P1-01
Computational approaches to drug-receptor binding
kinetics
Osamu Ichihara 1

osamu.ichihara@schrodinger.com

1

Schrödinger K.K., 17F Marunouchi Trust Tower North, 1-8-1 Marunouchi, Chiyoda-ku, Tokyo
100-0005, Japan

Keywords: Binding kinetics, Residence time, Solvation, Water molecules, Association rate,
Dissociation rate
It has been proposed by Pearlstein et al. that desolvation and resolvation of the binding sites ofproteins can be a key determinant of ligand binding kinetics. 1 They have shown that, for a set of
inhibitors of p38 MAP kinase, 2 calculated desolvation and resolvation costs during ligand binding
are qualitatively consistent with observed on and off rates respectively.
In this work, application of the hypothesis was further extended to rationalizing the binding
kinetics (residence time) of the inhibitors of Staphylococcus aureus enoyl-ACP reductase (saFabI),
where more complex two-step binding mechanism is suggested to be in operation. 3 It has been
proposed that, on ligand binding, saFabI may undergo a directional structural change, i.e. orderingof the substrate-binding loop to form an α-helical structure, and thereby locks the inhibitor into thecavity and increases its residence time. 3
We have calculated a set of thermodynamic parameters associated with the solvation properties
around the ligands bound to saFabI using molecular dynamics simulations (WaterMap). 4 The
observed rate of ligand dissociation (residence time) are shown to be in excellent correlation withthe parameters calculated. Moreover, the formulation of the parameters strongly supports the
expected two-step binding mechanism.

[1] Pearlstein, R. A., Sherman W., Abel, R., Proteins, 81:1509-1526, 2013.
[2] Regan, J., Pargellis, C. A., Cirillo, P. F., Gilmore, T., Hickey, E. R., Peet, G. W., Proto, A.,
Swinamer, A., and Moss, N., Bioorg. Med. Chem. Lett., 13(18): 3101-3104, 2003.
[3] Chang, A., Schiebel, J., Yu, W., Bommineni, G. R., Pan, P., Baxter, M. V., Avinash. K.,
Sotriffer, C. A., Kisker, C., and Tonge, P. J., Biochemistry, 52(24): 4217-4228, 2013.
[4] Abel, R., Salam, N. K., Shelley, J., Farid, R., Friesner, R. A., and Sherman, W.,
ChemMedChem, 6(6): 1049-1066, 2011.

P1-02
Exploration of Protein-Ligand Complex Configuration in
The Equilibrium State
Miwa Sato 1*

Yoshiki Namiuchi 2

sato-miwa@mki.co.jp

yoshiki.namiuchi@riken.jp

Mitsugu Araki 3*
mitsugu.araki@riken.jp

1

Mitsui Knowledge Industry Co., Ltd. Biomedical Dept. 2-5-1 Atago Minato-ku, Tokyo
105-6215, Japan
2
RIKEN HPCI Program for Computational Life Sciences, 7-1-16 Minatojima-minamimachi,
Chuo-ku, Kobe, Hyogo 650-0047, Japan
3
RIKEN AICS, 1-6-5 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan* corresponding authors

Keywords: Binding Free Energy Calculation, Protein modeling, Ligand docking, Extended
ensemble MD simulation
In drug discovery research, prediction of the most stable protein–ligand binding structure is oneof the key issues. To effectively and precisely calculate the binding free energies by using
MP-CAFEE method [1], which is based on the Jarzynski equality, it is essential to set the accurate
protein-ligand complex configuration in thermodynamic equilibrium state. But it is a challenging
task, if there is no protein-ligand crystal structure, or the ligand is de novo designed and is not yet
synthesized. Starting from a result of docking based studies, we explore the possibility to precisely
calculate the equilibrium state configuration of a protein-ligand complex (in this case,
CDK2-CS12 complex, whose crystal structure is registered in CSAR). For this purpose, we use
three molecular dynamics (MD) methods, long-time MD simulation, Temperature Replica
Exchange Method (T-REMD), and Hamiltonian Replica Exchange Method (H-REMD).
After the small-molecule compound (CSAR ID: CS12) was docked into the ATP-binding site of
the CDK2 kinase by use of GLIDE SP mode (Schrodinger, LLC, NY), two kinds of docking poses(gscore rank 1st and 3rd), in which overall or partial chemical structure is flipped with respect tothat observed in the crystal structure, were selected as initial structures for the MD simulations.
These binding modes did not drastically changed during 1μs conventional MD simulations. In
contrast, among trajectories obtained by T-REMD or H-REMD, we observed several kinds ofbinding poses distinct from the initial structures, including binding modes similar to the crystal
structure (Ligand RMSD<0.183nm between the poses and crystal structure). Although a molecularsize of CS12 is relatively small (MW=206.3) compared with general drug candidates, our study
might suggest that T-REMD and H-REMD have a potential to explore the protein-ligand complex
configuration. Based on these results, we are next planning to construct a prediction method of themost stable binding pose among the poses sampled by these extended ensemble MD simulations.
Acknowledgements: This research used computational resources of the K computer provided by
the RIKEN Advanced Institute for Computational Science through the HPCI System Research
project (Project ID:hp140042&140208). This work was supported in part by MEXT SPIRE Field 1
and K supercomputer-based drug discovery project by Biogrid pharma consortium.
[1] Fujitani, H., Tanida, Y., and Matsuura, A., Massively parallel computation of absolute binding
free energy with well-equilibrated states, Physical Review E, 79, 021914, 2009.

P1-03
Analysis of the SLP-76 ligand recognition mechanismof Mona/gads by molecular dynamics simulation
Yoshiyuki Uemura 1
uemura-yoshiyuki-td@ynu.jp

1

Kazuyoshi Ueda 1
k-ueda@ynu.ac.jp

Graduate school of Engineering Yokohama National University, 79-5
Tokiwadai, Hodogaya-ku, Kanagawa 240-8501, Japan

Keywords: Molecular Dynamics simulation, Ligand docking,Intracellular signal transduction is an important mechanism for the growth and the differentiationof the immuno competent cell (T cell, etc.) in vivo. [1] Mona/gads and SLP-76 are known as themember proteins of such signal transduction pathway in cell. Meanwhile, SH3 is a domain which is
known as one of the most abundant protein interaction modules in nature a nd can binds Pro-rich
peptide strongly. Mona/gads also includes a SH3 domain in its structure. However, SH3 domain in
Mona/gads does not recognize the Pro-rich peptide, instead, it recognizes the RxxK motif included
in SLP-76. [2] The mechanism of these different binding, nature has not been cleared yet. In thiswork, we tried to elucidate the binding mode and recognition mechanism between Mona/gads and
SLP-76 complex using the molecular dynamics simulation method.
We performed 10 ns MD simulation on the system of Mona/gads and SLP-76 complex. From the
trajectories, we calculated the interaction energy between them to elucidate the modes of thebinding. Molecular dynamics calculation was performed using t he package of GROMACS. The
force field of protein is AMBER03, and TIP3P model was used for water. The peptide sequence of
PAPSIDRSTKPPL is used as a binding motif instead of whole SLP-76 peptide. The results showthat the interaction between SLP-76 and Mona/gads was found to be divided into two parts; One is
the part where the coulombic electrostatic interaction works mainly, and the other is the part wherethe van der Waals interaction works mainly. The ratio of these interactions is about 2:1. On theother hand, the residues of Pro1,Ser4,Asp6,Ser8,Pro12 of Mona/gads were found not to involved in
the interaction.
We next prepared some mutants of SLP-76, which is substituted one of the residues of SLP-76 toAla, to investigate the importance of the specific residue. The calculations were also performed
with 10ns MD simulation. The results show that the hydrophobic residues in SLP-76 affect theinteraction between Mona/gads and SLP-76. On the other hand, charged groups in SLP-76 affectthe interaction through the conformation change. In other words, coulomb interactions of thecharged residues play an important role to maintain the conformation of SLP -76.
[1] Andre E.Nel, J. Allergy. Clin. Immunol. 109, 763 (2002) .
[2] Maria Harkiolaki, Marc Lewitzky, Robert J.C.Gilbert , E.Yvonne Jones, Roland P.Bourette, Guy
Mouchiroud, Holger Sondermann, Ismail Moarefi and Stephan M.Feller.
The EMBO Journal. 22, 2571-2582 (2003)

P1-04
Designed Autonomic Motion in
Novel Organized Self-oscillating Gels
D O Junghee

MUKAI Masaru

junghee-do@aist.go.jp

TOMINAGA Kenichi

ARIMURA Takashi
takashi-arimura@aist.go.jp

Institute of Advanced Industrial Science and Technology(AIST), AIST Tsukuba Central 5-2,
Tsukuba, Ibaraki 305-8565, Japan

Keywords: Ferroin, Self-oscillation, Polymer gel, Belousov-Zhabotinsky reaction, LCSTH

CH2 CH
CH2
CH2
CH
C
y
z
x
Self-oscillating polymer gel represent an intriguing
O
OO
class of stimuli-responsive materials, which an
NH
NNH
embedded system of chemistry can elicit responsive
2+/ 3+
CH2
forms of mechanics in dissipative/autonomous manner.
The
self-oscillating
properties
of
the
NH
N
N
Belousov-Zhabotinsky (BZ) gels result from theO
Fe NNincorporation of the BZ reaction catalysts within the
H2C
C
H
N
N
polymer that can cause periodic oscillations of chemical
1
composition. The self-oscillating BZ gels containing
Ferroin cross-linked gel
ferroin complex 2 , modified to provide a flexible
Fig. 1 Chemical structure of achemistry for photo-induced polymerization and theferroin cross-linked gel.
amplitudeofswelling-deswellinghas
beencharacterized.
The photopolymerization was carried out using a UV
light source (>300nm, SUPERCURE-204S) at 20 ℃ .
The behavior of NIPAAm hydrogel at the lower criticalsolution temperature (LCST) was analyzed. NIPAAm
hydrogels by polymerization via UV exposure showedstunning swelling and deswelling behavior with change ,inducing the volume phase transition at 29 ℃ lower
than that of NIPAAm hydrogel (32 ℃ ) by thermal
synthesis. 3 As shown in the Figure 2, the homogeneityhydrogel was induced the photo-induced polymerization
Fig. 2 Equilibrium swelling ratio of the NIPAAm
for 30 min. Reported below, synthesized a new a
as a function of temperature.
self-oscillating
ferroin-copolymerized
N-isopropylacrylamide (NIPAAm) cross-linked gel by the photo-induced polymerization (Figure1). The autonomous motion of the ferroin cross-linked gel on the groove was took place by the BZ
oscillating reaction driven from ferroin catalyst.

