P1-01
Development status of ABINIT-MP program in 2018
Yuji Mochizuki 1,2 Kota Sakakura 3 Yoshinobu Akinaga 4 Hiromasa Watanabe 5
fullmoon@rikkyo.ac.jp k-sakakura@ay.jp.nec.com akinaga@vinas.com hi-watanabe@hpc.co.jp

Kouichiro Kato 6 Kaori Fukuzawa 2,7 Tatsuya Nakano 8
kouichiro.kato@mizuho-ir.co.jp k-fukuzawa@hoshi.ac.jp nakano@nihs.go.jp

1

2

3

4
5
6

7

8

Department of Chemistry & Research Center for Smart Molecules, Faculty of Science,
Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
Institute of Industrial Science, University of Tokyo, 4 -6-1 Komaba, Meguro-ku, Tokyo
153-8505, Japan
1st Government and Public Solutions Division, NEC Corporation, 5-7-1 Shiba,
Minato-ku, Tokyo 108-8001, JapanVINAS Co. Ltd., 2-3-31 Dojima, Kita-ku, Osaka 530-003, Japan
HPC SYSTEMS Inc., Kaigan 3-9-15, Minato-ku, Tokyo 108-0022, Japan
Mizuho Information & Research Institute Inc., 2 -3 Kanda-nishiki-cho, Chiyoda-ku,
Tokyo 101-8443, Japan
School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara,
Shinagawa-Ku, Tokyo 142-8501, Japan
Division of Medicinal Safety Science, National Institute of Health, 3-25-26 Tonomachi
Kawasaki-ku, Kawasaki, Kanagawa 210-9501, Japan

Keywords: Fragment molecular orbital, FMO, ABINIT-MP, Interaction energy analysis
The fragment molecular orbital (FMO) method [1-3] has been one of the most widely used
schemes based on the fragmentations of molecular systems [4]. The popularity of FMO could be
related with the usefulness of inter-fragment interaction energy (IFIE) or pair interaction energy(PIE), in particular for medicinal chemistry and biophysics. We have been developing ABINIT-MP
as an original FMO program [3]. This program has several unique features, in comparison withother FMO programs, GAMESS-US [5] and PAICS [6]. In this poster presentation, we willsummarize the current development status of ABINIT-MP (Open series [7]) in 2018. New
functionalities such as energy decomposition analysis will be shown.
[Acknowledgment] This work was supported by MEXT as a social and scientific priority issue #6 to be
tackled by using post-K computer (FS2020) and also as a grant-in-aid (Kaken-hi) No. 16H04635.

[1] Fedorov, D. G., Kitaura, K. ed., The FRAGMENT MOLECULAR ORBIAL METHOD: Practical
Applications to Large Molecular Systems, CRC Press: Boca Raton, 2009.
[2] Fedorov, D. G.; Nagata, T.; Kitaura, K. Exploring Chemistry with the Fr agment MolecularOrbital Method, Phys. Chem. Chem. Phys., 14:7562-7577, 2012.
[3] Tanaka, S., Mochizuki, Y., Komeiji, Y., Okiyama, Y., Fukuzawa, K., Electron-Correlated
Fragment-Molecular-Orbital Calculations for Biomolecular and Nano Systems , Phys. Chem.
Chem. Phys., 2014, 16:10310-10344, 2014.
[4] Gordon, M. S., Fedorov, D. G., Pruitt, S. R., Slipchenko, L. V., Fragmentation Methods: A
Route to Accurate Calculations on Large Systems, Chem. Rev., 112:632-672, 2012.
[5] Fedorov D. G., The fragment molecular orbital method: theoretical development,
implementation in GAMESS, and applications, WIREs, Comp. Mol. Sci., e1322, 2017.
[6] Ishikawa T., Ishikura T., Kuwata K., Theoretical study of the prion protein based on the
fragment molecular orbital method, J. Comp. Chem., 30:2594-2601, 2009.
[7] Mochizuki Y., Sakakura K., Akinaga Y., Kato K., Watanabe H., Okiyama Y., Nakano T.,
Komeiji Y., Fukuzawa K., Tanaka S., Current Status of ABINIT-MP as a FMO Program andRelated Works with Machine Learning, J. Comp. Chem. Jpn., 16:119-122, 2018.

P1-02
Ab Initio Molecular Simulations on Specific Interactions
Between Amyloid-beta Peptide and Its Ligand
Tomoki Shinzato 1

Shogo Tomioka 1

Haruki Sogawa 1

shinzato@klab.cs.tut.ac.jp

tomioka@klab.cs.tut.ac.jp

sogawa@klab.cs.tut.ac.jp

Sergiy Shulga 2

Yaroslav Blume 2

Noriyuki Kurita 1
kurita@klab.cs.tut.ac.jp

12

Department of Computer Science and Engineering, Toyohashi University of Technology,
1−1 Hibarigaoka, Tempaku−cho, Toyohashi, Aichi, 441−8580, Japan
Institute for Food Biotechnology and Genomics, National Academy of Sciences of Ukraine,
2a. Osypovskogo Str., Kyiv-123, 04123, Ukraine

Keywords: Fragment molecular orbital, Molecular dynamics, Alzheimer’s disease, Aggregate,
EGCG
The molecular pathogenesis of Alzheimer’s disease (AD) is accompanied by senile plaques
comprised of amyloid-beta peptides (Aβs) in a diseased brain [1]. Since Aβ has severalhydrophobic amino acid residues, it can form strong aggregates in water due to the hydrophobic
interactions between these residues, leading to a formation of Aβ fibrils [2]. Therefore, agents witha strong binding affinity to Aβ are expected to inhibit Aβ aggregation and be a potent inhibitor
against AD pathogenesis [3].
The (‒)-epigallocatechin-3-gallate(denoted hereafter as EGCG) is a potential inhibitor against
Aβ aggregation [4], because EGCG consists of three aromatic rings, which can form π‒π stacking
contacts with the Aβ hydrophobic residues contributing to the formation of Aβ aggregates.
However, the relevant mechanism of interactions between Aβ and EGCG is not elucidated. To
clarify this mechanism, replica exchange molecular dynamics (MD) simulations as well as
quantum chemical calculations were performed for Aβ hexamers with and without EGCG [5].
However, since a small fragment Aβ(16−22) hexamer was considered in this study, it was notclarified if the other residues in a full length Aβ(1−42) effect the interactions with EGCG or not.
In the present study, we investigated the specific interactions between Aβ (1−42) and EGCG,
using ab initio molecular simulations based on protein-ligand docking, molecular dynamics andab initio fragment molecular orbital (FMO) calculations and elucidated the specific interactions
between a full length Aβ and EGCG. In addition, we performed similar molecular simulations forthe other ligands, which will be effective for inhibiting AD pathogenesis. Based on the results
simulated, we attempted to propose novel agents for inhibiting Aβ aggregations.
[1] F. M. Laird, et al., J. Neurosci. 25 (2005) 11693–11709.
[2] S. W. Pimplikar, Int. J. Biochem. Cell Biol. 41 (2008) 1261–1268.
[3] A. J. Doig, Curr. Opin. in Drug Disc. Devel. 10 (2007) 533–539.
[4] A. V. Kristen, et al., Clin. Res. Cardiol. 101 (2012) 805–813.
[5] S. T. Ngo, et al., J. Mol. Graph. & Model. 76 (2017) 1–10.

P1-03

Efficient Implementation of FMO-RI-MP3 method
in PAICS
Takeshi Ishikawa 1

Kota Sakakura 2

t-ishi@nagasaki-u.ac.jp

k-sakakura@ay.jp.nec.com

Yuji Mochizuki 3,4
fullmoon@rikkyo.ac.jp

1

2

3

4

Department of Molecular Microbiology and Immunology, Graduate School of Biomedical
Sciences, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan.
1 st Government and Public Solutions Division, NEC Corporation, 7-1, Shiba 5-chome,
Minato-ku, Tokyo 108-8001, Japan.
Department of Chemistry and Research Center for Smart Molecules, Faculty of Science,
Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan.
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo
153-8505, Japan.

Keywords: Electron correlation, fragment molecular orbital method, resolution of the identity
approximation, third-order Møller-Plesset perturbation theory, biomolecule
The third-order Møller−Plesset perturbation (MP3) theory using the resolution of the identity(RI) approximation [1] was combined with the fragment molecular orbital (FMO) method [2] toefficiently calculate a high-order electron correlation energy of biomolecular systems. We
developed a new algorithm for the RI-MP3 calculation [3] , which was implemented in PAICS [4,5] .
After test calculations using a small molecule, the FMO-RI-MP3 calculations were performed fortwo biomolecular systems comprising a protein and a ligand. The computational cost of thesecalculations was only around 5 and 4 times higher than that of the FMO-RHF calculations. The
error associated with the RI approximation was around 2.0% of the third-order correlation
contribution to the total energy. On the other hand, the RI approximation error in the interactionenergy between the protein and ligand molecule was insignificantly small, which reflected thenegligible error in the inter fragment interaction energy. Considering the importance of theaccurate evaluation of the molecular interaction in biomolecular systems, we can conclude that the
FMO-RI-MP3 method is a highly potent tool for biological studies.
[1] M. Feyereisen, G. Fitzgerald, A. Komornicki, Use of approximate integrals in ab initio theory.
An application in MP2 energy calculations, Chem. Phys. Lett., 1993, 208, 359.
[2] K. Kitaura, T. Sawai, T. Asada, T. Nakano, M. Uebayasi, Pair interaction molecular orbital
method: an approximate computational method for molecular interactions, Chem. Phys. Lett.
1999, 312, 319.
[3] T. Ishikawa, K. Sakakura, Y. Mochizuki, RI-MP3 calculations of biomolecules based on the
fragment molecular orbital method, J. Comput. Chem., in press.
[4] T. Ishikawa, T. Ishikura, K. Kuwata, Theoretical Study of the Prion Protein Based on the
Fragment Molecular Orbital Method, J. Comput. Chem. 2009, 30, 2594.
[5] http://www.paics.net/

P1-04
Ligand chirality can affect histidine protonation of vitamin-D
receptor: ab initio molecular orbital calculations in water
Yuta Terauchi 1
terauchi@klab.cs.tut.ac.jp

1

2

3

Rie Suzuki 1

Atsushi Kittaka 2 ,r-suzuki@klab.cs.tut.ac.jp

akittaka@pharm.teikyo-u.ac.jp

Midori Takimoto-Kamimura 3

Noriyuki Kurita 1

m.kamimura@teijin.co.jp

kurita@cs.tut.ac.jp

Department of Computer Science and Engineering, Toyohashi University of Technology,
1−1 Hibarigaoka, Tempaku−cho, Toyohashi, Aichi, 441−8580, Japan
Faculty of Pharmaceutical Sciences, Teikyo University,
2-11-1 Kaga, Itabashi, Tokyo, 173-8605, Japan
Teijin Institute for Bio-Medical Research, Teijin Pharma Ltd.,
4-3-2 Asahigaoka, Hino, Tokyo, 191-8512, Japan

Keywords: Chirality; Protonation of histidine; Molecular simulation; Fragment molecular orbital;
Vitamin D receptor; Protein ligand interaction

Vitamin D is recognized to play important roles in the onset of immunologicaldiseases as well as the regulation of the amount of Ca in the blood. Since these
physiological actions caused by active vitamin D are triggered by the specific interaction
between the vitamin D receptor (VDR) and active vitamin D, many types of compounds
have been developed as potent ligands against VDR. In the recent experiment [1], thebinding affinity between VDR and its ligand was found to depend significantly on thechirality of the ligand. However, the reason for the dependence has not been elucidated yet.
In the present study, we investigated the specific interactions between VDR andsome ligands having different chirality, using ab initio fragment molecular orbital (FMO)
calculations. The FMO results highlight that two histidine residues of VDR contributesignificantly to the binding between VDR and ligand and that their protonation states canaffect the specific interactions between VDR and ligand. We therefore considered otherpossible protonation states of these histidine residues and determined their most stablestates, using the ab initio FMO calculations. The results illustrate the possibility that thedifference in the chirality of a ligand can induce the change in protonation states of thehistidine residues of VDR existing near the ligand. This finding provides an import ant
warning that the protonation states of histidine residues existing near the ligand should beconsidered more precisely in the molecular simulations for investigating the specificinteractions between protein and ligand.
[1] D. Sawada et al., Org. Biomol. Chem., 16 (2018) 2448−2455.

P1-05Specific interactions between mycobacterial FtsZ andinhibitors derivatives: molecular docking and ab initio
molecular simulations
Haruki Sogawa 1 , Shogo Tomioka 1 , Tomoki Shinzato 1 , Pavel Karpov 2 , Sergiy Shulga 2 ,Yaroslav Blume 2 , Noriyuki Kurita 1
kurita@cs.tut.ac.jp

1 Department of Computer Science and Engineering, Toyohashi University of Technology,
1-1 Hibarigaoka, Tenpaku-cho, Toyohashi, Aichi, 411-8580, Japan
2 Institute for Food Biotechnology and Genomics, National Academy of Scien ces of Ukraine,
2a. Osypovskogo Str., Kyiv-123, 04123, Ukraine

Keywords: Cell division; Fragment molecular orbital; FtsZ; Inhibitor; Protein-ligand docking;
Tuberculosis; zantrin Z3; ZZ3;
Tuberculosis (TB) is one of the most widespread infectious diseas es and is caused by thebacillus Mycobacterium tuberculosis (Mtb). In the treatment of TB, various kinds of drugs such as
isoniazid, rifampicin, pyrazinamide and ethambutol have been administered. However, there is aconsiderable potential for Mtb to have resistance against these drugs. In particular, Mtb having
resistance against multiple drugs is called multidrug-resistant TB (MDR-TB), and the number of
MDR-TBs is increasing rapidly. It is thus necessary to develop new anti-TB drugs targeting themost conservative proteins, which cannot be mutated easily [1]. As a candidate of anti-TB drugs,
new compounds were proposed which suppress the growth of Mtb by inhibiting the division of Mtb
cells [2]. These drugs target the cytoskeletal protein FtsZ ( Filamentous temperature-sensitive Z ),which plays an essential role in the cell division mechanism [3]. In addition, the treatment of TB
should be completed in a short time, because prolongation of the TB treatment may cause new
MDR-TBs. Therefore, the development of novel potent anti-TB drugs, which can shorten thetreatment period, is required.
As a novel inhibitor against Mtb FtsZ, we here considered zantrin Z3 and ZZ3. Zantrin Z3 is an
Mtb FtsZ inhibitor proposed by D.N. Margalit et al [2]. Based on chemical experiments. In 2015,
ZZ 3, an improved inhibitor based on zantrin Z3, was announced by G. M. Nepomuceno et al[4]. However, since there are some ligand-binding pockets in Mtb FtsZ, the binding site of zantrin
Z3 and ZZ3 on FtsZ and the specific interactions between these inhibitors and FtsZ are not
elucidated yet.
In the present study, we investigated the specific interactions between Mtb FtsZ and someinhibitor derivatives, using ab initio molecular simulations based on protein-ligand docking,classical molecular mechanics optimization and ab initio fragment molecular orbital (FMO)
calculation. Based on the FMO results, we attempted to reveal which curcumin derivative can bindmore strongly to FtsZ. In addition, we elucidated which parts of FtsZ and inhibitors derivative areimportant for the specific interactions between them. The result will be useful for proposing novelanti-TB drugs based on zantrin Z3 and ZZ3 derivatives.
[1] P.A. Karpov, et al., Sci. Innov., 12: 43−55, 2016.
[2] D. N. Margalit, et al., PNAS, 101: 11821–11826, 2004.
[3] M. Osawa, et al., Science, 320: 792–794, 2008.
[4] G. M. Nepomuceno, et al., ACS Med. Chem. Lett. , 6: 308−312, 2015.

P1-06
Free energy analysis of b -sheet aggregation
by molecular dynamics simulation

1

2

Keiichi Masutani 1

Yu Yamamori 2

masutani-ke@cheng.es.osaka-u.ac.jp

yu.yamamori@aist.go.jp

Kang Kim 1

Nobuyuki Matubayasi 1

kk@cheng.es.osaka-u.ac.jp

nobuyuki@cheng.es.osaka-u.ac.jp

Division of Chemical Engineering, Graduate School of Engineering Science, Osaka University,
1-3 Machikaneyamacho, Toyonaka, Osaka 560-8531, Japan
National Institute of Advanced Industrial Science and Technology, Artificial Intelligence
Research Center, Tokyo, 135-0064, Japan(Eintra+∆µ)/monomer(kcal/mol)energy/monomer (kcal/mol)

Keywords: Molecular dynamics simulations, Aggregation, Cosolvent, Free energy【 Introduction】
Amyloid has b -sheet conformation and aggregation is considered to be a cause of amyloidosis
diseases such as Alzheimer's disease. In the field of protein engineering, the aggregation of theexpressed protein often results in the inclusion bodies, which are not suitable for a large-scaleprotein expression. Since the aggregation of proteins is affected not only by the interaction
between proteins but also by the interaction between a protein and the solvent, it is indispensable
to analyze the energetic aspects of the intermolecular interactions explicitly. In this study, we
focus on NACore, which corresponds to the 68th to the 78th residue of human a -synuclein made of
140 amino acids [1]. NACore is considered as a causal substance of Parkinson's disease and is the
key region of aggregation. Molecular dynamics simulations of
its monomer, octamer, and hexadecamer in a pure-water solvent
and mixed solvents of water with urea and DMSO are carried
20
out to study the relative stabilities of the aggregates.
0
Eintra【 Results & Discussion】
-20
intra
∆µ
Figure 1 shows the intra-aggregate structural energy (E ),
-40
intra
intra
(E
+∆µ)
the solvation free energy (D µ ), and their sum (E +D µ ) per-60
monomer as a function of the degree of oligomerization. The-80
E intra becomes more negative when the degree of1816
oligomerization is increased. That is, the intra-aggregate
degree of oligomerization
structure is more stable. In contrast, a more positive value of
Figure 1:E intra , D µ , andD µ indicates that the interaction between the aggregate and the(E intra +D µ ) as a function of thewater molecules is less favorable. The stability is characterized
degree of oligomerization.
by (E intra +D µ ) per monomer. This becomes more negative as thedegree of oligomerization increases. It means that the
-50
WATER
aggregate is more stable than the monomer. This can be-55
UREA
explained in terms of the interaction of aggregate, which is-60
DMSO
larger than the effect of destabilization due to the interaction-65
between the aggregate and the solvent water.
-70
Figure 2 shows (E intra +D µ ) per monomer in the pure-water-75
solvent and mixed solvents of water with urea and DMSO at
-80
1
8
16
each degree of oligomerization. The stability of the protein indegree of oligomerization
urea and DMSO mixtures can then be compared with that in
Figure 2:(E intra +D µ ) as awater through the theory of chemical equilibrium, and it isfunction of the degree ofshown that urea suppresses the tendency of aggregation byoligomerization in pure-waterorders of magnitude in terms of the protein concentration.
solvent and mixed solvents of[1] J. A. Rodriguez, et al., Nature, 525:486-490, 2015.
water with urea and DMSO.

P1-07
Effect of Zn on Aβ Nonamer Aggregates: Molecular
Dynamics and Ab Initio Molecular Orbital Calculations
Shogo Tomioka 1

Tomoki Shinzato 1

tomioka@klab.cs.tut.ac.jp

shinzato@klab.cs.tut.ac.jp

Sergiy Shulga 2

Yaroslav Blume 2

Haruki Sogawa 1
sogawa@klab.cs.tut.ac.jp

Noriyuki Kurita 1
kurita@klab.cs.tut.ac.jp

12

Department of Computer Science and Engineering, Toyohashi University of Technology,
1−1 Hibarigaoka, Tempaku−cho, Toyohashi, Aichi, 441−8580, Japan
Institute for Food Biotechnology and Genomics, National Academy of Sciences of Ukraine,
2a. Osypovskogo Str., Kyiv-123, 04123, Ukraine

Keywords: Amyloid-β; Alzheimer’s disease; Aggregate; Zn; Molecular simulation;
Molecular dynamics; Fragment molecular orbital; Protein -protein interaction.
Alzheimer's disease (AD) is the most common disease in dementia [1] . It is recognized that
aggregates of amyloid beta protein (Aβ) are deeply involved in the onset of AD [2]. Actually, Aβs
produced from APP (amyloid precursor protein) form aggregates of various sizes, such as dimers
and amyloid fibrils [3]. These aggregates were found to be self-organized in the experiment [4]. In
addition, it was confirmed that the brains of AD patients have abnormally higher concentrations ofmetal ions such as Zn and Al compared with the brains of normal people [5], and the association
between Aβ aggregation and these metal ions has been investigated widely [6].
In the present study, we investigated the influence of Zn ion on Aβ aggregation at atomic andelectronic levels, using molecular dynamics (MD) and ab initio fragment molecular orbital (FMO)
calculations in water. We employed Aβ nonamer with three-fold symmetry and analyzed the change
in its structure induced by the addition of Zn ions by MD simulations. Moreover, the electronic
states of the Aβ nonamer with and without Zn ions were investigated by ab initio FMO calculations
to elucidate how the specific interactions between Aβ monomers in the nonamer are changed by theaddition of Zn ions.
The MD simulations reveal that the interactions between the charged amino acid residues of Aβmonomers in the Aβ nonamer are significantly influenced by the addition of Zn ions to the Aβ
nonamer. The electronic states of the Aβ nonamer are also affected by the Zn ions. In particular,
the specific interactions between the charged residues of the Aβ monomers were found to bechanged significantly by the Zn addition. Based on these results, we propose a mode l of anaggregation mechanism of the Aβ monomers, which provides useful information for proposing anovel compound of suppressing Aβ aggregations and contributing to the development of new AD
therapeutic drugs.
[1] Scott, H., Anthony, F. J., Dementia, John Wiley & Sons Ltd, 2002.
[2] Selkoe, D. J., Physiol. Rev. 81 (2001) 741–766.
[3] Tycko, R., Q. Rev. Biophys. 39 (2006) 1–55.
[4] Antzutkin, O. N., Balbach, J. J., Leapman, R. D., Rizzo, N. W., Reed, J. , Tycko, R.,
Proc. Natl. Acad. Sci. U.S.A. 97 (2000) 13045–13050.
[5] Miller, L. M., Wang, Q., Telivala, T., Smith, R., Lanzirotti, A., Miklossy, J.,
J. of Structural Biology. 155 (2006) 30–37.
[6] Lovell, M. A., Robertson, J. D., Teesdale, W. J., Campbell, J. L., Markesbery, W. R.,
J. of the Neurolo. Sciences. 158 (1998) 47–52.

P1-08

Molecular Dynamics Simulations of HIV Tat protein andAmyloid-β peptides
Kazumi Omata 1
komata@hosp.ncgm.go.jp

12
3

Hisashi Okumura 2,3
hokumura@ims.ac.jp

Department of Data Science, National Center for Global Health and Medicine, 1-21-1 Toyama,
Shinjuku, Tokyo 162-0052, Japan
Institute for Molecular Science, 38 Nishigonaka, Myodaiji, Okazaki, Aichi 444-8585, JapanThe Graduate University for Advanced Studies, 38 Nishigonaka, Myodaiji, Okazaki, Aichi
444-8585, Japan

Keywords: HIV Tat protein, Amyloid-β, Ligand docking, Molecular dynamics, neurocognitive
dysfunction
To identify binding sites of an HIV transactivator of transcription (Tat) protein and an amyloid-β(Aβ) peptide, the present study has investigated their docking, using molecular dynamics (MD)simulations.
Recently, the experiments by Hategan et al. suggested that HIV Tat binds to the exterior surfaces ofAβ fibrils, increasing β-sheet formation and lateral aggregation into thick multifibrillar structures[1]. As a result, the fibers increase their rigidity and mechanical resistance. They also showed thatthe aggregate of Tat-Aβ complexes synergistically induced neurotoxicity both in vitro and inanimal models. Apart from such a study, Okumura and Itoh examined the structure of Aβ fibrilsby all-atom MD simulations, and demonstrated that structural fluctuations at the fibril ends play
an important role for the fibril formation [2]. Based on the knowledge about the structure of Aβfibrils derived from their MD simulations, the present study has attempted to find which parts ofAβ peptides are tightly associated with HIV Tat proteins, using MD simulations.
The all-atom MD simulations were carried out using the Generalized -Ensemble Molecular
Biophysics (GEMB) program [3]. The AMBER99SB force field was used for the HIV Tat and Aβ,
and the TIP3P rigid-body model was used for the water molecules. The electrostatic potential was
calculated by the particle mesh Ewald method. Temperature was controlled at 298 K with the
Nosé-Hoover thermostat, and pressure was controlled at 0.1 MPa with the Andersen barostat. The
symplectic quaternion scheme was employed for the rigid-body water molecules.
We have investigated three sets of initial condition. In the first set, a Tat protein was placed near
one of the two beta sheets constituting the Aβ. The Tat moved and was bound to the N-terminal
soft region of the Aβ. The second set in which a Tat was placed near the other beta sheet also
exhibited the movement and binding of the Tat to the N-terminal region. Considering these results,
in the third set, we placed a Tat protein near the loop region of the Aβ which is far from the
N-terminal soft region. Again, this set provided a similar movement and binding to the soft region.
These results allow us to conclude that the binding of a Tat and Aβ occurs only in the soft regionof the Aβ. This characteristic behavior may be attributed to the high concentration of charged
residues in the N-terminal region of the Aβ.
[1] Hategan, A., et al., Nature Structural & Molecular Biology 24, 379–386, 2017.
[2] Okumura, H. and Itoh, S. G., Scientific Reports 6, Article number:38422, 2016.
[3] Okumura, H., Proteins, 80: 2397-2416, 2012.

P1-09Ab initio Molecular Dynamics Simulation ofContinuous Production of Organic Moleculesin Alkaline Hydrothermal Vents
Yukimasa Muraki1

Kohei Shimamura2

181p305p@stu.kobe-u.ac.jp

shimamura@port.kobe-u.ac.jp

Ichiro Mori1

Shigenori Tanaka2ichiro.mori@port.kobe-u.ac.jp

tanaka2@kobe-u.ac.jp

1

Graduate School of Science Technology and Innovation, Kobe University, 1-1, Rokkodai, Nada, Kobe
657-0013, Japan
2
Graduate School of System Informatics, Kobe University, 1-1, Rokkodai, Nada, Kobe 657-8501, Japan

Keywords: Origin of Life, Alkaline Hydrothermal Vents, Ab initio Molecular Dynamics
Although there are several hypotheses attempting to interpret abiotic synthesis of organic
molecules, such as formic acids and amino acids, in the non-reduced environment of the primitive
Earth, the most of them lack the consideration in continuous production process. The continuous
production of organic molecules would be essential for maintaining primitive life’s metabolism.
We therefore focus on the environment of alkaline hydrothermal vents, which is regarded as oneof the most promising places as the origin of life [1]. Membranes consisting of FeS would beformed at the interface between seawater and hot water. Since the pH levels of the sea water andthe hot water were estimated to be 6 and 10, respectively, i.e., a pH gradient was formed acrossthe FeS membranes. In addition, the hot water contained a constant amount of H2. In considerationof such an environment, a mechanism has been proposed in which electrons released from
dissociation of H2 under the pH gradient were supplied to CO2 absorbed on FeS surface resulting
in continuous production of organic molecules.
However, the catalytic mechanism of FeS membranes is not fully understood yet. In order tograsp its microscopic insights, we performed ab initio molecular dynamics simulation, where theenvironment of alkaline hydrothermal vents was modeled. The model contained two domains
which are separated by a FeS slab: the primitive seawater domain consisting of H2O, CO2, andH2S, and the hot water domain consisting of H2O and H2. A pyrite structure was used for the FeS
slab because it was detected as the main component of the hydrothermal vents. To create a pH
gradient, H3O+ and OH- were added to the seawater and the hot water the domains, respectively.
In our simulation, a formic acid molecule was produced by the reduction of CO2, where both of
H2O and H2 were used as electron sources while H3O+ was used as H+ source. OH- was involvedin H2 and H2O dissociation on the surface of the pyrite surface. We also found the S-S bond on
the pyrite surface behaved like a disulfide bond (S-S) in current life systems. It has been reported
that the S-S bond on pyrite surface would help redox reactions [2]. The state of S-S bond is cycled
between oxidized (-S-S-) and reduced states (-SH HS-) in the redox reaction. In our simulation,
the S-S bond was changed to reduced state form the oxidized one while the CO2 molecule was
reduced on the S-S bond. However, when the CO2 was transformed into a formic acid, the bondstate cycled back to the oxidized one. Therefore, it is considered that organic molecules such as
formic acid would be continuously produced when membranes consisting of pyrite were formed
in alkaline hydrothermal vents.
Since the computational cost of density functional theory (DFT) used in this simulation was
expensive, the simulation model has to be kept very small (226 atoms). To further expand themodel size, we are now trying to employ density functional tight binding (DFTB) [3]. Although
DFTB would accelerate the simulation by introducing plausible parameters into DFT, theparameter for Fe-S is not available yet. In this poster session, the progress towards the simulation
utilizing the DFTB method will be also presented.
[1] Sojo, V., et al., Astrobiology, 16:181, 2016.
[2] Luther III, G., Geochim. Cosmochim. Acta 51:3193, 1987.
[3] https://www.dftb.org/

P1-10Is hydrophobic group in osmolyte hydrophilic? :A study by fragment based molecular theory
Yukina Uchino1

Nahoko Kuroki2

g1520307@edu.cc.ocha.ac.jp

g1670603@edu.cc.ocha.ac.jp

Hirotoshi Mori* 3
mori.hirotoshi@ocha.ac.jp

Department of Chemistry, Faculty of Science, Ochanomizu University, 2-1-1 Otsuka,
Bunkyo-ku, Tokyo 112-8610, Japan2
Department of Chemistry and Biochemistry, Graduate School of Humanities andSciences, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-ku, Tokyo 112-8610, Japan
3
Faculty of Core Research, Natural Science Division, Ochanomizu University, 2-1-1
Otsuka, Bunkyo-ku, Tokyo 112-8610, Japan
1

Keywords: Osmolyte, trimethylamine N-oxide (TMAO), ab initio effective fragment potential(EFP), hydrogen bond, hydrophobicity
Molecular architecture of aqueous osmolyte solution is one of the important chemical physics tounderstand biological mechanism of biological control of osmotic pressure and protein
denaturalization. It has been considered that complemental study of spectroscopy and ab initio
molecular dynamics (AIMD) simulations is powerful tool to clarify the mechanisms. However, as
the computational cost of AIMD simulation is very expensive at the moment, there still exist very
big wall to observe slight difference in various osmolytes with the above-mentioned schemes.
Recently, in our group, we have focused on effective fragment potential (EFP) [1], which is an abinitio polarizable force field defined by a set of quantum chemical wave function of compact
constituent molecules. Combining the EFP method with the MD technique, it is possible toperform AIMD level molecular simulation with much cheaper computational costs. Recently, we
have reported that it is possible to predict excess molar quantities of mixtures of organic liquids orradial distribution of molecules in ionic liquids. [2,3] In this poster session, applying quantum
chemical calculation and EFP theory to aqueous solution of trimethylamine N-oxide (TMAO), a
well-known osmolyte [4], we will discuss hydrogen bonding network and hydrophobicity of
TMAO in physiological condition.
[1] Gordon, M.S., Fedorov, D.G., Pruitt, S.R., and Slipchenko, L.V., Fragmentation Methods: A Routeto Accurate Calculations on Large Systems, Chemical Reviews, 112(1):632-672, 2012.
[2] Kuroki, N., and Mori, H., Applicability of effective fragment potential version 2 – Molecular
dynamics (EFP2-MD) simulations for predicting excess properties of mixed solvents, Chemical
Physics Letters,694:82-85, 2018.
[3] Kuroki, N., and Mori, H., Effective Fragment Potential Version 2 - Molecular Dynamics(EFP2-MD) Simulation for Investigating Solution Structures of Ionic Liquids, Chemistry Letters,
45:1009-1011, 2016.
[4] Zou, Q., Bennion, B.J., Daggett, V., and Murphy, K.P., The Molecular Mechanism of Stabilizationof Proteins by TMAO and Its Ability to Counteract the Effects of Urea, Journal of American
Chemical Society, 124 (7):1192–1202, 2002.

P1-11

Study of regulation mechanism of agonistic / antagonistic
activities of vitamin D receptor Ligand-Binding Domain
Takafumi Kudo 1
w185412d@yokohama-cu.ac.jp

Tsutomu Yamane 1
tyamane@yokohama-cu.ac.jp

1

Toru Ekimoto 1
ekimoto@yokohama-cu.ac.jp

Mitsunori Ikeguchi 1
ike@yokohama-cu.ac.jp

Graduate School of Medical Life Science, Yokohama City University, 1-7-29
Suehirotyou, Tsurumi-ku, Yokohama City 108-8639, Japan

Keywords: Molecular Dynamics simulation, Vitamin D receptor, pharmacophore
Vitamin D receptor (VDR) is one of the nuclear receptors (NR) and a significant target for drug
discovery. For example, VDR agonists are used as therapeutic agents for osteoporosis. In contrast,
although VDR antagonists are expected as therapeutic agents for marble bone diseases, its effective
therapeutic agents have not been found.
The mechanism of the agonistic/antagonistic activities is not understood clearly on the basis of X ray crystallography. According to X-ray crystal structures of NRs and other experimental results,
the VDR ligand-binding domain (LBD) undergoes the conformational change upon ligand binding,and a local conformational change around helix 12 is key to regulating agonistic/antagonistic
activities. However, all the crystal structures of VDR-LBD are almost identical, regardless ofagonist/antagonist binding.
In order to elucidate the regulation mechanism of agonistic/antagonistic activities depending on
VDR ligands, we performed pharmacophore analysis using crystal structures and solution structuresgenerated by molecular dynamic (MD) simulations . MD simulations are capable of simulating
structural fluctuation, and therefore, the dynamical pharmacophore model created by MD
simulations involves structural fluctuation of both the ligand and VDR-LBD. In addition, we also
examined solution structures of the antagonist bound form elucidated by a combi nation analysis ofsmall-angle x-ray scattering experiment and MD simulations [1]. The structure exhibits a different
conformation of helix 12. We created the dynamical pharmacophore models for both agonist andantagonist, and found each characteristic feature: In the agonist model, the hydrophobic interaction
between the agonist and helix 12 is formed. In contrast, in the antagonist model, the hydrophobic
interaction between the antagonist and helices 7 and 10/11 is formed, and the interaction between
the antagonist and helix 12 is not observed. These results indicate that the regulation mechanism
induced by agonist/antagonist may be originated from the different interaction patterns between theligand and VDR-LBD.
[1] Anami, Y., Shimizu, N., Ekimoto, T., Egawa, D., Itoh, T., Ikeguchi, M., Yamanoto, K., J. Med.
Chem., 59, 7888-7900, 2016.

P1-12    In silico protein-protein interaction analysis of axon
guidance molecule semaphorin and receptor plexin
Erena Shimoji

i175414b@yokohama-cu.ac.jp

  Toru Ekimoto

ekimoto@yokohama-cu.ac.jp

Tsutomu Yamane

     tyamane@yokohama-cu.ac.jp

Terukazu Nogi      nogi@yokohama-cu.ac.jp

Meri Nagatomo

Tsubasa Tanaka

w165430g@yokohama-cu.ac.jp	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  w185502c@yokohama-cu.ac.jp

Mitsunori Ikeguchi
ike@yokohama-cu.ac.jp

Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho,
Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

Keywords: Semaphorin, Guidance molecule, Protein-protein interaction, Molecular dynamics
simulation
Semaphorin has been known as an axon guidance molecule, secreted around cells. Plexin is a
receptor of semaphorin, and their interactions are guidance cues for axons of neurons during
development of the nervous system. Because the protein-protein interactions (PPI) of semaphorin
and plexin have wide interaction surfaces, it is not trivial to understand the determinants of PPI.
Semaphorin and plexin include “Sema domain” with high sequence similarity, and they form PPI
through the sema domain.
We analyzed interactions of two complexes which are SEM6A-PLXA2 and SEM6D-PLXA1.
Even though they have high sequence homology, they have different binding affinity[1].
Therefore, we analyzed the PPI using in silico approaches to recognize the feature of specificinteractions. We previously calculated the binding free energy and to perform “in silico alanine
scanning” [2,3], and identified the “hotspots” which are significant amino acids in binding.
Then, we analyzed structural dynamics for investigating interaction specificity. We have
performed molecular dynamics simulations for four complexes, SEM6A-PLXA2, SEM6DPLXA1, SEM6A-PLXA1 and SEM6D-PLXA2. To examine the interaction specificity, theexchange models, i.e., SEM6A-PLXA1 and SEM6D-PLXA2 were modeled by swapping
semaphorins of SEM6A-PLXA2 and SEM6D-PLXA1. The exchange models are supposed to
exhibit less affinity than wildtype models. We performed 1-µs molecular dynamics simulations
for each model. Amino acids contacts of binding interface were analyzed to identify the importantinteractions of the complexes.
[1] B. J. C. Janssen, et al. Structural basis of semaphorin–plexin signaling. Nature 467 1118-1122,
2010.
[2] T. Kortemme, et al. A simple physical model for binding energy hot spots in protein–protein
complexes. Proc. Natl. Acad. Sci. 99, 14116-14121, 2002.
[3] T. Kortemme, et al. Computational Alanine Scanning of Protein-Protein Interfaces. Sci. STKE
10, 2004.

P1-13

Protein-ligand binding process studied
by Markov state model
Toru Ekimoto 1

Mitsugu Araki 2

Yuta Isaka 3

ekimoto@yokohama-cu.ac.jp

araki.mitsugu.6w@kyoto-u.ac.jp

isaka@fbri.org

Tsutomu Yamane 1

Yasushi Okuno 2

Mitsunori Ikeguchi 1

tyamane@yokohama-cu.ac.jp

okuno.yasushi.4c@kyoto-u.ac.jp

ike@yokohama-cu.ac.jp

1

2

3

Graduate School of Medical Life Science, Yokohama City University, 1 -7-29 Suehiro-cho,
Tsurumi-ku, Yokohama 230-0045, Japan
Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto
606-8507, Japan
Center for Cluster Development and Coordination Research Development Group for in Silico
Drug Discovery, Foundation for Biomedical Research and Innovation at Kobe, KCMI building
213, 6-3-5, Minatojima-Minamimachi, Chuo-ku,Kobe, 650-0047, Japan

Keywords: Molecular dynamics simulation, Markov state model, binding free energy, association
rate constant, dissociation rate constantComputational approaches are routinely used in drug discovery and lead-optimization processes. Withadvances in computational power and softwares, all-atom molecular dynamics (MD) simulations can be
carried out easily, and (relative) binding free energy calculations with MD simulations become possible
at present. Therefore, further developments of methodology using MD simulations are demanded fornovel structure-based drug design, e.g., with considering dynamic structural change of proteinsinduced by a ligand binding and protein-ligand binding processes.
In this study, protein-ligand binding processes are analyzed by large amount of short MD simulations
unified by Markov state model (MSM) [1]. In this framework, the protein-ligand binding process is
described as a stochastic process among states under the assumption that the transition from the state
depends only on the current state. From the transition matrix, the probabilities of staying each state inequilibrium and of transitions among states are determined, and therefore, the binding free energy (ΔG)
and the association/dissociation rate constants (Kon/Koff) can be estimated by a ratio of thebound/unbound states and the mean first passage time between the two states. In addition, the MSM
provides an explicit picture of the binding process, and structures on the process can be also obtained.
To assess the performance of MSM, we execute a lot of 100 ns MD simulations for three systems
using K supercomputer (~200 μs in total), construct MSM s, and compare the estimated ΔG, Kon, andKoff to the experimental values. The three systems are Dihydrofolate reductase (DHFR)-inhibitor 1,
DHFR-inhibitor 2, and a mutated DHFR-inhibitor 2 systems, and exhibit different Kon/Koff. The MSM
is constructed by two steps: The first step is binding-pocket search by MD simulations which starts
from dissociated positions of the protein and ligand. U sing MSM constructed by the first step, boundstates are determined, and additional MD simulations start from the bound states to sample lig andbindings into the binding pocket at suitable poses. The estimated Kon is in good agreement with theexperimental value, and the estimated ΔG and Koff deviate ~2 kcal/mol and double-digit from theexperimental ones, respectively.

This research was done in activities of the K supercomputer-based drug discovery project byBiogrid pharma consortium (KBDD). The results were obtained using the K -computer (project ID:
hp150025 and hp160010, hp170036, hp180011).
[1] Husic, B.E. and Pande, V.S., Markov State Models: From and Art to a Science, J. Am. Chem.
Soc., 140:2386-2389, 2018.