[1] (a) S. Saito., et al., Effect of additives on phase transition of N -isopropylacrylamide gels,
Langmuir, 1992, 8, 687-690. (b) S. Takahashi., et al., Macromolecules, 1993, 26, 2194-2197.
[2 ] T. Arimura and M. Mukai, Chem. Commun., 2014, 50, 5861-5863.
[3] T. Still et al., Journal of Colloid and Interface Science, 2013, 405, 96–102

P1-05
Incorporation of Water Displacement Effect into Scoring
Functions for Protein-ligand Docking
Shota Uehara 1

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

1

Shigenori Tanaka 1

tanaka2@kobe-u.ac.jp

Department of Computational Science, Graduate School of System Informatics, Kobe
University, 1-1, Rokkodai, Nada-ku, Kobe, Hyogo, 657-8501, Japan

Keywords: Protein-ligand docking, Scoring function, Grid inhomogeneous solvation theory(GIST)
In structure-based drug design (SBDD), an accurate scoring function and an efficient
optimization algorithm are urgently required for successful virtual screening via docking [1]. In
the last years, a number of scoring functions have been developed to achieve accurate estimationof binding affinity between protein and ligand, but the correlations between the experimentally
determined binding energies and the calculated scores are still not satisfactory [2]. Most of thesescoring functions are formulated based on interatomic potentials, and give relevant estimations ofinteraction energy between protein and ligand. However, docking scoring functions often missvarious thermodynamic effects upon protein-ligand binding, such as water displacement effect.
The binding of a ligand to a protein leads to displacement of water molecules from the protein’s
pocket, and this displacement process significantly contributes to the overall thermodynamics ofprotein-ligand binding. For example, the waters in the protein hydrophobic regions which cannot
make appropriate hydrogen bonds are energetically unfavorable, and the displacement of such
waters into bulk region earns a large contribution in thermodynamics. In earlier works, important
contributions of active site waters were revealed using explicit solvation simulations with Water
Map [3], GIST [4] and other methods [5,6]. In this work, we incorporate the water displacement
effect into docking scoring function, to realize accurate and efficient docking simulation.
[1] Uehara, S., Fujimoto, K. J., and Tanaka, S., Protein-ligand docking using fitness learning-based
artificial bee colony with proximity stimuli. Physical Chemistry Chemical Physics.
17:16412–16417, 2015.
[2] Huang, S.-Y., Grinter, S. Z., and Zou, X. Scoring functions and their evaluation methods forprotein-ligand docking: recent advances and future directions, Physical Chemistry Chemical
Physics, 12:12899–12908, 2010.
[3] Abel, R., Young, T., Farid, R., Berne, B. J., and Friesner, R., Role of the active-site solvent in
the thermodynamics of factor Xa ligand binding, Journal of the American Chemical Society,130:2817–2831, 2008.
[4] Nguyen, C. N., Young, T. K., and Gilson, M. K., Grid inhomogeneous solvation theory:
Hydration structure and thermodynamics of the miniature receptor cucurbit[7]uril, Journal ofChemical Physics, 137:044101, 2012.
[5] Czapiewski, D., and Zielkiewicz, J., Structural properties of hydration shell around various
conformations of simple poly- peptides. Journal of Physical Chemistry B, 114:4536−4550, 2010.
[6] Haider, K., and Huggins, D. J., Combining solvent thermodynamic profiles with functionality
maps of the Hsp90 binding site to predict the displacement of water molecules. Journal ofChemical Informatics and Modeling, 53:2571−2586, 2013.

P1-06
Multiple binding poses of a fragment-like molecule to a
receptor and their affinities using metadynamics andmultiple trajectories multistate Bennett acceptance ratio
Yoshiaki Tanida

tanida.yoshiaki@jp.fujitsu.com

Azuma Matsuura
matsuura.azuma@jp.fujitsu.com

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

Keywords: free energy, multiple poses, metadynamics, alchemical transformation
Current “state-of-the-art” free energy calculations based on alchemical transformation for ligand
binding have been widely accepted in computational ligand design and optimization [1]. Here, theprediction of the binding poses of a ligand in the active site of a target receptor is crucial for theuse of quantitative evaluation of binding free energy.
In this work, we demonstrate the alchemical free energy calculation s coupled with exploringmetastable structures using metadynamics. Theophylline/RNA complex (PDB code 1O15 [2]) isused. Several metastable structures in the binding site can be found using the well-tempered
metadynamics in three collective variables. In alchemical free energy calculations, to restrict theconformation sampling of theophylline molecule to a finite volume within the binding site, we use
the harmonic potentials restraining the translational and rotational motions of the molecule relative
to RNA [3]. The multistate Bennett acceptance ratio (MBAR) [4] is employed in evaluating freeenergies. Multiple trajectories are also used for reducing the statistical error. After computing thefree energy difference for each metastable structure, we estimate the binding free energy using thesummation of the partition function. Our obtained value agrees well with the experimental one.

[1] Y. Tanida, アンサンブル vol. 17, No. 2, April 2015.
[2] G. M. Clore and J. Kuszewski, J. Am. Chem. Soc., 125, 1518 (2003).
[3] S. Boresch, F. Tettinger, F. Leitgeb and M. Karplus, J. Phys. Chem. B, 107, 9535 (2003).
[4] M. R. Shirts and J. D. Chodera, J. Chem. Phys., 129, 124105 (2008).

P1-07
Evaluation of protein-ligand binding affinity
by replica exchange thermodynamic integration
Osamu Takarada 1

Osamu Ichikawa 1

Kazushi Fujimoto 2

osamu-takarada@ds-pharma.co.jp osamu-ichikawa@ds-pharma.co.jp k-fuji@apchem.nagoya-u.ac.jp

Kazuto Yamazaki 1

Atsushi Yamada 2

kazuto-yamazaki@ds-pharma.co.jp ayamada@apchem.nagoya-u.ao.jp

1

2

Tomoko Mizuguchi 2

Susumu Okazaki 2

mizuguchi.tomoko@apchem.nagoya-u.ac.jp

okazaki@apchem.nagoya-u.ac.jp

Sumitomo Dainippon Pharma Co., Ltd., 3 -1-98, Kasugade-naka, Konohana-ku,
Osaka 554-0022, Japan
Department of Applied Chemistry, Nagoya University, Furo -cho, Chikusa-ku,
Nagoya, Aichi 464-8603, Japan

Keywords: Molecular dynamics simulation, Protein-ligand binding free energy, Replica exchange
thermodynamic integration (RETI), Induced -fit binding
The free energy calculation of protein-ligand binding is becoming an essential process for drug
discovery. But it is not always enough accuracy for any proteins. Especially, the free energy
calculation for induced-fit binding is known to have low accuracy [1]. In order to expand theversatility of the free energy calculations, we applied replica exchange thermodynamic integration(RETI) to the protein-ligand binding and discussed the efficacy of RETI for induced-fit binding.
We selected 9 protein-ligand complexes, which composed of 3 target proteins (Bcl-xL: B cell
lymphoma-extra large, MBP: maltose-binding protein, GR: glucocorticoid receptor) and 3 ligands
for each protein, as evaluation data of RETI. All complexes are known to be formed by induced-fit
binding mechanism, and each of them has respectively different binding affinities. Firstly, we
performed MD simulation at high temperatures in order to produce the diverse conformations foreach protein. And the obtained conformations were individually assigned to the temperature
replicas from 310K to 400K so that the neighboring replicas had similar conformations. Then, we
executed the replica exchange simulation and calculated the free energies of protein-ligand binding
from 310K replicas by the thermodynamic integration.
The calculated free energy values of 9 complexes by RETI were generally different from theexperimental values. However, the differences between the calculated and experimental values in
RETI tended to be smaller than the differences in TI. It means that RETI can be effective for
improvement the accuracy of the free energy calculation. It was considered that most of these
improvements come from more precise simulation of apo-form dynamics. On the other hand, it wassuggested that RETI could be also effective in the evaluation of induced fit binding, because somecomplexes showed higher binding affinity values by RETI than TI .
[1] de Rulter A. Oostenbrink C. Free energy calculations of protein -ligand interactions, Curr opin
Chem Biol, 15(4), 547-52, 2011

P1-08
An Experimental “Twist” to Conformer Generation
Scott A. Gothe 1gothe@ccdc.cam.ac.uk

1

2

Ilenia Giangreco 2
giangreco@ccdc.cam.ac.uk

Cambridge Crystallographic Data Centre Inc., 174 Frelinghuysen Road, Piscataway, NJ,
08854-8076, USA
Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, United
Kingdom

Keywords: Molecular Modeling, Knowledge-based Methods, Structure-Based Design
Understanding a molecule’s available “conformational landscape” is of central importance in the
success of modern Structure-Based Drug Design methods. A number of approaches have beendeveloped by the scientific community to map this landscape, utilizing either theoretical
calculations of knowledge-based methods. Many of these methods potentially suffer fromoversampling of areas of geometric space inaccessible to the molecule, or undersampling likely
conformations due to energetic considerations.
Utilizing information derived from over 750000 small -molecule crystal structures in the
Cambridge Crystallographic Database, we have developed a new, rapid, knowledge -based method
for enumerating and selecting the conformational profiles for molecules of interest. This
presentation will describe the science, methodology, validation and utilization of thisexperimentally-informed technique.

P1-09
Molecular Dynamics Simulation of Shiga Toxin , III
Kazumi Omata 1

Hisashi Okumura 2,3

komata@hosp.ncgm.go.jp

hokumura@ims.ac.jp

Yoshiharu Mori 2

Kiyotaka Nishikawa 4

ymori@ims.ac.jp

1

2
3

4

knishika@mail.doshisha.ac.jp

Medical Sociology Section, 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
Faculty of Life and Medical Sciences, Doshisha University, 1-3 Miyakotani, Tatara, Kyotanabe,
Kyoto 610-0394, Japan

Keywords: Shiga toxin, Ligand docking, Molecular dynamics, O157
Shiga toxin 1 B-subunit (Stx1B) is a pentamer protein produced by Escherichia coli O157:H7 [1].
The number of residues is 5 x 69 = 345. Its shape is relatively flat, and unlike other proteins, itdoes not have a distinct binding pocket; the number of binding sites is three per monomer, i.e. thetotal number is 5 x 3 = 15, and they are distributed on its surface. Using these binding sites, Stx1B
recognizes and binds receptors on a cell surface, and intrudes by endocytosis into the cell,accompanying the catalytic A-subunit. Nishikawa and collaborators have developed a ligand(MMAtet) by using a peptide library technique [2], which binds Stx1B. This ligand is a branchedpeptide oligomer designed such that it covers the greater part of the Stx1B surface. Stx1B-MMAtet
complexes cannot be crystallized, so their static structure is still unknown. As such, the present
study has investigated the docking of Stx1B and the ligand by use of molecular dynamics
simulations [3]. We have obtained the following results: (1) the ligand recognized and docked on
the three classes of binding sites in Stx1B, in agreement with the deduction based on theexperiments; (2) the Stx1B-ligand binding occurs inhomogeneously on the Stx1B surface,
implying that in fact more than one ligand bind to one Stx1B in actual situations; (3) it remained
unclear why the ligand binds strongly one of the three binding-site classes which consists ofarginine or tryptophan residues. In the meeting, quantitative analysis concerning the above
results will be presented in detail.
[1] Nishikawa, K., Watanabe, M., Kita, E., Igai, K., Omata, K., Yaffe, M.B., and Natori, Y., A
multivalent peptide library approach identifies a novel Shiga toxin inhibitor that inducesaberrant cellular transport of the toxin, The FASEB Journal, 20: 2597-9, 2006.
[2] Tsutsuki, K., Watanabe-Takahashi, M., Takenaka, Y., Kita, E., and Nishikawa, K., Infect.
Immun. 81: 2133-2138 (2013).
[3] Okumura, H., Temperature and pressure denaturation of chignolin: Folding andunfolding simulation by multibaric-multithermal molecular dynamics method , Proteins,
80: 2397-2416, 2012.

P1-10
Molecular simulations on specific interactions between
curcumin derivatives and amyloid-β peptide
Shintarou Ota 1 , Hiromi Ishimura 1 , Ryushi Kadoya 1 , Takeru Murakawa 1 , Sergiy Shulga 2 andNoriyuki Kurita 1
kurita@cs.tut.ac.jp
12

Department of Computer Science and Engineering, Toyohashi University of Technology
Institute for Food Biotechnology and Genomics, National Academy of Sciences of Ukraine

Keywords: curcumin; inhibitor; amyloid-β; fragment molecular orbital; protein-ligand docking
Alzheimer's disease (AD) is developed in relation to the accumulation of amyloid -β (Aβ)
peptides in a brain. Aβ peptides are produced from the cleavage of amyloid precursor protein(APP) by both β- and γ-secretases. Among the produced Aβ peptides species, Aβ40 and Aβ42
have a large population, and the amount of Aβ42 is larger than Aβ40 in the brains of the AD
patients. To suppress the production of Aβ peptides, it is effective to inhibit the APP cleavage
process by the secretases. However, the secretases also play key roles in production of other vital
proteins, so secretase inhibitors have an increased side effect risk. Therefore, it has been desired
to develop novel compounds which bind specifically to the cleavage site of APP and inhibit theγ-secretase attacks to APP.
To propose new agents for suppressing the generation of Aβ peptides, we [1] investigated
the interactions between a short APP peptide and derivatives of curcumin, a constituent ofturmeric, which is a spice used in Indian cuisine. We performed molecular simulations based onprotein-ligand docking as well as ab initio fragment molecular orbital (FMO) methods andinvestigated the specific interactions between APP and curcumin I, II and III. The results
simulated reveals that curcumin I binds to APP in a way as it covers over the γ-secretase cleavage
site of APP, although the binding of curcumin I with APP is not so strong. It is thus expected that
curcumin I can suppress the cleavage of APP by γ -secretase.
In the present study, in order to enhance the binding between APP and curcumin I, we
proposed novel curcumin derivatives by introducing some groups into curcumin I andinvestigated the specific interactions between these derivatives and APP. The results reveal thatthe introduction of CH 3 groups into the central part of curcumin I significantly enhances thebinding between APP and curcumin I. It is thus expected that the curcumin I derivative covers
over the γ-secretase cleavage site of APP to be a potent inhibitor for the production of Aβ40 andAβ42 peptides. The details of our molecular simulations and the results simulated will be shown inthe conference.
[1] H. Ishimura et al., Chem. Phys. Lett. 633, 139-145 (2015).

P1-11
Fragment Molecular Orbital Calculations on theInteractions of Enzyme Lipases and Organic
Compounds:Elucidation of the Enantioselectivity andReactivity of Lipases in Organic Synthesis
Yoichiro Yagi 1

Takatomo Kimura 2

yagi@sp.ous.ac.jp

kimura@konankako.co.jp

Makoto Kamezawa 2
makoto.kamezawa@konankako.co.jp

Yoshinobu Naoshima 1
naoshima@sp.ous.ac.jp
1

2

Research Institute of Natural Sciences, Okaya ma University of Science, 1-1 Ridai-cho,
Kita-ku, Okayama 700-0005, Japan
Konan Chemical Industry Co., Ltd., 5-12 Nakagawa-cho, Takatsuki-shi, Osaka 569-0066,
Japan

Keywords: FMO calculation, lipase, enantioselectivity, organic synthesis, azulene
Over the last several years we have been focusing our attention on the biomolecular
computational simulations of the complexes of lipases with organic compounds to elucidate theenantioselectivity and reactivity of the enzyme in organic synthesis . We have recently shown thatboth molecular dynamics (MD) and fragment molecular orbital (FMO) calculations are usefulmeans for the prediction of the enantioselectivity of Burkholderia cepacia lipase (BCL) [1, 2] andCandida antarctica lipase typeB (CALB) [1] toward a variety of aliphatic and aromatic alcohol
esters. This presentation describes the FMO calculations on the interactions of Candida antarctica
lipase typeA (CALA) and the enantiomers of trifluoroazulene alcohol esters. Azulene and its
derivatives are composed of a characteristic fused five-seven bicyclic aromatic ring system and are
typical nonbenzenoid aromatic hydrocarbons. Azulenes are of interest because of the expectedreaction specificities and physicochemical and pharmaceutical properties. [3]CALA-azulene alcohol esters complexes, like BCL- and CALB-aliphatic and aromatic alcohol
esters ones, in the presence of TIP3P water molecules were subjected to MD calculations over a
period of 2000ps with ABMER11. After MD calculations, surrounding water molecules were
removed and the FMO computations of the resulting CALA-azulene complexes were carried out at
FMO2-MP2/6-31G level using ABINIT-MP/BioStation program.
We found that for 3-chloro-and 3-bromotrifluoroazulene alcohol derivatives having highenantioselectivity, each fast reacting (S)-enantiomer strongly interacts with a definite amino acidresidue in CALA, that is, with ASP95; those in BCL and CALB were HIS286 and THR40,
respectively. Our FMO computations also indicated that for an azulene alcohol ester possessing
azulenothiophene ring with low enantioselectivity, both (R)- and (S)-enantiomers show interaction
with several identical amino acid residues including ASP95. We have reached a conclusion thatparticular amino acid residues in three different lipases, ASP95 in CALA, HIS286 in BCL, andTHR40 in CALB, can play an important role in the chiral recognition of substrate enantiomers
through the individual lipase-catalyzed biotransformations.
[1] Yagi, Y., Kimura, T., Kamezawa, M., and Naoshima, Y., Biomolecular Computational Simulations
and Organic Synthesis: Prediction of the Enantioselectivity of Lipase-Catalyzed Biotransformations,Chem-Bio Informatics Society Annual Meeting2014, P1-09, 2014.
[2] Yagi, Y., Tanaka, T., Imagawa, A., Moriya, Y., Mori, Y., Kimura, T., Kamezawa, M., andNaoshima, Y., Large-Scale Biomolecular Chemical Computations toward the Prediction of
Burkholderia cepacia Lipase Enantioselectivity, J. Adv. Simulat. Sci. Eng., 1:141-160, 2014.
[3] Naoshima, Y., Kimura, T., Mori, Y., Kamezawa, M., Tachibana, H., Kohara, K., and Ohtani, T., The
first synthesis of chiral azulene alcohols possessing a trifluoromethyl group by lipase -mediatedbiotransformation, Recent Res. Devel. Org. Bioorg. Chem., 6:1-9, 2004.