P1-14Microsecond Molecular Dynamics Simulations of Medium
Molecule Drugs and the docking with a target protein
Lisa Matsukura1

Kazuto Mochizuki 2

1833730007b@waka.kindai.ac.jpm1743011@edu.cc.uec.ac.jp

Naoyuki Miyashita 1,2,*
miya@waka.kindai.ac.jp

Masumi Taki2
taki@pc.uec.ac.jp

Shinichi Watanabe 2
shin.watanabe@uec.ac.jp

12

Department of Biological System Engineering, Faculty of Biological Oriented Science andTechnologies, KINDAI University, 930 Nishimitani, Kinokawa, Wakayama 649−6493, Japan
Department of Engineering Science, Informatics and Engineering, University ofElectro-Communications, 1-5-1, Chofugaoka, Chofu, Tokyo 182-8585 , Japan

Keywords: Molecular dynamics Simulation, Ligand Docking
Recently, Medium molecule drug, antibody drug and so on, have been developed as new drugs.
Medium Molecule Drugs is usually the complex of a low weight molecular compound and a
peptide, and have a ring shape and the medium size. The peptide portion has a specific sequence
for the target protein, and the component of the low weight molecular compound also has specificfor the target. Here, we focused on the Medium Molecule Drug for HSP90. The binding affinity ofthe peptide portion with HSP90 is low, while that of the complex with HSP90 is very high. The
results of CD spectroscopy, however, suggested that both compounds have similar turn structure in
the peptide regions[1]. To understand the differences between the affinities of them, we performed
the microsecond molecular dynamics (MD) simulations of the peptide portion and the complex,
and the docking them with HSP90. We have obtained the reasonable results that the both of thepeptide portion and the complex compound have Type-I beta-turn around PRO. Moreover, thedistribution of Type-I beta turn in the complex is much higher than that in the peptide portion, andthe turn structure stabilizes the tertiary structure of the complex. The stability would be supported
to the high docking affinity with HSP90.
[1] Keisuke Fukunaga, et al., Construction of a crown ether-like supramolecular library by
conjugation of genetically-encoded peptide linkers displayed on bacteriophage T7, Chemical
Communications, 50:3921-3923, (2014)

P1-15

Geometry Optimization Effect of Ligand on
Protein-Ligand Binding Energy Calculation Using
Fragment Molecular Orbital Method
Takao Otsuka 1

Noriaki Okimoto 1

totsuka@riken.jp

okimoto@riken.jp

Makoto Taiji 1
taiji@riken.jp

1

RIKEN Center for Biosystems Dynamics Research (BDR), Building B, 6-2-4 Furuedai, Suita,
Osaka 565-0874, Japan

Keywords: electronic structure calculation, fragment molecular orbital method, protein-ligand
binding energy, geometry optimization
Computational prediction of protein-ligand binding affinities is so important for rational drug design.
One of the most popular strategy to obtain the protein -ligand binding energy by electronic structure
calculations is to use the fragment molecular orbital (FMO) method [1]. The FMO method wasdeveloped as a way of calculating the QM electronic state of a whole molecular system through thefragment based type of strategy. Recently, we proposed an acceleration scheme for the binding energy
calculation of protein-ligand system by using FMO method [2]. This scheme is based on a multilayer
FMO method focusing on the protein-ligand interaction distance, reduces the computational costs,
while maintaining accuracy in the evaluation of binding energy. We also reported an application study
using this acceleration scheme and showed the high applicability of our scheme [3].
On the other hand, we still have some open questions for the protein-binding energy calculation using
FMO method; as one of the examples, the effect of geometry optimization on protein-ligand system.
(Although we have reported this kind of study by large-scale first-principles DFT calculation
previously [4].) In this study, we report the effect of the geometry optimization on protein -ligand
binding energy calculation using FMO method. In order to evaluate the difference of the binding
energy clearly, only the ligand structure was optimized. The geometry optimization was performed by
FMO with frozen domain and dimer (FDD) method [5]. The results in this study can compare with ourprevious study directly.

[1] Kitaura, K., Ikeo, E., Asada, T., Nakano, T., Uebayasi, M., Chem. Phys. Lett., 313, 701, 1999.
[2] Otsuka, T., Okimoto, N., Taiji, M., J. Comput. Chem., 36, 2209, 2015.
[3] Okimoto, N., Otsuka, T., Hirano, Y., Taiji, M., ACS Omega, 3, 4475, 2018.
[4] Otsuka, T., Okimoto, N., Taiji, M., Bowler, D. R., Miyazaki, T., J. Phys.: Conf. Ser., 454,
012057 (2013).
[5] Nakata, H., Fedorov, D. G., Nagata, T., Kitaura, K., Nakamura, S., J. Chem. Theory Comput.,
11, 3053, 2015.

P1-16Development

of

the

CHARMM

force

field

for

Cyclosporine A
Tsutomu Yamane 1

Ryo Takahashi 1

tyamane@yokohama-cu.ac.jp

i140382e@yokohama-cu.ac.jp

Toru Ekimoto 1
ekimoto@yokohama-cu.ac.jp

1

2

Mitsunori Ikeguchi 1,2
ike@yokohama-cu.ac.jp

1Graduate School of Medical Life Science, Yokohama City University, 1 -7-29 Suehiro-cho,
Tsurumi-ku, Yokohama 230-0045, JapanRIKEN Medical Sciences Innovation Hub Program, 1 -7-22 Suehiro-cho, Tsurumi-ku,
Yokohama 230-0045, Japan

Keywords: Cyclosporine A, CHARMM force field, Molecular dynamics simulation
Cyclosporine A (CsA), which is an 11-residue cyclic peptide, is an antibiotic found from a fungusof Tolypocladium inflatum and has been used as an immune suppressor. CsA shows biomembrane
permeability, despite high molecular weight (M. w. ~ 1200). This biomembrane permeability is
considered to be caused by the structural change between "open" and "closed” conformation,
which are hydrophilic and hydrophobic structure, respectively. Also, CsA contains eight
unnatural amino acids and a D-amino acid. Especially, four of eight unnatural residues are
methylated backbone amide group (N-methyl amino acids), which play a role in the structuralchange of CsA. In the present study, we developed the CHARMM force field fo r CsA, especially
we calculated the backbone conformational energy maps of N -methyl amino acids (alanine andglycine) through Quantum Mechanics (QM) calculation. These energy maps were entirely different
from those of natural amino acids and consistent wit h conformations of N-methyl amino acids ofcrystal structures of CsA. Based on these results of energy maps, we generated the parameters for
CMAPs energy term of CHARMM force field for N-methyl amino acids and performed 1smolecular dynamics simulations of CsA in aqueous solution.

P1-17

Free energy based structural refinement
Yoshiaki Tanida

tanida.yoshiaki@jp.fujitsu.com

Azuma Matsuura
matsuura.azuma@jp.fujitsu.com

Fujitsu Laboratories Ltd., Atsugi, Kanagawa 243 -0197, Japan

Keywords: X-ray crystal structure analysis, structure refinement, alchemical free energy
calculation
In drug discovery, it is an important issue to accurately determine the binding structure of theligand molecule and the target molecule, i.e., the complex structure. However, even usingsynchrotron radiation (SR) X-ray crystal structure analysis, since the thermal fluctuation is large ina solvent, a missing region often remains near the important binding pocket. On the other hand,current “state-of-the-art” free energy calculations based on alchemical transfo rmation for ligand
binding are widely accepted in computational ligand design and optimization. Consequently,
modern computational free energy calculations are expected to be useful for building the precise
model for ligand binding to the target molecule.
In this study, we demonstrate the ability of such free energy based modeling for complex
structure, CK2/cdk2 (PDB: 4FKL).

P1-18In silico binding affinity analysis forphosphodiesterase-10A inhibitors.
Chisa Yuasa 1
w175438e@yokohama-cu.ac.jp

1

Toru Ekimoto 1
ekimoto@yokohama-cu.ac.jp

Tsutomu Yamane 1

Mitsunori Ikeguchi 1

tyamane@yokohama-cu.ac.jp

ike@yokohama-cu.ac.jp

Graduate School of Medical Life Science, Yokohama City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama-shi, Kanagawa, Japan

Keywords: Ligand docking, free energy calculation, Molecular Dynamics simulation, residue
mutation
Phosphodiesterase (PDE) hydrolyzes cyclic-AMP (c-AMP) and cyclic-GMP (c-GMP), which play
important roles in signal transduction. PDE10A specifically expresses in brains, and PDE10A is
considered as a drug target of schizophrenia. Although 24 drug candidates are developed, t herelationship of their structures and drug activities has not been clear.
In order to understand the mechanism of the affinity of PDE10A and the ligands, we performed
free-energy simulations for a series of binding affinities of the ligands and the selec tivity of
PDE10A among the PDE family.
First, we calculated binding free energies of 24 ligands and compared them with experimental
value: IC 50 using 4 approaches: the Glide docking, MM-GBSA, FEP+ and MP-CAFEE. The binding
free energies calculated by FEP+ were correlated with IC 50 . From these examinations, we found two
important points for the high correlation: precise docking poses of ligands and account of hydrationwater molecules at the binding site.
Second, we calculated the binding-free-energy changes upon 68 single mutations of residues at thebinding sites using FEP. Mutations were chosen from the sequence alignment of the PDE family.
The free-energy simulations successfully detected key residues for sele ctivity of PDE10A among
the PDE family.
[1] J. Kunitomo, M. Yoshikawa, M. Fushimi, A. Kawada, J F. Quinn, H Oki, H. Kokubo, M.
Kondo, K. Nakashima, N. Kamiguchi, K. Suzuki, H. Kimura, and T. Taniguchi,
Discovery of 1‐[2-Fluoro-4-(1H‐pyrazol-1-yl)phenyl]-5-methoxy-3-(1- phenyl‐1H‐pyrazol-5yl)pyridazin-4(1H)‐one (TAK-063), a Highly Potent, Selective, and Orally Active
Phosphodiesterase 10A (PDE10A) Inhibitor .
J. Med. Chem. 2014, 57, 9627−9643[2] L. Wang, B. J. Berne and A. Friesner
On achieving high accuracy and reliability in the calculation of relative protein –ligand binding
affinities. Pr oc. Natl. Acad. S ci. USA 2012, 109, 1937-1942[3] E. Harder, W. Damm, J. Maple, C. Wu, M. Reboul, J. Y. Xiang, L. Wang, D. Lu pyan, M. K.
Dahlgren, J. L. Knight, J. W. Kaus, D. S. Cerutti, G. Krilov, W. L. Jorgensen, R. Abel, and R. A.
Friesner, OPLS3: A Force Field Providing Broad Coverage of Drug -like Small Molecules
and Proteins. J. Chem Theory Comput. 2016, 12, 281-296

P1-19Structural basis for pH-dependent ferric ion coordination modes ofTtFbpA, the periplasmic subunit of an ABC-type iron transporterfrom Thermus thermophilus HB8Peng Lu1

Yoshitaka Moriwaki2

zjhzlupeng@hotmail.com

moriwaki@bi.a.u-tokyo.ac.jp

Tohru Terada3

Kentaro Shimizu2

Koji Nagata1

tterada@iu.a.u-tokyo.ac.jp

shimizu@bi.a.u-tokyo.ac.jp

aknagata@mail.ecc.u-tokyo.ac.jp

Departments of 1Applied Biological Chemistry and 2Biotechnology, and 3Agricultural
Bioinformatics Research Unit, Graduate School of Agricultural and Life Sciences,
UTokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan

Keywords: ABC transporter; Bicarbonate-carbonate equilibrium; Ferric ion binding protein; Ferric ion
coordination mode; pH-dependent structural change; Quantum calculation
Iron is one of the vital elements in living organisms. While humans obtain iron primarily through the foodthey eat, bacteria have developed diverse mechanisms to obtain iron from different sources including Fe3+transferrin, Fe3+-lactoferrin, Fe-hemoglobin, Fe2+ ion,and Fe3+-siderophores depending on their living
environments. Therefore, specific receptors and transport systems are required to transport iron from theenvironment to the inside of cell [1].
In Thermus thermophilus HB8, a Gram-negative bacterium, an ATP-binding cassette (ABC) transport systems,
TTHA1628-1629-1630, is annotated to be involved in iron uptake and has been validated functionally. Thistypical iron-uptake ABC transporter consists of three subunits, namely the iron-binding subunit TtFbpA(TTHA1628) in the periplasm, the transmembrane subunit TtFbpB (TTHA1629) in the inner membrane bilayer
and the nucleotide-binding subunit TtFbpC (TTHA1630) in the cytoplasm. As an importer, TtFbpB and
TtFbpC subunits are highly conserved among other ABC transporters, however, TtFbpA subunit is exclusively
conserved in Gram-negative bacteria and of great interests [2].
Previously, two iron coordination modes, six- and five-coordinated, have been observed in the crystal
structures of TtFbpA (PDB: 3WAE and 4ELR) at pH 5.5 [3] and 7.5 [4], respectively. In this study, we
compared these two crystal structures and found that the different ferric ion coordination modes result fromthe pH-dependent equilibrium between bicarbonate and carbonate ions and also the conformational change ofTtFbpA between open and closed states. These observations are confirmed by the results of quantum
calculations for various types of possible ferric iron coordination modes.
[1]
Nikaido H (2003) Molecular Basis of Bacterial Outer Membrane Permeability Revisited. Microbiol.
Mol. Biol. Rev. 67, 593–656.
[2]
Rees DC, Johnson E & Lewinson O (2009) ABC transporters: the power to change. Nat. Rev. Mol.
Cell Biol. 10, 218–227.
[3]
Wang S., et al., (2014) A novel mode of ferric ion coordination by the periplasmic ferric ion-binding
subunit FbpA of an ABC-type iron transporter from Thermus thermophilus HB8. Acta Crystallogr. Sect. D
Biol. Crystallogr. 70, 196–202.
[4]
Wang Q., et al., (2013) Structural insights into the coordination of iron by Thermus thermophilus HB8
ferric binding protein A Biochem Biophys Res Commun, 434(1):48-53.

P1-20How proteins recognize a phosphorylated amino acid:
Comparative studies of an antibody and the other protein
families
Raiji Kawade 1
kawade@protein.t.u-tokyo.ac.jp

Makoto Nakakido 1

Daisuke Kudroda 1
d-kuroda@protein.t.u-tokyo.ac.jp

Jose Caaveiro 2

nakakido@protein.t.u-tokyo.ac.jp

jose@phar.kyushu-u.ac.jp

Toshiaki Maruyama 3
info@abwizbio.com

Shigeru Okumura 3
info@abwizbio.com

Kouhei Tsumoto 1,4
tsumoto@bioeng.t.u-tokyo.ac.jp

1, Department of bioengineering, school of engineering, University of Tokyo, 4-6-1 Shirokanedai,
Minato-ku, Tokyo 108-8639, Japan
2
School of engineering, University of Kyusyu, 744 Motooka Nishi-ku, Fukuoka 819-0395, Japan
3
Abwizbio Inc. 9823 Pacific Heights Blvd., Suite J, San Diego, CA 92121-4705
4
Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo
108-8639, Japan

Keywords: Protein’s phosphorylation, Molecular Dynamics simulations, Protein modelingProtein phosphorylation is one of the major post -translational modifications that occur in the
nucleus and cytoplasm of cells. Phosphorylation plays critical roles in a wide range of cellular
processes such as signaling and on/off switching of interactions . Because of the important effectsof phosphorylation on protein structure and function, the mechanisms by which phosphorylatedamino acids mediate protein–protein interactions have been widely investigated [1]. Especially,molecular dynamics (MD) simulations have been adopted to analyze proteins’ recognition
mechanisms of phosphorylated amino acids [2]. However, most of the previous studies assumed
that the protonation state of a phosphorylated amino acid was PO 3 2- despite the fact that at
physiological pH, the phosphate group (pKa ~7) would exist as an equilibrium mixture of PO 3 2–
and the singly protonated state (PO 3 H – ).
To better understand the molecular mechanism of recognition of a phosphorylated amino acid, we
performed MD simulations of 4 different natural protein families that recognize a phosphorylated
peptide both in the PO 3 2- and PO 3 H - states. Our study suggested PO 3 2- was more preferable toPO 3 H - in the interactions due to the larger mobility and higher amount of charge of the phosphategroup in the PO 3 2- state. Moreover, we evaluated the contribution of each residue to the recognition ,and revealed that, among the basic residues, Arg is the more feasible residue than Lys to capture aphosphate group.
Finally, we also obtained a high-affinity (~20 nM) and high-selective monoclonal antibody
against a phosphorylated antigen, analyzed its recognition mechanism based on in vitro and in
silico techniques, and compared the results with those of natural protein families. Putting together,
these comparative studies suggests strategies to design proteins that can recognize phosphorylatedamino acids.
[1] M.B. Yaffe, et al., Phosphoserine/threonine-binding domains, Current Opinion in Cell Biology,
13:131–138, 2001.
[2] V.M. Anisimov, et al., Computational and experimental studies of the interaction between
phospho-peptides and the C-terminal domain of BRCA1, Journal of Computer-Aided Molecular
Design, 11: 1071-1084, 2011.

P1-21Non-empirical Coarse-grained Simulations for Lipid
Bilayers and Proteins
Koji Okuwaki 1

Eiji Shinsho 2

okuwaki@rikkyo.ac.jp

m1813@hoshi.ac.jp

Yuji Mochizuki 1, 3

fullmoon@rikkyo.ac.jp

Hideo Doi 1
hideo-doi@rikkyo.ac.jp

Kaori Fukuzawa 2, 3

Etsuo Yonemochi 2

k-fukuzawa@hoshi.ac.jp

e-yonemochi@hoshi.ac.jp

1

Department of Chemistry, Rikkyo University, 3-34-1 Nishi-Ikebukuro, Toshima-ku, Tokyo, 1718501, Japan
2
School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara, Shinagawa,Tokyo, 142-8501, Japan
3
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo,
153-8505, Japan

Keywords: FMO-DPD simulation, Lipid bilayers, Protein modeling<Introduction> Recently, various researches and developments are actively conducted fordesigning lipids or proteins in the context of drug delivery systems (DDS) and sensors mimicking
the cell membranes. For that purpose, detailed views of the structures of membrane (e.g. distribution,
irregularity, and fluidity) should be useful, however such information is hardly obtainable throughonly experimental observations. Computational approaches, e.g. molecular dynamics (MD) anddissipative particle dynamics (DPD) [1], could thus be effective as complements. Recently, we havebeen developing a multiscale simulation method that the effective interaction parameters for DPD
are to be evaluated by the fragment molecular orbital (FMO) [2] calculation, as the FMO-DPD
scheme. [3,4] In this presentation, we report the results of FMO-DPD applied to the mixed lipid
bilayers and the protein folding.
<Mixed lipid bilayers> DPD simulations were performed for a mixed lipid model of positively
charged lipid (DOTAP) and neutral phospholipid (DPPC) used for DDS. As a result, we reproduced
the experimental results that the two lipids were heterogeneously mixed and the shape of liposome
became oblate spheroid. [5]<Protein folding> As a touchstone along the structural reproduction of proteins, we performed nonempirical DPD simulation for chignolin, [6] the smallest protein consisting of 10 residues. As shownin the Figure 1, the backbone of hairpin curve shape was formed. We will also report the results ofseveral proteins.
This research was supported from MEXT FS2020 projectpriority issue 6 and Grant-in-Aid for Scientific Research(16H04635).
[1] R. Groot et al., J. Chem. Phys. 107 (1997) 4423.
[2] "The Fragment Molecular Orbital Method: Practical
Applications to Large Molecular Systems", (2009, CRC).
[3] K. Okuwaki et al., J. Phys. Chem. B, 122 (2018) 338.
[4] H. Doi et al., Chem. Phys. Lett. 684 (2017) 427.
[5] E. Shinsho et al., J. Comp. Chem. Jpn., in press.
[6] S. Honda et al., Structure, 12 (2004)1507.

Figure 1. The DPD simulationof chignolin.

P1-22

Energy analysis of mixtures of associated liquids andnon-associated liquids using microcalorimetry andmolecular dynamics simulation
Masao Fujisawa 1

Hirohito Ikeda 2

fujisawa@waka.kindai.ac.jp

ikeda@fukuoka-u.ac.jp

Takayoshi Kimura 3
kimura@chem.kindai.ac.jp

1

2

3

Department of Biotechnological Science, Kindai University, 930 Nishimitani, Kinokawa-City,
Wakayama, Tokyo 649-6493, Japan
Faculty of Pharmaceutical Science, Fukuoka University, 8 -19-1 Nanakuma Fukuoka 814-0180,
Japan
Department of Chemistry, Kindai University, Kowakae, Higashi-osaka 577-8502, Japan

Keywords: microcalorimetry, excess enthalpy, molecular dynamics
Molecular recognition and discrimination may be caused by the interactions among contacting surfacesof colliding molecules in solutions and mixtures. In particular, stereospecific interactions due toneighbouring surfaces may play the leading role in, for examples, enzyme -substance reactions,antigen-antibody reactions, some kinds of mechanisms of the sense s of smell and taste, etc. The
influences of stereospecific interactions in the liquid state have been determined by microcalorimetry
and reported[1-5]. To understand interactions of associated liquids and non-associated liquids, theexcess enthalpies of 1-butylamine + 1-butanol, 1-butylamine + 2-butanol, 2-butylamine + 1-butanol,
2-butylamine + 2-butanol, t-butylamine + 1-propanol, t-butylamine + 2-propanol, 2-butylamine +1-propanol, 2-butylamine + 2-propanol, i-butylamine + 1-propanol, i-butylamine + 2-propanol,
1,2-propanediamine + 1,2-propanediol, 1,2-propanediamine + 1,3-propanediol, 1,3-propanediamine +1,2-propanediol, 1,3-propanediamine + 1,3-propanediol, 1,2-propanediamine + 1-propanol,
1,2-propanediamine + 2-propanol, 1,3-propanediamine + 1-propanol, 1,3-propanediamine + 2-propanol,
1-propylamine + 1,2-propandiol, 1-propylamine + 1,3-propandiol, 2-propylamine + 1,2-propandiol,
2-propylamine + 1,3-propandiol have been measured at 298.15 K using a twin -microcalorimeter. All
excess enthalpies were exothermic and large. In order to clarity that molecular structure effect onamine-alcohol interactions, molecular dynamics simulations were performed.

[1] Kimura, T., Suzuki, K., Takagi, S. Excess Enthalpies of o - and m- Isomers of Difluorobenzene,
Dichorobenzene, Dibromobenzene,+ Methyl Methylthiomethy Sulfoxide or + Dimethyl Sulfoxide
at 298.15 K, Fluid Phase Equilib., 136: 269-278, 1997.
[2] Kimura, T., Suzuki, K., Takagi, S. Excess Enthalpies of Alkylbenzene + Methyl Methyl thiomethy Sulfoxide or + Dimethyl Sulfoxide at 298.15 K, Thermochim. Acta, 328: 55-64, 1999.
[3] Kimura, T., Matsushita, T., Kamiyama, T. Excess Enthalpies of Alkane -1-amines {C n H 2n+1 NH 2 , n=
3 to n=8} + Methyl Methlthiomethyl Sufoxide, + Dimethyl Sufoxide at 298.15 K, Thermochim. Acta,
405: 129-139, 2003.
[4] Fujisawa, M., Kimura, T., Enthalpy and entropy changes on molecular inclusion of 1 -heptanol into
α- and β-cyclodextrin cavities in aqueous solutions
Thermochim. Acta, 416(1-2): 51-54, 2004.
[5] Kimura, T., Fujie. S., Yukiyama, T., Fujisawa, M., Kamiyama, T., Hatsumi, A., Enthalpy andentropy changes on molecular inclusion of pentane derivatives into α -cyclodextrin cavities inaqueous solutions, J. Incl. Phenom. Macrocycl. Chem. 70(3-4): 269–278, 2011.

P1-23In silico modeling of PAX8–PPARγ fusion protein withunknown three-dimensional structure in follicular thyroid
neoplasmKaori Sakaguchi 1

Yoshio Okiyama 2sakaguchi@eniac.scitec.kobe-u.ac.jp

okiyama@nihs.go.jp

Shigenori Tanaka 1
tanaka2@kobe-u.ac.jp

12

Department of Computational Science, Graduate School of System Informatics, Kobe
University, 1-1, Rokkodai, Nada, Kobe, Hyogo 657-8501, Japan
Division of Medicinal Safety Science, National Institute of Health Sciences, 3 -25-26
Tonomachi, Kawasaki-ku, Kanagawa 210-9501, Japan

Keywords: Follicular neoplasm, Fusion protein, Protein modeling, Molecular dynamics simulation
Follicular thyroid neoplasms include benign follicular adenoma and follicular thyroid carcinoma.
Notably, a gene fusion between paired box 8 (PAX8) and peroxisome proliferator-activatedreceptor γ (PPARγ) results in the production of a PAX8 –PPARγ fusion protein (PPFP) in the tumor
cells. PPFP is oncogenic and the PPARγ/PPFP ligand pioglitazone is highly therap eutic in atransgenic mouse model of PPFP thyroid carcinoma [1]. Furthermore, pioglitazone may betherapeutic in patients with PPFP [2]. Except for these issues, the functions of PPFP and its action
mechanisms are not well understood and the three -dimensional (3D) structures of PPFP have notbeen reported so far. Thus, elucidating the 3D structures of PPFP is strongly required to understandthe specific function and oncogenic mechanism of PPFP for drug discovery.
In this study, we propose plausible 3D structures of PPFP (see Figure below) by improving
the modeling method reported at CBI 2017 meeting. We use the amino acid sequence o f PPFP
consisting of PAX8 (Met1–Ala396) and PPARγ (Met1–Tyr505) [1]. The PPFP that binds to a DNA
sequence was constructed as follows: We first extended the structure from PAX8's paired domain
and PPARγ at the both ends of PPFP whose crystal structures are known, and finally linked them.
To validate the predicted PPFP models, we analyzed their dynamical sta bilities with molecular
dynamics simulation and investigated their various physicochemical characteristics.
[1] Giordano, T. J., et al., Pioglitazone Therapy of PAX8-PPARγ Fusion Protein Thyroid Carcinoma ,J. Clin. Endocrinol. Metab., 103: 1277–1281, 2018.
[2] Raman, P., and Koenig R.J., Pax-8-PPAR γ Fusion Protein in Thyroid Carcinoma, Nat. Rev.
Endocrinol, 10:616–623, 2014

Figure: Superposition of three model structures of PPFP

P1-24FMO Study on the Effects of Phosphorylation in Janus
Kinase (JAK) on Ligand Binding
Takahiro Hirata 1

Shusuke Tokutomi 1thirata@eniac.scitec.kobe-u.ac.jp

tokutomi@eniac.scitec.kobe-u.ac.jp

Hirofumi Watanabe 2

Kaori Fukuzawa 3

Shigenori Tanaka 1

hirofumi.watanabe@port.kobe-u.ac.jp

k-fukuzawa@hoshi.ac.jp

tanaka2@kobe-u.ac.jp

1

Graduate School of System Informatics, Kobe University, 1 -1 Rokkodai, Nada-ku, Kobe
657-8501, Japan
2
Education Center on Computational Science and Engineer ing, Kobe University 7-1-48,
Minatojimaminamimachi, Chuo-ku, Kobe 650-0047 Japan3
Department of Physical Chemistry, School of Pharmacy and Pharmaceutical Sciences, Hoshi
University, 2-4-41 Ebara, Shinagawa, Tokyo 142-8501, Japan

Keywords: Janus kinase; Ligand binding; Fragment Molecular Orbital (FMO) method;
Inter-Fragment Interaction Energy (IFIE)
Immunosuppressive drugs such as steroids have been used for a long time, but their targets arebroad and have many side effects. This is why molecular target ed drugs are expected, which act
only on a specific molecule and can be ingested orally with less side effects than the classicalinhibitor. Kinases are drawing attention as targets of these molecular targeted drugs, and among
them, Janus kinase (JAK) inhibitors are noted in the treatment of rheumatoid arthritis.
There are two kinds of complex structures of JAK and ligands: a state in which its own tyrosine(TYR) residue is phosphorylated and that has a phosphorylated tyrosine (PTR) residue, and a state
without it. However, the effect of PTR residues on interaction with ligands are not well known.
Here, the interaction energies between JAK family and ligand molecules were calculated by the
Fragment Molecular Orbital (FMO) method, focusing on the Inter-Fragment Interaction Energies(IFIEs). In this study, JAK with PTR residues and that with TYR changed from PTR werecomparatively analyzed. It was found that the IFIEs between the ligand and the residues which areimportant for ligand binding such as aspartic acid (ASP) and lysin (LYS) varied according to thepresence or absence of PTR. In addition, sometimes the histidine (HIS) residue can be protonatedand its conformation may be modified, so that the electrostatic interaction due to the negative
charge of PTR influences many residues. The IFIE-sum of the phosphorylated structure and that of
the non-phosphorylated structure were found to have a fair correlation, except for the discrepancy
of IFIE between the PTR / TYR site and the ligand. Correlation between the inhibition constant K i
and the IFIE-sum was investigated, but it was difficult to obtain good correlation. Thus, there isroom for further study as to whether and how to perform FMO calculations with or without PTR.
This study was performed in the framework of the FMO drug design consortium (FMODD). We
thank Dr. Fumio Nakajima (Carna Biosciences, Inc.) for useful discussions. The results of FMO
calculations were obtained using the K computer (project ID: hp170183 and hp180147).

P2-01

Design of Anti-Cancer Peptides
with Counterpropagation Artificial Neural Networks
Miyabi Hishinuma 1
hishinuma.m.aa@m.titech.ac.jp

Claudia Neuhaus 2
claudia.neuhaus@pharma.ethz.ch

Gisela Gabernet Garriga 2
gisela.gabernet@pharma.ethz.ch

Masaaki Kotera 4maskot@chemsys.t.u-tokyo.ac.jp

1

2

3

4

Francesca Grisoni 2,3
francesca.grisoni @pharma.ethz.ch

Jan A. Hiss 2
jan.hiss@pharma.ethz.ch

Gisbert Schneider 2gisbert@ethz.ch

School of Life Science and Technology, Tokyo Institute of Technology, 1-11-5, Midorigaoka,
Meguro-ku, Tokyo, 152-0034, JapanETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4,CH-8093 Zurich, Switzerland
University of Milano-Bicocca, Dept. of Earth and Environmental Sciences, piazza della Scienza
1, 20126 Milano, Italy
Department of Chemical System Engineering, School of Engineering, The University of Tokyo,
7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

Keywords: Anti-Cancer Peptides, Machine Learning, Pharmacological Activity Prediction
Anti-Cancer Peptides (ACPs) are drawing attention as potential cancer therapeutics because of
their pharmacological activity. The cancer cell selectivity of certain membranolytic ACPs
originates from their ability to detect differences between the plasma membrane composition andstructure of healthy cells and cancer cells, which means it is difficult for cancer cells to develop
resistance against drugs. [1] However, the structure-activity relationships of ACPs are not fully
understood yet. Effective computational approaches may help to identify new ACPs. [1] We
demonstrated a rational strategy to design ACPs with counterpropagation artificial neural networks(CP-ANNs), [2] which is a predictive model based on self-organizing maps. [3] We constructed avirtual library of 1000 potentially alpha-helical peptides sequences using modLAMP, a Python
library for generating antimicrobial peptides. [4] The generated peptides were subjected to virtual
screening with CP-ANNs models developed to recognize ACPs. 21 peptides picked from thevirtual library were chemically synthesized and tested for their cellular anticancer activity inbioassays.
[1] G. Gabernet, A. T. Müller, J. A. Hiss, G. Schneider, Membranolytic anticancer peptides. Med.
Chem. Commun. 7:2232–2245, 2016.
[2] R. Hecht-Nielsen, Counterpropagation networks. Appl. Opt. 26:4979–4983, 1987.
[3] D. Ballabio, M. Vasighi, A MATLAB toolbox for Self Organizing Maps and supervised neuralnetwork learning strategies. Chemometrics and Intelligent Laboratory Systems. 118:24–32,
2012.
[4] A. T. Müller, G. Gabernet, J. A. Hiss, G. Schneider, modlAMP: Python for antimicrobial
peptides. Bioinformatics (Oxford, England). 33:2753–2755, 2017.

P2-02A newly developed method based on AI-oriented aminoacid interaction mapping (AI-AAM) for efficient virtual
scaffold hopping1

Kyosuke Tsumura, Ph.D. 1

Shino Ohira, Ph.D. 1

kyosuke.tsumura@fujifilm.com

shino.ohira@fujifilm.com

Analysis Technology Center, R&D Management Headquarters, Fujifilm Corporation,
210 Nakanuma, Minamiashigara-shi, Kanagawa 250-0193, Japan

Keywords: Electronic structure calculation, Ligand docking, Protein modeling
When we acquired a seed or lead compound in drug discovery, the candidate often dropped out
during in vivo/vitro testing. We present a new methodology called AI-AAM to obtain multiple
candidate compounds to increase our success rate of new drug development.
For scaffold hopping, we needed a descriptor that could represent the binding affinities of aligand to its target protein. In order to find the descriptor, we assumed that protein-ligand binding
could be described as the set of interactions between a ligand and amino acids. Using quantum
chemical and molecular dynamic simulations 1 , we computed the interactions between a ligand andamino acids to create a new descriptor, Amino Acid Mapping 2 (AAM).
To understand the ability of AAM, we tested the new descriptor with public data sets
(DUD-E: ABL1, hit ratio = 1.7%) 3 . After computing AAM on each compound, we had classified
them based on Euclidean distance using Affinity Propagation 4 . Out of 221 groups, 183 hitcompounds were efficiently classified into small number of groups. It was shown that the potencyof binding affinities of a compound was related to AAM. When we compared the chemical
structures in the “hit” group, we could find the completely different scaffolds. In addition to the
public data sets, we applied AI-AAM to anti-cancer and antibiotic drug discovery projects andcould find new scaffolds with the same order of IC 50 or MIC. The hit ratio was about 10%. We
developed AI-AAM which enabled the efficient ligand-based virtual scaffold hopping.

[1] (a) Gaussian 09, M. J. Frisch, et al., 2009, (b) AMBER 11, D.A. Case, et al., 2011.
[2] Patent pending.
[3] DUD-E, http://dude.docking.org/.
[4] Brendan J. Frey; Delbert Dueck (2007). "Clustering by passing messages between data
points". Science. 315 (5814): 972–976.

P2-03Structure Information Management System in Asahi
Kasei Pharma
Kazufumi Ohkawa 1
okawa.kf@om.asahi-kasei.co.jp

Kenichiro Takaba 1
takaba.kb@om.asahi-kasei.co.jp

Kensuke Akaogi 2

akaogi.kb@om.asahi-kasei.co.jp

12

Laboratory for Medicinal Chemistry, Pharmaceuticals Research Center, Asahi Kasei Pharma
Corporation, 632-1 Mifuku, Izunokuni, Shizuoka 410-2321, Japan
Laboratory for Drug Discovery, Pharmaceuticals Research Center, Asahi Kasei Pharma
Corporation, 632-1 Mifuku, Izunokuni, Shizuoka 410-2321, Japan

Keywords: X-ray structure, FMO, database system
Nowadays, many laboratories have already accumulated a large number of in house X -ray data
(protein-ligand complex structures), which aren’t properly managed (assigned ID or stored in a
database).
Adding that situation, Fragment Molecular Orbital (FMO) and Molecu lar Dynamics (MD) havebecome popular methods for understanding the nature of inte raction between protein and ligand.
These structure related data are desirable to be integratedly managed, so we developed SIMS
(Structure Information Management System) which enable researchers to enter data, track analysis
report, and search protein-ligand interaction.

[1] Kitaura, K., Ikeo, E., Asada, T., Nakano, T., and Uebayasi, M., Fragment MolecularOrbital Method: an Approximate Computational Method for Large Molecules , Chem. Phys.
Lett. , 313:701– 706, 1999.
[2] Salentin,S. et al., PLIP: fully automated protein-ligand interaction profiler, Nucl. Acids Res, 43(W1): W443-W447, 2015.
[3] Gert-Jan Bekker. et al., Molmil: a molecular viewer for the PDB and beyond, Journal ofCheminformatics, 8:42, 2016.

P2-04

Implementation of protein-ligand docking engine
sievgene_M for many- and multi-core processors
Takanori Sugihara 1,2

Takashi Kurosawa 3,2

Hironori K. Nakamura 4

Yoshifumi Fukunishi 6,2Haruki Nakamura 7

t-sugihara@aist.go.jp

Tadaaki Mashimo 5,2
1
2
3
4
5
6
7

Japan Biological Informatics Consortium
Technology Research Association for Next Generation Natural Products Chemistry
Hitachi Solutions East Japan, Ltd.
Biomodeling Research Co., Ltd.
IMSBIO Co., Ltd.
National Institute of Advanced Industrial Science and Technology (AIST)
Institute for Protein Research, Osaka University

Keywords: Ligand docking, in silico screening, high-performance computing
We propose a method to accelerate in-silico screening and decrease the cost required to discoverpharmaceutical candidate chemical compounds. We develop a multithreaded high-performance versionof the protein-ligand docking engine for better performance. The new docking engine is calledsievgene_M. By the subscript M, we mean that it is a version customized for the state-of-the-art manyand multi-core processors such as Intel’s Xeon Phi 7210 and E5, respectively. It is easy to use
sievgene_M in many- and multi-core environment [1,2]. One can just compile and execute it with theIntel’s or GNU’s Fortran compilers like any other application software.
In drug development, it is quite important to decrease labor to find candidate compounds.
Usually, experimental methods are used for ligand screening. In those methods, one can analyze target
protein-ligand systems in detail but the cost increases in proportional to labor.
In-silico screening is a useful automated method based on high-performance computing. In
this method, we can evaluate millions of protein-ligand interactions for short time in an economical
way. We explain the details of the method to parallelize the docking engine for many- and multi-coreprocessors.
The docking engine sievgene_M is a part of the molecular simulation and analysis software
suite myPresto. The newest version of myPresto has just been released through the Internet. Source
codes of sievgene_M can be downloaded at the myPresto portal website and used for free [3].

[1] T. Sugihara, T. Kurosawa, H. K. Nakamura, T. Mashimo, Y. Fukunishi, and H. Nakamura, in
preparation.
[2] Y. Fukunishi, Y. Mikami, H. Nakamura, J. Mol. Graph. Model. 24, 34-45, 2005.
[3] https://www.mypresto5.jp/

P2-05

Bandit Ensemble FMO for Protein-Ligand Binding Affinity
Predictions
Kenichiro Takaba 1
takaba.kb@om.asahi-kaesi.co.jp

1

Kazufumi Ohkawa 2
okawa.kf@om.asahi-kasei.co.jp

Laboratory for Medicinal Chemistry, Pharmaceuticals Research Center, A sahi Kasei Pharma
Corporation, 632-1 Mifuku, Izunokuni, Shizuoka 410-2321, Japan

Keywords: Fragment orbital method, Molecular dynamics, Machine learning, Binding free energy
Fragment orbital method (FMO) has emerged as a powerful computational tool in structure-based
drug discovery. Pair interaction energy decomposition analysis (PIEDA) enables detailed analysis
of protein-ligand interactions and many studies has shown that its interaction energies can be used
to predict protein-ligand binding affinities [1,2]. However, the accuracy is not sufficient enough tobe applied for lead optimization.
To overcome this difficulty, we introduce ensemble FMO where MD simulations are used togenerate multiple protein-ligand complexes and FMO calculations are performed against
ensembles of conformers with explicit water molecules. To assess ensemble FMO, the correlations
between experimental and calculated binding affinities of two systems, internal project A and Pim1
kinase, were examined. Single conformer-based FMO results of Pim1 reported by Watanabe andcoworkers were used as reference in this study [2].
The correlations between experimental pIC 50 values and PIEDA binding energies which were
calculated from MM-optimized single conformers under vacuum conditions were R 2 =0.09 andR 2 =0.53 for internal project A and Pim1, respectively. In contrast, the application of ensemble
FMO was able to improve the correlation for internal project A (R 2 =0.67), whereas the correlation
was the same for Pim1 (R 2 =0.53). If combinations of different PIEDA energies were allowed, thenthe best correlation between the experimental pIC 50 values and ensemble FMO binding energies
were R 2 =0.76 and R 2 =0.62 for internal project A and Pim1, respectively.
Furthermore, inspired by the work from Terayama and coworkers [3], we discuss the applicationof a machine learning method, Best Arm Identification, where unpromising compounds are avoided
at an early stage and the limited amount of computational resources are allocated to more
promising compounds. This approach is expected to maximize the performance of ensemble FMO
where promising compounds are chosen from a set of candidate compounds within a limited
amount of computational resource.
[1] Heifetz, A., et al., The Fragment Molecular Orbital Method Reveals New Insight into theChemical Nature of GPCR-Ligand Interactions, J. Chem. Inf. Model. 2016, 56, 159-172.
[2] Watanabe, C., et al., Theoretical Analysis of Activity Cliffs among Benzofuranone -Class Pim1
Inhibitors Using the Fragment Molecular Orbital Method with Molecular Mechanics
Poisson-Boltzmann Surface Area (FMO+MM-PBSA) Approach, J. Chem. Inf. Model. 2017, 57,
2996-3010.
[3] Terayama, K., et al., Machine learning accelerates MD-based binding pose prediction between
ligands and proteins, Bioinformatics 2018, 34, 770-778.

P2-06

Rapid and Accessible In-silico Macrocycle Design

1

Giovanna Tedesco 1

Mark Mackey 1

giovanna@cresset-group.com

mark@cresset-group.com

Matthias Bauer 1

Paolo Tosco 1

matthias@cresset-group.com

paolo@cresset-group.com

Cresset, New Cambridge House, Bassingbourn Road, Litlington, Cambridgeshire, SG8 0SS, UK

Keywords: Macrocyclization, Bioisostere replacement
An increasingly common medicinal chemistry technique is conformational restriction throughmacrocyclization, in order to attain higher affinity and selectivity for the target coupled to
improved oral bioavailability. 1 Although the concept is simple, the execution is difficult: theproposed linker must have enough flexibility that it can join the proposed cyclization sites without
introducing too much steric strain, but not so much that the entropic benefits of cyclization are lost.
It must be synthetically feasible, must fit into the available space in the activ e site, and ideallyshould make favorable (or at least not unfavorable) interactions with the protein.
Macrocyclization can be seen as a special case of fragment linking, which in turn is a constrained
form of fragment growing. Fragment linking strategies have been recently reported as a highly
successful route to lead optimization. 2 In both linking and macrocyclization the design problem is
to find a moiety which enforces the required geometry between the two link sites.
We present a modification of Cresset’s bioisostere searching tool, Spark, to this problem.
Traditional bioisostere searches specify a fragment to replace in the starting ligand and look forsimilar fragments in a database. Instead, Spark assesses bioisosteric replacements in product space,which allows more complex experiments. A modification of the Spark scoring method to include
similarity to other ligands known to bind in the region of the linker, as well as constraints from theprotein active site and known pharmacophoric requirements, allows sensible ranking of potential
linkers.
Here we apply this technique to several data sets, including a set of recently -reported
pyridine-based BRD4 inhibitors. 3 Results were obtained that are identical or very similar toreported BRD4 macrocycle inhibitors. Also, the distribution of generated linker sizes was in good
agreement with experimental SAR data.
[1] Marsault, E.; Peterson, M. L. Macrocycles Are Great Cycles: Applications, Opportunities, andChallenges of Synthetic Macrocycles in Drug Discovery, Journal of Medicinal Chemistry,
54:1961-2004, 2011.
[2] Mondal, M.; Radeva, N.; Fanlo-Virgós, H.; Otto, S.; Klebe, G.; Hirsch, A. K. H. Fragment
Linking and Optimization of Inhibitors of the Aspartic Protease Endothiapepsin:
Fragment-Based Drug Design Facilitated by Dynamic Combinatorial Chemistry, Angewandte
Chemie International Edition, 55: 9422-9426, 2016.
[3] Wang, L.; McDaniel, K. F.; Kati, W. M. Fragment-Based, Structure-Enabled Discovery ofNovel Pyridones and Pyridone Macrocycles as Potent Bromodomain and Extra -Terminal
Domain (BET) Family Bromodomain Inhibitors. Journal of Medicinal Chemistry, 60:
3828-3850, 2017.