P1-12
Designing Noise-immune Gates
Tetsuya J. Kobayashi 1
tetsuya@mail.crmind.net
1

Institute of Industrial Science, University of Tokyo,108-8639, Japan

4-6-1 Komaba, Meguro-ku, Tokyo

Keywords: Bayesian inference, optimal decoding, molecular gates
Intracellular reactions are intrinsically noisy because of the small number of molecules and of theextrinsic fluctuation of environment and other factors [1]. Nonetheless, a variety of biologicalsystems and functions operate robustly even though they are implemented by noisy reactions [2].
This fact suggests that intracellular systems and networks are designed so as to be immune to theintrinsic noise from the molecular components. Such design may be employed to artificially design
intracellular reactions networks to conduct various functions. However, little is clarified on thedesign principle of such noise-immune systems. In our previous work, we derived a intracellular
network structure that can robustly sense the environmental change via noisy receptor by
employing Bayesian inference approach.
In this work, I extend our previous work for designing noise-immune gates that operate robustly
even with noise in the input signals to be computed. The properties of this gates are compared withpreviously proposed noise-immune gates based on logical stochastic resonance.
[1] http://www.nature.com/nature/focus/biologicalnoise/index.html, 2010.
[2] Perkins, T. J., & Swain, P. S. ,Strategies for cellular decision-making. Molecular Systems
Biology, 5, 326. doi:10.1038/msb.2009.83, 2009.
[3] Kobayashi T. J., Phys. Rev. Lett. 104: 228104 , 2010.
[4] Kobayashi T. J., and A. Kamimura, Phys. Biol. 8: 055007, 2011.
[5] Kobayashi T. J., Phys. Rev. Lett. 106:228101, 2011.

P1-13
Protein‒ligand binding pathways revealed bycoarse-grained molecular dynamics simulations
Tatsuki Negami

Kentaro Shimizu

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

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

Tohru Terada

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

Department of Biotechnology and Agricultural Bioinformatics Research Unit,
Graduate School of Agricultural and Life Sciences, The University of Tokyo,
1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan

Keywords: coarse-grained molecular dynamics simulation, protein ‒ligand binding, ligand-binding
pathway
Clarifying the mechanism of protein‒ligand interactions is one of the most important researchsubjects in computational chemistry and biology. However, most of the research efforts have been
devoted to predicting docked structures. The process of the ligand binding remains to be clarified.
Molecular dynamics (MD) simulation is a straightforward way to study the ligand -binding process.
However, reproducing the ligand-binding process in an all-atom MD simulation is still difficult
because it requires a very long time simulation. For this reason, we have explored the possibility of
the coarse-grained simulation.
Recently, we have shown that the ligand-binding processes can be reproduced in coarse -grained(CG) MD simulations with the MARTINI force field [1]. In this study, we performed CGMD
simulations for two protein–ligand pairs that have different physicochemical and structuralproperties. To make a complete comparison between protein –ligand pairs with different properties,
we classified all the protein–ligand complex structures in PDB into groups according to theseproperties, and chose two representative protein–ligand pairs from each group. For each pair, 1–5
microsecond CGMD simulations were performed 50 –100 times with different initial ligand
placement. For all the protein‒ligand pairs, the ligand molecules entered into the correct
ligand-binding pockets. To obtain the details of the ligand binding pathways, we calculated theflow of the ligand molecules on the protein surfaces. The results demonstrated that the CG ligand
molecules entered the ligand-binding pockets through specific pathways. Based on these results,
we will discuss the determinant of the ligand-binding pathway in relation to the physicochemicaland structural properties of the ligand and the protein surface.
[1] Negami, T., Shimizu, K., and Terada, T., Coarse-Grained Molecular Dynamics Simulations ofProtein‒Ligand Binding, Journal of Computational Chemistry, 35:1835-1845, 2014.

P1-14
Elucidation of transition state for the interactions
between ERK2 and inhibitors using SPR
Sou Yamaguchi 16711877632@mail.ecc.u-tokyo.ac.jp

Satoru Nagatoishi 1,2
nagatoishi@bioeng.t.u-tokyo.ac.jp

Kouhei Tsumoto 1,2,3
tsumoto@bioeng.t.u-tokyo.ac.jp

1

2

3

Department of Bioengineering, School of engineering, The University of Tokyo, 7 -3-1,
Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
Drug Discovery Initiative DDI, The University of Tokyo, 7 -3-1 Hongo, Bunkyo-ku,
Tokyo 113-8656, Japan
Institute of Medical Science, The University of Tokyo, 4 -6-1, Shirokanedai, Minato-ku,
Tokyo 108-8639, Japan

Keywords: Protein-small compound interaction, Transition state, Thermodynamic analysis
To develop drug discovery of small compounds, it is important to elucidate how small
compounds interact with protein. The purpose of this study is elucidating the process ofprotein-small compound interactions and proposing to the design of small-molecule drugs
using the process of small-molecule bindings.
To date, protein-small compound interactions have been evaluated in term of their final state
where a small compound is bound to protein. However, it is difficult to design compounds
which can bind to target protein specifically, because many binding sites of protein arepreserved among proteins [1]. This study focuses on the process of the interaction. Since it isassumed that a small compound forms some intermediate states with various amino acids andfinally binds to the energy-stable site of protein, the process is possibly differ among proteins.
It has been reported that the in silico studies have also proposed the possibilities, which isimportant for efficiency of drugs, and specificity of compound to t arget protein. Here, thebinding kinetic analysis was carried out to elucidate the binding process of the compound toprotein. In this study, complexed energy transition in the binding process [2] is simplified as
two state reaction and the state where a compound is in process is regarded as transition state.
From the analysis of thermodynamic parameters in transition state, the transition state is
examined at amino acid level and the relationship between transition state and binding process
is discussed. This allows us to verify the possibility of obtaining an important indicator for
drug design.
[1] D. Shukla, Y. Meng, “Activation pathway of Src kinase reveals intermediate states astargets for drug design”, Nat. Commun., 5, 3397(2014).
[2] F. Bai, Y. Xu, “Free energy landscape for the binding process of Huperzine A toacetylcholinesterase”, Proc. Natl. Acad. Sci., 110, 4273–4278 (2013).

P1-15
Tautomeric reactions of DNA base pairs induced by
H and OH radicals: DFT calculations in water
Naoko Okutsu 1

Eisuke Shimizu 1

Victor I Danilov 2

okutsu@klab.cs.tut.ac.jp

Sergiy Shulga 2

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

1
2

Toyohashi University of Technology, Toyohashi, Aichi, 441 -8580, Japan
National Academy of Sciences of Ukraine, Kyiv, Ukraine

Keywords: DFT, Electronic states, Molecular orbital calculation, DNA base pair, Radical,
Genetic information recorded in DNA can be damaged indirectly by radiations. Theirinteractions to the water molecules in living organs produce the ionization of water molecules,
leading to highly reactive H and OH radicals. These radicals react with organic cellularcomponents, especially DNA and lipids, so that DNA is damaged in its structure and electronic
properties seriously. OH radicals can reach an active site of DNA duplex and remove a hydrogenion from the sugar moiety or the nucleobase of DNA. As a result, the native double-helicalstructure of DNA is changed, leading to errors in the genetic information stored in DNA [1]. It isthus important to understand the change in DNA structure induced by the radical attack forrevealing the indirect effect of radicals on DNA.
To elucidate the effect of radicals, we have investigated the attacking mechanism of OH -radical
to the G-C and A-T base pairs, using the density functional theory (DFT) calculations [2]. The
effect of solvation on the mechanism was also revealed by pe rforming the same DFT calculations
under the continuum solvation approximation. The results elucidated that the hydrogen atom of
NH 2 group of G or A base is abstracted by the OH radical. The solvation around the base pairstabilizes the dehydrogenated structures significantly, indicating the acceleration of the attacking
reaction by OH-radical to the base pairs in water.
In the present study, we searched for the transition states of the attacking mechanism between
the base-pair and OH/H radicals in water, using the DFT calculations. The results elucidate thatOH radical can cause tautomeric reaction in A-T base pair, while H radical affects significantly on
G-C base pair to cause the tautomeric reaction into G* -C* form, as shown below. The details of thetautomeric reaction paths and their energetics will be described at the meeting.

(a) G-C+H-radical(b) A-T+OH-radical
Figure Chang in structure during tautomeric reaction for (a) G -C+H-radical and (b) A-T+OH-radical[1] M. S. Cooke, et al., FASEB J., 17, 1195-1214(2003).
[2] E. Shimizu, et al., J. of Biomolecular Structure and Dynamics,33,158-166(2015).

P1-16
Specific interactions between amyloid-β peptides
in three-fold symmetric hexamer:
ab initio molecular simulations
Hiromi Ishimura
Ryushi Kadoya

Shintarou Ota
Noriyuki Kurita

kurita@cs.tut.ac.jp

Depart. Computer Science & Engineering, Toyohashi University of Technology

Keywords: amyloid-β; fragment molecular orbital; protein-protein interaction; aggregation
Alzheimer's disease (AD) is most common symptoms of dementia, and the number of patients
increases remarkably all over the world, and the development of the cure and therapeutic drugs is
an urgent research theme now. The cohesion of amyloid-β peptides (Aβs) in the brain is related tothe onset of AD. Therefore small molecules inhibiting the Aβ cohesion are expected to be useful as
AD therapeutic drugs. A diagrammatical view of the cohesion process was elucidated by thesolution NMR analysis. However, it is not possible to elucidate the process at an atomic level by
only the experimental analysis. This fact is a bottleneck in developing effective inhibitors for Aβcohesion. To solve the problem, many classical molecular dynamics (MD) simulations have been
performed to analyze the tertiary structures of Aβ monomer, dimer and fibrils at an atomic level.
For example, MD simulations were performed for the pair of Aβ-fibrils [1] to predict the growthmechanism of Aβ fibrils. In addition, MD simulations were carried out for the Aβ-fibrils with athree-fold symmetric conformation, whose structure was obtained from the senile plaques of the
AD patient [2]. However, since a molecular force field was used in these MD studies, the relative
stability among the MD snapshots cannot be analyzed accurately. To predict the most stableconformation properly, ab initio molecular orbital (MO) calculations are necessary to be done formany conformations of Aβ fibrils.
In the present study, we performed ab initio fragment MO (FMO) calculation for the three-foldsymmetric structure of Aβ-fibrils obtained by the solid-state NMR [2]. Based on the results of the
FMO calculation, we investigated the specific interactions between Aβ monomers in the Aβ-fibrils
and elucidated which amino acid residues of Aβ monomers are important for stabilizing theAβ-fibril structure with a three-fold symmetry. The results will be useful for proposing novel
inhibitors to Aβ aggregation.
[1] Anna Kahler, Heinrich Sticht, Anselm H. C. Horn, Conformational Stability of Fibrillar
Amyloid-Beta Oligomers via Protofilament Pair Formation – A Systematic Computational Study,
PloS one, 8 (2013) e70521.
[2] Erik J. Alred, Malachi Phillips, Workalemahu M. Berhanu and Ulrich H. E. Hansmann, On thelack of polymorphism in Aβ-peptide aggregates derived from patient brains , Protein Science, 24(2015) 923-935.

P1-17
Acceleration of Protein-ligand Binding Energy Calculation
Using Fragment Molecular Orbital Method
Takao Otsuka 1

Noriaki Okiomoto 1

totsuka@riken.jp

okimoto@riken.jp

Makoto Taiji 1
taiji@riken.jp

1

RIKEN Quantitative Biology Center (QBiC), 1-6-5 Minatojima-minamimachi, Chuo-ku, Kobe,
Hyogo 650-0047, Japan

Keywords: electronic structure calculation, fragment molecular orbital method, protein-ligand
binding energy
Computational prediction of protein-ligand binding affinities is so important for rational drug
design. Especially computational methods using molecular docking and/or molecular dynamics
simulation are central methods for evaluation of protein -ligand binding affinity, and are expected
to improve the efficiency of the drug development in the near future [1].
Recently first-principles (or ab initio) electronic structure calculations are also used in this field
to evaluate accurate protein-ligand interactions. However, the electronic structure calculations areoften not available for the large molecular system because of its high computational costs. One of
the most popular calculation methods of electronic structure for large biomolecular system is thefragment molecular orbital (FMO) method proposed by Kitaura and co -workers [2]. Nowadays
many studies on protein-ligand interactions by FMO method have been reported [3].
In this study we report our recent proposed acceleration scheme for the binding energy
calculations of protein-ligand systems by using FMO method, which is based on a multilayer FMO
method focusing on the protein-ligand interaction distance [4]. Our acceleration scheme reducescomputational costs, while maintaining accuracy in the evaluation of binding energy.

[1] Gilson, M. K., Zhou, H., Annu. Rev. Biophys. Biomol. Struct., 36, 21-42, 2007.
[2] Kitaura, K., Ikeo, E., Asada, T., Nakano, T., Uebayasi, M., Chem. Phys. Lett., 313, 701, 1999.
[3] For example, Tanaka, S., Mochizuki, Y., Komeiji, Y., Okiyama, Y., Fukuzawa, K., Phys. Chem.
Chem. Phys., 16, 10310-10344, 2014.
[4] Otsuka, T., Okimoto, N., Taiji, M., J. Comput. Chem., 2015, DOI: 10.1002/jcc.24055

P1-18Specific interactions between M.tuberculosis CYP130 andeconazole derivatives:
Molecular docking and ab initio FMO calculations
Ryushi Kadoya 1 , Ryosuke Takeda 1 , Mitsuki Fujimori 1 , Hiromi Ishimura 1 , Pharit Kamsri 2 ,Pijittra Meewong 2 , Pornpan Pungpo 2 and Noriyuki Kurita 1
kurita@cs.tut.ac.jp
12

Department of Computer Science and Engineering, Toyohashi University of Technology,
Tenpaku-cho, Toyohashi, Aichi, 441-8580, Japan
Department of Chemistry, Faculty of Science, Ubon Ratchathani University,
85 Sthollmark Rd., Warinchamrap, Ubon Ratchathani 34190, Thailand

Keywords: Tuberculosis, Econazole, Cytochrome P450, CYP, Molecular docking,Fragment molecular orbital, Protein-ligand interaction, Molecular mechanics.
Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (M.
tuberculosis). In 2013, 9 million people were infected with TB, and 1.5 million were died from it.
This high rate of TB deaths was mainly caused from the strong drug -resistant of TB as well as
the concurrent illness of TB and HIV. Among the commercially available drugs for TB, ananti-tubercular rifampicin induces the expression of drug -metabolizing cytochrome P450 (CYP)
enzymes, which reduce the blood level of the anti -tubercular as well as the anti-HIV agents. As aresult, the effects of these agents are decreased by the simultaneous usage of rifampicin. It isthus needed to inhibit the expression of CYP enzymes under the existence of rifampicin.
The previous experiments [1,2] elucidated that azole antifungal agents can coordinate to the
Fe ion of HEME and inhibit the CYP function. For example, econazole was found to bind to CYP
and inhibit the growth of M. tuberculosis. In addition, various econazole derivatives such as
nitroimidazole and 1H-1,2,3-triazole were synthesized and their anti-TB functions were analyzed.
In the present study, to propose novel agents with large binding affinity with CYP130, weinvestigated the specific interactions between CYP130 and econazole as well as some azole
compounds at atomic and electronic levels, using molecular simulations based on protein-ligand
docking, classical molecular mechanics (MM) and ab initio fragment molecular orbital (FMO)
methods. The FMO results elucidate which residues of CYP130 and which parts of the ligands areimportant for the binding between CYP130 and the ligands. Based on the results, we designed
novel agents listed in Table1 and investigated their binding energies and specific interactions with
CYP130. The results will be shown in the poster.
Table1 Our proposed novel agents for CYP130
LigandEconazole
Eco-COH
Eco-OH
C-36
C-36-CN
C-36-NO
C-36-O
C-36-OH

R1
R2
2,4-diCl-Phenyl CH2-4-Cl-Phenyl
2,4-diCl-Phenyl CH2-4-Cl-Phenyl
2,4-diCl-Phenyl CH2-4-Cl-Phenyl
4-PhenylH
4-PhenylH
4-PhenylH
4-PhenylH
4-Phenyl
H

R3
H
H
H
NO2
NO2
NO2
NO2
NO2

R4
H
COH
OH
H
H
H
H
H

R5
H
H
H
H
CN
NO
O
H

[1] Z. Ahmad, et al., FEMS Microbiol. Lett., 2005, 251, 19.
[2] K.J. McLean, et al., Microbiology, 2002, 148, 2937.

R6
H
H
H
H
H
H
H
OH

X
CH
CH
CH
C-OCH3C-OCH3C-OCH3C-OCH3C-OCH3

P1-19Changes in conformation and electronic states of CAP dimer induced byligand-binding: molecular dynamics and FMO simulations
Kanako Shimamura, Ittetsu Kobayashi, Yuki Matsushita, Noriyuki Kurita*
Toyohashi University of Technology, *E-mail: kurita@cs.tut.ac.jp
Keywords: catabolite activator protein (CAP), MD, fragment MO, transcription, cAMP
1. Introduction
Catabolite activator protein (CAP) plays an important role in the transcription mechanism of genetic
information from DNA to RNA. CAP is activated by the binding of cyclic adenosine monophosphate(cAMP) as ligand, and CAP+cAMP complex binds specifically to near the transcription domain of DNA. As
a result, RNA polymerase can bind specifically to the binding site to start the transcription. In the present
study, we investigated the change in structure of CAP-dimer+DNA complex in water induced by the cAMPbinding, using classical molecular dynamics (MD) simulations. In addition, we elucidated the specificinteractions between CAP-dimer, DNA and cAMP at an electronic level, using ab initio fragment molecularorbital (FMO) calculations.
2. Details of molecular simulations
We obtained the structure of CAP-dimer+cAMP+DNA complex (PDB ID: 1ZRC) from PDB, and thedefect of amino acid residues of CAP was complemented. Because PDB structure has no information of H
atoms, we add H atoms as well as counter ions around DNA backbones. The structure was optimized inwater by classical molecular mechanics (MM) calculation, and MD simulations were carried out for 100 ns
under the conditions of temperature 300K and pressure 1 atom. For some MD snapshots, electronic states
were evaluated by ab initio FMO method, in order to elucidate the change in specific interactions.
3. Results and discussion
To elucidate the structural change, we first analyzed the Root Mean Square Distance (RMSD) between theinitial structure and the MD snapshot for the Cα atoms of CAP residues. RMSD became the largest 3.57 Å at
10.6 ns, and it became larger than 3.0 Å at several snapshots, indicating significant structural change of
CAP-dimer during the MD simulation. In particular, at 70.7 ns, the CAP-dimer conformation deforms
significantly from the initial one, and the displacement of the residues around Pro160 of CAP monomer-1
12

shown in Figure 1. Since these residues

10

are in the DNA binding domain of CAP,
the interactions between CAP and DNA
are expected to be changed significantly.
The details of structural change andelectronic states of CAP-dimer+DNA
complex evaluated by FMO will bepresent at the meeting.