P2-07
A web-based application for the visualization andexploration of the SAR matrix
Atsushi Yoshimori 1

Tohru Tanoue 2

yoshimori@itmol.com

tanoue.toru@infogram.co.jp

Jürgen Bajorath 3
bajorath@bit.uni-bonn.de
1

Institute for Theoretical Medicine, Inc., 4259-3 Nagatsuta-cho, Midori-ku, Yokohama,226-8510, Japan
2
Infogram, Inc., 2-17-19 Hakataekimae, Hakata-ku, Fukuoka 812-0011, Japan3
Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology andMedicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c,
D-53115 Bonn, Germany

Keywords: Structure-activity relationships, Matched molecular pair, Matching molecular series,
SAR matrix
SAR matrix is designed to elucidate SAR information associated with groups of structurally
related active compounds, which are extracted from large compound data sets [1]. The SAR matrix
data structure identifies and organizes compound series produced by structurally analogous
matching molecular series [1, 2]. SAR matrices contain not only active compounds but also many
virtual compounds generated by the matched molecular pair formalism [3]. These virtualcompounds are expected to be attractive candidates for hit-to-lead transformation and lead
optimization. Accordingly, a chemically intuitive and interpretable user-interface for the SAR
matrix method is expected to substantially aid in practical medicinal chemistry applications.
Index of SAR Matrices:

SAR Matrix:

Details of compound:

We are developing a web-based application for visualizing and exploring SAR matrices using
modern javascript libraries. This new application supports medicinal chemists in SAR analysis andlead optimization.
[1] Wassermann, A.M., Haebel, P., Weskamp, N., and Bajorath, J., SAR matrices: automatedextraction of information-rich SAR tables from large compound data sets, Journal of Chemical
Information and Modeling, 52:1769-1776, 2012.
[2] Wawer, M., and Bajorath, J. Local structural changes, global data views: graphicalsubstructure-activity relationship trailing. Journal of Medicinal Chemistry, 54:2944−2951, 2011.
[3] Kenny, P.W., and Sadowski, J., Structure modification in chemical databases. In Chemoinformatics
in Drug Discovery, Oprea, T. I., Ed.; Wiley-VCH: Weinheim, Germany, 271−285, 2005

P2-08

Peptide Docking Method using a Coarse-grained Potential
Sho Matsumoto 1

Ryosuke Tsukamoto 1

matsumoto@cch.cs.tut.ac.jp

tsukamoto@cch.cs.tut.ac.jp

Hitoshi Goto 1
gotoh@tut.jp

1

Computational Chemistry Laboratory, Computer Science and Engineering,
Toyohashi University of Technology, 1-1 Hibarigaoka, Tempaku-cho,
Toyohashi, 441-8580, Aichi, Japan

Keywords: Protein-peptide complex, Docking, Coarse-grained potential
A method for accurately evaluating the recognizing ability of a specific peptide to a target protein
plays a crucial role in research and development for new drug discovery such as antibody or
peptide medicines. In order to perform this in silico evaluation, it is necessary to find the peptide
binding site on the surface of the target protein and evaluate binding energy between protein andpeptide. However, the high affinity between them is possible to give a number of binding site
candidates. In this presentation, we will introduce our peptide docking method that we havedeveloped for accurately and quickly search of some plausible protein-peptide binding sites [1].
In this method, the binding site is scored by applying Krishnamoorthy potential [2] to tetrahedral
four-body relationships between the protein surface and peptide described by is a coarse-grained
protein model. We also adopted Defpol [3] and MSMS [4] methods that evaluate solvent contacting
surfaces, to put search grid points for placing amino acid residues of peptides on the protein
surface. As the exploring algorithm, we tried the elite tree search and Monte Carlo tree search. We
used 28 known crystal structures of protein-peptide complexes as a standard set for verifying theperformance of our methods. Furthermore, it is confirmed that the performance of our methods iscomparable to that of AutoDock Vina, a famous docking system using all-atom model.

[1]
[2]
[3]
[4]

R. Tsukamoto, H. Enomoto, H. Goto. 39th Symposium on Chemoinformatics, P11, 2016.
C. Deutsch, B. Krishnamoorthy, Bioinformatics, 19, 1540-1548, 2003.
C. S. Pomelli, J. Tomasi. J. Comput. Chem, 19, 1758-1776, 1998.
M. Sanner, A. J. Olson, J. C. Spehner, Biopolymers, Vol 38, (3), 305-320, 1996.

P2-09Current status of myPresto computer-aided drug
development suite
Tadaaki Mashimo 1

Yoshifumi Fukunishi 1,2

Takeshi Kawabata 3

mashimo-tadaaki@aist.go.jp

y-fukunishi@aist.go.jp

kawabata@protein.osaka-u.ac.jp

Takashi Kurosawa 1,4
takashi.kurosawa.uh@hitachi-solutions.com
1

2

3
4

5

Chisato Kanai 1,5Haruki Nakamura 3

kanai@k-ct.jp

harukin@protein.osaka-u.ac.jp

Technology Research Association for Next Generation Natural Products Chemistry, 2-3-26,
Aomi, Koto-ku, Tokyo 135-0064, Japan
Molecular Profiling Research Center for Drug Discovery (molprof), National Institute ofAdvanced Industrial Science and Technology (AIST), 2 -3-26, Aomi, Koto-ku, Tokyo 135-0064,
Japan
Institute for Protein Research, Osaka University, 3 -2 Yamadaoka, Suita, Osaka 565-0871, Japan
Hitachi Solutions East Japan, Ltd., Kawasaki Ekimae Tower Riverk, 12-1 Ekimae-honcho,
Kawasaki-ku, Kawasaki-shi, Kanagawa-ken 210-0007, japan
Kyoto Constella Technologies Co., Ltd., 4th Flr., Kyozome Kaikan, 481 Tourouyama -cho,
Nakagyo-ku, Kyoto, 604-8225, Japan

Keywords: myPresto5, GUI, PDBx/mmCIF, Molecular structure generation, G estimation“myPresto” [1] is a computer software suite for drug developments equipped with molecular
dynamics (MD) simulation, molecular docking simulation, virtual compound screening, etc.
myPresto is one of the few commercial-free MIT/free-BSD like computer programs and all thesource codes are available with user’s and programmer’s guides. myPresto5 was released at Feb.
2018 and has been updated every month . In the version upgrade, a graphical user interface(GUI) program "myPresto portal" was newly added to myPresto. The Protein Data Bank has
introduced new file format (PDBx/mmCIF) in addition to the conventional PDB format. To accept
this new format, myPresto provides the file conversion tools. ReCGen (by Kyoto ConstellaTechnologies Co., Ltd.) can generate new drug-like molecular structures based on compound
databases. We renewed some programs including our compound database [2]. In addition, we havedeveloped the docking-score QSAR
method [3] which is binding-freeenergyG) estimation method using
ChEMBL
database.
Thedocking-score-QSAR method approximate
the G values with a linear combinationof docking scores against multiple protein
pockets of the query compound inquestion. The accuracy was about Q 2 =0.7-0.8 (average error = 1 kcal/mol) in
4-fold cross validation tests. The training
session of myPresto will be opened [4].
[1] myPresto home page: https://www.mypresto5.jp/[2] LigandBox database: http://www.mypresto5.com/ligandbox/cgi -bin/index.cgi?LANG=ja[3] Fukunishi, Y., Yamashita, Y., Mashimo, T. and Nakamura, H. Prediction of Protein compound
Binding Energies from Known Activity Data: Docking-score-based Method and its Applications,
Molecular Infomatics, 37:1700120, 2018.
[4] See also <CBI2018 Sponsor's workshop> Biomodeling Research Co., Ltd. on Oct. 10 (Wed).

P2-10

Molecular Block Inserting: A Simple and Fast Algorithm
for Efficient Generation of Ring Conformations

1
2

Hajime Sugiyama 1

Akihiko Ueda 1

sugiyama.hajime@jp.fujitsu.com

a.ueda@jp.fujitsu.com

Azuma Matsuura 1, 2Atsushi Tomonaga 1

matsuura.azuma@jp.fujitsu.com

atomonaga@jp.fujitsu.com

Fujitsu Limited, 1-2-70 Konan, Minato-ku, Tokyo 108-0075, Japan
Fujitsu Laboratories Ltd., 10-1 Wakamiya, Morinosato, Atsugi, Kanagawa 243 -0124, Japan

Keywords: Molecular Mechanics Calculation, Conformation Search
Macrocycles, which are more than 12-membered ring molecules, has been emerging modality as
therapeutics agents, expanding drug target spaces which had not been covered with small
molecules and large biotics. The novel modality hides the possibility of causing a mazing successof drug development because of its flexibility and diversity being inherent in molecular size andring topology.
In contrast with those advantages, the use of rational design for macrocycles consisting ofstructural based drug design techniques and molecular simulations has been somehow limited yet.
Both greater degree of freedom and complicated ring closure condition are prone to make it
difficult to predict their stable conformation, not to mention their binding structure to a target
protein.
Here, the authors propose a simple and fast method to generate ring molecule conformations in areasonable time. It is based on an asymptotic molecular progression; a molecular block is inserted
between two bonded atoms of a ring molecule in a lowe r energy conformation, and then the entire
structure of the molecule is energy-minimized. Finally that of the target molecule conformations isgenerated.
Besides an introduction to the method, a result of its application is also shown.

P2-11
Comprehensive Protein-Ligand Interaction Analysis:
FMO Calculation on the complexes of a Human Protease
Renin and its Inhibitors
Yoichiro Yagi 1

Takatomo Kimura 2

yagi@ee.ous.ac.jp

kimura@konankako.co.jp

Chiduru Watanabe 3
chiduru.watanabe@riken.jp

Hirotomo Moriwaki 3
hirotomo.moriwaki@riken.jp

Yoshio Okiyama 4okiyama@nihs.go.jp

Shigenori Tanaka 5

Teruki Honma 3

Kaori Fukuzawa 6

tanaka2@kobe-u.ac.jp

honma.teruki@riken.jp

k-fukuzawa@hoshi.ac.jp

1

2
3

4
5

6

Department of Electrical and Electronic Engineering, Okayama University of Science,
1-1 Ridai-cho, Kita-ku, Okayama 700-0005, Japan
Konan Chemical Industry Co., Ltd., 5-12 Nakagawa-cho, Takatsuki, Osaka 569-0066, JapanRIKEN Center for Biosystems Dynamics Re search, 1-7-22 Suehiro-cho, Tsurumi-ku,
Yokohama, Kanagawa 230-0045, Japan
National Institute of Health Sciences, 1 -18-1 Kamiyoga, Setagaya-ku, Tokyo 158-8501, Japan
Department of Computational Science, Graduate School of System Informatics,
Kobe University, 1-1 Rokkodai, Nada-ku, Kobe, Hyogo 657-8501, Japan
School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara,
Shinagawa-Ku, Tokyo 142-8501, Japan

Keywords: Renin, Inhibitor, Fragment molecular orbital (FMO) calculation, Inter-fragmentinteraction energy (IFIE), Visualized cluster analysis (VISCANA)
It is well known that a human protease, renin, is the first component of the renin -angiotensin
system in hypertension. In our continuous efforts, we have performed the fragment molecularorbital (FMO) calculation on the renin-inhibitor complexes at FMO2-MP2/6-31G* level by using
ABINIT-MP/BioStation program [1]. We found that a strong correlation between the binding
energies of renin and inhibitors and the activity value on 50% inhibitory concentration (IC 50 ) of theinhibitors [2,3]. The aims of this study are to classify inhibitors from a viewpoint of the interaction
between renin and inhibitor, such as a value of interaction energy, interacting amino acid residues,etc., and to make good use of our computational methods to in silico drug design. In this research,
we examine the feature of the inter-fragment interaction energies (IFIEs) between renin andinhibitors that obtained from FMO calculation on the 20 structures of renin-inhibitor complexes byusing visualized cluster analysis (VISCANA) method implemented in BioStationViewer.
This research was done in activities of the FMO drug design consortium (FMODD). The results
were obtained using the K computer (project ID: hp180147). Pair interaction energy decomposition
analysis (PIEDA) calculation was carried out by using MIZUHO/BioStation software package.
[1] Tanaka, S., Mochizuki, Y., Komeji, Y., Okiyama, Y., and Fukuzawa, K., Electron -correlated
fragment-molecular-orbital calculations for biomolecular and nano systems, Physical Chemistry
Chemical Physics, 16:10310-10344, 2014.
[2] Yagi, Y., Kimura, T., Watanabe, C., Okiyama, Y., Tanaka, S., Honma, T., and Fukuzawa, K.,
Fragment Molecular Orbital Calculations on the Interaction and Binding Energies of Human
Protease Renin and its Inhibitors, CBI Annual Meeting 2016, P1-08, 2016.
[3] Yagi, Y., Kimura, T., Watanabe, C., Okiyama, Y., Tanaka, S., Honma, T., and Fukuzawa, K.,
The Role of Water Molecules in Protein-Ligand Binding: Fragment Molecular Orbital
Calculations on the Complexes of Renin with its Inhibitors, CBI Annual Meeting 2017, P2-08,
2017.

P2-12

Use of the multilayer fragment molecular orbital method topredict the rank order of protein-ligand binding affinities:
A case study using tankyrase2 inhibitors
Noriaki Okimoto 1
okimoto@riken.jp
1

Takao Otsuka 1
totsuka@riken.jp

Yoshinori Hirano 1
hirano@riken.jp

Makoto Taiji 1
taiji@riken.jp

RIKEN, Center for Biosystems Dynamics Research, Building B, 6-2-4 Furuedai, Suita, Osaka
565-0874, Japan

Keywords: affinity prediction, molecular docking, molecular mechanics calculation, quantum
chemical calculation
In computational drug discovery, ranking a series of compound analogues in the order that is
consistent with the experimental binding affinities remains a challenge. Many of the computational
methods available for evaluating binding affinities have adopted molecular mechanics (MM) based
force fields, although they cannot completely describe protein–ligand interactions. In contrast,
quantum mechanics (QM) calculations play an important role in understanding the protein-ligand
interactions; however, their huge computational costs hinder their application in drug discovery.
In this study 1 , we evaluated the ability to rank the binding affinities of tankyrase2 ligands by
combining both MM and QM calculations. Our computational approach uses the protein-ligand binding
energies obtained from a cost-effective multilayer fragment molecular orbital (MFMO) method 2,3
combinedwiththesolvationenergyobtainedfromthemolecular
mechanics-Poisson-Boltzmann/surface area method (MM-PB/SA) to predict binding affinity. Thisapproach enabled us to rank tankyrase2 inhibitor analogues, outperforming several MM-based methods,
including rescoring by molecular docking and the MM-PB/SA method alone. Our results show that thiscomputational approach using the MFMO method is a promising tool for predicting the rank order ofthe binding affinities of inhibitor analogues.

References1.

Okimoto, N.; Ohtsuka, T.; Hirano, Y.; Taiji, M. ACS OMEGA 2018, 3(4), 4475-4485.

2.

Fedorov, D. G.; Ishida, T.; Kitaura, K. J Phys Chem A 2005, 109(11), 2638-2646.

3.

Otsuka, T.; Okimoto, N.; Taiji, M. J Comput Chem 2015, 36(30), 2209-2218.

P2-13

Prediction of the synthetic accessibility oforganic compounds using computational technology
Kentaro Takai 1

Seiji Fukui 1

takai.kentaro@jp.fujitsu.com

fukui.seiji@jp.fujitsu.com

Akihiko Ueda 1

Atsushi Tomonaga 1

a.ueda@jp.fujitsu.com

1

atomonaga@jp.fujitsu.com

Fujitsu Limited, 1-2-70 Konan, Minato-ku, Tokyo 108-0075, Japan

Keywords: synthetic accessibility, retrosynthetic analysis, reaction scheme
To know the synthetic accessibility of the candidate compounds as drugs is important in drug
design procedure. For computer-derived compounds which are often difficult to synthesize, it isespecially important to know the accessibility before selecting more promising ones from themassive compounds. In our IT-based drug design (IT-Soyaku), compounds are designed using
OPMF (Optimum Packing of Molecular Fragments)[1] and the binding affinity between a target
protein and a compound is evaluated using MAPLECAFEE (MAssively ParalLEl Computation of
Absolute Binding FreE Energy)[2]. We can get a massive number of designed compounds,
however it is difficult for synthetic chemists to select synthetically feasible compounds in a shorttime. Therefore, an automatic synthetic accessibility predicting system is highly expected (Figure
1).
Our developed system includes three functions, i.e., retrosynthetic analysis to decompose a target
compound to starting compounds, reagent searching from commercially available or in-housecompound DB, and scoring the synthetic accessibility. Criteria of compounds to be syntheticallyaccessible are as follows, (i) all decomposed compounds exist in the DB. (ii) the score is below theallowable value determined by chemists empirically. In order to improve accuracy, we used not
only reaction schemes defined by rearrangement data of atom bonding status before and afterreactions but also limitation rules to applicability of the scheme to avoid inconvenience such asside reactions in the retrosynthetic analysis. In the system, we used our own method to decompose
the compounds and to select reactions[3].

Figure 1. Flow of IT-Soyaku.
[1] T. Yamashita, A. Ueda, T. Mitsui, A. Tomonaga, S. Matsumoto, T. Kodama, H. Fujitani, Molecular
dynamics simulation-based evaluation of the binding free energy of computationally designed drugcandidates: importance of the dynamical effects, Chem. Pharm. Bull. 62:661-667, 2014.
[2] W. Mitchell and S. Matsumoto, Large-scale integrated super-computing platform for next
generation virtual drug discovery, Curr. Opin. Chem. Biol. 15:553-559, 2011.
[3] A. Tomonaga and K. Kasai, Expression of reaction, reaction data base and recording medium ofreaction information, Japanese Patent No. JP-10/218813-A (1998).

P2-14

A Concept of Automated Lead Optimization Method byCompound Property Enhancement and Learning to Rank
Nobuaki Yasuo 1 , 2Keisuke Watanabe 1

Naoki Arai 1{yasuo, watanabe, arai}@cbi.c.titech.ac.jp

Hideto Hara 3

Kentaro Rikim aru 3{hideto.hara, kentaro.rikimaru}@takeda.com

1

2
3
4

Masakazu Sekijim a 1 , 4

sekijima@c.titech.ac.jp

De pa rtm e nt of Com pute r Sc ie nce , Tokyo Inst it ute of Te c hnol ogy, J 3-23, 4259,
Na ga t suta , Mi dori-ku, Yokoha ma , Ka na ga wa 226-8503, Ja pa n
J SPS Re se arc h fe ll ow DC1, J 3-23, 4259, Na ga t sut a , Mi dori -ku, Yokoha ma , Ka na ga wa
226-8503, J a pa n
Shona n Re sea rc h Ce nte r, Ta ke da Pha rma ceut i ca l Com pa ny L im i te d, 2-26-1,
Mura oka hi ga shi, Fuj i sa wa Ka na ga wa 251 -8555, Ja pa n
Adva nce d Com put at i ona l Drug Di sc ove ry Uni t, Tokyo Inst i t ut e of Tec hnol ogy, J 3-23,
4259, Na gat sut a, Mi dori -ku Yokoha ma , Ka na ga wa 226-8503, J a pa n

Keywords: Lead optimization, Drug discovery, Machine Learning, Property enhancement
Lead optimization is an important step in drug discovery, where the chemical structures of drugcandidates are modified to improve efficacy, selectivity, physicochemical properties, and toxicity.
There is much demand for the automation of lead optimization due to the difficulty and the cost of
the process. 1 Here we show a concept of automated lead optimization method, which outputs
optimized compounds from input compounds. The algorithm is basically the iterative“exploration” and “evaluation” of compounds. The exploration is the process to find next
candidate compounds based on the property enhancement system, which is previously reported byour group. 2 The evaluation is the process to score the candidates, which can be accomplished by
the prediction of the drug-likeness. However, previous drug-likeness prediction is not optimized to
distinguish the difference in the same series of compounds. In this study, we show the concept ofautomated lead optimization system and examined the evaluation module using learning to rank
methods. The dataset is the actual compounds used in drug discovery projects, which have beenpreviously synthesized at Takeda Pharmaceutical Company Limited. We predicted the order ofsynthesis of compounds. The advantage of this method is that all factors involved in the
optimization were implicitly considered in the order of synthesis. We compared six models oflearning to rank methods.
[1] S. M. Paul, D. S. Mytelka, C. T. Dunwiddie, C. C. Persinger, B. H. Munos, S. R. Lindborg, and
A. L. Schacht, How to improve R&D productivity: the pharmaceutical industry’s grandchallenge, Nature reviews Drug discovery, 9.3:203–214, 2010.
[2] N. Arai, Y. Shunsuke, N. Yasuo, R. Yoshino, M. Sekijima, Compound property enhancement byvirtual compound synthesis, Journal of Bioinformatics and Computational Biology (In Proc.
GIW2017), doi.org/10.1142/S0219720018400164, 2018.

P2-15

Establishment of a Method for Analyzing Transcriptional
Regulatory Mechanisms of Genes Grouped by Orthogonal
Linear Separation Analysis (OLSA)
Shotaro Maedera 1

Tadahaya Mizuno 1

mdera0130@gmail.com

tadahaya@mol.f.u-tokyo.ac.jp

Setsuo Kinoshita 1
setsuo.kinoshita@gmail.com

1

Hiroyuki Kusuhara 1
kusuhara@mol.f.u-tokyo.ac.jp

Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The
University of Tokyo, Japan

Keywords: bigdata analysis, in silico drug analysis, computational pharmacology
Recently, the technologies to obtain omics data have been advancing and studies utilizing omics
data analysis have clarified various biological phenomena. Previously, we developed a new omics
data analysis method, Orthogonal Linear Separation Analysis (OLSA), which contracts numerous
variables of omics data to mathematical vectors including biological responses based on factor
analysis. With OLSA, we analyzed the transcriptome data of breast cancer cell line (MCF -7)
responses to 318 drugs obtained from Connectivity Map and contracted the cell responses to the
118 vectors. We attributed 47% of the vectors to biological annotations and there were vectors
including autophagy induction effects, estrogen like effects, and others, which was consistent withliterature reports and in vitro analysis. However, the underlying molecular mechanisms of vectors
generated by OLSA are unclear because the method is unsupervised.
To understand the molecular mechanisms, we devised a method to discover transcription factors
regulating the coordination of gene expression changes which characterize each vector . Thismethod is divided into the following two steps: (i) extracting characteristic genes from the vector,and (ii) calculating the affinity between the DNA sequence around the transcription start site of thegenes and transcription factors with 941 position weight matrices obtained from JASPAR
automatically. We analyzed the 118 vectors with the method and detected 85 relationships between
the vectors and transcription factors including HSF1, EGR, p53, and IRF1. The result wasconsistent with literature reports and especially the drugs which strongly changed the expression
levels of coordinated genes corresponding to the p53 -related vector were mostly reported toactivate p53. We performed real time PCR and western blotting of MCF-7 stimulated with thedrugs which were not reported to activate p53 but predicted to have a p53 activating effect, andseveral of them actually increased the expression levels of p53 activation markers.
We succeeded in constructing the automatic method to elucidate the underlying molecularmechanisms of vectors generated by OLSA. This pipeline will contribute to understanding
pharmacological properties of drugs.
[1] Lamb J., Crawford E. D., Peck D., Modell J. W., Blat I. C., Wrobel M. J., Lerner J., Brunet J.
P., Subramanian A., Ross K. N., Reich M., Hieronymus H., Wei G., Armstrong S. A., Haggarty S. J.,
Clemons P. A., Wei R., Carr S. A., Lander E. S., and Golub T. R.., The Connectivity Map: using
gene-expression signatures to connect small molecules, genes, and disease. , Science, 2006

P2-16

Statistical analysis of inter- and intramolecularinteractions for drug design based on FMO database
Chiduru Watanabe 1

Daisuke Takaya 1

Shunpei Nagase 1

chiduru.watanabe@riken.jp

daisuke.takaya@riken.jp

shunpei.nagase@riken.jp

Kikuko Kamisaka 1

Kaori Fukuzawa 1,2

Teruki Honma 1

kikuko.kamisaka@riken.jp

k-fukuzawa@hoshi.ac.jp

honma.teruki@riken.jp

1

2

RIKEN Center for Biosystems Dynamics Research 1 -7-22 Suehiro-cho, Tsurumi-ku,
Yokohama, Kanagawa 230-0045, Japan
School of Pharmacy and Pharmaceutical Sciences , Hoshi University 2-4-41 Ebara,
Shinagawa-ku, Tokyo 142-8501, Japan

Keywords: interaction energy database, FMO, ligand binding, molecular recognition
We developed “FMO database” [1] to collect >2000 entries of fragment molecular orbital (FMO)
calculation [2] data using X-ray crystal structures, NMR structures, and MD snapshots obtained
from the activities of FMO drug design consortium (FMODD) [3]. This database contains data ofinter- and intramolecular interaction energies and atomic charge, etc., based on quantum
calculations.
In this work, we statistically examined distribution of inter-fragment interaction energies (IFIEs)
which were classified by fragment charge, type of amino acid residues, and energy components
such as electrostatic, charge transfer, and dispersion interaction energies by pair interaction energy
decomposition analysis (PIEDA). The purpose of this research is to reveal an essential
interaction(s) for the recognition of ligand and protein by the statistical analysis of IFIEs. In
addition, we analyzed the distributions of characteristic interaction energy values such as hydrogenbond and CH/ interactions. It is expected that criteria for interaction energy value to judge
whether or not interaction that is essential for the molecular recognition and the construction of
tertiary structure will be established. The FMO database is now opening to FMODD members;
however it will be released to the public by the end of fiscal year 2018.
Acknowledgement
This research was done in activities of the FMODD [3]. The results of FMO calculations wereobtained using the K computer (project ID: hp180147). PIEDA calculations were done by using
MIZUHO/BioStation software package. This research was partially supported by Platform Projectfor Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug
Discovery and Life Science Research (BINDS)) from AMED under Grant Number JP18am0101113.
Finally CW and DT acknowledge JSPS KAKENHI Grant Number JP18K06619.
[1] URL of FMO database: http://drugdesign.riken.jp/FMODB[2] Tanaka, S., Mochizuki, Y., Komeiji, Y., Okiyama, Y., Fukuzawa, K., Electron -Corrected
Fragment Molecular Orbital Calculations for Biomolecular and Nano Systems , Phys. Chem.
Chem. Phys. 16:10310-10344, 2014.
[3] http://eniac.scitec.kobe-u.ac.jp/fmodd/index.html

P2-17Development of FMO database and its recent updates
Daisuke Takaya 1

daisuke.takaya@riken.jp

Chiduru Watanabe 1

chiduru.watanabe@riken.jp

Kikuko Kamisaka 1
kikuko.kamisaka@riken.jp

1

Shunpei Nagase 1

shunpei.nagase@riken.jp

Teruki Honma 1
honma.teruki@riken.jp

RIKEN Center for Biosystems Dynamics Research 1 -7-22 Suehiro-cho, Tsurumi-ku, Yokohama,
Kanagawa 230-0045, Japan

Keywords: FMO, interaction energy database, protein-ligand interaction.
In structure-based drug design, it is important to understand protein -ligand interactions such ashydrogen bonds, electrostatic interactions and the van der Waals interactions. For further
understanding of more detailed interactions, our group has been focusing on the quantum
mechanics (QM) which calculate the effects of donating and withdrawing electrons and canappropriately deal with the cation-π, CH-π and π-π interactions. Generally, QM calculation takes
highly computational cost for applying biological macromolecules such as protein -ligand complex
structures. Fragment molecular orbital (FMO) method [1] enables us to efficiently perform ab
initio QM calculations by many-body expansion technique with fragmentation. The fragmentation
technique of FMO method leads to calculate fragment -fragment interaction energies, which areuseful for analyzing protein-ligand interactions. The pairwise fragment-fragment interactionenergies are called as pair interaction energy (PIE) or inter-fragment interaction energy (IFIE). The
FMO method is widely applied to analysis of the protein -ligand interaction. [2, 3]
Although IFIE data calculated by the FMO method provides rich information about theinteractions and will be useful for researchers in other fields, FMO calculation settings such as
preprocess of an input structure, parameter tunings are difficult for not experienced scientists.
Many computer resources are needed to calculate many protein-ligand complexes. From theviewpoint of big data, it is expected that IFIE between each amino acid residue and a ligand
molecule may be used as descriptors for statistical approaches such as linear regression andmachine learning for protein-ligand interaction analysis. Accumulation of large scale of IFIE data
is important for the purpose. In that situation, our group is developing FMO database [4], forproviding IFIE data. The FMO database provides user friendly interfaces to access IFIE data andsummarize IFIE and PIEDA data from ligand binding point of view. In this poster, we introduce
recent research progress and development of the FMO database.
Acknowledgement
This research was done in activities of the FMO drug design consortium [5]. The results of FMO
calculations were obtained using the K computer (project ID: hp180147). PIEDA calculations weredone by using MIZUHO/BioStation software package. This research was partially supported by
Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting
Innovative Drug Discovery and Life Science Research (BINDS)) from AMED under Grant Number
JP17am0101001. Finally DT and CW acknowledge JSPS KAKENHI Grant Number 18K06619.
1.
2.
3.
4.
5.

Kitaura, K. et al. Chem. Phys. Lett. 1999, 313, 701-706.
Takaya, D. et al. Bioorg. Med. Chem. 2018 submitted
Watanabe, C et al. J. Chem. Inf. Model. 2017, 57, 2996-3010
FMODB URL: http://drugdesign.riken.jp/FMODB
http://eniac.scitec.kobe-u.ac.jp/fmodd/index.html

P2-18Subtype specificity analysis of estrogen receptor using
fragment molecular orbital method
Yuya Seki 1

s151080@hoshi.ac.jp

Satoshi Anzaki 2

Shigenori Tanaka 2

anzaki@eniac.scitec.kobe-u.ac.jp

Takayuki Furuishi 1
t-furuishi@hoshi.ac.jp
1
2

Tsukasa Kato 1

m1814@hoshi.ac.jp

tanaka2@kobe-u.ac.jp

Kaori Fukuzawa 1

Etsuo Yonemochi 1

k-fukuzawa@hoshi.ac.jp

e-yonemochi@hoshi.ac.jp

Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 142-8501, Japan
Kobe University Graduate school, 1-1, Rokkodai-cho, Nada-ku, Kobe, Hyogo 657-8501, Japan

Keywords: Estrogen Receptor β, Fragment Molecular Orbital Method (FMO), PIEDA,
Protein-Ligand Interaction, Subtype Selectivity
In order to clarify subtype specificity between the estrogen receptor (ER) α/β and ligands, werevealed the interaction between the ligand and s urrounding amino acid residues using the
Fragment Molecular Orbital (FMO) method [1]. Structural features of various ligands which affectsubtype selectivity were revealed, based on similarity of binding mode with ER .
We calculated all the available PDB structures of the ER-ligand complex at the
FMO2-MP2/6-31G* level. We analyzed the interaction energies (IFIE) and their energy component(PIEDA) between the ligand and surrounding amino acid residues. Solvent effects were consideredby the FMO + MM-PBSA method [2]. We also performed clustering of ligands by similarity ofinteractions with receptor using the VISCANA analysis [3]. The ABINIT-MP program and K
computer were used for FMO calculations, and Amber program was used for solvation free energy
calculations.
By comparing the interaction between ligand and amino acid residue s of ER, it was found that theinteraction with mutated residues among subtypes (Met336Leu and Ile373Met) were different, andit was also suggested that such a difference affects the selectivity of subtypes. In addition, it was
revealed that both hydrogen bond with Glu305 and CH/π interaction with Phe356 were essential
for ligand binding of ERβ, and stable interaction with Met336 and His475 would be related to β
selectivity. Such FMO based specification of residues with key interaction is considered to be
useful for drug design.
This research was done in activities of the FMO drug design consortium (FMODD). The results
were obtained using the K computer (project ID: hp180147). PIEDA calculation was done by using
MIZUHO/BioStation software package.
[1] K. Kitaura, E. Ikeo, T. Asada, T. Nakano, and M. Uebayas hi, Chem. Phys. Lett., 313:701–706,
1999.
[2] C. Watanabe, H. Watanabe, K. Fukuzawa, L.J.Parker, Y. Okiyama, H. Yuki, S. Yokoyama, H.
Nakano, S. Tanaka, and T. Honma, J. Chem. Inf. Model., 57:2996 -3010, 2017.
[3] S. Amari, M. Aizawa, J. Zhang, K. Fukuzawa, Y. Mochizuki, Y. Iwasawa, K. Nakata, H.
Chuman, and T. Nakano, J. Chem. Inf. Model., 46:221–230, 2006.

P2-19Application of molecular dynamics simulation in drug
design: case study of tankyrase2
Yoshinori Hirano 1

Noriaki Okimoto 1

hirano@riken.jp

okimoto@riken.jp

Makoto Taiji 1
taiji@riken.jp

1

Laboratory for Computational Molecular Design, Center for Biosystems Dynamics Research(BDR), RIKEN, 6-2-4 Furuedai, Suita, Osaka 565-0874, Japan

Keywords: Molecular docking, Molecular dynamics, multiple receptor conformations
In early stage of drug discovery research, computational screening approach is widelyused to find hit compounds. Molecular docking is one of the computational methods for thecompound screening. This method is generally docks a flexible ligand to a specific (static)
protein structure and predicts the binding pose and binding affinity for each ligand in a practical
time. It is well known that proteins fluctuate dynamically under physiologic conditions, and thisflexibility is essential for ligand binding. Therefore, the ability of molecular docking using a
specific ligand binding is not always effective for prediction of binding pose and binding affinity.
To resolve these problems, incorporation of protein flexibility into molecular docking is required.
Molecular docking using multiple receptor conformations (“ensemble docking” [1-3]) is one ofapproaches to consider protein flexibility. These multiple receptor conformations can be obtainedby X-ray crystallographic structures and/or structures sampled by molecular dynamics (MD)simulations. However, there still remains difficulty on proper generation and selection of multiple
receptors used for ensemble docking.
In this study, we performed several MD simulations of tankyrase2 with and without ligand
and investigated the conformational changes of the ligand binding pockets. As a result, we foundthat MD simulations of apo X-ray structures induced reduction of ligand binding pocket, on theother hand, those of holo structures showed different behavior of the binding pocket. Furthermore,
we analyzed these characteristic binding pocket conformations and evaluated performance ofmolecular docking using these multiple conformations.
References:[1] Totrov M. and Abagyan R., Current Opinion in Structure Biology, 2008, 18:178-184[2] Barril X. and Morley S. D., J. Med Chem. 2005, 48: 4432-4443.
[3] Knegtel R. M. et al., J. Mol. Biol. 1997, 266:424–40.

P2-20
Bioisosteric conversion based on electrostatic potential
Shunpei Nagase 1

Daisuke Takaya 1

Chiduru Watanabe 1

shunpei.nagase@riken.jp

daisuke.takaya@riken.jp

chiduru.watanabe@riken.jp

Hitomi Yuki 1

Yoshio Okiyama 1,2

Teruki Honma 1 ,3

hitomi.yuki@riken.jp

o kiyama@nihs.go.jp

honma.teruki@riken.jp

1
2
3

RIKEN Center for Biosystems Dynamics Research, 1 -7-22, Suehiro-cho, Tsurumi-ku,
Yokohama, Kanagawa 230-0045, Japan
Division of Medicinal Safety Science, National Institute of Health Sciences, 3-25-26
Tonomachi, Kawasaki-ku, Kanagawa 210-9501, JapanRIKEN Cluster for Science and Technology Hub, 1-7-22, Suehiro-cho, Tsurumi-ku,
Yokohama, Kanagawa 230-0045, Japan

Keywords: quantum mechanics, electrostatic potential, electrostatic similarity, lead optimization
In lead optimization, a structure should be modified to improve its pharmacokinetic properties
while maintaining the activity. Several studies have reported in silico screening using theelectrostatic potential (ESP) obtained from a quantum mechanical (QM) calculation[1,2].
However, there are few researches on chemical structural conversion systems utilizing accurate
ESP based on a QM method.
In this research, we newly developed Tscore that characterizes the similarity of ESP for thischemical structural conversion system. Each ESP was calculated from a substructure obtained byremoving the maximum common structure, and expressed by grid data. A Tscore was calculated bycomparing the ESPs at the same coordinates of two compounds. To demonstrate the advantage ofthis method, we evaluated R 2 between the ESP similarity and activity similarity of compounddataset assayed in the process of search for inhibitors of hematopoietic cell kinase (HCK) [3]. As aresult, the R 2 value became higher in the case of the compounds over a certain threshold of
Tanimoto Combo[4] that indicates both of the shape and color similarities of two substructures tobe compared. It was shown that compounds with higher ESP similarities tend to have similar
activities. In addition, we found that if a substructure with Tscore exceedi ng a certain threshold isselected in a chemical conversion, the decrease in activity can be suppressed. We expect thissubstructure transformation method based on the QM-ESP approach can reasonably optimize leadstructures for drug design.
Acknowledgments: The results were obtained using the "HOKUSAI GreatWave" of RIKEN Head
Office for Information Systems and Cybersecurity(ISC).
[1] Muchmore S. W., Souers A. J., and Akritopoulou-Zanze I., The use of three-dimensional shapeand electrostatic similarity searching in the identification of a melanin -concentrating hormone
receptor 1 antagonist, Chem. Biol. Drug. Des., 67(2):174–176(2006).
[2] Bostrom J., Grant J. A., Fjellstrom O., Thelin A., and Gustafsson D., Potent fibrinolysis
inhibitor discovered by shape and electrostatic complementarity to the drug tranexamic acid , J.
Med. Chem., 56(8):3273–3280(2013).
[3] Saito Y., Yuki H., Kuratani M., Hashizume Y., Takagi S., Honma T., Tanaka A., Shirouzu M.,
Mikuni J., Handa N., Ogahara I., Sone A., Najima Y., Tomabechi Y., Wakiyama M., Uchida N.,
et al., A Pyrrolo-Pyrimidine Derivative Targets Human Primary AML Stem Cells in Vivo, Sci.
Transl. Med., 5(181):181ra52(2013).
[4] Hawkins P. C. D., Skillman A. G., and Nicholls A., Comparison of Shape-Matching andDocking as Virtual Screening Tools, J. Med. Chem., 50(1):74–82(2007).

P2-21

Toward Rational State-Selective Stabilization of GPCR
using Computational Protein Design Strategy
Masaya Mitsumoto 1,2

Ryosuke Nakano 3sanbon@ims.ac.jp

nryosu1995@chiba-u.jp

Takeshi Murata 3,4

Nobuyasu Koga 1,2t.murata@faculty.chiba-u.jp

1

2

3

4

nkoga@ims.ac.jp

Exploratory Research Center on Life and Living Systems, National Institutes of Natural
Sciences, 38, Nishigohnaka, Myodaiji-cho, Okazaki-shi, Aichi 444-8585, Japan
Department of Structural Molecular Science, Graduate School of Physical Sciences,
SOKENDAI, 38, Nishigohnaka, Myodaiji-cho, Okazaki-shi, Aichi 444-8585, Japan
Faculty of Science, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba-shi, Chiba, 263-8522,
Japan
PRESTO, Japan Science and Technology Agency, Kawaguchi Center Building, 4-1-8
Motomachi, Kawaguchi-shi, Saitama, 332-0012, Japan

Keywords: Protein design, GPCR, Intracellular loop 3, Stabilization, Structure infomatics
G-protein coupled receptors (GPCRs) are one of the major drug targets. The atomistic structures
are important clue for structure-based drug design. However, obtaining the structures is difficult,due to the structural instability and fragility caused by flexible intracellular loop 3 (ICL3). To
solve this problem, the GPCR structures were attempted to be stabilized by domain fusion [1],point mutation [2], antibody binding [3] etc. However, these approaches require many
experimental trials and thus very time-and-cost-consuming. In addition, most of solved GPCR
structures are in inactive state; active state structures are also important for drug design. Becauseof these reasons, rational approach to stabilize GPCR in the aimed state needs to be developed.
In contrast to GPCR, de novo designed proteins created by our group are extremely stable [4][5];they formed stable structures even under over 100 degrees C. One of the reasons for the super highstability is possibly because of their loops; we designed the proteins with typical short loop types
observed in naturally occurring proteins. By applying this protein design strategy, we
computationally redesigned ICL3 from backbone level to stabilize GPCR in the aimed state,targeting adenosine A2A receptors for a proof of concept.
[1] Cherezov et al., High-resolution crystal structure of an engineered human beta2-adrenergic G
protein-coupled receptor, Science, 318:1258-1265, 2007.
[2] Vega et al., Conformational thermostabilization of the beta1-adrenergic receptor in a detergent
resistant form, Proc. Natl. Acad. Sci., 105:877-882, 2008.
[3] Hino et al., G-protein-coupled receptor inactivation by allosteric inverse-agonist
antibody, Nature, 482:237-240, 2012.
[4] Koga et al., Principles for designing ideal protein structures, Nature, 491:222-227, 2012.
[5] Lin et al., Control over overall shape and size in de novo designed proteins, Proc. Natl. Acad.
Sci., 112:5478-5485, 2015.