Displacement [Å]was found to be remarkably large as

8
6
4
2
0

8

100

207

Amino acid residues of CAP M1
Figure 1

8

100

207

Amino acid residues of CAP M2

Displacement of Cα between the initial and 70.7 ns

P1-20
Molecular dynamics study of conformation change incalmodulin
Hiromitsu Shimoyama 1
shimoyamah@pharm.kitasato-u.ac.jp

Mayuko Takeda-Shitaka 1
shitakam@pharm.kitasato-u.ac.jp

1

Faculty of Pharmacy, Kitasato University, 9-1, Shirokane 5-Chome Minato-ku, Tokyo 108-8642,
Japan

Keywords: calmodulin, conformation change, conformational fluctuation
Calmodulin (CaM) is a calcium ion binding protein which mediates many essential biologicalprocesses such as fertilization, contraction, cell differentiation and proliferation[1, 2]. The informationof transient Ca 2+ is converted into a variety of biochemical changes by intracellular proteins [1, 3].
Some of these proteins can bind to Ca 2+ by itself, however, others requires intermediate Ca 2+ -binding
proteins such as CaM.
CaM is 148 amino acids long protein which forms approximately 7 -8 α-helices in solution. The Nand C-terminal domains (NTD, CTD) are mutually connected by long linker-region, then, the overall
CaM’s structure is coarsely a symmetrical dumbbell -like shape. The ion binding to CaM causes
conformation change[4, 5].
In order to understand the conformation change, structural fluctuations around holo -state[6] wasstudied by molecular dynamics simulation using GROMACS[7].
[1] Gifford, J., M. Walsh, and H. Vogel, Structures and metal -ion-binding properties of the
Ca 2+ -binding helix-loop-helix EF-hand motifs. Biochem. J, 2007. 405: p. 199-221.
[2] Berridge, M.J., M.D. Bootman, and P. Lipp, Calcium -a life and death signal. Nature, 1998.
395(6703): p. 645-648.
[3] Chin, D. and A.R. Means, Calmodulin: a prototypical calcium sensor. Trends in cell biology, 2000.
10(8): p. 322-328.
[4] Zhang, M., T. Tanaka, and M. Ikura, Calcium-induced conformational transition revealed by thesolution structure of apo calmodulin. Nature Structural & Molecular Biology, 1995. 2(9): p. 758 -767.
[5] Bertini, I., et al., Experimentally exploring the conformational space sampled by domain
reorientation in calmodulin. Proceedings of the National Academy of Sciences of the United States ofAmerica, 2004. 101(18): p. 6841-6846.
[6] Chattopadhyaya, R., et al., Calmodulin structure refined at 1.7 Å resolution. Journal of Molecular
Biology, 1992. 228(4): p. 1177-1192.
[7] Hess, B., et al., GROMACS 4: Algorithms for highly efficient, load -balanced, and scalable
molecular simulation. Journal of Chemical Theory and Computation, 2008. 4(3): p. 435 -447.

P1-21Development of hydration structure prediction method
around the protein with 3D-RISM theory
Shunsuke Chiba 1
pl10027@st2.pharm.kitasato-u.ac.jp

Yasuomi Kiyota 1
kiyotay@pharm.kitasato-u.ac.jp

Mayuko Takeda-Shitaka 1
shitakam@pharm.kitasato-u.ac.jp

1

School of Pharmacy, Kitasato University, 5-9-1 Shirokane, Minato-ku, Tokyo 108-8641, Japan

Keywords: Hydration structure, 3D-RISM method, Monte Carlo Sampling
Water plays important roles in protein function. In particular, i n the process of molecular
recognition, it is necessary to consider the hydration and dehydration. Determination of hydration
structure is a useful approach to analyze this influence. For predicting the hydration structure,
there are many theoretical methods like MD simulation, but these methods have complex processes
because of higher entropy of water. 3D-RISM (3-Dimensional Reference Interaction Site Model)
theory, the integral equation theory of liquids, can calculate the hydration structure as a3-dimensional distribution function (3D-DF), directly [1,2]. The 3D-DF, however, is difficult toimage the molecular shape in hydration structure [3]. Therefore, we developed a sampling method
for protein hydration structures based on the 3 D-DF with Inversed-transformed Monte-Carlosimulation. This method can reproduce the conformation of hydration structure accurately.
Our method gives higher prior conformation of water based on
the 3-dimensional DF obtained from 3D-RISM calculation. The
position where the peak of water tends to appear would be
replaced to explicit water molecule in accordance with theInversed-transformed Monte-Carlo method. The 3-dimensional
DF is the probability density function, then the peak heightindicates the degree of appearance and it can be used as theweight in the Monte-Carlo calculation.
In order to evaluate the prediction ability of our method, weapplied our method to 112 globular-protein structures which had
been solved by X-ray diffraction as monomer. For comparison of
Figure 1. A result of
the predicted and experimental hydration structures, we ca lculate
predicted hydration structure
the RMSDs between them. One of the results from accurate
aroundprotein,
Serinesamples is shown in Figure 1. It was displayed that the method
Protease (PDB ID: 1ARB).
can reproduce experimental hydration structure. Most predicted
The color code is assigned
hydration structures demonstrate small RMSDs whose range isto predicted (black) and
X-ray(white)waterroughly from 1.5 Å to 2.5 Å.
molecules.
References:[1] Kovalenko, A., Hirata, F., J. Chem. Phys., 110 10095 (1999).
[2] Kiyota, Y., Takeda-Shitaka, M., J. Phys. Chem. B., 118, 11496 (2014).
[3] Sindhikara, D. J. and Hirata, F., J. Phys. Chem. B, 117, 6718 (2013).

P1-22
Molecular dynamics simulation of a protein-ligand
binding process with conformational changes
Takashi Mitsui 1,2

Hajime Sugiyama 2tmitsui@jp.fujitsu.com

sugiyama.hajime@jp.fujitsu.com

Hideaki Fujitani 1
fjtani@lsbm.org

1

2

Laboratory for System Biology and Medicine, Research Center for Advanced Technology, The
University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan
Bio-IT R&D Office, Research & Development Division, Next-generation Healthcare Innovation
Center, Fujitsu Limited, 1-17-25 Shin-kamata, Ohta-ku, Tokyo 226-8503, Japan

Keywords: Molecular dynamics, Ligand docking
Understanding the physical process of protein-ligand binding has significant importance instructure-based drug design [1, 2]. In order to capture the binding processes of a protein-protein
interaction inhibitor and its receptor, we performed 600 unguided molecular dynamics simulationsof 300 ns length for the system of Mcl-1 and its ligand on K computer.
The initial structure of Mcl-1 was obtained from the crystal structure of PDB code 4HW4 which
is a complex structure with a peptide ligand. The ligand was obtained from PDB code 4HW2, andplaced by a distance of 10Å from the binding groove of Mcl -1. Bound structures in the groove
were observed in 473 trajectories of 600 MD simulations, and the binding poses within RMSD of 1
Å from the X-ray cocrystal structure were observedin 25 trajectories. One of typical ligand binding
processes found in this study was as follows: aindole ring approached to the binding groove, thensalt bridges were formed between a carboxy groupof the ligand and a side chain of Arg263 in Mcl -1,
finally the ligand changed its conformation and 4chloro-3,5-dimethylphenoxy group of the ligand
bound to the deep P2 pocket in the binding groove.

[1] Y. Shan, E. T. Kim, M. P. Eastwood, R. O. Dror, M. A. Seeliger,and D. E. Shaw, “How Does a
Drug Molecule Find Its Target Binding Site?”, J. Am. Chem. Soc., 133, 9181 (2011)[2] I. Buch, T. Giorgino, and G. De Fabritiis, “Complete reconstruction of an enzyme -inhibitor
binding process by molecular dynamics simulations ”, Proc. Natl. Acad. Sci., 108, 10184 (2011)

P1-23
Binding Affinity Prediction of Protein-Protein Complexes
Based on MM/3D-RISM Method
Narumi Sai 1

Yasuomi Kiyota 1

pl10017@st2.pharm.kitasato-u.ac.jp

kiyotay@pharm.kitasato-u.ac.jp

Mayuko Takeda-Shitaka 1
shitakam@pharm.kitasato-u.ac.jp

1

School of Pharmacy, Kitasato University, 5-9-1 Shirokane, Minato-ku, Tokyo 108-8641, Japan

Keywords: Protein-protein interaction, Binding free energy, MM/3D-RISM method, Protein
structure prediction
Protein-protein interactions (PPIs) play important roles in biological processes. In order tounderstand the binding mechanism of PPIs, quantitative information about protein-protein binding
affinity is essential. Therefore, computational methods topredict binding affinity are valuable. In the present work, wedeveloped a system for predicting binding free energies ofprotein-protein complexes using structure-based approach. In
this system, Template Based Modeling (TBM) method isused for preparing protein-protein complex structures ofvarious mutants, and MM/3D-RISM method is used forbinding free energy calculation. 3D-RISM theory is a
powerful tool for studying solvation thermodynamics andproperties of a solvation structure.
Figure 1 shows the prediction scheme of our system. First,
based on a template structure of a protein-protein complex,
monomer and complex model structures of various mutants
are constructed by TBM method. Next, binding free energiesof the various mutants are calculated by MM/3D-RISM
method based on the obtained model structures. Evaluationof this system shows that the predicted energies are in good
agreement with the experimental data. MM/3D-RISM
method improves the accuracy of binding free energy
prediction.
References:[1] Kiyota, Y. and Takeda-Shitaka, M., J. Phys. Chem. B.,
118:11496-11503, 2014.
[2] Kovalenko, A. and Hirata, F., J. Chem. Phys.,
110:10095−10112, 1999.