P2-22Development of new ranking method based on MMGBSA
for Virtual Screening
Naoya Asada 1

Yusuke Sakoh 1

naoya.asada@shionogi.co.jp

yusuke.sako@shionogi.co.jp

Kazuya Yasuo 1

Kazunari Hattori 1

kazuya.yasuo@shionogi.co.jp

kazunari.hattori@shionogi.co.jp

1

Shionogi Pharmaceutical Research Center, SHIONOGI & CO., LTD., 3-1-1,
Futaba-cho, Toyonaka-shi, Osaka 561-0825, Japan

Keywords: Virtual Screening, MMGBSA, pareto sort,
MMGBSA scoring method has been widely used for ligand virtual screening (VS) because of its
moderate accuracy and throughput higher than other scoring methods using molecular dynamics[1]. In spite of its numerous applications for VS, it remains some limits such as the dependency on
ligand’s molecular weight and the lack of ligand deformation energy contribution.
To overcome above described limits, we developed a new ranking method based on MMGBSA for
VS using pareto sort (PS) [2]. PS is one of the ranking algorithms, which has been applied for
multi-parameter optimization problem. In this method, six descriptors which represent a variety ofligand properties related to affinity gain were calculated and total rank was computed using PS. We
applied the developed method to six protein -ligand systems (MK14, PDE5A, FXa, PARP1 and two
in-house targets). In 5 out of 6 systems, our method gave higher hit ratio of active compounds thanthat of the conventional MMGBSA method, and in remaining one system, hit ratio was almost
equivalent in both methods. Furthermore, our method gave smaller molecules compared to the
MMGBSA method, because our method overcame the limit of MMGBSA: the dependency on
ligand’s molecular weight.
In conclusion, we developed a new ranking method for VS, which could expect higher hit rat e andmolecules suitable for a starting point for drug discovery.
[1] Bernd Kuhn, Paul Gerber, Tanja Schulz-Gasch, and Martin Stahl., J. Med. Chem. 2005, 48,
4040-4048.
[2] Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, T Meyarivan, Parallel Problem Solving fromNature PPSN VI, 2000, 849-858.

P2-23

Data Analysis Toolkits of Fragment Molecular Orbital
Calculations to Visualize Interaction Energies Using theGUI Plugin for PyMOL
Takaki Tokiwa 1,2
t.tokiwa@dc.tohoku.ac.jp

Takeshi Ishikawa 5
t-ishi@nagasaki-u.ac.jp

Yuji Mochizuki 4,7
fullmoon@rikkyo.ac.jp
1

2

3

4

5

6

7

8

Shogo Nakano 3snakano@u-shizuoka-ken.ac.jp

Sohei Ito 3
itosohei@u-shizuoka-ken.ac.jp

Yuta Yamamoto 4
17lb019f@rikkyo.ac.jp

Kaori Fukuzawa 6,7
k-fukuzawa@hoshi.ac.jp

Hiroaki Tokiwa 4

Yasuteru Shigeta 8

tokiwa@rikkyo.ac.jp

shigeta@ccs.tsukuba.ac.jp

Department of Chemistry, Graduate School of Science, Tohoku University, 6-3, Aoba, Aramaki,
Aoba-ku, Miyagi 980-8578, Japan
Department of Physics, Graduate School of Pure and Applied Sciences, University of Tsukuba,
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8571, Japan
School of Food and Nutritional Sciences, University of Shizuoka, 52-1 Yada, Suruga-ku,
Shizuoka 422-8526, Japan
Department of Chemistry, Rikkyo University, 3-34-1 Nishi-Ikebukuro, Toshima-ku, Tokyo
171-8501, Japan
Graduate School of Biomedical Sciences, Department of Molecular Microbiology and
Immunology, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan
School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara,
Shinagawa-Ku, Tokyo 142-8501, Japan
Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo
153-8505 Japan
Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki
305-8577, Japan

Keywords: FMO calculation, Analysis Toolkit, PyMOL Plugin, Ruby, Python, Visualized energy
Data analysis and visualization toolkits for fragment molecular orbital (FMO) calculations weredeveloped by Ruby and Python programs and named “AnalysisFMO” [1, 2]. AnalysisFMO toolkits
can assist in analyzing the results obtained by FMO calculation program packages such as
GAMESS-FMO, PAICS, and ABINIT-MP [3]. These toolkits include two tools: one is Ruby script
to be able to extract inter-fragment interaction energies (IFIEs) or pair interaction energies (PIEs)
from output file of the FMO calculation packages, and another is a Python [4] script for a PyMOL
plugin [5] to visualize IFIEs or PIEs which displayed on protein structure in the PyMOL Viewer.
These plugin scripts can analyze output data using two analysis modes: “All-pairs” and“Selected-pairs” modes. The former mode can analyze and visualize the residue–residue
interactions such as domain interaction. The later mode, i.e., the script and plugin of a “Selectedpair” mode, can visualize protein–ligand interaction such as protein-drug interactions.
[1] T. Tokiwa, S. Nakano, Y. Yamamoto, T. Ishikawa, S. Ito, K. Fukuzawa, Y. Mochizuki, H.
Tokiwa, Y. Shigeta, in preparation.
[2] AnalysisFMO: http://dfns.u-shizuoka-ken.ac.jp/labs/proeng/custom20.html[3] D. Fedorov and K. Kitaura eds., "The Fragment Molecular Orbital Method: Practical
Applications to Large Molecular Systems", CRC Press (2009).
[4] M. F. Sanner, J. Mol. Graphics Model.17, 57-61 (1999).
[5] PyMOL: http://www.pymol.org/ (accessed August 1, 2018).

P2-24

Theoretical Interaction Analysis between Muscarinic
acetylcholine receptors and Nobiletin by using fragment
molecular orbital calculations
Kanade Shimizu 1

Sundarama Arulmozhiraja 1,2

Yuta Yamamoto 1

18lb026b@rikkyo.ac.jp

raja@rikkyo.ac.jp

17lb019f@rikkyo.ac.jp

Yasuo Watanabe 4

Shizuo Yamada 5

WOO Je-Tae 3
jwoo@isc.chubu.ac.jp

yasuwat@yok.hamayaku.ac.jp

yamada@u-shizuoka-ken.ac.jp

Hiroaki Tokiwa 1,2

tokiwa@rikkyo.ac.jp

1

Department of Chemistry, Rikkyo University, 3-34-1, Nishi-Ikebukuro, Toshima-ku,
Tokyo, 171-8501, Japan
2
AMED-CREST, Japan Agency for Medical Research and Development (AMED), 1-7-1,
Ohte-machi, Chiyoda-ku, Tokyo, 100-0004, Japan3
Department of Biological Chemistry, Research Institute for Biological Functions,
ChubuUniversity;1200 Matsumoto, Kasugai, Aichi 487–8501, Japan
4
General Health Medical Center, Yokohama College of Pharmacy, 601 Matano, Totsuka,
Yokohama, 245-0066, Japan
5
Center for Pharma-Food Research, University of Shizuoka, 52-1 Yada, Suruga,
Shizuoka, 422-8526 Japan

Keywords: GPCR, FMO calculation, Drug design
In recent years, G protein-coupled receptors (GPCRs) are intensively studied as drug targets .
Muscarinic M 1 -M 5 acetylcholine receptors (mAChRs) that belong to the GPCR family regulate
many vital functions of the central and peripheral nervous systems. Although sequence similarity
among the subtypes M 1 to M 5 is very high, only subtype M 1 receptor has been vigorously
investigated as a target for the treatment of neurological disorders. Our recent study reveals thatnobiletin, a flavonoid isolated from citrus peels, selectively binds and activates the M 1 receptor[1]; however, the structure of the complex (M 1 receptor with nobiletin) and the origin of nobiletin
selectivity are unknown. In this study, we aim to elucidate the mechanism of action by which
nobiletin, as a food ingredient, selectively binds to the M 1 receptor and to establish drug designing
targeting M1 receptor. The structure of M 1 receptor ligated with nobiletin was constructed using
homology modeling by following normal procedures. The crystal structure of the activated form ofM 2 receptor (PDBID: 4MQT) was considered as the template for this purpose. The protein-ligand
binding analysis were made by using ab initio Fragment Molecular Orbital (FMO) method at
RI-MP2/cc-pVDZ level of theory.
[1] S.Onoue, S.Yamada, et al., J. Pharm. Sci., vol. 11, no. 9, Sep. 2011 .
[2] Molecular Operating Environment (MOE), 2016.08, Chemical Computing Group Inc.: 1010
Sherbrooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2018.
[3] Ishikawa, T.; Kuwata, K., Fragment molecular orbital calculation using the RI-MP2
method. Chemical Physics Letters, 474, 195-198, 2009.

P2-25Interaction analysis of selective JAK inhibitors by the FMO
methodYuma Handa 1

s141205@hoshi.ac.jp

Shigenori Tanaka 2s151233@hoshi.ac.jp

Takayuki

Furuishi 1

Tanaka2@kobe-u.ac.jp

t-furuishi@hoshi.ac.jp

Kaori Fukuzawa 1

Etsuo Yonemochi 1

k-fukuzawa@hoshi.ac.jp

1
2

Taiki Machida 1

e-yonemochi@hoshi.ac.jp

Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 142-8501, Japan
Graduate School of System Informatics, Kobe University, 1 -1 Rokkodai, Nada-ku, Kobe
657-8501, Japan

Keywords: Janus kinase, Subtype selectivity, Ligand binding, Fragment Molecular Orbital (FMO)
method, Interacting energy analysis
The Janus (JAK) family of cytoplasmic protein tyrosine kinases consist of four subtypes; JAK1, JAK2,
JAK3 and TYK2. Since these subtypes are target receptors of specific disease, their selectivity
becomes important in drug discovery. Clinically available JAK inhibitors have been Zeruzants ®
(tofacitinib) and Olmient ® (baricitinib), where tofacitinib has no selectivity and baricitinib is selective
for JAK1 and JAK2. In this study, FMO calculations [1,2] were performed to investigate molecularinteractions between each subtype and two JAK inhibitors and to identify interaction for design ofmore potent inhibitors.
The Molecular operating environment (MOE) was used for structure preparation. The Amber10EHT
force field was used for energy minimizations. FMO calculations were carried out at the MP2/6-31G*
level by using ABINIT-MP software. Cocrystal structures between tofacitinib and JAK were obtained
from Protein Date Bank (PDBID: 3EYG, 3FUP, 3LXK and 3LXN). For the complex structures between
baricitinib and each subtype, docking study was performed. We calculated differences of energy
component in the pair interaction energy decomposition analysis (PIEDA) [3,4] of each subtype.
PIEDA calculation was done by using MIZUHO/BioStation software package.
For tofacitinib, there was no particular difference in binding energy and therefore selectivity was notobserved. On the other hand, there were several amino acid residues with different interaction.
Electrostatic interaction at glutamic acid (E883 in JAK1 numbering. The same has applied hereafter.),which was mutated to lysine in JAK2 and JAK3, was different due to the difference in charge of aminoacid, and JAK 2 and JAK 3 were more stable. G884 of JAK1 is conserved in other subtypes, however,differences of dispersion interaction were observed at the position of the main chain due to thedifference of next residues. L1010 of JAK1 was conserved in other subtypes, but there were also
differences in dispersion interaction because the number of CH /π interactions was different. In addition,
F958 of JAK1, which was mutated to tyrosine in other subtypes, were involved in importantinteractions, but they were not directly contributed in selectivity. These residues were considered to beimportant in the design of JAK inhibitors.
This research was done in activities of the FMO drug design consortium (FMODD). The
results were obtained using the K computer (project ID: hp180147)[1] D. G. Fedorov , T. Nagata, K. Kitaura, Phys. Chem. Chem. Phys. 14, 7562 (2 012).
[2] S. Tanaka, Y. Mochizuki, Y. Komeiji, Y. Okiyama, and K. Fukuzawa, Phys. Chem. Chem. Phys.,
16, 10310 (2014).
[3] D. G. Fedorov, and K. Kitaura, J. Comput. Chem., 28, 222, (2007).
[4] T. Tsukamoto, et al., J. Comput. Chem. Jpn., 14, 1 (2015).

P2-26

Computational Analysis of Potential Compounds bound to
GPR35 by using Fragment Molecular Orbital Calculations
Yuta Yamamoto 1

Sota Nagashima 1

Sundaram Arulmozhiraja 1

17lb019f@rikkyo.ac.jp

15cc147j@rikkyo.ac.jp

raja@rikkyo.ac.jp

Takuto Koide 2

Miyako Naganuma 2

Tomohiro Watanabe 2

bm18018@ns.kogakuin.ac.jp

s115059@ns.kogakuin.ac.jp

matz@cc.kogakuin.ac.jp

Kazuyuki Furuta 3

Tomoko Hashimoto 2

Osamu Ohno 2

furutak@okayama-u.ac.jp

bm18018@ns.kogakuin.ac.jp

ohno@cc.kogakuin.ac.jp

Satoshi Tanaka 4

Kenji Matsuno 2

Hiroaki Tokiwa 1

tanaka-s@mb.kyoto-phu.ac.jp

matz@cc.kogakuin.ac.jp

tokiwa@rikkyo.ac.jp

1

2

3

4

Department of Chemistry, Rikkyo University, 3-34-1, Nishi-Ikebukuro, Toshima-ku, Tokyo,
Japan
Department of Chemistry and Life Science, Kogakuin University, 2665-1, Nakanochou,
Hachiouji-shi, Tokyo, Japan
Division of Pharmaceutical Sciences, Okayama University, Graduate School of Medicine,
Dentistry and Pharmaceutical Sciences, 1 -1-1, Tsushima-naka, Kita-ku Okayama, Japan
Department of Biopharmaceutics, Kyoto Pharmaceutical U niversity, 5, Misasaginakauchi-cho,Yamashina, Kyoto 607-8414, Japan

Keywords: Fragment molecular orbital calculations, Ligand docking, Protein modeling, and GPCR
In recent years, multi-scale molecular simulation has been wildly applied to study various
biological systems like medicinal/clinical target proteins. We studied such targets by using
multi-scale molecular simulation combined with ab initio Fragment Molecular Orbital (FMO)
calculations 1,2 . In the FMO framework, the entire protein system is divided into single -residue
fragments and the total properties of large molecular systems are derived in a many -body
expansion by combining the properties of fragments.
G protein-coupled receptors (GPCRs) are intensively studied as drug targets. GPR35 has
attracted as a target protein for various anti-allergic drug candidates; however, since activity of theagonists highly depends upon the target species, clinical experiments on animals are difficult. Recently,
Lodoxamide and Bufrolin have been reported as GPR35 agonists that eliminate species differences 3 ,but their activity was low. Matsuno et al., on the other hand, succeeded in the development of highly
active compounds that eliminate species differences 4 . In this study, we have theoretically constructed
the complex structure of the newly synthesized ligands bound to human/rat GPR35, and analyzed theinteraction mechanisms using molecular simulations and FMO calculations .
[1] Yamamoto, Y. et al., Molecular association model of PPARalpha and its new specific andefficient ligand, pemafibrate: Structural basis for SPPARMα. Biochem Biophys Res Commun
499, 239-245, 2018.
[2] Arulmozhiraja, S. et al., Comparative Binding Analysis of Dipeptidyl Peptidase IV (DPP -4)
with Antidiabetic Drugs - An Ab Initio Fragment Molecular Orbital Study. PLoS One 11,
e0166275-1-15, 2016.
[3] MacKenzie, A. E. et al., The anti-allergic mast cell stabilizers lodoxamide and bufrolin as thefirst high and equipotent agonists of human and rat GP R35. Mol Pharmacol 85, 91-104, 2014.
[4] http://nenkai.pharm.or.jp/138/pc/ipdfview.asp?i=2028

P2-27Site Identification by Ligand Competitive Saturation(SILCS) reproduces experimental binding trends for 31
TrmD ligands
Clinton Threlfall 1

Sirish Kaushik Lakkaraju 1 Sunhwan Jo 1

clinton@silcsbio.com

Olgun Guvench 1 Alexander D. MacKerell Jr 1 , 2

1
2

SilcsBio, LLC. 8 Market Pl, Baltimore MD 21202 USA
Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland,
Baltimore, 20 Penn St, Baltimore MD 21201 USA

Keywords: Site-identification, Ligand, GFE, FragMaps
Site-Identification by Ligand Competitive Saturation (SILCS) computational functionalgroup mapping provides insights into the binding preferences of a target protein that can beused qualitatively and quantitatively to drive ligand design. SILCS is a robust structurebased approach that gives information-rich Grid Free Energy (GFE) FragMaps thatencompass critical aspects such as protein flexibility and explicit solvation. Here we
describe the use of the SILCS approach on tRNA methyltransferase (TrmD) and 31 ligands
belonging to two series originally donated by GSK and made publicly-available throughCommunity Structure-Activity Resource (CSAR) and the D3R Database. SILCS-MC
sampling of ligands in the field of the FragMaps yields Ligand Grid Free Energy (LGFE)
scores. SILCS scoring correctly predicts favorable vs. unfavorable modifications relative toa reference ligand (27/30 predictions correct). Additionally, SILCS FragMaps recapitulate
functional group patterns of both series of ligands. This information can be used to drive
design and optimization visually.

P3-01

Consideration of excessive metabolites on dynamical
analysis of bacterial secondary metabolic pathways
Daisuke Tominaga 1

Hideo Kawaguchi 2

Daisuke-tominaga@aist.go.jp

kawaguchi_h@port.kobe-u.ac.jp

1

2

3

Yoshimi Hori 2

Tomohisa Hasunuma 2

Chiaki Ogino 3

hasunuma@port.kobe-u.ac.jp

ochiaki@port.kobe-u.ac.jp

Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced
Industrial Science and Technology, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan
Graduate School of Science, Technology and Innovation, Kobe University, 1-1-3 Minatojima,
Chuo ward, Kobe ciry 650-8586, Japan
Graduate School of Engineering, Kobe University, 1-1-3 Minatojima, Chuo ward, Kobe ciry
650-8586, Japan

Keywords: Electronic structure calculation, Ligand docking, Protein modeling
Analyses on secondary metabolic pathways that include industrial useful metabolites are often
done to optimize production of the useful materials by microorganisms. One of the mostly
established analysis for that is Flux Balance Analysis (FBA), however, it can not deal withdynamic changes of concentrations of metabolites that are often essential for industrial
production[1]. So that mathematical models for dynamics, such as ordinary differential equation(ODE) systems are introduced[2]. Some chemical molecules, such as ATP, NAD or NADPH and soon, are often omitted or ignored at constructions of ODE system models because of observationdifficulties, or the models can not be stable for large systems that consist of much number of
metabolites[3]. This omission can improve mathematical stability of the models, and reduce theexperimental observation costs. The omission is reliable when dynamic changes of omitted
metabolites are negligible because of its' excessiven ess.
We tested the influence of the omission on reliabilities of ODE system models of dynamicalchanges of metabolite concentrations in log-phase of Escherichia coli cultivation.
Concentrations of 131 metabolites are measured at ten time points. Enzymic re actions are searched
comprehensively for each metabolite by KEGG search, and we selected 22 out of 131 on which an
ODE (the S-system form) can be applicable (number of relating reactions is sufficiently small
comparing with the number of sampling time poin ts). Dynamical changes of each metabolite aremodeled in two approaches; with and without excessive molecules, ATP, ADP, AMP, NAD+,
NADH, NADP+ and NADPH.
The results show that dynamic changes of concentrations of some metabolites can not be modeledwith the omission of the excessive molecules, and including excessive molecules improves
precision of models.
[1] Palsson, B. O., Systems Biology: Constraint-based Reconstruction and Analysis, Cambridge
University Press, 2015.
[2] Palsson, B. O., Systems Biology: Simulation of Dynamic Network States, Cambridge
University Press, 2011.
[3] Voit, E.O., Computational Analysis of Biochemical system: A Practical Guide for Biochemists
and Molecular Biologists, Cambridge University Press, 2000.

P3-02

Pathway prediction of natural products
by reverse synthetic analysis
Tsubasa Matsumoto1
matsumoto.t.ap@m.titech.ac.jp

Kohei Amano 1
amano.k.ac@bio.titech.ac.jp

Miyabi Hishinuma1

Kimito Funatsu 2

hishinuma.m.aa@m.titech.ac.jp

funatsu@chemsys.t.u-tokyo.ac.jp

Masaaki Kotera 2
maskot@chemsys.t.u-tokyo.ac.jp
12

School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 Ookayama,
Meguro-ku, Tokyo 152-8550, Japan
Department of Chemical System Engineering, School of Engineering, The University of Tokyo,
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

Keywords: Natural products, Pathway analysis, Reverse synthetic analysis
Considerable amounts of pharmaceuticals were originated from natural products. Drug discovery
process typically requires 10-18 years and 200-300 billion dollars, and it is extremely important todecide desirable lead compounds from natural products. Organic synthesis had been almost the onlymeans of drug discovery, especially for compounds with complex molecular skeletons. Recently,
synthetic biology approach has been caught attention. The flow of the synthetic biology approachconsists of the following three steps; metabolic pathway elucidation, genetic recombination, andclinical trial. In order to effectively produce valuable natural products in synthetic biology, it isessential to understand the relationship between natural products and their biosynthetic machinery [1] .
However, the number of natural products whose biosynthetic pathways are unknown, and the number of
unknown enzyme genes involved in the biosynthetic pathways are both increasing [2] . Therefore, amethod for exhaustively connecting these two types of information has been desired.
In this study, we have developed a reverse synthesis-based method for predicting the biosynthetic
pathway of various natural products for the purpose of extracting responsible biosynthetic genes.
Compared with the predictive performance presented in last year [3] , the current algorithm greatly
improved the performance by utilizing biosynthetic building block information [4] to limit the available
reaction types. The performance was verified with KEGG's secondary metabolic map, and we predicted
the metabolic pathway of unknown compounds.
[1] Moriya Y, Yamada T, Okuda S, Nakagawa Z, Kotera M, Tokimatsu T, Kanehisa M, Goto S.
Identification of Enzyme Genes Using Chemical Structure Alignments of Substrate-Product Pairs. J
Chem Inf Model. 56(3):510-516, 2016.
[2] Tabei Y, Yamanishi Y, Kotera M. Simultaneous prediction of enzyme orthologs from chemicaltransformation patterns for de novo metabolic pathway reconstruction. Bioinformatics, 32, i278-i287,
2016.
[3] Matsumoto T, Amano K, Hishinuma M, Kotera M. Prediction of natural biosynthetic pathway
by reverse synthetic analysis, Chem-Bio Informatics Society (CBI) Annual Meeting 2017, P3-04,
2017.
[4] Amano K, Matsumoto T, Hishinuma M, Funatsu K, Kotera M. Prediction of biosynthetic
building blocks in complicated natural products, Chem-Bio Informatics Society (CBI) Annual
Meeting 2018.

P3-03

Prediction of biosynthetic building blocks
in complicated natural products
Kohei Amano 1

Tsubasa Matsumoto 1

amano.k.ac@m.titech.ac.jp

matsumoto.t.ap@m.titech.ac.jp

Miyabi Hishinuma 1
hishinuma.m.aa@m.titech.ac.jp

Kimito Funatsu2
funatsu@chemsys.t.u-tokyo.ac.jp

Masaaki Kotera2
maskot@chemsys.t.u-tokyo.ac.jp
12

School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 Ookayama,
Meguro-ku, Tokyo 152-8550, Japan
Department of Chemical System Engineering, School of Engineering, The University of Tokyo,
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

Keywords: Natural products, Pathway analysis, Chemical graph, Building block
Many natural products are important sources of pharmaceuticals. The identification of their
metabolic pathways is beneficial for natural drug discovery. We previously developed methods todeal with unknown metabolic pathways by predicting an enzyme gene or an enzyme protein thatcatalyzes a specified reaction [1,2 ] . However, it is still difficult to predict biosynthetic pathways ofcomplicated natural products with consideration of several unknown intermediates. It is necessary
for reverse synthetic analysis to identify the starting material of the target product in its
biosynthetic pathway.
In this study, we have developed the program to automatically predict a combination of
biosynthetic building blocks in complicated natural products. It enabled to reveal the basic
metabolites constructing the target product, and showed better interpretive capacity than everbefore [3 ] .
Natural products were represented as chemical graphs converted from MDL Molfile. We havecreated the biosynthetic building block library by extracting frameworks from molecules that areimportant stating materials or intermediates in the metabolic pathways. RDKit's substructure
matching judged whether the target product contained a biosynthetic building block in the library.
Finally, possible combinations of biosynthetic building blocks were output. This program correctlypredicted about 90% of compounds in biosynthetic pathways of secondary metabolites.
[1] Tabei Y, Yamanishi Y, Kotera M. Simultaneous predicti on of enzyme orthologs from chemicaltransformation patterns for de novo metabolic pathway reconstruction. Bioinformatics, 32,
i278-i287, 2016.
[2] Moriya Y, Yamada T, Okuda S, Nakagawa Z, Kotera M, Tokimatsu T, Kanehisa M, Goto S.
Identification of Enzyme Genes Using Chemical Structure Alignments of Substrate -Product Pairs.
J. Chem. Inf. Model. 56(3):510-516, 2016.
[3] Amano K, Matsumoto T, Hishinuma M, Kotera M. Prediction of Biosynthetic Basic Parts ofCompounds with Complicated Structures, Chem-Bio Informatics Society (CBI) Annual Meeting
2017, P3-06, 2017.

P3-04Morphology-based analysis of myoblasts
for prediction of myotube formation
Kei Yoshida 1

Kiyoshi Ishikawa 2

yoshida.kei@a.mbox.nagoya-u.ac.jp

ishikawa.kiyoshi@ps.nagoya-u.ac.jp

Kei Kanie 1

Kenji Omori 2

kanie-k@ps.nagoya-u.ac.jp

omori-k@ps.nagoya-u.ac.jp

Ryuji Kato 1
kato-r@ps.nagoya-u.ac.jp
12

Graduate School of Pharmaceutical Sciences, Nagoya University, Furocho, Chikusa-ku,
Nagoya, 464-8601, Japan
Mitsubishi Tanabe Pharma Corporation, 1000, Shida -cho, Aoba-ku, Yokohama, 227-0033,
Japan

Keywords: Cellular morphology, morphology-based analysis, myoblast differentiation, cGMP
The development of new drugs depends on the efficiency of drug screening. Phenotype -based
screening has attracted interest due to its considerable potency for the discovery of first -in-classdrugs 1 . In general, fluorescently-labeled imagery is the leading technique for phenotype-based
screening 2 , however there are growing requirements to understand total culture profiles, which areunclear from end-point assays.
In this study, we demonstrate the morphology-based cellular evaluation of unlabeled cells toadvance myotube formation assays. One of our aims was to study the myogenic differentiation
process in C2C12 cells to discern the differences between cellular responses to different medium
conditions (serum concentrations and insulin dosages). Our results show ed that predictive
morphological profiles that strongly correlate with myogenic differentiation can be generated frommyotube images, even in the confluent stage. Differentiation rate after 14 days can b e quantitatively
predicted with highest accuracy from images taken at 0 –11.5 days. In addition, for the applicationof our morphology-based cellular evaluation of C2C12, the effect of cyclic guanosine
monophosphate (cGMP) on myogenic differentiation was an alyzed. Our results show that thequantitated morphological profile from non-labeled time-course images can be an effective
descriptor for analysis of the myotube differentiation screenings.
[1] Moffat, J. G., Vincent, F., Lee, J. A., Eder, J. and Prunotto, M., Opportunities and challenges inphenotypic drug discovery: An industry perspective , Nat. Rev. Drug Discov, 16:531–543, 2017.
[2] Singh, S., Carpenter, A. E. and Genovesio, A., Increasing the content of high-content screening:
An overview, journal of biomolecular screening, 19:640–650, 2014.

P3-05Development of Image Cell Picker for Cancer Spheroid Analysis
Hirohito Kato 1

Mayu Shibuta 1

katou.hirohito@a.mbox.nagoya-u.ac.jp

Ryoko Kusakabe 2ryokokskb@gmail.com

Kei Kanie 1

3
4

hmatsui@md.tsukuba.ac.jp

Masumi Yanagisawa 3

Toshiyuki Kanamori 4

yanagisawa@engineeringsystem.co.jp

t.kanamori@aist.go.jp

shinji.sugiura@aist.go.jp

2

Yuji Matsui 2

kanie-k@ps.nagoya-u.ac.jp

Shinji Sugiura 4

1shibuta.mayu@e.mbox.nagoya-u.ac.jp

Ryuji Kato 1
kato-r@ps.nagoya-u.ac.jp

Department of Basic Medicinal Sciences, Graduate School of Pharmaceutical Sciences, Nagoya
University, Furocho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan
Division of Gastroenterology, Faculty of Medicine, University of Tsukuba, 1 -1-1, Ten-noudai,
Tsukuba, Ibaraki 305-8575, Japan
Engineering System Co., Ltd., 5652-83, Sasaga, Matsumoto, Nagano 399-0033, Japan
Drug Assay Device Research Group, Biotechnology Research Institute for Drug Discovery,
National Institute of Advanced Industrial Sc ience and Technology (AIST), Central 5th, 1 -1-1,
Higashi, Tsukuba, Ibaraki 605-8565, Japan

Keywords: Image analysis, Cancer spheroid, 3D culture, Morphology
Recently, three dimensionally (3D) cultured cells, such as spheroid , has become one of theimportant disease models for drug screening. Compared to the two dimensionally culture cells, 3D
cultured cells are known to show higher functions that can represent partial biological functions
and structures 1 . Therefore, there are growing attentions to establish new assay platforms using
spheroids for more functional drug screening. 3D cultured cancer spheroids derived fromcancer/tumor cell lines had been expected to lead novel understanding of the complexity of tumor
drug responses. However, since there are still rare techniques to understand thefunctions/responses of 3D cultured spheroids cultured in ECM-derived hydro-gels withhigh-throughput manner, new quantitative assay platforms have been expected.
Here, we introduce an automated Image Cell Picker to quantitatively screen/study 3D cultured
cancer spheroids by combining label-free live morphology-based analysis with photodegradablehydrogel cell isolation technique 2,3 . From the aspect of computer-assisted quantitative
analysis of cancer spheroids, we here discuss the effectiveness of using time -course
morphological information of cancer spheroids in our system .
[1] Wenzel, C. et al. 3D high-content screening for the identification of compounds that target cells
in dormant tumor spheroid regions. Cancer Research 75, doi:10.1158/1538 -7445.am2015-317(2015).
[2] Tamura, M., Yanagawa, F., Sugiura, S., Takagi, T., Sumaru, K., Matsui, H., and Kanamori, T.:
Optical cell separation from three-dimensional environment in photodegradable hydrogels forpure culture techniques, Sci Rep, 4, 6 (2014).
[3] Shibuta, M. et al. Imaging cell picker: A morphology -based automated cell separation systemon a photodegradable hydrogel culture platform. Journal of Bioscience and Bioengineering,
doi:https://doi.org/10.1016/j.jbiosc.2018.05.004 (2018).

P3-06

Adding trans-omics analysis features to TargetMine
Tatsuya Kameyama 1,2

Yi-An Chen 1

Yosui Nojima 1

Takeshi Fujiwara 1
takeshi.fujiwara@nibiohn.go.jp

tatsuya@nibiohn.go.jp

chenyian@nibiohn.go.jp

yosui@nibiohn.go.jp

Tsuyoshi Esaki 1

Lokesh P. Tripathi 1

Mari Ito 1

tsuyoshi-esaki@nibiohn.go.jp

lokesh@nibiohn.go.jp＆

Kenji Mizuguchi 1,2

mari@nibiohn.go.jp

kenji@nibiohn.go.jp

1

Laboratory of Bioinformatics, National Institutes of Biomedical Innovation, Health and Nutrition,
7-6-8, Saito Asagi, Ibaraki-shi, Osaka 567-0085, Japan
2
Graduate School of Frontier Biosciences, Osaka University (Building A, B, C)
1-3 Yamadaoka, Suita, Osaka 565-0871, Japan

Keywords: Omics analysis of disease, Clinical Bioinformatics, pathway analysis
A major problem in research on the mechanisms of complicated disease and drug target discovery
is that despite the accumulation of various omics data, analysis methods associated with them have
not yet been fully established.
To solve this problem, we have tried to establish a system for trans-omics analysis, with thecapability of easily connecting and analyzing “three-layer” omics data, i.e., profiles ofgenes/transcripts, proteins and metabolites.
The integrated database TargetMine 1-3) , developed in our laboratory, integrates more than 50
biological public databases including functional annotations for genes, transcripts, proteins andsmall molecule compounds and has the ability of data mining and statistical analysis.
In this study, we first enhanced TargetMine by incorporating relevant data to facilitate mapping
compounds onto metabolic pathways. Then we automated the following analysis flow using theTargetMine API: ⑴Generating a KEGG 4) web site address, which maps known molecules onto themetabolic pathway. ⑵Ranking the disease specificity of the metabolic pathway with differentiallyexpressed omics data by the enrichment analysis. ⑶ Referencing the molecules of other layers thatwill be involved in a metabolically regulated molecule in the public database. For example, thesystem can retrieve the associations between genes and enzymes and the relations between
metabolites and enzymes. These automated features were applied to published mouse omics data
and the results are consistent with the discoveries in the publication.
Our study shows that TargetMine is able to analyze metabolomic data efficiently, and it enables us
to grasp relations between pathways. Moreover, it can analyze various omics data integrally.
[1] Chen, Y.A., Tripathi, L.P., Mizuguchi, K. (2011) TargetMine, an Integrated Data Warehouse for
Candidate Gene Prioritisation and Target Discovery. PLoS ONE 6(3): e17844.
[2] Chen, Y.A., Tripathi, L.P., Mizuguchi, K. (2016) An integrative data analysis platform for geneset analysis and knowledge discovery in a data warehouse framework. Database (Oxford). 2016 Mar
17;2016. pii: baw009.
[3]http://targetmine.mizuguchilab.org[4]https://www.genome.jp/kegg/kegg_ja.html

P3-07
A Study of Transcription Factor Network to Distinguish
the Difference of Normal and Cancer Cells
Bharata Kalbuaji 1
kalbuaji.b.aa@m.titech.ac.jp

1

Konagaya Akihiko 1
kona@c.titech.ac.jp

School of Computing, Tokyo Institute of Technology, 4259 Nagatsuta, Mirodi, Yokohama, 2268502, Japan

Keywords: cancer, gene regulatory network, transcription factor
Gene regulation hold an important role in understanding how cell behave, including in cancer
development. Interaction that happen from gene regulation process can be modeled using a gene
regulatory network. In this study, we use transcription factor binding information in the form of
position weight matrices (PWM) to generate a gene regulatory network among transcription factors.
We applied this technique to five cancer dataset: bile duct cancer (BDC), hepatocyte carcinoma(HCC), colorectal cancer (CRC), lung adenocarcinoma (LUAD), and osteocarcoma (OSSRC).
Although the transcription factor networks obtained from those five cancer dataset are socomplicated, we found a common subnetwork among four cancer dataset: BDC, HCC, CRC, and
LUAD. We cannot find any common subnetwork that covers OSSRC data. To further investigate theresult, we use pathway analysis to understand what kind of pathway is affected from genes thatappear in the common subnetwork.
We use two pathway database to check, KEGG pathway and WikiPathway database. From both
pathway analysis, several important pathways in cancer development appear. Those are:adipogenesis, circadian rhytm, transcriptional misregulation in cancer, and pathways in cancer.
Adipogenesis and circadian rhytm has been known to be involved in cancer development. We also
suspect that there is similarity in cancer cell characteristic from fo ur cancer cell because all cellline is rich in adipocyte. So, adipogenesis may hold an important role to discover common
mechanism regarding cancer in adipocyte rich cell.
In conclusion, we believe that understand how transcription factor interact with ea ch other hold a
key to discover the mechanism in cancer. We also show that it is probable that several cancer cellfrom different cell line to share similar characteristic if they also share similar normal cellcharacteristic. This may lead to find a candidate for drug target that can be used for several cancers.
[1] Saurabh Sinha; On counting position weight matrix matches in a sequence, with application todiscriminative motif finding, Bioinformatics, Volume 22, e454 –e463, 2006.
[2] Xuhua Xia, “Position Weight Matrix, Gibbs Sampler, and the Associated Significance Tests in
Motif Characterization and Prediction,” Scientifica, vol. 2012, Article ID 917540, 15 pages, 2012.
[3] Wang, S., Li, X., Xu, M., Wang, J., Zhao, R.C.: Reduced adipogenesis after lung tumor exosomes
priming in human mesenchymal stem cells via TGFβ signaling pathway. Molecular and Cellular
Biochemistry 435(1-2), 59-66 (2017).
[4] Amemori, S., Ootani, A., Aoki, S., Fujise, T., Shimoda, R., Kakimoto, T., Shiraishi, R., Sakata,Y., Tsunada, S., Iwakiri, R., Fujimoto, K.: Adipocytes and preadipocytes promote the proliferationof colon cancer cells in vitro. American Journal of Physiology-Gastrointestinal and Liver
Physiology 292(3), 923-929 (2007).
[5] Savvidis, C., Koutsilieris, M.: Circadian rhythm disruption in cancer biology. Molecular
Medicine 18(9), 1 (2012).

P3-08Modeling and simulations of the kinetics of antigens andantibodies towards personalized medicine for allergies
Mizuka Komatsu 1
m-komatsu@stu.kobe-u.ac.jp

1

2

Takaharu Yaguchi 1,2
yaguchi@pearl.kobe-u.ac.jp

Graduate School of System Informatics, Kobe University, Rokkodai-cho 1-1, Nada-ku, Kobe,
657-8501, JapanJST PRESTO, K's Gobancho, 7, Gobancho, Chiyoda-ku, Tokyo 102-0076 Japan

Keywords: Allergy, Parameter estimation, Personalized medicine, Differential Algebraic Equation
Recently allergies are drawing attention as systemic diseases. Although whole-body simulations
are needed for analysis of such diseases, most of mathematical models of allergies are built
typically at the cellular or molecular level. In this talk, by applicatio n of physiologically based
pharmacokinetic models, we propose a whole-body model of allergies, more precisely, of the
kinetics of antigens and antibodies. Physiologically based pharmacokinetic models are models thathave been developed in the pharmaceutical field for describing the kinetics of drugs at thewhole-body level [1]. As a first step, we have developed a model [2] for an experimental data,
thereby exploring how the model works in simulations of allergies. Through our work, we clarify
the underlying difficulties and how to address them, which are shortly explained as follows.
Firstly, because the model describes antigen-antibody interactions, the systems of equations
consist of differential algebraic equations, which are known to be hard to solve numerically. We
show that the equations have a certain property, with which suitable numerical solvers can be
designed. This work confirms the reliability of the simulations.
Secondly, we apply the experimental data from some subjects obtained by Husby et. al [3] toour model and estimate parameters for each subject. Shortly speaking, the parameter estimations
here reduced to undetermined problems thus parameters are not determined uniquel y but form amanifold, which makes the estimations hard. Against the challenge, we propose a method ofparameter estimations which is of multi-stage multi-start type to obtain multiple possible
parameters.
Thirdly, we propose a method of analysis for the obtained parameters. These parameters are
intuitively “samples” from the manifolds, each of which is uniquely determined by each subject.
Therefore, by the analysis, the features of the manifolds corresponding to each subject are
explored, which must be helpful for investigation of individual physiological or immunologicalvarieties between subjects. Our result indicates that development of personalized medicine forallergies would become possible by our model and simulations.
[1] Peters, S. A., Physiologically-based pharmacokinetic(PBPK) modeling and simulations :principles, methods, and applications in the pharamaceutical industry, Wiley, NJ, 2011.
[2] Komatsu, M. and Yaguchi, T., Mathematical modeling of kinetics of antigens and antibodies
based on experiments toward analysis of the mechanism of allergies, submitted.
[3] Husby, S., Jensenius, J. C. and Svehag, S.-E., Passage of undegraded dietary antigens into theblood of healthy adults. Quantification, estimation of size distribution, and relation of uptake tolevels of specific antibodies, Scand. J. Immunol., 22 83-92, 1985.