Figure 1. Prediction scheme.
Barnase(bn)-barstar(bs) complex
is shown as an example.

P1-24Novel insights into drug hypersensitivity by long-term
molecular dynamics simulations.
Takahiro MURAI 1

Norihito KAWASHITA 1,2

u879782i@ecs.osaka-u.ac.jp

kawashita@gen-info.osaka-u.ac.jp

Yu-Shi TIAN 3
yushi-tian@ist.osaka-u.ac.jp

1

2

3

Tatsuya TAKAGI 1,2
ttakagi@phs.osaka-u.ac.jp

Graduate School of Pharmaceutical Sciences, Osaka University, 1 -6 Yamadaoka, Suita, Osaka
565-0871, Japan
Research of Institute for Microbial Diseases, Osaka University, 3 -1 Yamadaoka, Suita, Osaka
565-0871, Japan
Graduate School of Information Science and Technology, Osaka University, 1 -5 Yamadaoka,
Suita, Osaka 565-0871, Japan

Keywords: drug hypersensitivity, HLA, abacavir, MD simulation
The discovery that several drug hypersensitivity reactions (DHRs) are associated with specifichuman leukocyte antigen (HLA) alleles attracted increasing interest [1, 2]. However, theunderlying mechanisms of these HLA-induced DHRs, especially those of drug-induced immediate
activation of T cell clones (TCCs) remain unclear. Recently a novel hypothesis, the partial
detachment between HLA molecule and self-peptide(s), is proposed to explain this unresolved
mechanism [3]. Here in order to validate this hypothesis long-term molecular dynamics (MD)simulations were performed.
We focused on HLA-B*57:01-restricted abacavir (ABC) hypersensitivity, one of the most famous
DHRs. Firstly the models of four self-peptides were constructed and next complexes of theseself-peptides and the HLA-B*57:01-peptide were generated. Subsequently we performed 500ns
MD simulations of all these complexes.
One of the simulation results showed that the distance between the specific residues of
HLA-B*57:01 and those of self-peptide(s), which are implicated in ABC binding, was changed tobe larger during the period of simulations. A detailed analysis also revealed that the distance
between α1 and α2 helix of HLA-B*57:01 became large enough to allow the entrance of ABC into
the peptide binding cleft. These results implied that some self-peptides are partially dissociatedfrom HLA-B*57:01, which enables some ligands to accommodate and stabilize the peptide binding
cleft. Our findings provided novel insights into ABC-induced immediate activation of TCCs andthese findings might also be applied to other DHRs, such as HLA-B*58:01-restricted allopurinol
hypersensitivity reactions.
This work was supported by the TSUBAME grand-challenge program and the numericalcalculations were carried out on the TSUBAME2.5 supercomputer in Tokyo Institute ofTechnology.
[1] Pavlos, R., et al., T cell-mediated hypersensitivity reactions to drugs, Annu Rev Med,
66:439-454, 2014
[2] White, K.D., et al., Evolving models of the immunopathogenesis of T cell -mediated drug
allergy: The role of host, pathogens, and drug response, J Allergy Clin Immunol, 136:219-234,
2015[3] Yun, J., et al., Oxypurinol directly and immediately activate s the drug-specific T cells via thepreferential use of HLA-B*58:01, J Immunol, 192:2984-2993, 2014

P1-25Comprehensive Analysis of Protein-Ligand Interactionsin Estrogen Receptor α
Using Fragment Molecular Orbital Method
Satoshi Anzaki 1

Chiduru Watanabe 2

Yoshio Okiyama 2

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

chiduru.watanabe@riken.jp

yoshio.okiyama@riken.jp

Teruki Honma 2

Kaori Fukuzawa 3

Shigenori Tanaka 1honma.teruki@riken.jp

fukuzawa.kaori@nihon-u.ac.jp

tanaka2@kobe-u.ac.jp

1

2

3

Department of Computational Science, Graduate School of System Informatics, Kobe
University, 1-1, Rokkodai, Nada-ku, Kobe, Hyogo 657-8501, JapanRIKEN Center for Life Science Technologies, 1 -7-22 Suehiro-cho, Tsurumi-ku, Yokohama,
Kanagawa 230-0045, Japan
School of Dentistry at Matsudo, Nihon University, 2 -870-1 Sakaecho-Nishi, Matsudo,
Chiba, 271-8571, Japan

Keywords: Estrogen receptor α (ERα), Fragment molecular orbital (FMO) method, Visualized
cluster analysis (VISCANA)
Estrogen receptors play important roles in genesis of reproductive functions and multiplicationsof cells. Estrogen receptor α (ERα) binds either agonists which have the same functions as
estrogens or antagonists which do not. In a previous study[1], we have analyzed a positional
difference of helix12 between agonist complexes and antagonist complexes.
In this research, we calculate protein-ligand interactions with fragment molecular orbital (FMO)
method[2] comprehensively after preparing computational models using 22 PDB entries with IC 50
values under the standardized condition (residue complementation, hydrogen addition, energy
minimization). Moreover, using FMO method, we obtain the inter-fragment interaction energy(IFIE) based on quantum chemical calculations which can incorporate electron correlationenergy[3]. We study the correlation of the IFIE with IC 50 and the binding specificities of ligandswith visualized cluster analysis (VISCANA)[4] for drug design.
Acknowledgment: This work is supported by the FMO Drug Design Consortium (FMODD) and the“Construction of platform of FMO-based drug discovery using HPCI system” project (hp150160)
using K computer.
[1] Watanabe, C., et al., Charge Clamps of Lysines and Hydrogen Bonds Play Key Roles in the
Mechanism to Fix Helix 12 in the Agonist and Anta gonist Positions of Estrogen Receptor alpha:Intramolecular Interactions Studied by the Ab Initio Fragment Molecular Orbital Method, J. Phys.
Chem. B, 118(19):4993-5008, 2014.
[2] Tanaka, S., et al., Electron-Correlated Fragment-Molecular-Orbital Calculations for Biomolecular
and Nano Systems, Phys. Chem. Chem. Phys. 16:10310-10344, 2014
[3] Fukuzawa, K., et al., Molecular Interactions between Estrogen Receptor and Its Ligand Studied by
Ab Initio Fragment Molecular Orbital Method, J. Phys. Chem. B, 110:16102-16110, 2006
[4] Amari, S., et al., VISCANA: Visualized Cluster analysis of Protein-Ligand Interaction Based on the
ab Initio Fragment Molecular Orbital Method for Virtual Ligand Screening , J. Chem. Inf. Model.,46:221-230, 2006.

P1-26
Analysis of Interaction between Glucagon-like Peptide-1
and Lipid Bilayers by Molecular Dynamics Study
Sakiko Mori 1

Yamada Hironao 1

Yoh Noguchi 1

s086232@toyaku.ac.jp

s077112@toyaku.ac.jp

s086181@toyaku.ac.jp

Takeshi Miyakawa 1

Ryota Morikawa 1

Takuya Watanabe 1

takeshi@toyaku.ac.jp

morikawa@toyaku.ac.jp

watanabe@toyaku.ac.jp

Masako Takasu 1

takasu@toyaku.ac.jp

1

School of Life Sciences, Tokyo University of Pharmacy and Life Sciences, Horinouchi,
Hachioji, Tokyo 192-0392, Japan

Keywords: Molecular dynamics, Peptide-bilayer interaction, Glucagon-like peptide-1,
Phospholipid membrane
Glucagon-like peptide-1 (GLP-1), a 30-amino acid peptide hormone, promotes insulin secretion
to lower plasma glucose levels in pancreas. When GLP-1 binds to GLP-1 receptor, which is a class
B G protein-coupled receptor (GPCR), GLP-1 shows single-stranded α-helical structure [1]. On theother hand, GLP-1 is highly flexible and the α-helicity is small in physiological saline [2]. It isinteresting to study how α-helical structure of GLP-1 is formed in the circulating blood. Two-stepligand transportation model [3] is a scheme to transport a ligand, which is in α-helical state, to thereceptor. In this model, a ligand binds to the cell membrane before binding to class B GPCR, anddiffuses along the membrane surface. Similarly, lipid micelles in the circulating blood may help
the ligand to form α-helix.
We have carried out molecular dynamics (MD) simulations of GLP-1 with dodecylphosphocoline(DPC) micelle to investigate the interaction between GLP-1 and micelle [4]. From the results ofthe simulations, among all the residues of GLP-1, Arg 30 , a C-terminal residue of GLP-1, has
extremely high tendency to make hydrogen bonds with oxygen atoms of DPC lipids. The binding
state of GLP-1 and micelle were stabilized when Arg 30 made hydrogen bonds with DPC lipids.
Since the hydrophobic amino acids of GLP-1 are squeezed into a space in the lipid head groups of
DPC micelle, GLP-1 stuck along the micelle surface and kept the α-helical structure.
We have also performed MD simulations of GLP-1 with phospholipid bilayer in physiologicalsaline to investigate the interaction between GLP-1 and bilayer. We found that GLP-1 also boundto the bilayer. The details of the results will be discussed.
[1] Underwood C. R., Garibay P., Knudsen L. B., Hastrup S., Peters G. H., Rudolph R., and Runge
S. R., Journal of Biological Chemistry, 285:723-730, 2010.
[2] Mori S., Yamada H., Fukuda, M., Fukasawa Y., Miyakawa T., Morikawa R., Watanabe T., andTakasu M., Peptide Science 2013, The Japanese Peptide Society, 285-288, 2014.
[3] Inooka H., Ohtaki T., Kitahara O., Ikegami T., Endo S., Kitada C., Ogi K., Onda H., Fujino M.,
and Shirakawa M., Nature Structural Biology, 8:161-165, 2001.
[4] Mori S., Yamada H., Noguchi Y., Miyakawa T., Morikawa R., Watanabe T., and Takasu M.,
Peptide Science 2014, The Japanese Peptide Society, 213-216, 2015.