P3-09Three-dimensional Loop Fragment Analysis of Proteins
Focused on Neighboring Ligands
Hiroaki Kato 1
kato @ hiroshima-cmt.ac.jp

Akihiro Hirama 2
a133364 @ edu.tut.ac.jp

1

Department of Distribution and Information Engineering, National Institute of Technology,
Hiroshima College, 4272-1 Higashino, OsakiKamijima-cho, Hiroshima 725-0231 Japan2
Department of Computer Science and Engi neering, Toyohashi University of Technology,
1-1 Hibarigaoka, Tempaku-cho, Toyohashi, Aichi 441-8580 Japan

Keywords: loop fragment, neighboring ligand, fragment library, structural similarity, protein motif,
EF-handMany proteins express their functions by interacting with other compound ligands or proteins.
It is well known that particular structural feature called motif is closely related to the function.
In the present work, we have constructed the protein 3D fragment library focused on the loop
region and the neighborhood ligands. It has also applied to the structural feature analysis using
known motif patterns.
In the present work, the protein structural data taken from Protein Data Bank (PDB) were
employed with corresponding secondary structure information by DSSP program. A loop region
is defined as a segment connecting two consecutive secondary structure (helix or strand) thatcomposed four or more residues. We have focused on a peptide fragment that contained the target
residue in loop region and its n neighboring residues in N -, C-terminal sides. The loop fragment
is characterized with coordinates of the residues, distance matrix between residues, secondary
structure information and fragment size (FS=2n+1). In addition, in the bounding sphere, a centeris gravity coordinates of each Cα of a fragment, and a radius is the longest Cα distant from thecenter. The neighborhood ligands are defined as a hetero atom in the bounding sphere. The
ligand binding residues are amino acids that bind to the neighborhood ligands. The loop fragment
library have been constructed with the representative dataset (29,840 proteins).
We have selected 474 loop fragments from FS = 17 loop fragments library (2,655,070
fragments) with the condition of ligand binding residues number = 5. They were clustered by
Ward’s method. As the result, maximum cluster was composed of loop fragments that contained
the EF-hand motif sequence pattern. In another cluster, we confirmed the calcium-binding site
appeared on the N-terminal side of S100 protein family. Moreover, the other binding site was
detected that both ends of loop region were strand region. The structural pattern of these loop
fragments were different from the structure of the typical EF -hand motif.
In addition, fragment search trial was carried out using the above novel structural motif
candidates as query. As the result, the loop fragments without the ligand information in PDB
were also detected. Because of the radius of these loop fragments are larger than the radius ofquery, it is expected that they are binding site in the unliganded state. These results shows t hepotential applicability of my approach for structural data mining of proteins.
References:[1] Lewit-Bentley, A., Rety, S., EF-hand calcium-binding proteins, Current Opinion in Structural
Biology, 10, 637-643 (2000).
[2] Donato, R., et al., Functions of S100 Proteins, Current Molecular Medicine, 13, 25-57(2013).

P3-10

Literature-based functional network predictor for
Down-Syndrome
Kazunori Hamada 1
j13431@kisarazu.kosen-ac.jp
1

Achyut Sapkota 2sapkota@ieee.org

Department of Information and Computer Engineering, N ational Institute of Technology,
Kisarazu College, 2-11-1, Kiyomidai-Higashi, Kisarazu, Chiba 292-0016 Japan

Keywords: Literature mining, Data mining, Down Syndrome, HSA21, Intellectual Disability
Down syndrome (DS), caused by the presence of an additional copy of a chromosome (No 21, i.e.
HSA21 in human), is one of the major genetically induced disorders that significantly limit
intellectual ability. It is also understood to be the cause of increased risk of other major diseases
like congenital heart diseases, leukemia, hypotonia and Alzheimer’s diseases [1-2]. Various mousemodels are developed for the understanding of functions of the genes experimentally. There areabout 200 genes associated with HSA21, however only the functions of few of them is understood[3]. An in-depth understanding of all the individual genes and their functions (isolated function orfunction as a group) is important for effective therapeutic discovery. Our current work is part ofthe integrated study approach to reveal the various genetic aspects of Down Syndrome on the basisof existing experimental and in-silico studies of genes, omics data and related diseases.
In this work, we developed an algorithm for the extraction and visualization of network ofinterrelated biological words that comprise diseases, genes, proteins and various symptoms etc.
The abstracts of articles available in digital archive of biomedical and life sciences journal
literatures (U.S. National Institutes of Health (NIH), PubMed Central) were screened iteratively
based on set of search keywords created after every search iteration [4]. A filter that comprises thelist of non-biological words prepared from selectively chosen non -biological literatures wasapplied to determine the search keywords for iterative operations. The components (the words) offinal network were then scored using rule-based fuzzy logic [5]. The validity of the algorithm wastested with the well-known subnetwork consisting Down Syndrome mouse modes, genes related to
HSA21 and (Ts65Dn, Ts1Cje), the human chromosome 21 (HSA21), the major region of study for
Down Syndrome and its identical chromosome 16 (MMU16) in mouse and other related terms.
[1] Patterson, D., Molecular genetic analysis of Down syndrome. Human genetics, 126:, 195-214,
2009[2] Wilcock, D.M., Schmitt, F.A. and Head, E., Cerebrovascular contribution s to aging and
Alzheimer's disease in Down syndrome. Biochimica et Biophysica Acta (BBA) -Molecular
Basis of Disease, 1862: 909-914, 2016[3] Nordquist, S.K., Smith, S.R. and Pierce, J.T., Systematic functional characterization of human
21st chromosome orthologs in Caenorhabditis elegans. G3: Genes, Genomes, Genetics,
g3-200019, 2018.
[4] PubMed Central, https://www.ncbi.nlm.nih.gov/pubmed/[5] Sapkota, A. and Ohmi, K., 2009., Error detection and performance analysis scheme for particle
tracking velocimetry results using fuzzy logic. International Journal of Innovative Computing,Information and Control, 5(12B): pp.4927-4934, 2009

P3-11

Orthogonal Linear Separation Analysis (OLSA): an
Approach to Decompose the Complex Effects of a Drug toUnderstand Its Pharmacological Properties
Tadahaya Mizuno 1
tadahaya@mol.f.u-tokyo.ac.jp

Shotaro Maedera 1
mdera0130@gmail.com

Setsuo Kinoshita 1
setsuo.kinoshita@gmail.com

Takuya Ito 1
ito32539@gmail.com

Hiroyuki Kusuhara 1
kusuhara@mol.f.u-tokyo.ac.jp
1

Molecular Pharmacokinetics, Department of Pharmaceutical Sciences, the University of Tokyo,
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

Keywords: Profiling, Profile data analysis, OLSA, Connectivity Map, transcriptome
The response to a drug can be a complex of the entire biological responses to the perturbagen
and to separate the complex effects of a drug into basic components is a prerequisite for a deep
understanding of the pharmacological properties of drugs. Therefore, we investigated whether it ispossible to decompose the complex effect of a drug into basic components described by variable
patterns using profile data analysis, particularly focusing on factor analysis (FA). A concern ofutilizing FA in profiling is that the centroid in the novel coordinate space has no biologicalmeaning and varies among data sets, which means that the obtained factors (vectors) in such asituation may not correspond to consistent biological meanings. To address that concern, we haveextended FA by using “response profiles” and “mirror data” of the examined data set. We havenamed this slightly modified FA as orthogonal linear separation analysis (OLSA) and have tested
its performance in this study.
Transcriptome profile data of MCF-7-cells treated with 318 low molecular compounds wereobtained from Connectivity Map and subjected to OLSA. Consequently, 118 factors contracting
11,911 genes were obtained. To examine the relevancy of those factors with biological responses,
we investigated the property of each factor focusing on gene ontology of main genes consisting of
the factor. Significant enrichment of the ontology in 65 of 118 factors were o bserved and similar
results were obtained in two other data sets. One factor discriminated two Hsp90 inhibitors,
geldanamycin and radicicol, while clustering analysis could not. Doxorubicin and othertopoisomerase inhibitors were estimated to inhibit Na + /K + ATPase, one of the suggested
mechanisms of doxorubicin-induced cardiotoxicity. Based on the factor including
PI3K/AKT/mTORC1 inhibition activity, 5 compounds were predicted to be novel inducers ofautophagy, and other analysis including western blotting revealed that 4 of the 5 actually induced
autophagy.
These findings indicate the potential of OLSA to decompose the effects of a drug and identify its
basic components.
[1] Lamb, J., Crawford, E.D., Peck, D., Modell, J.W., Blat, I.C., Wrobel, M.J., Lerner, J., Brunet,
J.P., Subramanian, A., Ross, K.N., Reich, M., Hieronymus, H., Wei, G., Armstrong, S.A.,
Haggarty, S.J., Clemons, P.A., Wei, R., Carr, S.A., Lander, E.S., Golub, T.R., The Connectivity
Map: using gene-expression signatures to connect small molecules, genes, and disease., Science,
313:1929-35, 2006.

P3-12
Investigation of the common sequence-structural
patterns in different folds
through cross-profile comparison andmolecular dynamics simulation
Yu Yamamori 1
yu.yamamori@aist.go.jp

1

Kentaro Tomii 1
k.tmoii@aist.go.jp

Artificial Intelligence Research Center, National Institute of Advanced Industrial Science andTechnology (AIST), Tokyo, 135-0064, Japan

Keywords: Cross-profile analysis, Molecular dynamics simulation, replica-exchange molecular
dynamics
Structurally similar segments can be occurred in different protein folds. Combining the database
analysis and molecular dynamics simulation, we investigated the evolutionary meaning of theexistence of those segments. Previously, using cross-profile analysis, we found that profiles ofsome structural-segment clusters show the strong
correlation between particular sequence profiles[1].
In this study, in order to validate thesequence-structure
relationship,
foldingsimulations of the peptides derived from twosequence profiles were performed using thereplica-exchange molecular dynamics [2]. One of
the sequence profiles is related to β-hairpin-likestructure, and another is related to α-helix structure.
For all of these simulations, 20 replicas were usedand 50-ns simulations were performed for each
replica. Water molecules are treated explicitly. The
AMBER99SB force field was adopted for peptide,and the TIP3P model was used for water molecules.
The potential of mean forces along the radius of
gyration of the peptides were calculated fromtrajectories of replica-exchange simulations (Figure1). One of the structures corresponding to theglobal minima of each potential of mean force waspicked up (Figure 1). We confirmed that the
peptides can have stable folded structures
corresponding to the structures expected fromcross-profile analysis. We expected that thesequence-structure relationship can be discoveredby cross-profile analysis can give a new insight toFigure 1 potential of mean force (PMF) along
de novo protein design.
the radius of gyration of the peptides. Each

[1] Kentaro Tomii, Yoshihiro Sawada and Shinya
Honda BMC Bioinfomatics 2012 13:11[2] Yuji Sugita and Yuko Okamoto Chemical
Physics Letters 1999 314 141-151

snapshot structure is corresponding to theglobal minima of PMF.

P3-14

Kampo drug repositioning: Analysis of the mode-of-action
and prediction of new indications of Kampo medicines
Ryusuke Sawada 1
rsawada@bioreg.kyushu-u.ac.jp

Masahito Umezaki 3

Michio Iwata 2
iwata121@bio.kyutech.ac.jp

Yoshihiko Usui 3masume@inm.u-toyama.ac.jp

Toshikazu Kobayashi 3

Takaki Kubono 4

koba.takaoka@zspec.net

m1661217@ems.u-toyama.ac.jp

Shusaku Hayashi 4
hayashi@inm.u-toyama.ac.jp

Makoto Kadowaki 4
makotok@inm.u-toyama.ac.jp

Yoshihiro Yamanishi 2,5yamani@bio.kyutech.ac.jp

1

2

3

4

5

Medical Institute of Bioregulation, Kyushu University, 3 -1-1 Maidashi, Higashi-ku, Fukuoka,
Fukuoka 812-8582, Japan.
Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems
Engineering, Kyushu Institute of Technology, 680 -4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
Division of Chemo-Bioinformatics, Institute of Natural Medicine, University of Toyama,
Toyama 930-0194, Japan.
Division of Gastrointestinal Pathophysiology, Institu te of Natural Medicine, University of
Toyama, Toyama 930-0194, Japan.
PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332 -0012, Japan.

Keywords: Kampo medicines, functional analysis, target prediction, Kampo repositioning
Kampo medicines are useful for treatment of multifactorial and chronic diseases. In this study wepropose new computational methods for analyzing the mode -of-action and predicting new
indications of Kampo medicines. Potential target proteins of the constituent compounds of Kampo
medicines were predicted by docking simulations and machine learning methods based onlarge-scale omics data (e.g., genome, proteome, metabolome, interactome). For example, we
predicted that daikenchuto would work for chronic inflammation-associated cancer, and we
confirmed that the daikenchuto treatment indeed significantly suppressed the development ofchronic colitis-associated colon cancer in a murine experimental model . Finally, we establishedKampoDB (http://wakanmoview.inm.u-toyama.ac.jp/kampo/), a novel database of Kampo
medicines, which provides various useful scientific resources on Japanese traditional formulas
Kampo medicines, constituent herbal drugs, constituent compounds, and target proteins of these
constituent compounds. The current version of KampoDB contains 42 Kampo medicines, 54 crude
drugs, 1230 constituent compounds, 460 known target proteins, and 1369 potential target proteins,and has functional annotations for biological pathways and molecular functions. KampoDB isuseful for mode-of-action analysis of Kampo medicines and prediction of new indications for awide range of diseases.

P4-01Modelling Neonatal and MODY Diabetes in vitro
Using iPS Cell-Derived Human Pancreatic Beta Cells
Filipa Soares 1 , Marcus Yeo 1 , Kalpesh Jhaveri 1 , Masashi Matsunaga 1 , Ludovic Vallier 1,2
1

DefiniGEN Ltd., Babraham Research Campus, Cambridge CB22 3AT, U.K.
Cambridge Stem Cell Institute, Universit y of Cambridge, U.K.
* Contact: enquiries@definigen.com
2

Keywords: iPS drug discovery platform, disease modelling, stem cell, regenerative medicine
DefiniGEN is a spin-out company from the University of Cambridge which utilizes its directeddifferentiation platform OptiDIFF to provide high -functionality human cells to the drug discovery
sector to enable the development of preclinical screens with improve d predictivity of human healthand efficacy. This platform has been combined with the CRISPR gene-editing platform by leading
Cambridge life science company Horizon Discovery Ltd. The platform is generating monogenic IPS
disease models for A1ATD, Familial Hypercholesterolemia, Neonatal and MODY diabetes which
will be validated at both the genotypic and phenotypic level using an array of biochemical
methodologies. Future work will focus on combined directed differentiation and genome -editing
approaches to generate disease models for complex diseases such as diabetes type 2.
Advantages of combining IPSC direct differentiation with CRISPR technology
Human IPSC circumvent many of the ethical and commercial barriers associated with embryonicstem cells as they are derived from adult cells that have been reprogrammed to the stem cell state.
Stem cells have two major properties:
1)2)

they can form any cell type of the body.
they can self-renew indefinitely, producing a limitless supply of cells.

Genome editing and CRISPR/CAS9 technology are an effi cient system to introduce mutations tohealthy donor lines whereby isogenic control can be used to distinguish disease -relevant changes.
Healthy human IPSC lines can be edited using CRISP R/CAS9 technology to create KI/KO mutants
that will help study gene function and develop therapeutic approaches.

OptiDiff Platform. DefiniGEN is focussed on the production and disease modelling of related cell types
including liver, pancreas, lung, intestinal and stomach cells.

P4-02Development of an in vitro evaluation system for
drug-induced hepatotoxicity using PXB-cells 
Mutsumi Inamatsu 1 , Mikaru Yamao 1 , Yuko Ogawa 1 , Chihiro Yamasaki 1 , Yuji Ishida 1,2 ,minamatsu@phoenixbio.co.jp

Chise Tateno 1,2
12

PhoenixBio Co., LTD. Research and Development Unit , 3-4-1, Kagamiyama,
Higashi-Hiroshima, Hiroshima 739-0046, Japan
Research Center for Hepatology and Gastroenterology, Hiroshima University, 1-2-3, Kasumi,
Minami-ku, Hiroshima 734-8551, Japan

Keywords: Toxicokinetics, ADMET, Regulating enzyme activity
Fresh human (h)-hepatocytes are considered to be the best in vitro model to study xenobiotic
metabolism and hepatotoxicity. However, hepatocytes cannot be maintained for more than a week
in two-dimensional cultures. Previously, we had shown that freshly isolated h -hepatocytes(PXB-cells ® ) from chimeric mice with humanized livers (PXB-mice ® ) can be maintained as ahigh-density culture (2.1 x 10 5 cells/cm 2 ), expressing major hepatic genes up to 3 weeks.
In physiological conditions, hepatocytes generate almost all adenosine triphosphate (ATP) in the
mitochondria via aerobic respiration. However, hepatocytes in conventional culture systems
generate the most ATP via cytosolic glycolysis because of high glucose content and limited oxygen
supply. Such anaerobically poised cells are resistant to xenobiotics that impair mitochondrial
function and are not suitable for mitochondrial toxicity evaluation. We examined thehepatotoxicity of mitochondrial complex-I inhibitor, rotenone, using PXB-cells  based on thereports that these hepatocytes were cultured in galactose-based medium (which substituted theconventional glucose based medium), and hyperoxia, to increase the susceptibility to
mitochondrial impairment 1,2) . To increase the supply of oxygen, we utilized Gas Permeable
VECELL Plate (Vessel Inc.) 3) and multi-gas incubators.
Lactate dehydrogenase assays showed that rotenone treatment decreased the viability of
PXB-cells  in a dose-dependent manner. PXB-cells  cultured in galactose-based medium andhyperoxia showed the highest sensitivity to rotenone. Since this condition was maintaining
expressions of several CYP mRNAs and CYP3A activity in PXB-cell  at least equivalent toconventional condition, compounds that show mitochondrial dysfunction after metabolism might
be evaluated in this condition.
In conclusion, mitochondrial toxicity might be determined using PXB-cells  cultured ingalactose-based medium and hyperoxia.
[1] Marroquin, L.D.,Hynes, J.,Dykens, J. A., Jamieson, J. D., and Will, Y., Circumventing the
crabtree effect: replacing media glucose with garactose increases susceptibility o f HepG2 cells
to mitochondrial toxicants, Toxicol Sci., 97: 539-547, 2007
[2] Liu C., Sekine, S., and Ito, K., Assessment of mitochondrial dysfunction-related, drug-induced
hepatotoxicity in primary rat hepatocytes, Toxicol Appl Pharmacol., 302: 23-30, 2016[3] Hamon M., Hanada, S., and Sakai, Y., Direct oxhgen supply with polydimethylsiloxane(PDMS) membranes induces a spontaneous organization of thick heterogeneous liver tissues
from rat fetal liver cells in vitro, Cell Transplant, 21: 401-410, 2012

P4-03

Implementation of PRED subroutine of NONMEM 7 forversatile pharmacokinetic analysis using fast inversion of
Laplace transform (FILT)

1

Ryota Jin 1

Akihiro Hisaka 1ryota-j@chiba-u.jp

hisaka@chiba-u.jp

Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of
Pharmaceutical Sciences, Chiba University, Chiba 260 -8675, Japan

Keywords: FILT, NONMEM, Transfer function, Napp, Laplace transform【 Background and Objective】 Fast Inversion of Laplace Transform (FILT) is a method which
numerically solves various models defined by transfer function, i.e. Laplace transform ofdifferential or partial differential equations. FILT was developed by Hosono et al 1 mainly for
research in electrical engineering field and was applied to pharmacokinetic analysis by Yano et al 2 .
Although FILT is precise and relatively efficient, and furthermore it is flexible then applicable tovarious pharmacokinetic analysis, available programs (MULTI(FILT) 2 and Napp 3 ) are now outdated
or inapplicable in Windows environment. In this study, we implemented a PRED subroutine of thelatest version of NONMEM 7, a widely-used pharmacokinetic analysis program, by using FILT tosolve the problem.
【Methods】We made a new PRED subroutine for implementation of FILT of the secondary orderprecision scheme using Fortran 95 which is now being adopted throughout in NONMEM 7. PRED
is an internal model unit in NONMEM, To confirm the precision of calculation by the new PRED,
we compared the simulation with results obtained by ADVAN (NONMEM predefined models) forthe conventional calculation method and by Napp for the FILT method. The model used by
ADVAN was 1-compartment model, and by Napp were an enterohepatic circulation model, a tankin series model, and a dispersion model.
【Results】The simulated values calculated by the new PRED agreed well with those calculated
by ADVAN and Napp for the four models above, which confirmed that the new PRED was
versatile and precise. By using FILT, it would be possible to expand potential of pharmacokinetic
analysis of NONMEM 7.
[1] Hosono, T. Numerical inversion of Laplace transform and some applications to wave optics.
Radio Science 16, 1015–1019 (1981).
[2] YANO, Y., YAMAOKA, K. & TANAKA, H. A Nonlinear Least Squares Program, MULTI(FILT),
Based on Fast Inverse Laplace Transform for Microcomputers. Chemical and Pharmaceutical
Bulletin 37, 1035–1038 (1989).
[3] Hisaka, A. & Sugiyama, Y. Analysis of nonlinear and nonsteady state hepatic extraction with thedispersion model using the finite difference method. Journal of pharmacokinetics andbiopharmaceutics 26, 495–519 (1998).

P4-04Development of an informatics system for diversity ofcompound library
Yugo Shimizu 1

shimizu-yg@pha.keio.ac.jp

Masanori Osawa 1

osawa-ms@pha.keio.ac.jp

Kazuyoshi Ikeda 1

ikeda-kz@pha.keio.ac.jp

1

Keio University, Faculty of Pharmacy, Division of Physics for Life Functions, 1-5-30
Shibakoen, Minato-ku, Tokyo 105-8512, Japan

Keywords: Library design, Structure-blinded analysis, New chemotype, Structural alert
Open innovation and collaborative drug discovery are expanding due to increasing the cost of drug
development. Informatics approach is a key to analyze diversity of HTS compound library without
disclosure of chemical structure. To expand collaborative drug discovery between industry andacademia, the Drug-Discovery Innovation and Screening Consortium (DISC) of over 20
pharmaceutical companies in Japan has been established [1]. Our group is developing an in silico
platform to analyze the consortium-based compound library without exact structure data.
We have updated our analysis system applied to larger compound libraries and developed
informatics protocols to improve quality of compound library as pharmaceutical agents. The latest
pharmaceutically developing compound data was collected from open drug discovery databases suchas ChEMBL, PubChem, and SureChEMBL. Multiple structural alert (SA) filters were newly
developed by combining different structural alert lists. The different criteria can be used for filtering
whether the input compound is suitable as a pharmaceutical product or a chemical library compound.
To search for chemical space suitable for new targets such as protein-protein interaction (PPI),
compound library containing different chemotypes from conventional small molecules is required.
We introduced a new filter to classify different chemotypes of three-dimensional shape based on the
results of analysis of the relation between the existence probability of bioactive compounds and the
drug-likeness parameters such as Fsp3. The filters would be useful for acquisition of new
chemotypes as well as expansion of current chemical space.
In order to estimate the distribution of diversity in bioactive space, we created ligand-based target
models by using several machine learning (ML) methods such as naïve Bayes, random forest,
support vector machine, and gradient boosting. We extracted compounds and their bioactivity
information from public databases such as ChEMBL and PubChem to train and test the models.
Over 1,500 target models were trained for each target by employing both molecular properties andfingerprints as descriptors. The prediction performances of the ML methods were evaluated bycross-validation and were compared each other.
[1] https://www.amed.go.jp/program/list/06/03/001_02-01.html
This work is conducted as a part of “Construction of Drug Discovery Informatics System” supported
by Japan Agency for Medical Research and Development (AMED).

P4-05Development of a pharmacokinetics prediction systemusing multiscale integrated modeling:
8. Web application and database consisting of curated
public data and newly acquired experimental data
Hitoshi Kawashima 1

Reiko Watanabe 1

Tsuyoshi Esaki 1

hkawashi@nibiohn.go.jp

reiko-watanabe@nibiohn.go.jp

tsuyoshi-esaki@nibiohn.go.jp

Rikiya Ohashi 1,2
rikiya-ohashi@nibiohn.go.jp

1
2
3
4

Daisuke Satoh 3

Chioko Nagao 1,4

daisuke.satoh@lifematics.co.jp

chio@nibiohn.go.jp

Yayoi Natsume-Kitatani 1,4

Kenji Mizuguchi 1,4

natsume@nibiohn.go.jp

kenji@nibiohn.go.jp

Laboratory of Bioinformatics, National Institutes of Biomedical Innovation, Health andNutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan
Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corp., 2-2-50 Kawagishi, Toda,
Saitama 335-8505, Japan
Software Division, Lifematics Inc., 2-25 Kanda Suda-cho, Chiyoda, Tokyo 101-0041, Japan
Laboratory of In-silico Drug Design, Center of Drug Design Research, National Institutes ofBiomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan

Keywords: Pharmacokinetics, Database, Prediction models, Web application
We started an initiative “Development of a Drug Discovery Informatics System”, with support
from the Japan Agency for Medical Research and Development (AMED) in collaboration withseveral other research groups to develop more accurate prediction systems for ADME and tox
properties primarily targeting academic scientists.
We collected pharmacokinetic and physicochemical parameters from the public bioactivity
database, ChEMBL. The data were curated according to a curation workflow and stored in the
database with a curation flag. In addition to the public data, we have been acquiring both in vitro
and in vivo experimental data using consistent protocols. In addition to the parameters collected
previously, we collected efflux ratio of P-glycoprotein (P-gp), which is the major transporter in gut
and brain.
We stored all the data in a PostgreSQL database and are developing a web application to view
and analyze the database content. The database can be searched by key words, several IDs including
ChEMBL ID, Standard InChI, Standard InChI Key and canonical SMILES, by drawing compound
structure or by activity range of several parameters. Detail view of a compound shows the chemical
structure, the activity of each parameter, the link to other databases and the position in the chemical
space.
We are currently developing prediction models using these data as presented in other posters, andwe plan to make it possible to have functions of database search of existing data and parameter
prediction of new compounds with similar interfaces.
This work was conducted as part of “Development of a Drug Discovery Informatics System”
supported by Japan Agency for Medical Research and Development (AMED).

P4-06Development of a pharmacokinetics prediction systemusing multiscale integrated modeling:
9. Development of Regression Model of Unbound Fraction
to Brain Homogenate from Chemical Structure
Tsuyoshi Esaki 1
tsuyoshi-esaki@nibiohn.go.jp

Reiko Watanabe 1

Rikiya Ohashi 1,2

reiko-watanabe@nibiohn.go.jp

rikiya-ohashi@nibiohn.go.jp

Yayoi Natsume-Kitatani 1,3

Hitoshi Kawashima 1

Chioko Nagao 1,3

natsume@nibiohn.go.jp

hkawashima@nibiohn.go.jp

chio@nibiohn.go.jp

Kenji Mizuguchi 1,3kenji@nibiohn.go.jp
1

2

3

Laboratory of Bioinformatics, National Institutes of Biomedical Innovation, Health andNutrition, 7-6-8, Saito Asagi, Ibaraki, Osaka 567-0085, Japan
Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corporation, 2-2-50, Kawagishi,
Toda, Saitama 335-8505, Japan
Laboratory of In-silico Drug Design, Center of Drug Design Research, National Institutes ofBiomedical Innovation, Health and Nutrition, 7 -6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan

Keywords: Brain homogenate, Brain distribution, Fraction unbound, Machine learning
Estimating the penetration of drugs into the central nervous system (CNS) in the early stages of drug
development can accelerate the development of CNS drugs. It is necessary to predict the concentrationof unbound drug in the brain (C u,brain ) for predicting the efficacy and toxicity of a drug. Fraction unboundin brain homogenate (f u,b ) is one of the most critical parameters to estimate the distribution of drug inthe brain, but no available models to predict f u,b from chemical structure alone have been proposed. In
the academic drug discovery environment, it is unfeasible to collect a large volume of new
experimental data and although a considerable amount of measurements are available from public
databases, but these databases typically contain f u,b under cover of unbound data measured using
other tissues. In this study, we developed regression models to predict f u,b using curated data collectedfrom literatures and an open database.
The experimental data were collected from literatures and ChEMBL, an open database. Our data set
included more than 250 compounds. Three freely available software packages, CDK, Mordred and
PaDEL-Descriptor, were used to calculate descriptors for the compounds. T hree machine learningalgorithms (Random Forest (RF), Support Vector Machine with Radial Kernel function (SVM) andPartial Least Square (PLS)) were employed for model development and the performance of each model
was evaluated.
The RF and SVM model scored higher than PLS in both cross-validation and the evaluation on a test
set The R2 scores for RF, SVM and PLS were from 0.52 to 0.56 in the cross validation, but R2 score of
RF was the highest (R2 = 0.614) followed by SVM and one of PLS was less than 0.5 on the test
compounds. The non-linear model (RF and SVM) may have higher predictive ability than the linear
algorithm (PLS), since the binding of a compound to the brain tissues depends on numerous complex
factors.
We presented an in silico model to predict f u,b without experimental procedures. Our study could open
the possibility to access the development for all researchers interested in the brain target drugs
This work was conducted as part of “Development of a Drug Discovery Informatics System”
supported by Japan Agency for Medical Research and Development (AMED).

P4-07Development of a pharmacokinetics prediction systemusing multiscale integrated modeling:
10. Prediction of renal clearance in humanutilizing structural information
Reiko Watanabe 1

Tsuyoshi Esaki 1

reiko-watanabe@nibiohn.go.jp

tsuyoshi-esaki@nibiohn.go.jp

Yayoi Natsume-Kitatani 1

1

2

3

Hitoshi Kawashima 1
hkawashi@nibiohn.go.jp

Chioko Nagao 1,3

natsume@nibiohn.go.jp

chio@nibiohn.go.jp

Rikiya Ohashi 12

Kenji Mizuguchi 1,3

rikiya-ohashi@nibiohn.go.jp

kenji@nibiohn.go.jp

Laboratory of Bioinformatics, National Institutes of Biomedical Innovation, Health andNutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan
Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corporation, 2-2-50,
Kawagishi, Toda, Saitama, 335-8505, Japan
Laboratory of In-silico Drug Design, Center of Drug Design Research, National Institutes ofBiomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085,
Japan

Keywords: plasma protein binding, protein unbound, machine learning
Renal clearance is the prime route for the elimination of drugs with low to negligible
metabolism and also of drug metabolites. It also represents the net result of glomerular filtration,
active secretion and reabsorption. In silico approaches that variously employ physicochemical andmolecular properties of compounds to quantify pharmacokinetic processes of interest are highly
effective in the early stages of drug discovery because they are noninvasive and havehigh-throughput capacity. We had previously developed in silico models to predict the values offraction unbound in plasma, which is an important determinant of drug efficacy inpharmacokinetic and pharmacodynamic studies. In this study, we have extended our approach tobuild models to predict renal clearance using the values of fraction unbound in plasma as
calculated by our previous models. Experimental values of the renal clearance and fraction
unbound in plasma were extracted from the literature and the ChEMBL database, and classification
models using different machine learning methods were constructed to classify the compounds into
three groups, namely, the reabsorption, intermediate and secretion types. Next, we generated
regression models to predict the renal clearance, the prediction processes were examined by
combining other prediction models such as classification models of elimination types or predictedvalue of fraction unbound in plasma. Our predictive models can be integrated into otherpharmacokinetic modeling systems, which would be highly useful in academic drug discovery.
[1] Bohnert T, Gan L. S, Plasma protein binding: from discovery to development. J Pharm Sci 2013,
102 (9), 2953-94.
[2] Varma M, Obach Bo R, Troutman M, Physicochemical determinants of human renal clearance.
J Med Chem 2009, 52, 4844-52.
[3] Ito S, Ando H, Ose A, Kitamura Y, Ando T, Kusuhara H, Sugiyama Y, Relationship between theurinary excretion mechanisms of drugs and their physicochemical properties. J Pharm Sci 2013,
102(9), 3294-301
This work was conducted as part of “Development of a Drug Discovery Informatics System”
supported by Japan Agency for Medical Research and Development (AMED).

P4-08Development of a pharmacokinetics prediction systemusing multiscale integrated modeling:
11. Prediction of sites of drug metabolism by CYP3A4 bymolecular simulation
Hiroaki Saito,

Takao Otsuka,

hiroaki.saito@riken.jp, totsuka@riken.jp,

Noriaki Okimoto,

Makoto Taiji

okimoto@riken.jp,

taiji@riken.jp

Laboratory of computational molecular design computational biology, RIKEN Center for
Biosystems Dynamics Research (BDR)

Keywords: Molecular dynamics, Molecular docking, Site of metabolism, Cytochrome P450 (CYP)
Cytochrome P450s (CYPs), a superfamily of haem-containing enzymes, are the major enzymes
involved in drug metabolism. In humans, it has been estimated that CYPs metabolize
approximately 75% of all marketed drugs, 95% of which are metabolized by CYP3A4, 2D6, 2C9
and 1A2 [1]. In the drug metabolism by the CYPs, identification of sites of metabolism (SOMs) on
molecules and the structure of their metabolites can be decisive for the design of molecules withfavorable metabolic properties. However, experimental techniques to determine SOMs andstructures of metabolites are still highly resource-demanding and challenging [2]. Thus,
developing fast and accurate computational methods to predict the SOMs/products of compounds
metabolized by the CYPs is one of the important tasks for the optimization of ADME and toxicity
properties.
In this study, we present a new computational method (score function) to predict SOMs ofcompounds metabolized by CYP3A4. The new score function is composed of accessibility andreactivity scores. The accessibility scores for the sites (atoms) of compounds are estimated byensemble docking simulation, while the activation energies of atoms estimated from the
SMARTCyp software[3] are used as the reactivity scores in the method. We carried out a molecular
dynamics (MD) simulation of apo type of CYP3A4 for 10 micro second to sample the receptor(pocket) structures for the ensemble docking. For the molecular docking, DOCK program with
Grid score, which are optimized for CYP system [4], was used. We prepared 47 test set compounds
metabolized by the CYP3A4 and 100,000 receptor snapshots (which correspond to the 10 micro
second MD simulation) for the ensemble docking simulation. The top three ranked atoms by the
new score function are predicted as possible SOMs for each compound in this study. We found thesuccess rate of the predicted SOMs was 86 %, showing better result than those by the ensemble
docking and SMARTCyp program alone.
[1] Wienkers LC, heath TG., Nature Rev. Drug. Discov. 4 (2005), 825-833.
[2] Johannes Kirchmair et al., J. Chem. Inf. Model. 52 (2012), 617−648.
[3] P. Rydberg et al., ACS Med. Chem. Lett., 1 (2010), 96-100[4] Kiumars Shahrokh, Anita Orendt, Garold S. Yost, Thomas E. Cheatham, III, J. Comp. Chem.,
33 (2012), 119-33.
This work is conducted as a part of “Construction of Drug Discovery Informatics System”
supported by Japan Agency for Medical Research and Development (AMED).

P4-09Development of a pharmacokinetics prediction system using
multiscale integrated modeling:
12. Evaluation of the functionalities and the performance ofthe revised custom SoC LSIGentaro Morimoto

Yousuke Ohno

Teruhisa Komatsu

gentaro.morimoto@riken.jp

ohno@riken.jp

teruhisa.komatsu@riken.jp

Hao Zhang

Itta Ohmura

Keigo Nishida

Yohei M. Koyama

hao.zhang@riken.jp ohmura@riken.jp keigo.nishida.jg@riken.jp ym.koyama@riken.jp

Hiroshi Koyama

Makoto Taiji

hkoyama@riken.jp

taiji@riken.jp

Laboratory for Computational Molecular Design, RIKEN Center for Biosystems Dynamics
Research (BDR), 6-2-4 Furuedai, Suita, Osaka 565-0874, Japan

Keywords: Molecular dynamics simulations, Ligand binding, Site of metabolism, Drug design
We have been developing a series of special purpose computers for molecular dynamics
simulations. The current system is the MDGRAPE-4[1]. The whole system consists of 512 custom
System on Chip (SoC) LSI chips in 3D torus network. The target performance is to make itpossible to simulate typical protein-ligand complex surrounded by water for 10 microseconds perday, which is two orders of magnitude faster than commer cially available systems. One of theapplications of long-term MD simulations of proteins is to predict the site of metabolism.
The MDGRAPE-4 hardware development completed in 2014, and the stabilization of operationand software development process succeeded for two years. However, the final performance does
not reach the expected one. In the process of the development, we realized that the classical MD
simulation of the typical system size has already reached the strong scaling limit of parallel
computation and not only accelerating the computationally demanding part but also more
specialized hardware for the type of computation is required to improve the performance.
To achieve the target performance, we have started the development of the improved system thatis named MDGRAPE-4A. The system architecture is basically common with the MDGRAPE-4.
The enhancement of the functionalities are as follows:
1) support of management and migration of atoms by the memory hardware
2) more sophisticated treatment of exclusion and reduced non-bonded interaction pairs in thespecial purpose pipelines3) implementation SIMD functions and special instructions required in the MD simulations in thegeneral-purpose cores4) reduction of the latency and more flexible routing patterns in the network interface5) support of a long-range electrostatic interaction calculation met hod that is suitable for theparallel systems with torus network topology
Currently the development process is under the evaluation of the revised LSI on the revised
system board. The new system hardware will be completed in the end of 2018. In this presentation,
we would like to show the expected performance of the revised system.
[1] Ohmura, I., et al., MDGRAPE-4: a special-purpose computer system for molecular dynamics
simulations, Phil. Trans. R. Soc. A, 372:20130387, 2014.
Some of this work is conducted as a part of “Construction of Drug Discovery Informatics System”
supported by Japan Agency for Medical Research and Development (AMED).

P4-10Development of an informatics system for predicting
cardiotoxicity: 4. Update of integrated cardiotoxicity
database
Hitomi Yuki

hitomi.yuki@riken.jp

Tomohiro Satotomohiro.sato@riken.jp

Teruki Honma
honma.teruki@riken.jp

RIKEN Center for Biosystems Dynamics Research, 1 -7-22, Suehiro-cho,
Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

Keywords: Cardiotoxicity, Database, hERG, Ion channels, QT prolongation
The inhibition of hERG potassium channel is closely related to the prolonged QT interval [1], andto assess the risk could greatly contribute to the development of safer therapeutic compounds. To
utilizing the recent increase of information about hERG inhibitors in public databases, we built
integrated database for hERG blocking compounds. The merging procedure were reported in CBIannual meeting 2017. The database was released as “AMED Cardiotoxicity Database” [2] and thepaper was also published [3]. You can find cardiotoxicity assay results quickly on the web by
substructure or similarity compound searches. We will introduce the database contents, theanalysis results, and future plans such as the scaffold difference between hERG actives andinactives, the user interface and assay results of other ion channels. We would like to discuss theusabilities and requests for the database.
This work was done as a part of “Construction of Drug Discovery Informatics System” supported
by Japan Agency for Medical Research and Development (AMED).
[1] Picard, S., Goineau, S., Guillaume, P., Henry, J., Hanouz, J.-L. and Rouet, R., Supplemental
studies for cardiovascular risk assessment in safety pharmacology: a critical overview,
Cardiovasc. Toxicol.11:285–307, 2011[2] http://drugdesign.riken.jp/hERGdb/[3] Sato, T., Yuki, H., Ogura, K. and Honma, T., Construction of an integrated database for hERG
blocking small molecules, PLoS ONE, 13(7):e0199348, 2018

P4-11Development of an informatics system for predicting
cardiotoxicity: 5. Quantitative model for hERG blocking
small molecules based on the integrated database.
Tomohiro Satotomohiro.sato@riken.jp

Hitomi Yuki

Teruki Honma

hitomi.yuki@riken.jp honma.teruki@riken.jp

Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro-cho,
Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan

Keywords: hERG, QT prolongation, Support vector regression
The inhibition of hERG potassium channel is closely related to the prolonged QT interval [1], andto assess the risk could greatly contribute to the development of safer therapeutic compounds. In
the optimization stage of drug development, quantitative prediction of hERG inhibitory activity is
crucial to design drug candidates without cardiotoxicity risk. Here, we developed a hERG
regression model combining Support Vector Regression (SVR) and descriptor selection by
Non-dominated sorting genetic algorithm (NSGA-II) based on the hERG integrated databasereported as the largest dataset for hERG inhibitors in CBI meeting 2016. From the 291,219
compounds in the integrated database, 6,563 compounds with IC 50 values were selected, andrandomly separated into training set (70%) and test set (30%) to build a regres sion model andevaluate its prediction performance. To avoid overfitting by employing too many non-relevant
explanatory variables, NSGA-II, which is a variation of genetic algorithm for multiple objective
optimization, is used to select descriptors in order to maximize Q 2 and minimize RMSE in 5-foldcross validation, and minimize the number of used descriptors spontaneously. The prediction
performance was then compared to those of ADMET predictor [ 2], commercial software providing
various ADMET property predictions. The SVR model recorded R 2 of 0.595 and RMSE of 0.607
for test set, clearly exceeding those of ADMET predictor (0.134 and 0.960, respectively).
This work was done as a part of “Construction of Drug Discovery Informatics System” supported
by Japan Agency for Medical Research and Development (AMED).

Figure 1. Prediction of hERG inhibition for test set using (a) AMED SVR model and (b) ADMET
predictor (Horizontal axes: experimental pIC 50 values, vertical axes: predicted pIC 50 values).
[1] S. Picard, S. Goineau, P. Guillaume, J. Henry, J.-L. Hanouz, R. Rouet, Supplemental studies for
cardiovascular risk assessment in safety pharmacology: a critical overview, Cardiovasc. Toxicol.11,
285–307, 2011[2] https://www.simulations-plus.com/software/admetpredictor/

P4-12

Use of Markov Chain Monte Carlo Method to Integrate
In Vitro & In Vivo Data for Prediction of Drug
Interactions Caused by Inhibition of Multiple CYP Species

1

Shizuka Hozuki 1

Hideki Yoshioka 1

hozuki0163@chiba-u.jp

afga7643@chiba-u.jp

Hiromi Sato 1

Akihiro Hisaka 1

hiromi-s@chiba-u.jp

hisaka@chiba-u.jp

Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences,
Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba, Chiba 260-8675, Japan

Keywords: Pharmacokinetics, Cytochrome P450, Drug-drug interaction, Bayesian inference,
MCMC, WinBUGSPrediction of AUC changes in drug-drug interaction (DDI) are not easy if involving CYP species
are multiple or are uncertain with insufficient data. The objective of this study is to construct asystematic and exhaustive prediction method of DDIs involving multiple CYP species based on all
available information (in vitro & in vivo) by using Bayesian inference which allows flexible error
model.
Based on a static pharmacokinetic model taken account of appropriate error distributions, aframework was constructed to estimate substrates’ contribution ratio (CR) and inhibitors’ inhibition
ratio (IR) of five CYP species (1A2, 2C9, 2C19, 2D6, 3A) by Markov Chain Monte Carlo method(software; WinBUGS1.4.3.). Model was constructed as the hierarchical Bayesian model. In addition
to CR and IR, distribution volume of each inhibitor was estimated as a scaling factor between invitro and in vivo data. The in vitro data of fm (fraction metabolized in vitro)/Ki and in vivo data of
AUC changed ratio (AUCR) caused by DDI or polymorphism were collected from University ofWashington's Metabolism and Transport Drug Interaction Database (DIDB ® ). We selected major 68
substrates and 32 inhibitors for the analysis in this study. Simulation analysis was extensively
performed by using virtual data sets beforehand to confirm appropriateness and predictability of themethod.
The simultaneous analysis of fm for 27 substrates, Ki for 32 inhibitors and AUCR for 201 drug
pairs (9.2% of 2,176 all possible drug pairs) resulted in the prediction of AUCR of all possible drug
pairs within reasonable estimation errors. In the real data analysis, the standard errors of fm, Ki and
AUCR were estimated as 0.013 (normal distribution), 251%CV and 32.5%CV (lognormal
distribution), respectively..
The present analysis allowed efficient use of observed data, which are limited in most cases, forproper management of DDIs. It may also contribute to appropriate classification of CYP substrates
and inhibitors in the labeling. Furthermore, our simulation suggested that the present predictabilityof DDI would be improved noticeably if in vitro data (experimental fm/Ki) will be collected
systematically in the future.
[1] Yoshiyuki Ohno, Akihiro Hisaka and Hiroshi Suzuki, General framework for the quantitative
prediction of CYP3A4-mediated oral drug interactions based on the AUC increase bycoadministration of standard drugs, Clin Pharmacokinet. 46(8):681-696, 2007

P4-13

Individual data analysis of patients participated inclinical studies: relationship between longitudinal changes
in cardiac functions and mortality risk in CHF
Sayuri

Guro 1

adwa1668@chiba-u.jp

AkihiroHideki

Yoshioka 1

afga7643@chiba-u.jp

Hisaka 1

hisaka@chiba-u.jp

1

Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences,
Chiba University, Chiba, Japan.

Keywords: chronic heart failure, clinical trial data, cardiac function
Chronic heart failure (CHF) is a serious disease with a high mortality rate (five-year survival rate,
50-60%) [1], and its patients often experience longitudinal decline of cardiac physical functions,
resulting in death. By using literature information of aggregated data (i.e. mean and sd), we
previously found that the reduced mortality risk in CHF patients by treatment of various drugs was
excellently associated with decrease in myocardial oxygen consumption (MVO 2), a cardiac load
index. Further, the degree of their longitudinal changes was fluctuated by patient characteristics[2]. In this study, by using individual data of patients participated in clinical studies, we aimed toelucidate the relationship between the changes in cardiac functions and mortality risk in CHF
patients in further detail focusing on heart rate (HR) and systolic blood pressure (SBP).
Based on approval of IRB of Chiba university, graduate school of pharmaceutical sciences, dataof two randomized controlled trials in CHF patients (SOLVD Treatment and HF-ACTION) wereobtained from Biologic Specimen and Data Repositories Information Coordinating Center(BioLINCC) [3]. SOLVD Treatment assessed the effect of enalapril (N=1285) vs placebo (N=1284),
while HF-ACTION assessed the efficacy and safety of exercise training (N=1172) vs placebo(N=1159). We compared changes in HR and SBP from the randomization date between survivors
and the deceased. For the deceased, the changes during 50 months before death was also compared.
We observed that SBP of the deceased was constantly decreased (~5 mmHg/year) after the
randomization despite that SBP of the survivors was almost stable in both trials. Furthermore, ouranalysis showed that the decline of SBP in the deceased was more remarkable during two years
before death. These suggests that the excessive decrease of SBP in the deceased should be
distinguished from the therapeutic effect and could be a risk factor for death in CHF patients. For
HR, no distinct difference between the deceased and survivors was observed in both trials.
Assessing longitudinal change in cardiac functions may provide detailed understanding of the
progression of CHF and its mortality risks.
[1] Shiba, N. and Shimokawa, H., Chronic heart failure in Japan: implications of the CHART
studies, Vasc Health Risk Manag, 4:103-13, 2008.
[2] Guro, S., Takaoka, R., Hatakeyama, H., Hisaka, A., Model based meta-analysis to investigate
therapeutic responses of β-blockers and ACE inhibitors in progression of chronic heart failure , The
37 th Annual Scientific Meeting of the Japanese Society of Clinical Pharmacology and Therapeutics ,2016,1-O-12.
[3] https://biolincc.nhlbi.nih.gov/home.html

P4-14Model-based meta-analysis of the relationship between HbA1cchange and urinary glucose excretion in subjects treated with six
SGLT2 inhibitors aiming early prediction of efficacy in drug
development
Ayana Ishikawa 1

Hideki Yoshioka 1

Hiromi Sato 1

Akihiro Hisaka 1

aeta1660@chiba-u.jp

afga7643@chiba-u.jp

hiromi-s@chiba-u.jp

hisaka@chiba-u.jp

1

Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical
Sciences, Chiba University, Inohana, Chuo-ku, Chiba-City, Chiba, 1-8-1, Japan

Keywords: Prediction of drug efficacy, Biomarker, Model-based meta-analysis
In drug development, the drug efficacy is generally evaluated in Phase II study and later. If we
can predict drug efficacy in Phase I, we may decide whether or not to continue development andmay improve the efficiency of later clinical studies with proper dosage selection . In this study, we
focused on six SGLT2 inhibitors used for pharmacotherapy of type 2 diabetes in Japan. In order tocompare the efficacy of these SGLT2 inhibitors, the relationships between the daily urinary
glucose excretion measured in healthy subjects in phase I studies and the HbA1c decrease
evaluated in diabetes patients in later clinical studies were investigated by model-based
meta-analysis (MBMA).
First, the dosages of six drugs were normalized based on the daily urinary glucose excretion(UGE) of healthy subjects in phase I single dose study described in the evaluation reports released
from PMDA. Next, MBMA was performed by using the Emax model for the relation between thenormalized dose and HbA1c changes from the baseline observed in 83 clinical studies collectedfrom the literature. The model analysis was performed using a first-order conditional estimation
with interaction (FOCE-I) implemented on NONMEM (v7.3.0).
As a result, urinary glucose excretion (UGE) was consistently elevated as the dose increased forall six SGLT2 inhibitors. It was confirmed that the daily UGE was approximately 50g at theefficacious clinical dose of these SGLT2 inhibitors. In addition, once their doses are normalized
to the clinical dosage, the dose-response relationship for HbA1c decrease was almost comparable
between these drugs. It was suggested that the clinical properties of six SGLT2 inhibitors are very
similar, and the proper dose selection for later clinical studies can be made based on daily urinary
glucose excretion which is evaluable even in phase I study.
Exploratory analysis of data in Phase I study relating the drug efficacy might be of help in
improving the efficiency of later studies and selecting proper dosage as demonstrated inthis study. Although this is a retrospective analysis, in the future , analogous prospective
analysis might contribute to improvement in the efficiency of drug development.

P4-15

Evaluation of correlation of newly developing noninvasivecell culture profiling system based on LC-MS/MS
measurement of medium components to the functionalchanges during cell culture
Ryuya Fujii 1

Kenichi Toyoda 2

Shinichiro Horiuchi 1

r-fujii@nihs.go.jp

Yukie Kuroda 1

Su-Ryang Kim 1

Yasunari Kanda 1

Seiichi Ishida 1

1

Division of Pharmacology, National Institute of Health Sciences,
3-25-26 Tonomachi, kawasaki-ku, Kawasaki, 210-9501, Japan
2
Shimadzu Corporation,
1,Nishinokyo Kuwabara-cho, Nakagyo-ku, Kyoto 604-8511, Japan

Keywords: HepG2, LC-MS/MS, cell culture profiling system , medium components[Background]
In recent years, in vitro cell-based assays with devices capable of culturing cells with highcell-function in vitro have been developed in order to reduce the cost for the drug -candidate safety
evaluation in the early stage of drug discovery proce ss. Liver has many functions for the catabolism ofnutrients or xenobiotics, and development of a noninvasive measurement method is useful for theevaluation of the fundamental functions of hepatocyte (e.g., production of albumin and urea andexpression of oxidase relating to drug metabolism) during cell culture on such devices. Therefore,
application of a cell culture profiling system capable of quantifying 95 medium components andsecretory metabolites simultaneously using an LC -MS/MS was examined. We have already reported anevaluation case in which differentiated and undifferentiated states of multifunctional stem cells werediscriminated based on the measurement of changes in medium components (The Society for
Biotechnology, Japan/Annual Meeting 2016). In this study, the application of this system to theanalysis of hepatocyte functions was evaluated using a liver cancer cell line HepG2.
[Method]
HepG2 cells was cultured with high glucose DMEM supplemented with 10% fetal bovine serum and
1% antibiotic and antimycotic solution for 5 days, and the culture supernatant was collected over time.
Acetonitrile was added to the culture supernatant to remove the protein, and then the medium
components were quantitated according to the protocol pre viously established using LC-MS/MS(LCMS-8050, Shimadzu).
[Result]
LC-MS/MS analysis resulted in the detection of several medium components successfully. We would
like to discuss the relation of changes in medium components to the HepG2 cell function s in thepresentation.

P4-16

Characterization of human hepatocytes isolated fromchimeric mice with humanized liver (PXB-cells) by DNA
microarray analysis for the evaluation of the applicability
to cell-based drug safety tests
Shinichiro Horiuchi

Yukie Kuroda

Su-Ryang Kim

Ryuya Fujii

Hori542@nihs.go.jp

Yasunari Kanda

Seiichi Ishida

Division of Pharmacology, National Institute of Health Sciences,
3-25-26 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa, 210-9501, Japan

Keywords: PXB-cells, DNA microarray, drug safety test, drug metabolism
Evaluation of drug metabolism in humans is important for the safety test of drug candidate
compounds. Human hepatocytes are mainly used in in vitro cell-based test at the early stage of drug
development. However, there are some drawbacks, such as differences between lots caused by theindividual donor variations or limited supply of each lot . Therefore, development of alternative cellsource which circumvents these points is expected. When human hepatocytes are transplanted into theuPA/SCID mouse liver via the spleen, mouse hepatocytes are largely repopulated with the transplanted
human hepatocytes. Stable supply of human hepatocyte derived from the same donor is possible byisolating hepatocytes from chimeric mice with humanized liver. We compared functions andcharacteristics among various hepatocytes by gene expression analysis to evaluate whether the humanhepatocytes isolated from chimeric mice with humanized liver has characteristics similar to humanhepatocytes.
PXB-cells (PhoenixBio, Co., Ltd., Hiroshima, Japan) were used as a human hepatocytes isolated fromchimeric mice. Liver function-related 183 gene expressions was measured by Genopal focused DNA
microarray (Mitubishi Chemical, Co., Ltd., Tokyo, Japan). The gene expression data obtained from
PXB-cells, cryopreserved hepatocytes (cryo-hep), hepatocyte-like cells differentiated from human iPS
cells (hiPSC-hep) and human liver cancer cells (HepG2) was compared by Principal Component
Analysis (PCA), hierarchical clustering analysis and enrichment analysis.
When gene expression data of Genopal was analyzed by PCA, PXB-cell was located near cryo-hep
and far from iPSC-HEP and HepG2 cell in principal component one (PC1) and PC2. In addition,
PXB-cells was classified into the same cluster that includes cryo-hep by hierarchical clustering. These
results shows that PXB-cells has properties closer to that of cryo-hep than to those of hiPSC-hep and
HepaG2 cell. Genes of phase I drug metabolizing enzyme and hepatocyte-specific transporter wereenriched in gene cluster highly expressed in PXB-cells and cryo-hep.
From the above, we consider that PXB-cells is the candidate for alternative cell source of humanprimary / cryo-preserved hepatocytes in in vitro cell-based drug metabolism test.

P5-01

AI for Chemistry Optimisation: Combining Machine
Learning and Domain Knowledge

12Matthew Segall 1

Sumie Tajima 2

Edmund Champness 1

matt@optibrium.com

tajima@hulinks.co.jp

ed@optibrium.com

Peter Hunt 1

Tamsin Mansley 1

peter@optibrium.com

tamsin@optibrium.com

Optibrium Ltd., F5-6 Blenheim House, Cambridge Innovation Park, Denny End Road
Cambridge, CB25 9PB, UK
Hulinks Inc., 5-14 Nihonbashi Hakozaki-cho, Chuo-ku, Tokyo 103-0015, Japan

Keywords: Multi-parameter optimisation, QSAR, Structural transformation, Evolutionary
algorithm, de novo design
Artificial Intelligence involves the application of machine learning algorithms in the context of
domain knowledge. In the case of compound design, this involves integratio n of information frommultiple perspectives: understanding of structure-activity relationships (SAR), based on data frompreviously studied compounds; expertise from diverse fields to define the multi -parameter
optimisation (MPO) objectives of a project; and knowledge of synthetic strategies that may be
applicable to create the next round of compounds for investigation. All of these forms ofknowledge can be captured and applied computationally: Machine learning methods can generatequantitative structure-activity relationship (QSAR) models to predict the properties of novel,virtual compounds; MPO methods capture the desired property criteria for a successful compound
for a specific project and rigorously define an objective function to guide optimisation; and,evolutionary algorithms can be applied to explore optimisation strategies captured as structuraltransformations that reflect steps made in previous chemistry projects.
In this presentation, we will describe these methods and illustrate how they can b e seamlessly
combined to rigorously explore new, relevant compound ideas and prioritise those most likely toachieve a project objective. This approach can help to stimulate the search for new optimisation
strategies and explore a much broader range of compounds than could be achieved based on asingle chemist’s or even a project team’s experience. Example applications include theoptimisation of compounds with a desired polypharmacology or selectivity profile and explorationof lead hopping strategies to overcome pharmacokinetic issues, while maintaining target potency.
[1] O. Obrezanova, M.D. Segall, J. Chem. Inf. Model., 50(6):1053-1061, 2010.
[2] M.D. Segall, Curr. Pharm. Des., 18(9):1292-1310, 2012.
[3] M.D. Segall et al., J. Chem. Inf. Model., 51(11):2967-2976, 2011.
[4] I. Ujváry, J. Hayward, In N. Brown ed., Bioisosteres in Medicinal Chemistry, 2012.

P5-02Development of Computer Aided Retrosynthesis system
Takayuki Serizawa 1serizawa.tf@om.asahi-kasei.co.jp

Kazufumi Ohkawa 1
okawa.kf@om.asahi-kasei.co.jp

Takayuki Kochi 1
kochi.tb@om.asahi-kasei.co.jp
1

Laboratory for Medicinal Chemistr y, Pharmaceuticals Research Center, AsahiKASEI Pharma
corporation, 632-1 Mifuku, Izunokuni, Shizuoka 4102321, Japan

Keywords: Artificial Intelligence(AI), Machine Learning, Deep Learning, Retrosynthesis,
Synthetic route prediction
Back ground:
Recently Artificial Intelligence is attracting attention not only Computer Vision but also Drug
Discovery.
We have been using de novo molecular design system. The system can propose molecules
but can’t propose synthetic route. There is a problem that de novo molecular generator sometimes
proposes synthetic infeasible compounds. To address this issue, we tried to develop Computer
Aided Retrosynthetic Analysis system.

Results:
We developed Retrosynthetic AI with neural network and Monte Carlo Tree Search (MCTS) [1]with python and JavaScript as programming language . This approach is the same as Google’s Alpha Go[2]. The AI can learn reaction pattern from more than a million of reaction data and proposes
synthetic route of given molecules within several minutes. And we developed Web user interface for
medicinal chemists.

[1] Marwin H. S. Segler, Mike Preuss, Mark P. Waller: Nature. 2018 Mar 28;555(7698), pp.
604-610.
[2] David Silver et al.: Nature 2016 January 28 529, pp. 484–489.
[3] CB Browne et al.: IEEE Transactions on Computational Intelligence and AI in Games (Volume:
4, Issue: 1, March 2012), pp. 1-43

P5-03In silico Prediction of Severe Cutaneous Adverse Drug
Reactions Based on the Adverse Event Reporting Database
Kazuyuki Ohya

Kaori Ambe

c142017@ed.nagoya-cu.ac.jp

ambek@phar.nagoya-cu.ac.jp

Masahiro Tohkintohkin@phar.nagoya-cu.ac.jp

Graduate School of Pharmaceutical Sciences, Nagoya City University,
3-1 Tanabe-dori, Mizuho-ku, Nagoya 467-8603 Japan

Keywords: JADER, Machine learning method, Stevens Johnson Syndrome, Toxic Epidermal
Necrolysis.
Stevens Johnson Syndrome (SJS) and Toxic Epidermal Necrolysis (TEN) are idiosyncratic
adverse drug reaction (iADR). Since iADR is hard to be predicted from animal experiments andclinical trials due to its high species specificity and low occurrence frequency, it is desired toestablish a prediction method of iADR at the new drug development stage. Large-scale adverse
drug effect data of post-marketing can grasp the relevance between medicinal drugs and various
adverse reactions. Therefore, we focused on Severe Cutaneous Adverse Reactions (SCAR), such as
SJS/TEN [1] based on adverse event reporting system, and built a predictive classification modelby machine learning method using the drugs structure information.
The Japanese Adverse Drug Event Report database (JADER) [2] constructed by the
Pharmaceutical and Medical Devices Agency (PMDA) has accumulated spontaneous adverse drug
effects reports for more than a dozen years in Japan. SCAR-positive or negative drugs were
defined from JADER database using the Proportional Reporting Ratios (PRR) [3] method and thenumber of reports. Chemical structure information on extracted drugs was obtained from PubChem[4] and molecular descriptor calculations were performed using Dragon 7 [5]. A predictive model
was built by Deep Learning using the calculated molecular descriptors. In addition, in order tomake more reliable prediction, applicability domain was defined [6]. As a result, we achieved
classification of SCAR with approximately 70% prediction accuracy from only the structure
information of drugs. This method is might be useful to improve efficiency of new drugs
development.
[1] https://www.meddra.org/sites/default/files/guidance/file/smq_intguide_20_1_japanese.pdf[2] https://www.pmda.go.jp/safety/info-services/drugs/adr-info/suspected-adr/0005.html[3] Kubota K., Signal Detection from Spontaneous Reports: New Methods in MCA in the UK, FDA
in the US and WHO, Jpn J pharmacoepidemiol, 6 (2) Dec 2001: 101-108.
[4] https://pubchem.ncbi.nlm.nih.gov/[5] https://chm.kode-solutions.net/products_dragon.php[6] OECD, ENV/JM/MONO, 2, 2007.

P5-04

Analyzing Deep Neural Networks on Molecular Activity
Prediction using Cross Validation Approach
Yoshiki Kato 1

Shinji Hamada 1

katoh@cch.cs.tut.ac.jp

hamada@adsim.tut.ac.jp

Hitoshi Goto 1
gotoh@tut.jp

1

Computational Chemistry Laboratory, Graduate School of Computer Science and Engineering,
Toyohashi University of Technology, 1 -1 Hibarigaoka, Tempaku-cho, Toyohashi, 441-8580,
Aichi, Japan.

Keywords: QSAR(Quantitative Structure-Activity Relationship), Deep Neural Networks, Cross
Validation, Drug Discovery
Deep Neural Networks (DNNs), which are neural networks composed of many hidden
layers, are constructed by applying a deep learning algorithm using a large number oftraining data. DNNs technique is applied in various fields, and especially, in Merck
Molecular Activity Challenge (MMAC), the prediction accuracy was equal to or higher
than those of other conventional machine learning methods [1,2].
We have performed verification experiments to the equivalent DNNs using the training andevaluation data (descriptors and activity values) sets of various targets adopted by MMAC, andreproduced almost the same results [3]. From this verifications, we found that there are a few
target data sets that show high prediction accuracy, whereas many data sets cannot enough achieve
the expected learning effect. To elucidate the cause of these large differences and to derive some
improvement strategies, we optimized hyperparameters of DNNs and analyzed feature quantities
between training and evaluation data set of each target [4,5].
In this work, we have further tried various learning methods based on the k-fold Cross Validation
approach and analyzed the differences with general learning algorithms used in deep learning andthe effects on prediction accuracy. Comparison with the differences in the prediction accuracy
among learning methods suggests that applications of the Cross Validation may suppress the overfittings for some targets.
This research is performed with the support of the Toyohashi University of Technology Leading
Program.
[1] J. Ma; R. P. Sheridan; A. Liaw; G. E. Dahl; V. Svetnik. J. Chem. Info. Model. 2015, 55 (2),263-274.
[2] Y. Xu; J. Ma; A. Liaw; R. P. Sheridan; V. Svetnik. J. Chem. Inf. Model. 57, 10, 2490-2504.
[3] Y. Kato; S. Hamada; H. Goto. Conference: The 2016 International Conference on Advanced
Informatics: Concepts, Theory and Application (ICAICTA2016), At 2016 -10-43-0081.
[4] Y. Kato; S. Hamada; H. Goto. SCCJ Spring Annual Meeting 2017, 2O06.
[5] Y. Kato; S. Hamada; H. Goto. SCCJ Spring Annual Meeting 2018, 2P08.

P5-05Deep-learning of cancer stem cell morphology for
anti-cancer stem cell molecule screening
Sakae Nishisako 1

Saori Aida 2b0115176c0@edu.teu.ac.jp

aidasor@stf.teu.ac.jp

Hiroyuki Kameda 2

Tomonari Kasai 1

kameda@stf.teu.ac.jp

kasaitnr@stf.teu.ac.jp

Atsushi Sato 1

Tomoyasu Sugiyama 1

atsato@stf.teu.ac.jp

tsugiyama@stf.teu.ac.jp

1

School of Bioscience and Biotechnology, Tokyo University of Technology, 1404 -1 Katakura,
Hachioji, Tokyo 192-0982, Japan
2
School of Computer Science, Tokyo University of Technology, 1404 -1 Katakura, Hachioji,
Tokyo 192-0982, Japan

Keywords: Artificial intelligence, Cancer stem cell, miPS-LLCcm cells
The purpose of this study is to identify cancer stem cells (CSCs) by using artificial intelligence
technology (AI). Recently, it is proposed that to suppression of CSCs could be important andeffective for suppressing tumour growth.
One of the most effective cancer therapies is to use anti -cancer agent. However, these drugs are
not always efficient to prevent tumour recurrences. This phenomenon is thought to be due to thepresence of CSCs. The surviving CSCs after the therapy might form the tumours which are notsuppressed. Thus, tumour could be reproduced unless eradicatin g CSCs. Conversely, tumour
recurrence might not be occurred if CSCs were removed completely.
Inefficiency of killing CSCs by existing agents is not surprising because these agents wereselected through cell assay using cancer cell lines which lost CSC characteristics. Unfortunately,
there are no straightforward cell assays to measure the ability of affecting CSC characteristics ofundifferentiated state. Here, we describe an automated method of identifying CSC based on thecell morphology of CSC characteristics.
Methods: We utilized mouse iPS-LLCcm cell line as a CSC model which was previously
developed (PLoS ONE 2012, 7: e33544). Pair of cell images of phase contrast and Nanog -GFP
reporter fluorescence was obtained using fluorescence microscopy equipped with digital camera
system. The images were used as the input into deep-learning software Conditional Generative
Adversarial Nets (CGAN).
Results and Discussion: miPS-LLCcm cells were grown on Petri dish coated with porcine gelatin.
Almost 90% of cells showed Nanog-GFP fluorescence indicating the maintenance of CSC
characteristics under the cell culture condition. Rest of cells showed no fluoresce indicating thatthese cells did not maintain the characteristics. Using these cell images as teacher, phase cont rast
bright-field images were examined for CGAN. It took many hours for the deep -learning. For
example, over 24 hours were required to learn about seven thousands images. One of the bottle
necks was the main memory size of hardware. After the deep -learning, AI distinguishedundifferentiated CSCs from differentiated cells using phase contrast cell image in high correlation
with the corresponding pair fluorescence cell image. Finally, we will develop an AI system that canevaluate whether a test cell is a CSC or not by using phase contrast bright-field cell image.

P5-06
Simulation study comparing non-linear mixed-effectmodeling and machine learning: efficient integration ofindividual patient-level data from multiple clinical trials
Hideki Yoshioka 1

Hiromi Sato 1

afga7643@chiba-u.jp

hiromi-s@chiba-u.jp

Akihiro Hisaka 1
hisaka@chiba-u.jp
1

Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical
Sciences, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi, Chiba, 260-8675, Japan

Keywords: clinical trial data, machine learning, artificial intelligence
Today, individual patient-level anonymous data from thousands of clinical trials are available via
several means such as ClinicalStudyDataRequest.com, which is currently the largest consortium
organized by clinical study sponsors/funders ( https://clinicalstudydatarequest.com ). Furthermore,
submission of electronic trial data for new drug approvals is recently obliged in various countries.
Therefore, a comprehensive framework to integrate big data derived from numerous studies
considering various covariates as well as complicated inter-study differences would be urgently
required. Based on such outlook, we attempted to perform a simulation analysis to examine how
the conventional approach using non-linear mixed-effect modeling works in such a situation incomparison with machine learning that are becoming the current major stream.
A total of ~10,000 patients, each of whom has ~100 feature records and belong to any of ~100
trials, were synthesized by Monte Carlo method. Assuming clinical trials that follow up drug
effects on a target biomarker, a longitudinal change in the virtual biomarker was individuallysimulated according to the double exponential function (y=a*exp(b*exp(c*x))), where each of thethree parameters (a, b, c) has both inter-individual variability (IIV) and inter-study variability(ISV) and ~5 covariates that are randomly selected from the features synthesized. To simplify the
analysis, data were transformed into a time-independent format (i.e. change from baseline at thetrial completion) and then analyzed by non-linear mixed effect modeling (NONMEM v7.3;
FOCE-I) and machine learning (Python v3.7.0; scikit-learn). Results were comprehensively
reviewed from the viewpoints mainly of covariate identification.
In NONMEM, the hierarchical structure of variability as well as a model function need bedescribed as a control file by the user, and this is directly passed to the software. Consequently, in
our analysis, the magnitudes of IIV and ISV were successfully separated, and all the covariates thatwere incorporated into the model during the data synthesis were identified with an extremely highsignificance level by the stepwise search. However, considering the execution time required
drastically increased as the data got bigger, it would not be practical to explore significant
covariates by this method when the number of features to be tested is huge. On the other hand,machine learning methods generally can handle data of numerous patients with more than hundredsof features easily compared to the conventional model-dependent approach. In the present analysis,
we confirmed that even without any information to distinguish clinical trials which each patient
belongs to, the random forest model explained data without distinct bias and correctly proposed all the
pre-incorporated covariates as features with higher importance. However, in contrast to its usability,
the inside structure was highly complex, so that we were able to rank features according to importancelevel, but not able to quantify their impact on outcomes as clearly as given by NONMEM.
Taken together, our analysis provided a better understanding of both aspects of advantages anddisadvantages for the model-dependent approach and analyses using machine learning. To make fulluse of clinical trial data to be accumulated in the future, further efforts to incorporate theories oranalytical hypotheses based on scientific evidences into machine learning models would be necessary.

P5-07

Auto Cell Image Classification System
for Micronucleus Assay by Deep Learning

1

2

Hiroshi Ueda 1

Yoshinori Wakabayashi 2

Hiroshi_Ueda@nts.toray.co.jp

wakabayashi@by-hex.com

Ryuji Tanimura 1

Tomohide Masuda 1

Ryuji_Tanimura@nts.toray.co.jp

Tomohide_Masuda@nts.toray.co.jp

Pharmaceutical Research Laboratories, Toray Industries, Inc. 6-10-1 Tebiro, Kamakura,
Kanagawa, 248-8555, Japan
BY-HEX LLP, 1-19-14 Shimizu, Suginami-ku, Tokyo 167-0033, Japan

Keywords: Micronucleus Assay, Deep Learning, Cell Image Classification
Micronucleus assay is a genotoxicity test method to observe "micronuclei" appearing due tochromosomal abnormality.
Micronucleus assay is very labor-intensive, the experts of
micronucleus assay visually check thousands of cultured cells by microscopic observation andclassify the presence or absence of micronuclei. So, using deep learning, we have developed asystem to calculate the micronucleus ratio by automatically classifying the presence or absence of
micronuclei just by inputting the microscopic image.
In order to prepare the training set, we have picked out about 61,000 images that contains single
cell from 48 microscopic images by using auto cell pick-out module “Kiridasi-kun” and then we
have classified the picked-out single cell images to the micronuclei presence images or absence
images by using user-assist GUI module “Bunrui-kun”. Using the prepared training set, we havecreated a classification model with the correct answer rate 92%. Genetic programming
technique[1], which is an efficient method to obtain the optimum configuration of the network
layers automatically, has been applied to decide the network configuration.
On the auto classification module “Hanbetu-kun” of cell images in micronucleus assay that
implements the classification model above, just by inputting a microscopic image, one can obtainthe micronucleus ratio by automatically classifying the presence or absence of micronuclei.
[1] Wakabayashi, Y., Mashimo, T., Wada, M., A Genetic Approach to Deep Leaning in Predictionof Molecular Properties, Chem-Bio Informatics Society (CBI) Annual Meeting 2017, P2-21,
2017.

P5-08
Toward AI-based Molecular Force Fields
Koichiro Kato 1
Ryuji Tanimura 2
Naoki Miyagawa 1
Shunpei Nagase 5
Kaori Fukuzawa 7
Shuntaro Chiba 6

Tomohide Masuda 2
Tomohide_Masuda@nts.toray.co.jp

Kanji Oshima 3
Hideo Mizouchi 1
Kikuko Kamisaka 5Atsushi Tokuhisa 8,9Masateru Ota 6

Hiroshi Ueda 2
Satoshi Ono 4
Chiduru Watanabe 5
Teruki Honma 5,6
Ryo Kanada 8
Mitsunori Ikeguchi 6,10

1

Mizuho Information & Research Institute, Inc., 2-3, Kanda-Nishikicho,
Chiyoda-ku, Tokyo 101-8443, Japan
2
Pharmaceutical Research Laboratories, Toray Industries, Inc., 6-10-1, Tebiro,
Kamakura, Kanagawa 248-8555, Japan
3
Biotechnology Research Laboratories, KANEKA CORPORATION, 1-8, Miyamae-cho,Takasago-cho, Takasago, Hyogo 676-8688, Japan
4
Modality Laboratories, Innovative Reaearch Division, Mitsubishi Tanabe Pharma Corporation,
1000, Kamoshida-cho, Aoba-ku, Yokohama, Kanagawa 227-0033, Japan
5
RIKEN Center for Biosystems Dynamics Research, 1-7-22, Suehiro-cho, Tsurumi-ku,
Yokohama, Kanagawa 230-0045, Japan
6
RIKEN Cluster for Science and Technology Hub, 1-7-22, Suehiro-cho, Tsurumi-ku,
Yokohama, Kanagawa 230-0045, Japan
7
School of Pharmacy and Pharmaceutical Sciences, Hoshi University,
2-4-41, Ebara, Shainagawa-ku, Tokyo 142-8501, Japan
8
RIKEN Cluster for Science and Technology Hub, 6-3-5, Minatojima-minamimachi,
Chuo-ku, Kobe, Hyogo 650-0047, Japan
9
RIKEN Center for Computational Science, 6-3-5, Minatojima-minamimachi,
Chuo-ku, Kobe, Hyogo 650-0047, Japan
10
Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Suehiro-cho,
Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

Keywords: artificial intelligence, machine learning, deep learning, molecular force field
The accuracy of molecular simulation such as molecular dynamics (MD) simulation is strongly
dependent on the molecular force field. We have been developing artificial intelligence (AI) -based
molecular force fields (AI models) which enable us to conduct various kinds of molecularsimulations with quantum mechanics (QM) accuracy at molecular mechanics (MM) cost.
Concretely, two types of AI models have been worked on. One is constructed by learning thepotential energy obtained by QM simulation without the conventional potential energy function
based on physical chemistry description [1]. Since the model is designed to be differentiable,
forces acting on each of atoms should be obtained by analytically differentiating the learned
potential energy surface. This model is intended to be used as high-accuracy force field. The otheris constructed by learning the environmental depending atomic charges obtained by QM simulation.
Using fragment molecular orbital (FMO) method among QM simulations [2] allows us to prepare
training data of atomic charges for protein systems with acceptable computational cost.
“On-the-fly” atomic charges generated by the model lead to improve the accuracy of molecularsimulations, since it is a part of parameters in the conventional molecular simulations.
We will discuss the prediction accuracy of the two models including the future applications.
Acknowledgments: This research has been conducted by cooperation of LINC (Life Intelligence
Consortium) and FMODD (FMO Drug Design Consortium). The results of FMO have beenachieved using the “K computer” (Project ID：hp180147). The authors are deeply grateful to them.
[1] J. Behler and M. Parrinello, Phys. Rev. Lett., 98, 146401 (2007)[2] S. Tanaka et al., Phys. Chem. Chem. Phys., 16, 10310 (2014)

P5-09Effects of single mutations on STING activation
Yuko Tsuchiya 1

Kei Taneishi 2

yuko.tsuchiya@aist.go.jp

kei.taneishi@riken.jp

Yasushige Yonezawa 3
yonezawa-wk@waka.kindai.ac.jp

1

2

3

Artificial Intelligence Research Center, Advanced Industrial Science and Technology,
2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan
Medical Sciences Innovation Hub, RIKEN, 54 Shogoin Kawaharacho, Sakyo-ku, Kyoto,606-8507, Japan
High Pressure Protein Research Center, Institute of Advanced Technology, Kindai University,
930 Nishimitani, Kinokawa, Wakayama, 649-6493, Japan

Keywords: Molecular dynamics simulation,
Conformational change by mutations

Featureextraction,

Deepneuralnetwork,

STING, a protein that plays an important role in innate immunity, induces type I interferon(especially IFN-ß) production when cell is infected with intracellular pathogens, such as viruses.
The phosphorylation of C-terminal tail (CTT) of STING by TBK1, subsequent to theconformational change of CTT via ligand binding, leads to the IRF3-dependent IFN-ß production[1]. Recent studies, however, have revealed that several single mutations of STING, found inpatients with autoimmune disorders, trigger constitutive IFN-ß productions, IRF3-indepedently,
via CTT.
In this study, we tried to elucidate effects of conformational changes triggered by such mutations
on constitutive activations, based on the molecular dynamics and feature extractions by deepneural network.

[1] Tsuchiya , Y., et al., Ligand-induced ordering of the C-terminal tail primes STING forphosphorylation by TBK1, EbioMedicine, 9:87-96, 2016.

P5-10Deep Learning-aided Label-free, Non-invasive Method for
Live/dead Cell Discrimination and Counting
Yuta Kawamura 1 , Miho Hosoi 1 , Mie Tsuchida 1 , Makoto Hasegawa 1 , Ryuzo Sasaki 1, 2,
Shigeru Nemoto 3 , Katsumi Kishimoto 2 , Tamio Mizukami 1, 2
mizukami@nagahama-i-bio.ac.jp

1

2
3

Nagahama Institute of Bio-Science and Technology, 1266 Tamura, Nagahama, Shiga, 526-0829,
Japan
Frontier Pharma, 1281-8 Tamura, Nagahama, Shiga, 526-0829, Japan
Broad Band Tower, 2-1-6 Uchisaiwaicho, Chiyoda-ku, Tokyo 100-0011, Japan

Keywords: Deep learning, Imaging, Drug sensitivity test
Live and dead cell discrimination and counting are fundamental techniques used in various
aspects of life science, medicine and drug discovery. At present, representative techniques include
trypan blue dead cell staining, and measurement of the number of living and dead cells by ahemocytometer, but they have a disadvantage that it is difficult to measure multiple specimens.
Furthermore, though it is possible to measure the number of viable or dead cells by image analysis
of cells treated with a fluorescent reagent which stains viable or dead cells using high-content
imaging equipment, it has a disadvantage that it is difficult to apply to cell types with complex
morphology and besides expensive equipment is necessary. In addition, a common disadvantage ofthe above-mentioned techniques is necessity of invasive interruption of cell culture due to additionof staining reagents to cells.
Therefore, we devised and completed the cell viability identification and counting technology
which solves the above problem. The present technology is aided by deep learning that enabled theimage generation and position determination of cells as follows.
Firstly, we built a model that learned the relationship between the fluorescent microscopic imageof the cells treated with the live and dead cell staining fluorescent reagents and the corresponding
bright field image by deep learning. Without experimental work such as fluorescent labeling, it
was possible to predict and generate pseudo-fluorescent cell images with high correctness frombright-field cell images input to the constructed model.
Secondly, we built another model (position determination model) to determine the position of thecell to be detected on the generated pseudo-fluorescent cell image. This model learned therelationship between the image created by manually striking the center position of the cell on the
fluorescent cell image and the corresponding fluorescent cell image by deep learning. By inputting
a pseudo-fluorescent cell image to this model, an image in which the center position of the cell on
the fluorescent cell image was struck was generated.
By the separately prepared program, the number of detected representative points was
automatically counted, and the cell number could be measured with high accuracy.
These findings will allow us to create the next-generation drug sensitivity test with such
advantageous characteristics that are not only label-free and non-invasive, but also simple, fast,
inexpensive, and versatile.

P5-11

Investigation of general physical observation method ofmonkeys using deep learning
Yosuke Ochiai, Tetsuo Kitamura,ochiai.yousuke@mn.medience.co.jp

kitamura.tetsuo@mh.medience.co.jp

Tomoharu Osada, Yoshinori Yamamoto,osada.tomoharu@mg.medience.co.jp

yamamoto.yoshinori@mg.medience.co.jp

Munehiro Nakagawa, Hiroyuki Izumi,
nakagawa.munehiro@md.medience.co.jp

izumi.hiroyuki@mu.medience.co.jp

Kaname Takahashi,
takahashi.kaname@mm.medience.co.jp

Nonclinical Research Center, Drug Development Service Segment, LSI Medience Corporation.
14-1, Sunayama, Kamisu-shi, Ibaraki 314-0255, Japan

Keywords: Deep learning[Introduction] Recent technological innovations have greatly influenced the development of new
drugs. Deep learning is one of those innovations. With the use of deep learning, it has become
realistically possible to utilize more experimental data for drug development than when it was
conducted by humans alone. We have done this research with hopes that observation of animals,
which had been carried out by humans, can be conducted for much longer periods of time with theutilization of deep learning. The final goal of this research is to create a model that can classify
toxic behaviors of animals such as changes in activity, abnormal posture, ataxia and vomiting
and/or retching from video recorded over long periods of time. As a preliminary step, we herein
report classification of monkey postures using still images.
[Methods] Still images taken from monkeys’ videos were used for classification. Still images of six
monkeys were used as training data. For classification, postures that are typically taken bymonkeys in addition to sitting and standing postures (including on four legs) such as walking andso on were labeled. The same still images were used for both training data and test data.
[Results] The accuracy of classification was 75%.
[Conclusion] Despite the need for improvement of the labels of model and classifications, it waspossible to classify still images of sitting posture, standing posture, and walking style. In the
future, training data will be increased, more models will be created and classification of still
images of monkeys other than the ones acquired from the training will be attempted.

P5-12Development of Template-Based Protein Structure Modeling Method
Using Deep-Autoencoder with a Denoising Algorithm.
Masaya Furue 1

shiver@miyashita-lab.net

Mitsutaka Nemoto 1

nemoto@waka.kindai.ac.jp

Naoyuki Miyashita 1*
miya@waka.kindai.ac.jp

1

Department of Biological System Engineering, Faculty of Biological Oriented Science andTechnologies, KINDAI University, 930 Nishimitani, Kinokawa, Wakayama 649−6493, Japan

Keywords: Deep Learning, Protein Structure Prediction, Molecular Dynamics Simulation
Information about the dynamics and tertiary structure of a target protein is essential to understandthe function of the target protein and the drug discovery and so on. However, the tertiary structuresof many proteins have not been solved, while the genome sequences have solved by DNA
sequencing technique. Thus, a protein, which tertiary structure is unknown, have sometimes
modeled by template structures of other similar proteins, which have tertiary structure data, e.g.
Modeller [1] and SWISS - MODEL [2] etc. Recently, the technique of Deep Learning has beendeveloped, and it works very well in an image recognition and so on. We, thus, have developed thetemplate based structural modeling program with Deep-Autoencoder. The trajectories of themolecular dynamics simulations of a target protein were used as training data, which is considered
the molecular fluctuations. Moreover, to ease the sequence mismatch between the target protein andtemplate proteins, we have adopted a denoising algorithm, which shifts the coordinate of atoms, inthe program.

[1] Fiser A, Sali A, et al, Methods in Enzymology 374, pp 461-491, (2003)[2] Andrew Waterhouse et al, Nucleic Acids Research, 46, pp.W296–W303, (2018)[3] Li H et al, J Proteomics Bioinform 9, pp306-313, (2016)

P5-13Three-dimensional Quantitative Structure-Activity
Relationship Analysis using Convolutional Neural
Network
Hirotomo Moriwaki 1

Yu-Shi Tian 2

hirotomo.moriwaki@riken.jp

yushi-tian@phs.osaka-u.ac.jp

Norihito Kawashita 3

nkawashita@life.kindai.ac.jp

1

2

3

Tatsuya Takagi 2ttakagi@phs.osaka-u.ac.jp

RIKEN Center for Biosystems Dynamics Research, 1 -7-22 Suehiro-cho, Tsurumi-ku,
Yokohama, Kanagawa, 230-0045, Japan
Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita City,
Osaka 565-0871, Japan
Faculty of Sciences and Engineering, Kindai University, 3-4-1 Kowakae, Higashiosaka City,
Osaka 577-8502, Japan

Keywords: 3D-QSAR, convolutional neural network
Quantitative structure–activity relationship (QSAR) techniques, especially those that possess
three-dimensional (3D) attributes, such as the comparative molecular field analysis (CoMFA) [1] arefrequently used in drug design and other related research domains since the late 1990s. However,
the requirement for accurate multiple alignments of compounds increases the difficulties
encountered in its use. This has led to the development of several techniques —such as VolSurf[2],GRIND[3], and Anchor-GRIND[4]—which do not require such alignments. We propose a new 3DQSAR technique that makes use of molecular-interaction-field (MIF) grid potentials as
inputs to a convolutional neural network. The model built by our new method has demonstrated
higher discrimination accuracy compared to the conventional descriptor-based QSAR as well as
Anchor-GRIND techniques. In addition, the method doesn’t require a dataset consisting of acommon chemical series, thus can apply to diverse datasets. The method is capable of providing
useful information regarding the importance of individual atoms for enhancement of potency
contained in the chemical dataset used in the analysis. In view of these advantages, the propo sed
method is expected to find wide applications in future drug-design operations.

[1] Cramer, R. D.; Patterson, D. E.; Bunce, J. D. Comparative Molecular Field Analysis (CoMFA).
1. Effect of Shape on Binding of Steroids to Carrier Proteins. J. Am. Chem. Soc. 1988, 110 (18),5959–5967.
[2] Cruciani, G.; Pastor, M.; Guba, W. VolSurf: A New Tool for the Pharmacokinetic Optimizationof Lead Compounds. Eur. J. Pharm. Sci. 2000, 11 (Supplement 2), S29–S39.
[3] Pastor, M.; Cruciani, G.; McLay, I.; Pickett, S.; Clementi, S. GRid-INdependent Descriptors(GRIND): A Novel Class of Alignment-Independent Three-Dimensional Molecular Descriptors.
J. Med. Chem. 2000, 43 (17), 3233–3243.
[4] Fontaine, F.; Pastor, M.; Zamora, I.; Sanz, F. Anchor-GRIND: Filling the Gap between Standard
3D QSAR and the GRid-INdependent Descriptors. J. Med. Chem. 2005, 48 (7), 2687–2694.

P5-14
Text mining for expand coverage/combination
therapy/adverse effect about immune checkpoint blockade
Kosuke Negishi 1
k.negishi@elsevier.com

1

Life Science, Elsevier Japan K.K,1-9-15 Higashi-Azabu Minato-ku, Tokyo 106-0044 Japan

Keywords: Drug discovery, Natural language processing, Text Mining.
After marketed Nivolumab, some immune checkpoint blockade for anti-cancer agent have beendeveloped. By this trend, pharmaceutical companies have considered these points.
1: expand coverage of efficacy
2: combination therapy with small molecule(s)3: data collection about adverse effectTo solving these points, comprehensive journal information search and curation are effective. On theother hands, this is so hard to solve it. Because,
1: Data preparing from full text journal/PubMed/PubMed Central
2: Search/curation method from huge data contents (one of Big data)
3: Comprehensive journal information visualization

By this session, I would like to propose text mining solution to solve these points. It has natural
language processing algorithm and comprehensive journal information from over 1400 title full text
journal/PubMed/PubMed Central

P5-15

ChemAtlas: chemical space extractor
Tetsuro KITAJIMA 1

Hitoshi KAMIJIMA 1

kitajima@isp.co.jp

1kamijima@isp.co.jp

Hideo TSURU 1

Shinji YAMAMOTO 1

tsuru@isp.co.jp

ymt@isp.co.jp

Rese ar ch I ns tit ute o f S y ste ms P la n ni n g Inc., K as u ya B ld g. II 2 n d flo o r, 2 3 -2 3
Sak ur a gao ka, S hib u ya, To k yo 1 5 0 -0 0 3 1 , J ap an

Keywords: Deep Learning, chemical space
We are developing chemical space extractor "ChemAtlas". ChemAtlas can be used forvisualizing chemical space, QSPR/QSAR task, and si milar tasks.
Approaches that make use of intermediate layers of deep neural networks are reported tobe effective for image recognition tasks and for tasks involving chemical compounds.
Based on these reports, we decided to develop a new chemical space extractor based on theapproach. ChemAtlas uses multitask deep learning and graph convolutional networks in
obtaining intermediate states and aims to be useful for wide range of applications.
ChemAtlas has performance equivalent to ECFP 3072 -bit hash in PubChem Assay
prediction and its performance in SLogP prediction is greater than that of ECFP hash.
(Shown in the figure 1)
We will report our results of chemical space visualization using ChemAtlas and the results ofcarrying out QSPR task.
R^2 Correlation: sLogP(mordred) - sLogP(Machine Learning)
1R^2 Correlation

0.8
0.6
0.4
0.2
0
-0.2
0

500

1000

1500

2000

2500

3000

3500

Training data #ChemAtlas

ECFP 3072bits hash

Figure 1 SLogP prediction: using ChemAtlas and ECFP
calculated by mordred - predicted by machine learning R^2 scores, plots

P5-16Using domain-specific vocabulary to detect multiple-wordphrases to improve word2vec embedding performance in
Medical literature
Attayeb Mohsen 1

Yi-An Chen 1

attayeb@nibiohn.go.jp

chenyian@nibiohn.go.jp

Kenji Mizuguchi 1
kenji@nibiohn.go.jp

1

National institutes of Biomedical Innovation, Health, and Nutrition, 7 -6-8, Saito-Asagi, Ibaraki
City, Osaka 567-0085, Japan

Keywords: Word2vec, Deep learning, Medical literature.
Word2Vec[1] used neural network to represent text in a vector space. Each word is represented in aform of continuous vector. The values of these vectors are influenced by the context they are used in.
Word2Vec representation can be used to understand the relations between words; It can also show thesimilarity between the words in usage and meaning.
Many medical terms consist of more than one word, for example, “Diabetes mellitus” and“Myocardial infarction.” These words combined can refer to a medical entity like a disease, sign, or
procedure among others while using one word of them does not tell the same meaning.
Multiple-words phrases affect the training of word2vec model. There are different methods to detect
these phrases to feed them to the machine-learning model as one word. In this study, we trained threemodels using PubMed abstracts (https://www.ncbi.nlm.nih.gov/pubmed/). The first model trained using
one-word representation without considering multiple -words terms. In the second model, we used
Phrase2Vec to detect them without human interference. The third model utilized a preprocessing step toconvert multiple-word terms using a vocabulary list to one word by replacing the space with a hyphen.
We assessed these three models using a set of random terms. We found out that using a vocabulary list
to detect multiple-words phrases and concatenate them showed better performance among these threemodels.
[1] Mikolov, T., Sutskever I., Chen K., Corrado G, Dean J. Distributed Representations of Words andPhrases and their Compositionality. arXiv:13104546. 2013. http://arxiv.org/abs/1310.4546.

P5-17Development of a novel linear notation of chemicalcompounds for deep learning
Juanjuan Lu 1

lu.j.ac@m.titech.ac.jp

Keisuke Yanagisawa 1

yanagisawa@bi.c.titech.ac.jp

Takashi Ishida 1

ishida@c.titech.ac.jp

1

School of Computing, Department of Computer Science, Tokyo Institute of Technology,
2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan

Keywords: Variational auto-encoder, SMILES, Molecule generation, chemical compound
The variation of chemical compounds in this world is very large, and thus those we have alreadyknown are only a small part of them. Within those unknown part, there can be many drug-likemolecules. In 2016, Gómez-Bombarelli et al. 1 proposed a variational auto-encoder model togenerate drug-like molecules. They used SMILES 2 to represent molecules and let them be the input
data of this model. However, in this model, it usually generated invalid molecules. To improve themodel, Kusner et al. 3 proposed a grammar variational auto-encoder. In this model, they used an
encoder to account the SMILES grammar, and the generation rate of valid molecules is twice higher
than former one. However, even using such techniques, the generation rate is only 0.31. It is still
low and not practical.
Through these researches we found that some rules of SMILES may cause harmful influences forgenerating valid molecules, and it is difficult to overcome if we continue to use SMILES. For
example, changing one number in a fragment will generate invalid molecules. Thus, here we tried
to create a new notation to represent molecules instead of SMILES. The notation is much easier toseparate molecules into fragments, and then we use a sequence of fragments as the input data ofvariational auto-encoder to generate drug-like molecules.
[1] Gómez-Bombarelli, Rafael, et al. "Automatic chemical design using a data-driven continuous
representation of molecules." ACS central science 4.2 (2018): 268-276.
[2] Weininger, David. "SMILES, a chemical language and information system. 1. Introduction tomethodology and encoding rules." Journal of chemical information and computer sciences 28.1
(1988): 31-36.
[3] Kusner, Matt J., Brooks Paige, and José Miguel Hernández-Lobato. "Grammar variational
autoencoder." arXiv preprint arXiv:1703.01925 (2017).

P5-18Development of interatomic potential building package based on
artificial neural network and an application for the alkaline deep sea
hydrothermal vent environments
Kota Endo1

180x203x@stu.kobe-u.ac.jp

Kohei Shimamura1

shimamura@port.kobe-u.ac.jp

Shigenori Tanaka1

tanaka2@kobe-u.ac.jp
1

Graduate School of System Informatics, Kobe University, 1-1, Rokkodai, Nada, Kobe 657-8501, Japan

We aim at the development of interatomic potential building package based on artificial neural network (ANNpotential) for molecular dynamics (MD) simulation on extensive biomolecular systems. This is because the ANN
potential is an empirical potential with the same accuracy as the ab initio MD (AIMD) method, [1] which can simulate
chemical reactions including proton transfers that occur in living bodies. In the development procedure, a set of
Atomic FingerPrint (AF) [1] which quantifies the characteristic local structure around each atom is constructed foreach element. Using the AF sets as feature quantities, the learned ANN potential can completely reproduce the abinitio interatomic potential within the range of the training data. Combining AIMD simulation and learning ANN
potential enables us to accelerate the MD simulation while maintaining high accuracy. [2] Specifically, we first learn
ANN potential while conducting AIMD simulation, and then switch to ANN potential when it can predict referenceenergies with accuracy of more than 99%. On the other hand, if the simulation encounters an atomic structure withpoor prediction accuracy, switch to AIMD simulation while learning ANN potential again. The unpredicted structure
can be judged by the values of AF sets. [2]However, since many of existing ANN potential building packages are targeted for CPU calculators, expensive
large scale parallel computers are necessary. Therefore, we have developed a package using the Tensorflow library[3] executed on GPU calculators because GPU is cheaper and matrix calculation performed during learning isoverwhelmingly faster than CPU.
We use our package to simulate the alkaline deep sea hydrothermal vent environment. [4] This environment is astrong candidate as the place of birth of life due to abundant electron sources and catalysts, where chemical reactions
can be sustained. This environmental condition is essential to support metabolism and homeostasis of primitive life.
We have already carried out AIMD simulations reproducing the hydrothermal vents environment, but problems arise
in terms of calculation cost. Because of AIMD method with calculation cost of O(N3) (N is the number of atoms inthe system), calculation time and the number of atoms are limited to several ps and ~1,000 atoms, respectively. We
are therefore trying to execute a simulation in combination of the AIMD method with the ANN potential. We would
like to report the simulation results at the poster presentation.
[1] Behler, J., J. Phys.: Condens. Matter, 26:183001, 2014.
[2] Botu, V., et al., Int. J. Quan. Chem. 115:1074, 2015.
[3] https://www.tensorflow.org/guide/using_gpu[4] Sojo, V., et al., Astrobiology, 16:181, 2016.

P5-19Prediction of toxicity through the chemical space
generated by deep learning

Yoshinori Wakabayashi 1

Kazuyoshi Ikeda 2

wakabayashi@by-hex.com

ikeda-kz@pha.keio.ac.jp

Kenichi Naito 1

naito@by-hex.com
1
2

BY-HEX LLP, 1-19-14 Shimizu, Suginami-ku, Tokyo 167-0033, Japan
Keio University Faculty of Pharmacy Division of Physics for Life, 1-5-30 Shibakoen,
Minato-ku, Japan

Keywords: deep learning, toxicity, chemical space, fingerprint, descriptor, ChEMBL
Exploring the chemical space spanned by various compound libraries is beneficial to understandthe structure-activity relationship or to develop potential drugs. Chemical space maps arecommonly constructed by a principal component analysis from high dimensional molecular
descriptors such as fingerprints and physical properties. However, there seems to be no distinctive
criteria about the choice of descriptors for deep learning by the conventional methods. In this work,
we present a new method to automatically depict chemical space map from SMILES strings
without complicated procedures of generation and selection of descriptors. A precursor layer ofthree nodes was appended just before the output layer on the DeepChem framework to retrieve thevalues for mapping chemical space. As a preliminary study, we applied this technique to theprediction of toxicity.
We will discuss validity of the prediction results and its applicability for bioactive chemical space
using curated drug discovery data from ChEMBL.

Diagram of our developed DNN method and chemical spaceP5-20

Performance Evaluation of Compound-Protein Interaction
Prediction using Graph Convolutional Network
Hiroaki Iwata 1

Taro Kiritani2

iwata.hiroaki.3r@kyoto-u.ac.jp

taro.kiritani@exwzd.com

Shouichiro Ishida 1
ishida.shouichi.57a@st.kyoto-u.ac.jp

1

2
3

Ryosuke Kojima1
kojima.ryosuke.8e@kyoto-u.ac.jp

Takeshi Fujiwara 3
takeshi.fujiwara@nibiohn.go.jp

Mari Ito 3
mari@nibiohn.go.jp

Graduate School of Medicine, Kyoto University, 53 Kawahara-cho, Syogoin, Sakyo-ku, Kyoto
606-8507, Japan
ExaWizards Inc. 1-6-15, Hamamatsu-cho, Minato-ku, Tokyo, 105-0013, Japan
National Institutes of Biomedical Innovation, Health and Nutrition Bioinformatics project,
7-6-8 Saito-Asagi, Ibaraki City, Osaka, 567-0085, Japan

Keywords: graph convolutional network, compound-protein interaction, ChEMBL database
The graph convolutional network (GCN) learns chemical and biophysiological properties ofcompounds based on their structural information [1,2]. We developed and evaluated performanceof the GCN model for compound-protein interaction using bioassay dataset from the ChEMBL
database[3]. The comparison between single-task model, and multitask model indicated themultitask approach was superior. We further conducted an accuracy assessment between the
proposed GCN model, the conventional deep neural network model using compound descriptors[4], and the target prediction model that was developed at ChEMBL group[5,6]. Finally, we discuss
an extension algorithm of the prediction model and its application to drug repositioning.
[1] Wu, Z., et al. MoleculeNet: a benchmark for molecular machine learning. Chemical science,
9.2: 513-530, 2018.
[2] DeepChem: Deep-learning models for Drug Discovery and Quantum Chemistry,
http://github.com/deepchem/deepchem, accessed 2018-08-02.
[3] Gaulton, A., et al. The ChEMBL database in 2017. Nucleic acids research, 45.D1: D945-D954,
2016.
[4] Ma, J., et al. Deep neural nets as a method for quantitative structure–activity relationships.
Journal of chemical information and modeling, 55.2: 263-274, 2015.
[5] Chen, B., et al. Evaluation of machine-learning methods for ligand-based virtual screening.
Journal of computer-aided molecular design, 21.1-3: 53-62, 2007.
[6] chembl_target_predictions: https://github.com/chembl/chembl_target_predictions, accessed
2018-08-02.

P6-01

Universal Read-Across Approach To Predict Toxicities

1

Hirohisa Nagahori 1

Masayuki Sato 1

nagahori@sc.sumitomo-chem.co.jp

satom13@sc.sumitomo-chem.co.jp

Environmental Health Science Laboratory, Sumitomo Chemical Co., Ltd., 1-98,
Kasugadenaka 3-chome, Konohana-ku, Osaka 554-8558, Japan

Keywords: Structure-Activity Relationships, Animal Testing Alternatives, Read-Across Approach,
Binomial Distribution, Validation Studies
To make read-across (RA) approach more reliable, robust, reproducible, easy, and high-throughput,the universal method was developed by using database and function in the QSAR toolbox. The
approach consisted of three steps: negative and positive local category approaches, where thecategory was locally defined by functional group in the target substance, and analogue approach(Figure 1). Acute oral toxicity, bacterial reverse mutation, eye and skin irritation, and sensitization
were practically predicted with clarification of mechanistic categories of the positive substances.
Reliability in local category approaches was assessed by both the p-values calculated bycumulative distribution function of the binomial distribution, and similarity index (SI) of
optimized k-nearest neighbors (k-NN). Applying these approaches to known 31 positives and 15
negative reference substances as the validation data set, the prediction was highly accurate withidentification of known toxicity mechanisms. Then the toxicities of over 200 test and 50 in-housedatasets were predicted and compared with the experimental results. High accuracy of generally>60% was kept in wide ranges of molecular weight (0 to 1200 Da) and lipophilicity (log K ow -2 to≥6). By considering the criterion for reliable prediction as the p-value of <0.05 and SI of ≥50%,
the relatively high performance of prediction was obtained with accuracy of ≥95%, the coverage of≥30%, and positive and negative likelihood ratio of ≥30 and <0.1, respectively, which suggested
this approach was a practically useful even for screening and definitive diagnoses. The accuracy
and coverage in all data sets and endpoints were high (80 to 100% and 20 to 60%, respectively),
which revealed the wide applicability of the universal RA approach. The results suggested thisuniversal RA approach was useful and reliable with wide-application to reduce animal toxicity
studies.
Local category Approach
Local category approach-Negative RA-Positive RACategorization by
Mechanistic
functional groupcategorization
Statistical andstructural analyses

Statistical andstructural analyses

Reliable negative

Reliable positive

Analogue approach Closest NN
Negative

Positive

Figure 1. An illustration of universal RA approach.

P6-02
Application of association rule mining in adverse event
reporting system

1

2

3
4

5

Shiori Hasegawa 1

Haruna Hatahira 2135056@gifu-pu.ac.jp125063hz@gmail.com

Misa Naganuma 1

Junko Abe 1, 3135049@gifu-pu.ac.jp

junko.a822@gmail.com

Sayaka Sasaoka 4

Ryogo Umetsu 1, 5

ssayaka3307@gmail.com

umetsur.ayni@gmail.com

Satoshi Nakao 1

Mitsuhiro Nakamura 1145045@gifu-pu.ac.jp

mnakamura@gifu-pu.ac.jp

Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4, Daigaku-Nishi, Gifu,
501-1196, Japan
Department of Pharmacy, Kizawa Memorial Hospital, 590 Kobi-cho Shimokobi, Minokamo,
Gifu, 505-8503, Japan
Medical Database Co., LTD; 3-11-10 Higashi, Shibuya-ku, Tokyo 150-0011, Japan
Department of Pharmacy, Hokushin General Hospital, Nishi 1-5-63, Nakano, Nagano,383-8505, Japan
Micron Inc., Nihonbashi Nishikawa Building, 1-5-3, Nihonbashi, Chuo-ku, Tokyo, 103-0027
Japan

Keywords: association rule mining, adverse event, spontaneous reporting system, JADER
Spontaneous reporting systems (SRSs) are useful for the detection of rare adverse events (AEs)and have been recognized as primary tools for pharmacovigilance that reflect the realities ofclinical practice. Association rule mining has been proposed in as an analytical approach in orderto study rare AE, and is a well-established method for discovering undetected relationships such as
the possible risk factors between variables in huge databases.
Data were extracted from the Japanese Adverse Drug Event Report (JADER) database on the
PMDA website (www.pmda.go.jp). The association rule mining was performed using the apriorifunction of the arules library in the arules package of the R software.
We demonstrate several association rules from the JADER database as follows: renal impairment
related to the administration of platinum compounds, drug-induced gingival hyperplasia related tosuspected drugs (immunosuppressants, calcium channel blockers, and anticonvulsants), andthromboembolic AEs related to combined estrogen-progestin preparations.
[1] Hasegawa S., Matsui T., Hane Y., Abe J., Hatahira H., Motooka Y., Sasaoka S., Fukuda A.,
Naganuma M., Hirade K., Takahashi Y., Kinosada Y., Nakamura M., Thromboembolic adverse
event study of combined estrogen-progestin preparations using Japanese Adverse Drug Event
Report database, PLoS One, 12, e0182045, 2017.
[2] Hatahira H., Abe J., Hane Y., Matsui T., Sasaoka S., Motooka Y., Hasegawa S., Fukuda A.,
Naganuma M., Ohmori T., Kinosada Y., Nakamura M., Drug-induced gingival hyperplasia: aretrospective study using spontaneous reporting system databases, Journal of Pharmaceutical
Health Care and Sciences, 3, 19, 2017.
[3] Naganuma M., Motooka Y., Sasaoka S., Hatahira H., Hasegawa S., Fukuda A., Nakao S.,
Shimada K., Hirade K., Mori T., Yoshimura T., Kato T., Nakamura M., Analysis of adverse
events of renal impairment related to platinum-based compounds using the Japanese Adverse
Drug Event Report database, SAGE Open Medicine, 6, 1-11, 2018.

P6-03
Quantitative Structure-Activity Relationship (QSAR) analysis using deep learning
based on Deep Snap, a novel molecular image input technique

Yasunari Matsuzaka

Yoshihiro Uesawa

matsuzys@my-pharm.ac.jp

uesawa@my-pharm.ac.jp

Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo,
Keywords: DeepSnap, Tox21, Deep learning, QSAR, artificial intelligence
As for the safety assessment, a repetition dosage subacute toxicity test is used for 2 8 to 90 days in mammals.
However, in general, the cytotoxicity in human could be caused by long -term exposure of various chemicals. Because
it is necessary to perform the examination for a large number of chemical substances, high-throughput (HTP) assay
and reduction in cost are required. However, prediction programs that precisely and highly evaluate the cytotoxicity
for various chemical compounds are limited. Alternative methods based on in silico experiments focusing on computer
prediction are essential for safety evaluation of chemical substances with high risk. Among them, Quantitative
Structure-Activity Relationship (QSAR) analysis can predict physiological activity, toxicity, enzymatic reactions,
receptor agonist/antagonist activities, and environmental fate, etc., from formulating establishment rule between
chemical structure and its activity using the structural, quantum chemical, and physicochemical features represented
as various numerical molecular descriptor [1-4]. More recently, deep learning, machine learning method designed torecognize pattern by multilayer neural network consisted of input, intermediate, and output layers , reported very highprediction accuracy, especially in the field of imaging and toxicology, and possess a possible function that cancalculate feature values from molecular structure in QSAR analysis without human intervention [5-8]. However, there
are some points to be improved due to increase prediction accuracy for application of the deep learning into QSAR
analysis, that is, one is a systematic and suitable input for complicated data such as three-dimension structure ofchemical compound, another is inadequate data volume for training data. To dissolve these tissues, we developed anovel QSAR model using deep learning based on three-dimension molecular imaging of chemical compounds [9].
Deep Snap, which is procedure capturing the omnidirectional snapshot , portray three-dimensional figures of chemicalcompounds using chemical structure drawing software, Jmol [10] based on the SDF file format developed by grant inLong-Range Research Initiative (LRI), Japan Chemical Industry Association (JCIA) . To input the three-dimensional
information into deep learning models without calculating structural descriptors, the three -dimensionally molecular
structure is rotated in 45°increments on the X-, Y-, and Z-axes, and photographed for eight viewing directions. A totalof 512 images were captured per one molecule and saved as PNG files due to be applied into deep learning model,
where actual images of the three-dimensionally molecular structure were indicated as a ball -and-stick model for eachatomic symbol with different colors representing different atoms . Using all imaging data obtained from approximately
7,000–9,000 different chemical structures on Tox21 data challenge 2014, a QSAR competition held by the National
Institutes of Health in the United States [11], a deep learning model was constructed by AlexNet [12], a convolutional
neural network implemented on the deep learning framework Caffe [13]. As results, the deep learning model using
Deep Snap procedure successfully predicted chemical compounds that induce mitochondrial membrane potential
disruption indicated by scoring an AUC value of 0.921 despite using unadjusted AlexNet, resulted in within top 10 at
this competition [11]. We would use the novel prediction method using deep learning based on Deep Snap as one of
QSAR analysis in AI-Substances Hazardous Integrated Prediction System (AI -SHIPS) project.
[1] Dougall, I. G. J. Recept. Signal Transduct. Res. 2001, 21, 117–137.
[2] Fang, H.; Tong, W.; Welsh, W. J.; Sheehan, D. M. J. Mol. Struct. 2003, 622, 113–125.
[3] Roy, K.; Das, R. N. Curr. Drug Metab. 2014, 15, 346–379.
[4] Silva, F. T.; Trossini, G. H. G. Med. Chem. 2014, 10, 441–448.
[5] LeCun, Y.; Bengio, Y.; Hinton, G. Nature. 2015, 521, 436–444.
[6] Zhang, L.; Han, D.; Tan, J.; Zhu, H. Drug Discov. Today 2017, 22, 1680–1685.
[7] Pastur-Romay, L. A.; Cedron, F.; Pazos, A.; Porto -Pazos, A. B.; Int. J. Mol. Sci. 2016, 17.
[8] Mayr, A.; Klambauer, G.; Unterthiner, T.; Hochreiter, S. Front. Environ. Sci. 2016.
[9] Uesawa, Y., "Prediction systems, prediction methods, prediction programs, learning model input data generation
systems, and learning model input data generation programs" , Pending Japanese patent application 2017, 129823.
2017.
[10] Jmol: an open-source Java viewer for chemical structures in 3D. http://www.jmol.org/
[11]
Tox21
data
challenge
2014,
National
Centerfor
AdvancingTranslational
Sciences:
https://tripod.nih.gov/tox21/challenge/[12] ImageNet Classification with Deep Convolutional Neural Networks. Alex Krizhevsky, Ilya Sutskev, Geoffrey E.
Hinton. Advances in Neural Information Processing Systems 25 (NIPS 2012), pp.1 -9 (2012)[13] Jia, Y.; Shelhamer, E.; Donahue, J.; Karayev, S.; Long, J.; Girshick, R.; Guadarrama, S.; Darrell, T. CVPR. 2014,
675–678.

P6-04

Co-Creation and Communication for Real-Time
Technology Assessment (CoRTTA) on Molecular Robotics
Ryuma Shineha 1r_shineha@seijo.ac.jp

Ken Kawamura2 1
kawamura310@u.seijo.ac.jp

23
4gy20@jcom.home.ne.jp

4Mikihito Tanaka 4sc.yoshinaga@gmail.com

steman@waseda.jp

Daisuke Yoshinaga

1

Go Yoshizawa 3

Faculty of Arts and Literatures, 6-1-20 Seijo University, Seijo Setagaya-ku, Tokyo 157-8511,
Japan.
The center for social studies of science and technology, 6-1-20 Seijo University, Seijo
Setagaya-ku, Tokyo 157-8511, Japan.
Oslo Metropolitan University, Norway.
Waseda University, 1-6-1 Nishiwaseda Shinjuku-ku, Tokyo 169-8050, Japan

Keywords: Real-Time Technology Assessment, Molecular Robotics, Biotechnology in Society
Our Co-Creation and Communication for Real-Time Technology Assessment (CoRTTA) project
aims to extract agendas of discussion by media analysis and by the predictive estimation method(horizon scanning) regarding the Ethical, Legal and Social Issues (ELSI) of molecular robotics.
Furthermore, through development of a "subject of discussion co -creation platform (NutShell)," inwhich high-tech information technology specialists as well as various stake holders participate, we
formulate a "Real-Time Technology Assessment (RTTA)" system to swiftly focus social
discussions in the relevant field.
In this paper, we would like to present the summary of Technology Assessment Note (TA-note)
concerning molecular robotics. This TA-note is to encourage further discussion on ELSIs ofmolecular robotics by conducting focus groups workshop discussions and reviewing previous
studies on ELSIs in fields of GMO, nanotechnology, regenerative medicine, genomics, and so on.
Through these trials, we propose realization of a better agenda building process concerning ELSI
and how feedback of the knowledge to the researchers at field sites should be channeled. .

P6-05Boundary Work of Risk: A Case Study on a Molecular
Robotics Laboratory in JapanKen Kawamura 1

Daisuke Yoshinaga 2

kawamura310@u.seijo.ac.jp sc.yoshinaga@gmail.com

1

2
3
4
5

Go Yoshizawa 3
gy20@jcom.home.ne.jp

Mikihito Tanaka 4

Ryuma Shineha 5steman@waseda.jp

r_shineha@seijo.ac.jp

Center for Science, Technology and Society Studies, Seijo University, Seijo 6-1-20, Setagaya,
Tokyo, Japan
Waseda University, Japan
Osaka University, Japan
Waseda University, Japan
Faculty of Arts and Literature, Seijo University, Seijo 6-1-20, Setagaya, Tokyo, Japan

Keywords: Molecular robotics, ELSI, Dual Use
The problem of dual use of technologies——the situation where it is unclear how to prevent
misuse without foregoing beneficial applications——not only poses a real challenge for civilsociety in the effort to prevent harmful misuses but is also a cause of serious concerns forscientists working to develop such technologies, especially new inventions at the research anddevelopment stages. While scientists are seriously worried about potential misuses of theirresearch outcomes, at the same time they are possibly hampered by the rigid regulations thatseverely limit their research activities. In this paper, I shed light on how scientists deal withdual-use concerns, based on an interview survey conducted on researchers working for a molecularrobotics laboratory in Tokyo. Molecular robotics is an emerging discipline involving the designand creation of biological structures that serve various purposes by manipulating the order ofnucleotides in DNA. In other words, it is the process of creating nanoscale robots utilizing theself-replicating capacity of DNA. Although the current accomplishments of the field remain
experimental, the potential applicability of these structures is immense——as are theconsequences of their misuse. Based on the analysis of the interview data, I show how the
researchers in this molecular robotics laboratory organize their research projects while facing andcoping with the dilemma posed by the potential risk of misuse and the concerns regarding
excessive control. Especially I delve into their boundary work of risk——the practice demarcating
the intrinsic risk of a given technology. This paper details a case study on how the researchers’
own considerations regarding the ethical, legal, and social implications (ELSI) of technology
shape their research practices.
[1] Gieryn, Thomas F., 1983, “Boundary-Work and the Demarcation of Science from Non-Science:
Strains and Interests in Professional Ideologies of Scientists,” American Sociological Review
48(6): 781-795, 1983.
[2] U.K. Parliamentary Office of Science and Technology, Postnote 340, 2009.
[3] Balmer, Andrew S, Jane Calvert et. al., “Taking Roles in Interdisciplinary Collaborations:
Reflections on Working in Post-ELSI Spaces in the UK Synthetic Biology Community,” Science& Technology Studies 28(3): 3-25, 2015.

P6-06
Overview of Molecular Robot Ethics
Akihiko Konagaya 1
kona@m.titech.ac.jp

Satoshi Murata 2
murata@molbot.mech.tohoku.ac.jp

1 School of Computing, Tokyo Institute of Technology
2 Department of Robotics, Graduate School of Engineering, Tohoku University

Keywords: molecular robotics, molecular robot ethics
DNA synthetic technology opens the doors for not only genome science but also formolecular robotics which aims for bio-molecular artifacts with sense and intelligence [1]. In
molecular robotics, DNA nano technologies such as DNA origami and DNA computing play
an essential role in construction and integration of molecular channels, molecularactuators and molecular control circuits [2]. These technologies enable us to develop
movable molecular robots with motor proteins and microtubules [3], light-induced peptide
nanofibre growth [4] and nematic alignment of confined actin [5]. Emergence of suchmolecular robots raises new research issues in ethics with regards to molecular robot
guideline and dual-use issues, to name a few [6]. In order to deal with these issues, we havestarted molecular robot ethics project [7] in collaboration with real time technology
assessment project [8]. Related information are now available at molecular robot ethics site
[9].

[1] Murata, S., et al., M. Molecular Robotics: A New Paradigm for Artifacts. New Generation
Computing 2013, 31 (1), 27–45.
[2] Hagiya, M., et al., Molecular Robots with Sensors and Intelligence. Accounts of Chemical
Research 2014, 47 (6), 1681–1690.
[3] Yusuke Sato, et al., Micrometer-sized molecular robot changes its shape in response to signal
molecules, Science Robotics, 2, eaal3735 (2017).
[4] Hiroshi Inaba, et al., Light-induced propulsion of a giant liposome driven by peptide nanof iber
growth, Science Report, 8, 6243 (2018).
[5] Shunske Tanaka, Kingo Takiguchi, Masahiro Hayashi, Repetitive stretching of giant
liposomes utilizing the nematic alignment of confined actin, Communications Physics, 1, 18(2018).
[6]http://en.molecular-robotics.org/2017/02/the-1st-international-symposium-on-molecular-robotethics/[7] https://ristex.jst.go.jp/hite/en/community/project000291.html[8] https://ristex.jst.go.jp/hite/en/community/project000290.html[9] http://molecular-robot-ethics.org/en/

P6-07Comparison of health hazard reporting of conventional
food poisoning and designated ingredient containing foodby Food Sanitation Law of Japan
Mitsuo Saito 1,2
mitsuosaito-kkr@umin.ac.jp

Yuzuru Hayashi 1
lin.rang@outlook.com

Takehiko Yajima 1
qzf11372@nifty.com
1
2

Institute for Health Vigilance, 4-25-5, Tanashi, Nishi-Tokyo City, Tokyo 188-0011, Japan
Japan Pharmaceutical Information Center, 2 -12-15, Shibuya, Shibuya-ku, Tokyo 150-0002,
Japan

Keywords: Designated ingredient containing food, Food poisoning, Health hazard reporting, Food
Sanitation Law of Japan[Introduction] The Food Sanitation Law of Japan was amended on 13th June 2018, and new
article 8 defined designated ingredient containing food that special caution should be required. It
will be effective within two years. In this presentation, we compared health hazard reporting
requirements of conventional food poisoning and designated ingredient containing food.
[Method] Amended article and related documents were obtained from the website of MHLW andNational Diet.
[Result] Current article 58 imposes the physician mandatory reporting of conventional foodpoisoning case to the public health center immediately. Adverse event reporting caused by thehealth food also follows this scheme. In contrast, new article 8 just requires physician, dentist, andpharmacist etc. (called as medical staffs) to grasp the health hazard caused by d esignatedingredient containing food, and collaborate the survey of local government if required. The new
article 8 solely impose mandatory reporting to the local government on the whole seller of the bydesignated ingredient containing food, and request local government to inform MHLW.
[Discussion] In spite of the potential risk of designated ingredient containing food, the legal
obligation of the physician is obviously mitigated. Under current regulation, destination of adverse
event reporting of the Food for Specified Health Uses (Tokuho) and the Food with Functional
Claims (Kinousei Hyoji Shokuhin) is Consumer Affairs Agency. However, the new article 8 does
not mention the destination of the health hazard report if the d esignated ingredient containing foodis also the Food for Specified Health Uses or the Food with Functional Claims. Hence if nocoordination is taken, double administrative management may occur. Before the implementation of
new article 8, careful designing of the system that is a ccurate and without omissions or
duplications is expected.

P6-08Formulating Ethical Principles of Molecular Robotics(ver. 1.1)
Naoto Kawahara 1

Ryuma Shineha 2n-kawaha@med.kyushu-u.ac.jp

r_shineha@seijyo.ac.jp

Akihiko Konagaya 3
kona@dis.titech.ac.jp

1
2
3

Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
Seijyo University, Seijo 6-1-20, Setagaya-ku, Tokyo 157-8511, JapanTokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa
226-8503 Japan

Keywords: Ethical principles, Molecular robotics, Risk, Benefit, Safety, Security, Dual-use,
Accountability, Transparency
Nowadays, it is an issue of extreme importance to establish an ethical framework with a new viewof material, information and life according to a technological development. With ever -increasing
progress in creativity and ingenuity of technology, new devices and systems appear continuously.
However, there are concerns about the ethical scope of molecular robotics. In Japan, research anddevelopment of molecular robotics has been promoted, ta king advantage of an important elemental
technology concerning senses, motions and intelligence. More complicated configurations ofsystems in molecular robotics are conceivable, which will be applied to informatics, engineering,chemistry, biology and medicine in near future. In this context, we examined various ethicalprinciples, codes, guidelines, etc. in the related fields. Then, we formulated the ethical principlesof Molecular Robotics (ver. 1.1). We also request any person who engages in molecular r obotics tocomply with these principles.
[1] G7 Academies’ Joint Statement, New Economic Growth: The Role of Science, Technolo
gy, Innovation and Infrastructure, https://royalsociety.org/~/media/about-us/international/g-sc
ience-statements/2017-may-3-new-economic-growth.pdf?la=en-GB, 2017.
[2] European Commission, Synthetic biology ethics: managing risks without limiting benefits,
http://ec.europa.eu/environment/integration/research/newsalert/pdf/233na1_en.pdf, 2011.
[3] U.S. Presidential Commission for the Study of Bioethical Issues New Directions: The Ethics ofSynthetic Biology and Emerging Technologies: Executive Summary and Recommendations,
2010.
[4] U.K. Engineering and Physical Sciences Research Council (EPSRC), Principles of Robotics,
2010.
[5] Peter Kemp, Jacob Dahl Rendtorff, The Barcelona Declaration: Towards an Integrated
Approach to Basic Ethical Principles, SYNTHESIS PHILOSOPHICA 46: 239–251, 2008.
[6] U.S. National Society of Professional Engineers, Code of Ethics for Engineers, 2007.
[7] UNESCO World Commission on the Ethics of Scientific Knowledge and Technology(COMEST), Nanotechnologies and Ethics: Policies and Actions, 2007.
[8] WHO/CDS/EPR, Biorisk management Laboratory biosecurity guidance, 2006 .
[9] Nicholas H. Steneck, Fostering Integrity in Research: Definitions, Current Knowledge, andFuture Directions, Science and Engineering Ethics 12(1): 53-74, 2006.

P7-01

Refined method for designing functional siRNAs
specific for any genes in mammalian cells
Yoshiaki Kobayashi 1

yoshiaki-kobayashi@g.ecc.u-tokyo.ac.jp

Zhiyi Li 1,2

lizhiyi16@mails.tsinghua.edu.cn

Kumiko Ui-Tei 1

ktei@bs.s.u-tokyo.ac.jp

12

Department of Biological Sciences, Graduate School of Science, the University of Tokyo,
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
Department of Biological Sciences, the University of Tsinghua, Haidian, Beijing, China

Keywords: RNA interference (RNAi), Small interfering RNA (siRNA), Off-target effect (OTE),
Base-pairing stability, Chemical modification, Nucleic acid therapeutics
RNA interference (RNAi) is a mechanism of post-transcriptional gene silencing. In RNAi
pathway, a small interfering RNA (siRNA) is loaded onto Argonaute (AGO) protein, and the guidestrand of siRNA base pairs with a target mRNA with perfect complementarity of nucleotide
sequence to repress its expression by cleavage by AGO protein.
We previously demonstrated that functional siRNA in mammalian cells satisfied four sequence
rules [1]; (1) 5’ end of the guide strand is A/U, (2) 5’ end of the passenger strand is G/C,
(3) 5’ one-third region of the guide strand is A/U rich, and (4) no long GC stretch.
On the other hand, it is known that siRNA often exhibits off-target effect (OTE) which represses
the expression of unintended mRNAs with partial sequence complementarities with the seed region(2-8 bases from the 5’ end) of the guide strand. We revealed that the degree of OTE is correlated
positively with the thermodynamic stability in base-pairing between the seed region of the guidestrand and unintended mRNAs [2]. Therefore, it was expected that the siRNA with low
base-pairing stability in the seed region is capable to avoid OTE. However, it was also revealed
that functional siRNAs with reduced OTE are not selectable for about 6000 kinds of human genes
due to such strict constraints for nucleotide sequences [3].
The purpose of this research is to design the refined method which is capable of selecting highly
functional siRNA without off-target effect in mammalian cells. Therefore, we introduced chemical
modifications or mismatches to regulate base-pairing stability, and to overcome the limitation for
selecting siRNAs based on nucleotide sequences.
[1] Ui-Tei K, Naito Y, Takahashi F, Haraguchi T, Ohki-Hamazaki H, Juni A, Ueda R, Saigo K,
Nucleic Acids Research, 32:936-948, 2004.
[2] Ui-Tei K, Naito Y, Nishi K, Juni A, Saigo K, Nucleic Acids Research, 36:7100-7109, 2008.
[3] Naito Y, Yoshimura J, Morishita S, Ui-Tei K, BMC Bioinformatics, 10:392, 2009.

P7-02Analyzing Co-author Networks to Search for Young
Promising Researchers in Biological Science Fields
Hiroto Inoue 1

Kenta Ishido 1

Yuri Suzuki 1

h-inoue@mail.dendai.ac.jp

18fmi02@ms.dendai.ac.jp

15nc023@ms.dendai.ac.jp

12

3

Masanori Fujita 2

Takao Terano 2,3

fujita.m.ai@m.titech.ac.jp

tterano@computer.org

Tokyo Denki University, 5 Senju Asahi-cho, Adachi-ku, Tokyo 120-8551, JapanTokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa
226-8503, Japan
National Institute of Advanced Industrial Science and Technology, 2-12-1 Ookayama,
Meguro-ku, Tokyo 152-8550, Japan

Keywords: Big Data, Academic Literature Database, Co-author Network
Researchers are often evaluated their performance by various citation indices of their
publications. However, popular ones such as h-index are not suitable for young researchers,
because they monotonically increase and only reflect the past achievements [1]. Thus, to search foryoung promising researchers, novel quantitative measures are required for both academism andindustries. For the purpose, using network analysis techniques, we examine a large databaseprovided by JDreamIII [2] on biological science fields, which includes over 1.92 million
literatures and 4.14 million authors during 2001 and 2015 years [3].
In this paper, we focus on co-author networks of the Japan Society for the Promotion of Science(JSPS) research fellows, who represent the excellent talents in Japan. We calculate thebetweenness centralities of all the authors in the database. Then, the temporal changes of the
proposed measure and h-index are compared with JSPS research fellows and the other ordinal
researchers. As, depicted in Figure 1, the proposed measure is effective for the evaluation.
Our future work includes the development of a practical searching system based on the JSPS
model under the support of the Life Intelligence Consortium (WG3-PJ08) [4].

●

1.5

●
●

0.4

Means (2004 − 2011)
Means (2005 − 2012)
Means (2006 − 2013)
Means (2007 − 2014)
Means (2008 − 2015)
Logistic Curve (JSPS)
Logistic Curve (Newly Appeared)

●
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0.5

0.2

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●

1

2

3

●

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●

4

5

6

Time Series [year]

7

8

●

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0.0

●

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Figure 1. Growth models based on
logistic regression analysis. The blue
curves show JSPS models and the red
curves show average young researchers
who have careers as Ph.D. students,
postdoctoral fellows, assistant professors, and others.

●

●
●

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●

1.0

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h−Index

0.6

Medians (2004 − 2011)
Medians (2005 − 2012)
Medians (2006 − 2013)Medians (2007 − 2014)
Medians (2008 − 2015)
Logistic Curve (JSPS)
Logistic Curve (Newly Appeared)●

0.0

Normalized Betweenness Centrality (y *)

●

2.0

b.
0.8

a.

●

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●

1

2

3

4

5

6

7

8

Time Series [year][1] R.N. Zare, Assessing Academic Researchers, Angewandte Chemie International Edition,
51:7338-7339, 2012.
[2] http://jream3.com (in Japanese)[3] M. Fujita, H. Inoue and T. Terano, Searching Promising Researchers through Network
Centrality Measures of Co-Author Networks of Technical Papers, Proc. 41st Annual Computer
Software and Applications Conference, IEEE, 2017.
[4] https://rc.riken.jp/life-intelligence-consortium/wg/

P7-03

Profiling of FFPE Tumor Samples by ChIP-Seq
Masato Yonezawa 1

Sarantis Chlamydas 1

Adam Blattler 1

yonezawa@activemotif.com

Tiffany Yen 1
1

Paul Labhart 1

Brian Egan 1

Active Motif, Inc. Carlsbad CA, USA.

Keywords: Epigenome, ChIP-seq, FFPE, biomarker
Formaldehyde-fixed paraffin embedded (FFPE) specimens are a valuable resource for retrospective
research on diseases and biomarker discovery. Clinical information, treatments and outcomes
available from patient samples may reveal molecular mechanisms underlying diseases anddiagnosis. Unlike fresh tumor samples, FFPE samples are more challenging for ChIP-seq(chromatin immunoprecipitation sequencing) analysis due to inefficiency in chromatin extraction
arising from extensive crosslinking. However, recent advances at Active Motif have enabled theroutine generation of high quality ChIP-seq data sets from limited amounts of FFPE tumor
samples 1, 2 . In the present study, we show 1) chromatin extracted from FFPE samples work forprofiling of histone modifications and transcription factors; 2) FFPE ChIP-seq profiles are highly
concordant with those generated from matched frozen tumor samples; 3) FFPE ChIP-seq profiles
reveal tumor specific patterns. Availability of our reliable ChIP-seq protocol for FFPE samples
will bring advances in our understanding to diseases and in discovery of novel biomarkers.
[1] http://www.activemotif.com/catalog/1185/ffpe-chip-seq[2] http://www.activemotif.com/catalog/937/chip-it-ffpe

H3K4me3

H3K36me3

ChIP-seq profiling using 20-year-old colon tumor FFPE blockP7-04Novel mechanism of lung cancer stem cells growth by
tobacco-specific nitrosamine NNK.
Naoya Hirata 1, 2

Shigeru Yamada 1, 2n-hirata@nihs.go.jp

shyamada@nihs.go.jp

Yasunari Kanda 1
kanda@nihs.go.jp

1

2

Division of Pharmacology, National Institute of Health Science, 3-25-26 Tonomachi,
kawasaki-ku, Kanagawa, 210-9501, Japan
Pharmacological Evaluation Institute of Japan, 3-25-22 Tonomachi, kawasaki-ku, Kanagawa,
210-9501, Japan

Keywords: lung cancer stem cell, NNK, ROS, Wnt
Epidemiological studies have suggested that tobacco smoking increases the risk of lung cancer.
Tobacco specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is one of the
major carcinogenic components in tobacco smoking. However, the mechanisms by which NNK
promotes lung cancer development are not fully understood. Growing evidence suggests that lungcancer is originated from cancer stem cells (CSCs) , which have been identified by aldehyde
dehydrogenase (ALDH) activity using various cell lines and clinical samples.
In the present study, we investigated the effect of NNK on CSC proliferation in human lungcancer cell line A549. We found that NNK increased ALDH -positive cells in a dose-dependent
manner. To examine the downstream signaling, we focused on Wnt, Hedgehog and Notch signaling
as self-renewal pathways. Stimulation with NNK induced the expression of Wnt target gene Dkk1.
Notch target gene Hes1 was also induced slightly. In contrast, NNK did not affect the expression of
Hedgehog target gene Gli1. A selective Wnt signaling inhibitor PNU74654 blocked the
NNK-induced Dkk1 expression and ALDH-positive cells. In addition, NNK induced nuclear
translocation of β-catenin. The Notch inhibitor DAPT had little effects. These data suggest that
NNK increases CSCs via Wnt signaling in lung cancer.
We further investigated the mechanism by which NNK activates Wnt signaling. We found that
NNK induced reactive oxygen species (ROS) production in A549 cells. The ROS scavenger
N-acetylcysteine (NAC) inhibited the NNK-induced nuclear translocation of β-catenin and theexpression of Wnt/β-catenin target gene Dkk1. NAC also inhibited the NNK-increased
ALDH-positive cells. Since PI3K-Akt pathway has been reported to be involved in the nuclear
translocation of β-catenin via ROS, we examined whether PI3K-Akt is involved in the
NNK-induced CSC proliferation. NAC inhibited the NNK-induced phosphorylation of Akt. The
selective PI3K-Akt inhibitor wortmannin inhibited the NNK-induced Dkk1 expression and increasein ALDH-positive cells in A549 cells.
Taken together, these data suggest that NNK induces ROS production, activates PI3K/Akt andsubsequently induces Wnt/β-catenin signaling, which results in expansion of lung CSCs. Thesefindings might explain the development of lung cancer in cigarette smokers.

P7-05

Immune system simulation based on multi-agent model
1)Hiromichi Tsurui,tsurui@juntendo.ac.jp

2)Yuko Matsuda

matsudayuko@gmail.com

1)Department. Pathology, Juntendo University School of Medicine.,
2)The University of Tokyo, Interfaculty Initiative in Information Studies,

Graduate School of Interdisciplinary Information Studies

Keywords: immune-system, multi-agent system, object-oriented programing, Mathematica

We have been building an immune system simulation tool based on multi-agent
model. The system is coded with Mathematica, especially based on its ability to describe
in an object oriented manner. The system deals with T cells, B cells macrophages,
dendritic cells, antibodies and bacteria as agents. Immune response in a lymph node,
which is divided into 8 components like Harel's model, is chiefly treated. The
system includes specific interaction between antigen – antibodies, and that between T
cell receptor – epitopes presented on B cells based on a homology scoring matrix, blosum
80. The system could run more than 10000 agents simultaneously and could simulate
bacterial infection and suppression.

P7-06

Simulation study of 3D-reconstruction of large
biomolecule from the diffraction images obtained by X-ray
free electron laser experiment
Miki Nakano 1
miki.nakano@riken.jp

Slavica Jonic 2
slavica.jonic@upmc.fr

1

2

3

4

Osamu Miyashita 1
osamu.miyashita@riken.jp

Florence Tama 1,3,4

florence.tama@riken.jp

RIKEN Center for Computational Science, 6-7-1, Minatojima-minamimachi, Chuo-ku, Kobe,
650-0047, Japan
IMPMC, Sorbonne Universités – CNRS UMR 7590, UPMC Univ Paris 6, MNHN, IRD UMR
206, 75005 Paris, France
Department of Physics, Graduate School of Science, Nagoya University, Furo -cho, Chikusa-ku,
Nagoya, 464-8602, Japan
Institute of Transformative Bio-Molecules, Nagoya University, Furo-cho, Chikusa-ku, Nagoya,
Aichi 464-8602, Japan

Keywords: X-ray free electron laser, Single particle analysis , Protein Structure
Single particle analysis using X-ray free-electron laser (XFEL) is a novel method to observe
biomolecules under conditions close to nature. This bright and coherent beam allows us to obtaindiffraction data without crystallization, and their short femtosecond pulses enables measurements
without radiation damage. 1,2 However, resolving 3D structure from XFEL single particle
diffraction data is still challenging because of the low signal to noise ratio and the lack of beamincidence angle information. Therefore, it is important to quantitatively assess how theexperimental conditions such as the amount of data and their quality affect the expected resolutionof the resulting 3D models. In this presentation, we show the results of our computational
investigation using simulated diffraction patterns. We show that the resolution of the reconstructed
3D structure depends on the amount and quality of the diffraction images, and further discuss therequirement for experimental condition to restore molecular structures of a few nanometer
resolution. 3[1] Hirata, K., Shinzawa-Itoh, K., Yano, N., Takemura, S., Kato, K., Hatanaka, M., Muramoto, K.,
Kawahara, T., Tsukihara, T., Yamashita, E., et al. Determination of Damage-Free Crystal
Structure of an X-Ray-Sensitive Protein Using an XFEL. Nat. methods, 11:734–736, 2014
[2] Chapman, H. N., Barty, A., Bogan, M. J., Boutet, S., Frank, M., Hau -Riege, S. P., Marchesini,
S., Woods, B. W., Bajt, S., Benner, W. H., et al. Femtosecond Diffractive Imaging with aSoft-X-Ray Free-Electron Laser. Nat. Phys., 2:839–843, 2006
[3] Nakano, M., Miyashita, O., Jonic, S., Tokuhisa, A., and Tama, F. Single-particle XFEL 3D
reconstruction of ribosome-size particles based on Fourier slice matching: requirements toreach subnanometer resolution. J. Synchrotron Rad. 25:1010–1021, 2018

P7-07

Image-based Morphological Analysis for Visualization and
Optimization of Stem Cell Culture
Masaya Fujitani 1

Kei Kanie 1

fujitani.masaya@h.mbox.nagoya-u.ac.jp

kanie-k@ps.nagoya-u.ac.jp

Ryuji Kato 1
kato-r@ps.nagoya-u.ac.jp

1

Graduate School of Pharmaceutical Sciences, Nagoya University, Furo -cho, Chikusa-ku,
Nagoya, 464-8601, Japan

Keywords: Cell morphology, Image analysis, Transcriptome analysis, Stem cell culture
By the advances of stem cell culture technology, various cul ture protocols have been reported formore cost effective and stable cell culture [1]. There are numerous protocols by the combination ofculture media, plates, coating materials, and handling skills. However, it is difficult to find themost appropriate protocol since there are too many parameters to be evaluated. Therefore,
commonly, once examined and succeeded protocol is used for long time and it is considered to berisky to examine new protocols. Such conservative protocol preservation is sometimes cos tly, andlosing the chance to establish better protocol with new materials. Moreover, when a facility require
new and optimized protocol for establishing cost effective original manufacturing process, orsearch for suitable protocol for new cells that have not yet been investigated in the past, we stillneed a laborious and costly examination process. In many cases, only the proliferation rate or
endpoint assay is examined in such protocol exploration process.
Cellular morphology has long been known as an important indication to evaluate the cellular
culture status. However, the rule of cellular morphology and its fitness to the culture condition has
not been quantitatively defined. Commonly, the cellular morphology is evaluated ambiguously
based on feeling and experiences.
We have been proposing morphology-based evaluation concept by quantifying cellularmorphology as cellular fingerprint, and have verified the effectiveness of its application to boththe cellular quality evaluation and its culture conditio n [2]. By converting the statistics of cellularmorphological parameters as morphological fingerprint, we have found that delicate cellularresponse to the effect of culture condition can be recorded as quantitative data, and can becompared with bioinformatics analysis. In this poster, we show that such morphology-based
analysis is effective to profile and compare the optimum culture conditions rapidly, feasibly, andcost effectively by using only the non-stained time-course microscopic images. In addition, we
conducted transcriptome analysis to reveal the biological difference among such culture conditions.
We here show the practical examples of phenotypic evaluation method of various types of culture
conditions for stem cell.
[1] Chen, K., Mallon, B., McKay, R., Robey, P., Human Pluripotent Stem Cell Culture:
Considerations for Maintenance, Expansion, and Therapeutics, Cell Stem Cell, 14(1):13-26,
2014[2] Hiroto, S., Ichiro, T., Mai, O., Rumi, S., Kei, K., Yasujiro, K., Hiroyuki, H., Ryuji, K.,
Label-Free Morphology-Based Prediction of Multiple Differentiation Potentials of Human
Mesenchymal Stem Cells for Early Evaluation of Intact Cells, PLOS ONE,9 (4): e93952, 2014

P7-08Development of Mixed Reality-based Protein and Ligand
Tertiary Structure Visualization System
Atsushi Koyama 1
Atsushi.k.ab@m.titech.ac.jp

Nobuaki Yasuo 1
yasuo@cbi.c.titech.ac.jp

Masakazu Sekijima 1,2
Sekijima.m.aa@m.titech.ac.jp

12

Department of Computer Science, Tokyo Institute of Technology, J3-23, 4259, Nagatsuta,
Midori-ku, Yokohama, Kanagawa 226-8503, Japan
Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3-23, 4259,
Nagatsuta, Midori-ku, Yokohama, Kanagawa 226-8503, Japan

Keywords : Mixed Reality, Visualization, Protein tertiary structure, Structure-based Drug Design
It is important to understand the tertiary structures of protein-ligand complexes for efficient drug
discovery, since the cost of drug discovery is getting higher 1 . Many molecular visualization
systems have been developed such as Pymol 2 and Jmol 3 . However, these systems do not fully
represent the tertiary structures since they must project the 3D structures to 2D planes.
Recently, virtual reality technology has been rapidly progre ssed to obtain immersive experience,
where enhanced 3D environments can be experienced as physically present in the virtual world.
Mixed reality is also an emerging technique. In mixed reality experience, virtual world and realworld are merged and co-exist so that we can interact both real and virtual object at the same time.
In this study, we developed HoloMol, which visualize 3D structure s of protein s and ligand s as it
is. Microsoft HoloLens, a head-mounted mixed reality device is used. One advantage of HoloLens
is that it is a see-through device that makes the communication easier. Also, it works without theconnection to other computers. The 3D models can be manipulated and changed by hand gesture(Air-tap). Our application can show arbitrary complex by replacing models on containers at cloud.
HoloMol can be downloaded at [URL] and will be released on Windows Store.
[1] Asher Mullard.: New drugs cost US$2.6 billion to develop. Nature reviews Drug Discovery, Vol.
13, No.12, p.877, 2015[2] https://pymol.org/2/[3] http://jmol.sourceforge.net

P7-09Interactive Molecular Scale Soft Matter Simulation in VR
Gregory Gutmann 1

gutmann@c.titech.ac.jp

1

Akihiko Konagaya 1
kona@c.titech.ac.jp

Tokyo Institute of Technology, J3-25, 4259, Nagatsuta, Midori-ku, Yokohama, Kanagawa
226-8503, Japan

Keywords: Soft Matter, VR, Simulation, Interactive
With recent efforts in gaining more understanding and control over emergent molecular scale
dynamics and the challenges involved in doing so; there has been an interest in using simulation toaid in research. With this aim, we have been developing an Interactive VR simulation system toexperiment with virtual molecules as an extension of our real-time particle simulation system for
microtubule gliding assay [1]. The virtual molecules can be represented by dynamic ball and stickmodels which can be manipulated using a hands-on approach in VR. In other words, virtual
hands created using a Leap Motion controller allow the user to touch and change the forms ofvirtual molecules. This has allowed us to simulate various deformable objects in our system. So far
we have successfully simulated tube structures and simple objects; however, there are stillchallenges as well, both of which will be covered in this poster.
[1] Gutmann, G., Inoue, D., Kakugo, A., & Konagaya, A. (2017) Parallel Interaction Detection
Algorithms for a Particle-based Live Controlled Realtime Microtubule Gliding Simulation
System Accelerated by GPGPU. New Generation Computing, Vol. 35 No. 2 Springer Publishing.

P7-10All-atom molecular dynamics simulation approach to film
supported DNA origami in water
Ryuzo Azuma
azuma.r.ac@m.titech.ac.jp

Greg Gutmann
gutmann.g.aa@m.titech.ac.jp

Sae Kishi
kishi.s.aa@m.titech.ac.jp

Akihiko Konagaya
kona@c.titech.ac.jp

Tokyo Institute of Technology, J3-25, 4259, Nagatsuta, Midori-ku, Yokohama, Kanagawa
226-8503, Japan

Keywords: molecular robotics, DNA origami, NAMD2, Interface MD, TSUBAME3.0
DNA origami technology has been expanding its application fields such as molecularrobotics [1-2]. The structure of a DNA origami object is essentially dependent on thesequence design of staples. Although this is one of the key benefits of the DNA origami
method allowing nano-structure designs, atomic-level DNA origami structure modeling is
also important to assess structural properties of crossovers [3]. Moreover, assessing thephysicochemical properties of their structure has become increasingly important recently
since there have been known facts about shape deformation dynamics under low ionicstrength [4-5]. In this study, we focus on these dynamics of DNA origami structures using
an all-atom MD simulation approach. The hallmark of our approach is to incorporate an
inorganic supporting film, Muscovite mica film in this study, as a substrate. We developed
a workflow for the combined system by utilizing caDNAno [6], Interface MD [7], andNAMD2 [8] with the latest refined force field AMBER PARMBSC1 [9].
We then observed continual stretching motion for a DNA origami in MD runs in theabsence of cations. This result is reminiscent of the experimental observation for a DNA
origami in a solution of EDTA, a chelating agent for cations [4]. We also show that underrealistic water permittivity (dielectric constant >> 1), DNA strands may withstanddenaturation and exhibits a regular pattern similar to argyle lattice. By taking histogramsof Zp' (a measure of duplex forms) we analyze these characteristics more quantitatively.
Financial supports: Strategic Advancement of Multi-Purpose Ultra-Human Robot andArtificial Intelligence Technologies, NEDO; Platform Project for Supporting Drug
Discovery and Life Science Research (BINDS), AMED.

[1] Murata, S., et al., M. Molecular Robotics: A New Paradigm for Artifacts. New Generation
Computing 2013, 31 (1), 27–45.
[2] Hagiya, M., et al., Molecular Robots with Sensors and Intelligence. Accounts of Chemical
Research 2014, 47 (6), 1681–1690.
[3] Bai, X. -c., et al., Cryo-EM Structure of a 3D DNA-Origami Object. Proceedings of the
National Academy of Sciences 2012, 109 (49), 20012 –20017.
[4] Kim, H., et al., Stability of DNA Origami Nanostructure under Diverse Chemical Environments.
Chemistry of Materials 2014, 26 (18), 5265–5273.
[5] Kielar, C., et al., On the Stability of DNA Origami Nanostructures in Low -Magnesium Buffers.
Angewandte Chemie International Edition 2018.
[6] Douglas, S. M., et al., Rapid Prototyping of 3D DNA-Origami Shapes with caDNAno. Nucleic
Acids Research 2009, 37 (15), 5001–5006.
[7] Heinz, H., et al., Thermodynamically Consistent Force Fields for the Assembly of Inorganic,
Organic, and Biological Nanostructures: The INTERFACE Force Field. Langmuir 2013, 29 (6),1754–1765.
[8] Phillips, J. C., et al., Scalable Molecular Dynamics with NAMD. Journal of Computational
Chemistry 2005, 26 (16), 1781–1802.
[9] Ivani, I., et al., A. et al. Parmbsc1: A Refined Force Field for DNA Simulations. Nature Methods
2015, 13 (1), 55–58. Nature methods. 2016, 13 (1), pp 55-58.

P7-11

Self-Assembly of a Flexible Multi-Joint Ring Motif
Shiyun Liu 1
hikari@molbot.mech.tohoku.ac.jp

Ibuki Kawamata 1

kawamata@molbot.mech.tohoku.ac.jp

Nomura M. Shin-ichiro 1

nomura@molbot.mech.tohoku.ac.jp
1

2

Yuki Suzuki 2

ysuzuki79r@molbot.mech.tohoku.ac.jp

Satoshi Murata 1

murata@molbot.mech.tohoku.ac.jp

Department of Robotics, Graduate School of Engineering, Tohoku University, 6 -6-01, Aobayama, Sendai 980-8579, Japan
Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, 6 -3, Aoba-yama,
Sendai 980-8578, Japan

Keywords: DNA origami, DNA nanotechnology, Self-assembly, Molecular robotics.
During the past few decades, the field of structural DNA nanotechnology has grown enormously[1]. It enables us to design a variety of self-assembled structures ranging from 2D to 3D crystalline
structures to algorithmically self-assembled aperiodic structures [2-3]. The invention of DNA
origami, which has greatly expanded the geometric complexity of DNA nanostructures and has
accelerated research development in this area [4]. Making a large (mm-scale) planer lattice with nodefect is one of remained problems of structural DNA nanotechnology. 2D crystal of DNA tiles or
DNA origami motifs can grow up to several hundred micrometer s scale but no more because it isdifficult to avoid the interstices among the clusters.
Here, we propose a flexible DNA origami multi-joint ring motif, which has a potential to selfassemble a large lattice on a 2D substrate surface. We designed the motif as a DNA origami heptagon
by a software called caDNAno [5]. The ring motif has an ability to connect with others through thehybridization of DNA strands, which are aligned symmetrically on all the edges. The edges of the
motif are linked by flexible hinges, which allow the motif to deform into different shapes. If the
motif stays rigid as a heptagon, it is impossible to cover 2D surface without gaps . Since the motif
is flexible, it is expected to fill the interstices to cover the entire surface of the substrate. The
geometrical design of the ring motif and some preliminary experimental results will be presented
on the poster.
The proposed multi-joint ring motif can provide a novel possibility to create nanostructures ofhigh complexity and high flexibility, which may have a capacity of self-recovery.
[1] Seeman, N. C, An overview of structural DNA nanotechnology. Mol. Biotechnol, 37: 246–257,
2007.
[2] Rothemund P. W. K, Papadakis N, Winfree E., Algorithmic Self-Assembly of DNA Sierpinski
Triangles, PLoS Biol 2(12): e424,2004.
[3] Lin, C., Liu, Y., Rinker, S. and Yan, H., DNA Tile Based Self-Assembly: Building Complex
Nanoarchitectures, ChemPhysChem, 7: 1641-1647, 2006.
[4] Rothemund, P. W. K, Folding DNA to create nanoscale shapes and patterns. Nature, 440: 297–
302, 2006.
[5] Shawn M. Douglas, Adam H. Marblestone, Surat Teerapittayanon, Alejandro Vazquez, George
M. Church, William M. Shih; Rapid prototyping of 3D DNA -origami shapes with caDNAno, Nucleic
Acids Research, Volume 37, Issue 15: 5001–5006, 2009.

P7-12Integrated gene logic-chip functioning in an artificial cellTakeya Masubuchi 1,9 , Masayuki Endo 2,3 , Ryo Iizuka 4 , Ayaka Iguchi 5 , Dong Hyun Yoon 6 ,Tetsushi Sekiguchi 6 , Hao Qi 1,7 , Ryosuke Iinuma 1 , Yuya Miyazono 1 , Shuichi Shoji 5 ,Takashi Funatsu 4 , Hiroshi Sugiyama 2,3 , Yoshie Harada 3,8 , Takuya Ueda 1
and Hisashi Tadakuma 1,3,8,
tadakuma@protein.osaka-u.ac.jp
1

Graduate School of Frontier Science, The University of Tokyo, 2 Department of Chemistry, Kyoto
University, 3 Institute for Integrated Cell-Material Sciences (iCeMS), Kyoto University, 4 Graduate Schoolof Pharmaceutical Sciences, The University of Tokyo, 5 Department of Nanoscience and Nanoengineering(ASE Graduate School), Waseda University, 6 Research Organization for Nano & Life Innovation,
Waseda University, 7 Department of Chemical Engineering and Technology, Tianjin University, , 8 Institutefor Protein Research, Osaka University, 9 Present address: Institute for Quantitative Biosciences, The
University of Tokyo

Keywords: Gene expression, Synthetic Biology, Molecular Robots
In synthetic biology, the control of gene expression requires a multi-step computation of
biological signals. However, the complexity of genetic circuits remains low because it is difficult
to completely avoid crosstalk between genetic circuits. Here, we made an orthogonal
self-contained device by integrating an actuator and sensors onto a DNA origami-based nano-chipthat contains an enzyme, T7 RNA polymerase, and multiple target gene substrates. This genenano-chip orthogonally transcribes its own genes, and the nano-layout ability of DNA origami
allows us to rationally design the gene expression levels by controlling the intermoleculardistances between the enzyme and the substrate genes. We further integrated logic gates so that thenano-chip responds to water-in-oil droplets and computes their miRNA profiles, which
demonstrates that the nano-chip can function as a gene logic-chip. Our approach to component
integration on a nano-chip may provide a basis for large-scale, integrated genetic circuits.
[1] Masubuchi T et al., Construction of integrated gene logic-chip. Nature Nanotechnology in press(doi: 10.1038/s41565-018-0202-3).

P7-13

Design of a new reaction diffusion system for patternformation in hydrogel medium
Keita Abe 1
abe@molbot.mech.tohoku.ac.jp

Shin-ichiro M. Nomura 1
nomura@molbot.mech.tohoku.ac.jp
1

Ibuki Kawamata 1
kawamata@molbot.mech.tohoku.ac.jp

Satoshi Murata 1
murata@molbot.mech.tohoku.ac.jp

Department of Robotics, Graduate School of Engineering, Tohoku University, Aramaki-azaAboba 6-6-01, Abobaku, Sendai, Miyagi 980-8579, Japan.

Keywords: Pattern formation, Reaction diffusion system, DNA logic gate
Reaction diffusion mechanisms are widely found in early stages of biological development. It isthought to be one of the main mechanisms to form highly ordered patterns of various biol ogicalstructures [1] [2]. Imitating these processes by programming an artificial reaction diffusion system
has a large potential to engineer self-organization process for various applications. To achieve this,we need to manipulate the processes of “reaction” and “diffusion” independently. DNA is known
for its capability to imitate arbitrary chemical reactions, thus is suitable as the material for artificial
reaction diffusion systems [3]. Several DNA-based frameworks for artificial pattern formation havebeen reported so far [2] [4], however, little attention has been paid to control the “diffusion” term.
Here, we propose a technique to design a DNA-based reaction diffusion system with an ability tocontrol the diffusion speed of DNA species. As a demonstration, a DNA logic gate system to form
a bisector pattern is designed. Two types of DNA called inputs A and B diffuse from different sources
placed in the gel medium. A bisector pattern is formed where the distributions of the species A andB overlap. The overlap is visualized by an AND gate pre-mixed in the gel, which outputs
fluorescence if both of the inputs A “AND” B exist. The relative positioning of the bisector in
between the two sources can be moved by tuning the diffusion speed of DNA [5]. In this diffusion
modulation method, we can adjust the diffusion speed of the input DNA by changing concentrationof additional DNA molecule called competitor.
Experimental results show that the bisector pattern was successfully formed and the position of
the pattern was successfully controlled by the proposed reaction system with the diffusion
modulation. This approach has a potential to scale up the molecular programming from nano - tomicro- and larger scales, which provides us a useful tool to engineer self-organizing material forvarious application such as artificial organs and brains.
[1] Wolpert, Lewis, Cheryll Tickle, and Alfonso Martinez Arias. Principles of development. Oxford
University Press, USA, 2015.
[2] Anton S. Zadorin, Yannick Rondelez, Guillaume Gines, Vadim Dilhas, Georg Urtel, Adrian
Zambrano, Jean-Christophe Galas & André Estevez-Torres, “Synthesis and materialization of areaction–diffusion French flag pattern.” Nature chemistry, 9(10), 990, 2017.
[3] Soloveichik, David, Georg Seelig, and Erik Winfree. “DNA as a universal substrate for chemical
kinetics.” Proceedings of the National Academy of Sciences 107.12 (2010): 5393-5398.
[4] John Zenk, a Dominic Scalise,Kaiyuan Wang, Phillip Dorsey, Jo shua Fern, Ariana Cruz, andRebecca Schulman. Stable DNA-based reaction–diffusion patterns. RSC Advances, 7(29), 1803218040, 2017[5] Thanapop Rodjanapanyakul, Fumi Takabatake, Keita Abe, Ibuki Kawamata, Shinichiro M.
Nomura, and Satoshi Murata. “Diffusion modulation of DNA by toehold exchange”. PhysicalReview E, 97(5), 052617, 2018.

P7-14

Chain aggregation of liposomes through DNA Hairpin
assembly
Yuto Otaki 1

Ibuki Kawamata 1

otaki@molbot.mech.tohoku.ac.jp

Yuki Suzuki 2

kawamata@molbot.mech.tohoku.ac.jp

Satoshi Murata 1

ysuzuki79r@molbot.mech.tohoku.ac.jp

murata@molbot.mech.tohoku.ac.jp

Nomura M. Shinichiro 1
nomura@molbot.mech.tohoku.ac.jp

12

Department of robotics, Graduate School of Enginee ring, Tohoku University, 6-6-01 Aramaki- aza-Aoba, Aoba-ku, Sendai, Miyagi, 980-8579, Japan.
Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, 6-3 Aramaki aza Aoba, Aoba-ku, Sendai, Miyagi, 980-8578, Japan

Keywords: DNA nanotechnology, Liposome, Drug delivery system (DDS)
Liposome, a capsule like structure formed by lipid bilayer, has been received attention as carriers
for target-specific drug delivery [1]. Capability for recognition and binding to a target cell is basedon specific binding between sensing molecules on liposomes and tar get molecules on the cell.
However, there are some obstacles for recognition and binding, such as small number of target
molecules and small size of target cell and steric hindrance caused by liposomes covering surfaceof target cell. Therefore, a new strategy, that signalize and amplify molecular recognition andbinding events to overcome those obstacles, is needed.
DNA is known as a programmable molecule that offers methods for molecular detection [2].
Recently, molecular detection system with signal amplification, called Exponential hairpin
assembly (EHA), was developed [3]. The system consists of four hairpin DNAs, forming
exponentially growing dendritic structure in the present of target DNA.
Here, we propose a novel method, based on molecular robotics, to induce chain aggregation ofliposomes around target via DNA. The method employs liposomes covered with two kinds ofhairpin DNAs, two kind of other free hairpin DNAs and target DNA. Detection of the target isachieved by hybridization between hairpin on the liposomes and a target DNA, causing
chain-branching reaction induced by EHA. The dendritic DNA structures of EHA are formed on
the surface of liposomes and spread to other liposomes through the hairpin chain reaction ,resulting chain aggregation around target. In experiments, we confirmed that target DNA triggered
liposome aggregation. This method is expected to provide new functionality for various targetingstrategies for DDS.
[1] Torchilin, Vladimir P. "Recent advances with liposomes as pharmaceutical carriers." Nature
reviews Drug discovery 4.2 (2005): 145.
[2] Dirks, Robert M., and Niles A. Pierce. "Triggered amplification by hybridization chain
reaction." Proceedings of the National Academy of Sciences 101.43 (2004): 15275-15278.
[3] Ma, Cuiping, et al. "Three-dimensional DNA nanostructures for colorimetric assay of nucleic
acids." Journal of Materials Chemistry B 3.14 (2015): 2853-2857.

P7-15

Improved the conjugation yield between AuNP and thiolgroup in DNA origami by solution freezing
Shumpei Ishikawa

Islam Md. Sirajul

Naohide Akamatsu

k529897@kansai-u.ac.jp

Yuichi Ohya

Akinori Kuzuya

ohya@kansai-u.ac.jp

kuzuya@kansai-u.ac.jp

Department of Chemistry and Materials Engineering, Kansai University,
3-3-35, Yamate, Suita, Osaka564-8680, Japan

Keywords: DNA nanostructures, DNA origami, AuNP
We have previously developed a DNA origami motif with 9 wells in the structure (DNANanostick). In previous study, we succeeded in conjugating 4 AuNPs in the wells as designed (wellnumber is 2, 4, 6, 8) in DNA Nanostick [1 ] . We used excess AuNPs such as 100 eq and longincubation such as 4 days for the reaction at 4°C. However, the yield of DNA Nanostick with 4
AuNPs was about 20%. This corresponds to the conjugation yield of 68.8% per well. Very recently,
B. Liu and J. Liu have reported that the reaction yield was increased for conjugation of AuNP withthiol modified ssDNA by freezing the solution [2] . Upon decreasing the temperature, ice crystals aregradually formed, pushing AuNPs, DNA, and salt (Na + , citrate ions) out to the “ micropockets” tobe concentrated, which significantly enhances the reaction rate. In this study, we applied thisfreezing method to DNA origami conjugation.
For conjugation of DNA Nanostick with AuNPs, we used 2 eq. of AuNPs and incubated them for2 hours at -20 ̊ C in a freezer or 2 min at -80 ̊ C in dry ice. After that, excess AuNPs were removed
by gel filtration, and the structure was observed using AFM in fluid. As a result, DNA Nanostick
was found to be intact even the solution was frozen. Moreover, the yield of DNA Nanostick with 4
AuNPs (about 50%) was increased compare to that in the previous study. In the case of freezing at-20 ̊ C for 2 hours (freezer method), the conjugation yield was 85.8% per well. In the case of
freezing at -80 ̊ C for 2 min (dry ice method), the conjugation yield was also increased (about 10%),
however, it was lower than freezer method. In dry ice method, the velocity of ice crystal formed
was too fast to concentrate solute not efficiently.
In summary, we succeeded in increasing the conjugation yield for reaction of DNA Nanostickwith AuNP by freezing the solution. We also succeeded in reducing the cost that necessary amountof AuNP was reduced from 100 eq excess to 2 eq. The reaction time was also reduced from 4 days
to 2 hours. Moreover, the conformation of DNA origami was not broken by freezing the solution.
[1] A. Kuzuya, M. Komiyama et al., Small, 2010, 6, 2664–2667.
[2] B. Liu, J. Liu, J. Am. Chem. Soc., 2017, 139, 9471–9474.

P7-16

Toward automatic-control of motility-protein crowds by DNA circuit
Shosei Ichiseki 1
ichiseki@molbot.mech.tohoku.ac.jp

Keya Jakia Jannat 2
zakiazannat@yahoo.com

Akira Kakugo 2
kakugo@sci.hokudai.ac.jp

Ibuki Kawamata 1
kawamata@molbot.mech.tohoku.ac.jp

Satoshi murata 1
murata@molbot.mech.tohoku.ac.jp

Shinichiro M. Nomura 1
nomura@molbot.mech.tohoku.ac.jp

1

Department of Robotics, Graduate School of Engineering, Tohoku University, 6-6-01,
Aramaki aza-aoba, Aobaku, Sendai, Miyagi 980-8579, Japan.
2
Department of Chemical, Graduate School of Science, Hokkaido University, Kita10,
Nishi8, Sapporo, Hokkaido, 060-0810, Japan.

Keywords: Motility proteins, molecular circuit, molecular programming, motion control
In the living organisms, various molecules work cooperatively, realizing complex reaction andvarious functions. For example, animal muscles realize macroscopic motion by cooperative work ofmyosins and actins. Recently, by learning from such living molecular systems, molecular robots havebeen developed by integrating molecular devices which have designed for realizing desired function[1][2].
Last year, J.J.Keya et al. demonstrated a study which switching assembly and disassembly of motility
microtubules on kinesins by DNA hybridization[3]. In their work, photo -reactive DNA molecules
switched the assembly/disassembly by alternately irradiating visible light and UV externally. Here, we
aim to control such a collective behavior of kinesins -microtubules automatically by designed chemical
reaction. In order to enable automatic output of desired DNA seq uence, we adopted a DNA computer,universal strand generator (USG) [4]. This is the device which generates any sequence of ssDNA
designed in advance at arbitrary time. We adjusted the experimental condition, such as temperature,buffer, ionic strength, etc. for coexistence of kinesin-microtubules griding assay with USG.The
microtubules were modified with fluorescence dye and ssDNAs.
By using fluorescence microscopy, it was confiermed that ~70% of the microtubules were assembled
by adding the ssDNA (for assemble). Then, we observed that the motility microtubules on kinesins
assembled through USG output. Now we are seeking a suitable condition to conduct the
automatic-disassemble process through USG.
This work would be useful not only for automatic control o f motility-protein crowds but also themolecular robots control by the chemical signal, similar to the natural chemotaxis.
[1] S.Murata, et al. "Molecular robotics: A new paradigm for artifacts." New Generation
Computing 31(1), (2013): 27-45.
[2] Y. Sato, et al. "Micrometer-sized molecular robot changes its shape in response to signal
molecules." Science Robotics 2(4), (2017): eaal3735.

[3] J.J.Keya, et al. "DNA-assisted swarm control in a biomolecular motor system." Naturecommunications 9(1), (2018): 453.
[4]Team sendai. "Universal strand generator." Biomod 2014, http://teamsendai2014.github.io/

P7-17Single molecule real-time observation of dynamicallyfunctioning DNA origami molecular machines
Yuta Yamasaki

Naohide Akamatsu

Ryosuke Watanabek122988@kansai-u.ac.jp

Yuichi Ohyaohya@kansai-u.ac.jp

Akinori Kuzuya
kuzuya@kansai-u.ac.jp

Department of Chemistry and Materials Engineering, Kansai University, 3-3-35
Yamate, Suita, Osaka 564-8680, Japan

Keywords: Molecular Robotics, DNA nanostructures
We have reported nanomechanical DNA origami devices, DNA pliers [1] and DNA chopsticks that
can transform in response to binding of target molecules. The former device is two-dimensional andthe latter is three-dimensional. Although the latter showed more rigid nature and equivalentpinching rate to the target substrates, no remarkable advantage of DNA chopsticks was been
observed. If the interaction between both structures and mica is different and DNA chopsticks ismore mobile on mica than DNA pliers, it is advantageous in real time observation. In this study, we
fix both structures on mica and add Na + as monovalent cations, to weaken the interaction between
each structure and mica. Then, we investigated the difference in behavior of both structures.
Both structures were annealed and deposited to mica. Then, 1×TAE/Mg 2+ buffer containing NaCl
was added, and the system was observed with AFM. By counting numbers, the strength of theinteraction between both structures and mica was compared. AFM observation revealed that, as theconcentration of Na + increased, the number of DNA chopsticks became less than DNA pliers. It was
found that the mobility of DNA chopsticks is higher than DNA pliers at a lower Na + concentration.
We also attempted real-time observation by using high-speed AFM. After DNA chopsticks was tomica, NaCl was added during observation to give the structures mobility. Then, the target,
streptavidin (SA) was added. As a result, selective closing was clearly observed upon capturing of
SA. This suggested that DNA chopsticks could be applied to devices that can detect of target
substrates in real time.
[1] A. Kuzuya, et al., Nat. Commun. 2011, 2, 499.

P7-18Making pores on biomembrane using DNA origami:
Design and evaluation of φ12 nm artificial channel
Shoji Iwabuchi 1

iwabuchi@molbot.mech.tohoku.ac.jp

Yuki Suzuki 1

ysuzuki79r@molbot.mech.tohoku.ac.jp

Ibuki Kawamata 1

kawamata@molbot.mech.tohoku.ac.jp

Satoshi Murata 1

murata@molbot.mech.tohoku.ac.jp

Shin-ichiro M. Nomura 1

nomura@molbot.mech.tohoku.ac.jp

1

Molecular Robotics Laboratory, School of Engineering, Tohoku University, 6-6-01
Aoba Aramaki-Aza, Aoba, Sendai City, Miyagi 980-8579, Japan

Keywords: DNA nanotechnology, channel, nanopores, molecular transport, DNA origami
Development of a molecular robot, as a small and autonomous system that can work in solution,
has attracted attention[1]. Closed-lipid bilayer membrane called liposome is mainly adopted as therobot body. The flexible membrane enables to deform in response to their internal molecularactuators with DNA logic gate[2].
However, since the lipid bilayer membrane prevents to pass hydrophilic molecules, there is an
essential problem that the robot cannot respond to external signals or stimulations. The artificial
channel on the lipid bilayer[3][4] is a solution to this problem. Though these channels are reported,it has been difficult to transport large molecule (such as dsDNA) or to control the selective
transportation because the pore size of channels is small.
In this study, we designed an artificial channel with a large-size pore. It is made by DNA
origami[5]. It is designed to bind to the bilayer membrane by cholesterol-modified DNA and topass through the molecule by simple diffusion. To evaluate the function, giant unilamellar vesicles
were used as the biomembrane model, and hydrophilic fluorescence dye was entrapped the outside(or inside) of the vesicles as the molecule to be transported. Then we mixed the artificial channel
to the system and observed the transportation of the dye molecules directly by confocal
microscopy.
As a result, the channel was attached to the GUV membrane and it is confirmed that thefluorescence dyes are transported through the channel. Now we are trying to increase theprobability of transportation and to control the limitation of the size of the transportable molecule.
The large pore can pass more kind of molecules, and the function of permeability control will leadto the realization of a molecular robot that can control its action by importing specific signal
molecule.
[1] Hagiya, M., et al. "Molecular robots with sensors and intelligence." Accounts of chemical research ,47.6 : 1681-1690, 2014.
[2] Sato, Y ., et al., Micrometer-sized molecular robot changes its shape in response to signal
molecules, Science Robotics, 2:eaal3735, 2017.
[3] Langecker, M., et al., Synthetic lipid membrane channels formed by designed DNA
nanostructures, Science, 338: 932-936, 2012.
[4] Göpfrich, K., et al., Large-conductance transmembrane porin made from DNA origami, ACS
nano, 10:8207-8214, 2016.
[5] Douglas, S. M ., et al., Rapid prototyping of 3D DNA-origami shapes with caDNAno. Nucleic
acids research, 37:5001-5006, 2009.