P1-27
Ligand Binding Analysis of p38 MAP Kinase with the
Fragment Molecular Orbital Method
Yinglei Sheng 1* , Keiya Maruyama 1 , Hirofumi Watanabe 2 , Yoshio Okiyama 3 , Chiduru
Watanabe 3 , Teruki Honma 3 , Kaori Fukuzawa 4 , Shigenori Tanaka 1*
1
2
3
4

Department of Computational Science, Graduate School of System Informatics, Kobe
University, 1-1, Rokkodai, Nada-ku, Kobe, Hyogo 657-8501, Japan
Education Center on Computational Science and Engineering , 7-1-48,
Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, JapanRIKEN Systems and Structural Biology Center, Tsurumi, Yokohama 230 -0045, Japan
School of Dentistry at Matsudo, Nihon University, 2 -870-1, Sakaecho-nishi, Matsudo, Chiba,271-8587, Japan*email: sheng@eniac.scitec.kobe-u.ac.jp, tanaka2@kobe-u.ac.jp

Keywords: p38 MAP kinase, Interaction energy analysis, Ligand binding analysis, Fragment
Molecular Orbital (FMO) method, IFIE (Inter -Fragment Interaction Energy), Structure preparation
p38 mitogen-activated protein (MAP) kinase [1] plays an important role in the functional
expression of various cellular processes which ca use cell aging and autoimmune diseases, and isactivated by phosphorylation with external stress such as heat, osmotic pressure and ultraviolet
rays. In the present study, we investigate the calculated results of the IFIE [2,3] (Inter-Fragment
Interaction Energy) between p38 MAP kinase and various binding inhibitors using Fragment
Molecular Orbital [4] (FMO) method, as one of the activities of “FMO Drug Design Consortium”
which was established last year. The p38 MAP kinase possesses more than 85 ligand binding
structures in PDB with the activity data on IC 50 . Moreover, p38 MAP kinase is the key for the
database of interaction analysis among the kinase family. [5] This time, the structure preparations
before carrying out the FMO-MP2/6-31G* calculations on the K computer (hp150160 for
industrial use: “Construction of platform of FMO-based drug discovery using HPCI system”) were
performed carefully. Structural optimizations for the added hydrogen atoms were performed byusing MOE [6] with classical force fields (Amber10:EHT and MMFF94x etc.) under a realistic
situation as much as possible on chemical basis: protonated state, bond order of ligands,
complement of missing residues and consideration of crystal water.
References[1] K. P. Wilson, P. G. McCaffrey, K. Hsiao, S. Pazhanisamy, V. Galullo, G. W. Bemis, M. J.
Fitzgibbon, P. R. Caron, M. A. Murcko, M. S. Su, The structural basis for the specificity ofpyridinylimidazole inhibitors of p38 MAP kinase, Chem. Biol., 4: 423, 1997.
[2] T. Nakano, T. Kaminuma, T. Sato, Y. Akiyama, M. Uebayasi, K. Kitaura, Fragment molecularorbital method: application to polypepties, Chem, Phys. Lett., 318: 614, 2000.
[3] S. Tanaka, Y. Mochizuki, Y. Komeiji, Y. Okiyama, K. Fukuzawa, Electron -Correlated
Fragment-Molecular-Orbital Calculations for Biomolecular and Nano Systems, Phys. Chem.
Chem. Phys., 16: 10310, 2014.
[4] K. Kitaura, E. Ikeo, T. Asada, T. Nakano, M. Uebayasi, F ragment molecular orbital method: anapproximate computational method for large molecules, Chem. Phys. Lett., 313: 701, 1999.
[5] D. Pearlman, P. S. Charifson, Are free energy calculations useful in practice? A comparison
with rapid scoring functions for the p38 MAP kinase protein system., J. Med. Chem. 44: 3417,
2001.
[6] Molecular Operating Environment (MOE), Version 2013.08; Chemical Computing Group Inc.:
Montreal, Quebec, Canada, 2013.

P1-28
Molecular dynamics simulation of unbinding pathway

Teruhisa S. Komatsu 1

Noriaki Okimoto1

teruhisa.komatsu@riken.jp

okimoto@gsc.riken.jp

Makoto Taiji1

taiji@riken.jp

1

QBiC, RIKEN, 1-6-5 Minatojima-minami, Chuo-ku, Kobe, Hyogo 650-0047, Japan

Keywords: molecular dynamics simulation, protein-ligand binding affinity
In order to develop a new drug design or to improve the tentative drugs for the target receptor, theprecise estimation of the binding free energies for the various ligands is essential. Aiming at the application
to drug improvements, rather high precision ( around the order of thermal energy k BT=2.5kJ/mol ) for thebinding free energies should be established. Atomistic (classical mechanical) forcefields optimized fororganic molecules have been developed[1-2], and methods to calculate binding free energies by molecular
dynamics simulations are proposed[3-7]. Nevertheless, it seems that the blind use of the result from themolecular dynamics simulation is not yet guaranteed, and there is a room for further investigations toclarify the difficulties and for developing more efficient schemes.
Here we try to simulate unbinding process of protein-ligand system. The typical time scale of suchprocess is order of second. It is far beyond to reach directly from all-atom molecular dynamics simulationby the state of the art computer architecture. Thus a kind of biased sampling method should be employed.
We try molecular dynamics simulation of unbinding pathway by following PaCS-MD method [8] and its
variants and present preliminary results and discuss its application to free energy calculations.
[1] S. J. Weiner, P. A. Kollman, D. T. Nguyen, and D.A. Case, An All Atom Force Field for Simulations ofProteins and Nucleic Acids. J. Comp. Chem. 7, 230-252 (1986).
[2] J.W. Ponder and D.A. Case. Force fields for protein simulations. Adv. Prot. Chem. 66, 27-85 (2003).
[3] Y. Deng and B. Roux, Computations of Standard Binding Free Energies with Molecular Dynamics
Simulations, J. Phys. Chem. B 113, 2234–2246 (2009).
[4 ] J. Wang, Y. Deng, and B. Roux, Absolute Binding Free Energy Calculations Using Molecular
Dynamics Simulations with Restraining Potentials, Biophys. J. 91, 2798-2814 (2006).
[5] H. J. Woo and B. Roux, Calculation of absolute protein–ligand binding free energy from computer
simulations, PNAS 102, 6825-6830 (2005).
[6] D. L. Mobley, J. D. Chodera, and K. A. Dill, Confine-and-Release Method: Obtaining Correct Binding
Free Energies in the Presence of Protein Conformational Change, J. Chem. Theory Comput. 3,
1231-1235 (2007).
[7] H. Fujitani, Y. Tanida, and A. Matsuura, Massively parallel computation of absolute binding free energy
with well-equilibrated states, Phys. Rev. E 79, 021914:1-12 (2009).
[8] R. Harada and A. Kitao, Parallel cascade selection molecular dynamics (PaCS-MD) to generate
conformational transition pathway, J. Chem. Phys. 139, 035103 (2013).

P1-29State Transition Analysis of H-Ras with PaCS-MD
Simulation
Yuta Saito 1

Yuta Hano 1

Kazuhiro J. Fujimoto 2

saito@eniac.scitec.kobe-u.ac.jp hano@eciac.scitec.kobe-u.ac.jp k-fujimoto@hokuriku-u.ac.jp

Takashi Kumasaka 3kumasaka@spring8.or.jp

Shigeyuki Matsumoto 4
smat@med.kobe-u.ac.jp

Fumi Shima 4
sfumi@med.kobe-u.ac.jp
1

2

3

4

Tohru Kataoka 4kataoka@people.kobe-u.ac.jp

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

Department of Computational Science, Graduate School of System Informatics, Kobe
University, 1-1, Rokkodai, Nada-ku, Kobe, Hyogo 657-8501, Japan
Faculty of Pharmaceutical Sciences, Hokuriku University, Ho -3 Kanagawa-machi,
Kanagawa-machi, Kanazawa 920-1181, Japan
Protein Crystal Analysis Division,Japan S ynchrotron Radiation Research Institute(JASRI/SPring-8),1-1-1 Kouto, Sayo, Hyogo 678-5198 Japan
Division of Molecular Biology, Department of Biochemistry and Molecular Biology, Kobe
University Graduate School of Medicin, 7 -5-1 Kusunoki-cho, Chuo-ku, Kobe 650-0017,Japan

Keywords: Ras protein, State transition, Hydrogen bond network, Information entropy, Parallelcascade selection molecular dynamics (PaCS-MD)
Small GTPases Ras (H, K-, and N-Ras), the products of the ras proto-oncogenes function as
guanine nucleotide-dependent molecular switches regulating cell growth, development andapoptosis by cycling between GTP-bound (Ras-GTP) and GDP-bound (Ras-GDP) forms. Ras are
frequently activated in a wide variety of human cancers, making them some of the most promisingtargets for anti-cancer drug development. Ras-GTP adopts two inter-converting conformational
states, state 1 and state 2, corresponding to inactive and active forms, respectively [1,2]. Since thestate 1 conformation possesses the drug-accessible surface pockets between the two switch regions,
named Switch I (32-38) and Switch II (60-65), the information on the transition mechanism
between the states is essential for the efficient development of Ras-specific inhibitors. We
investigated herein the state transition mechanism of H-Ras-GTP using molecular dynamics [3,4].
First, we performed PaCS-MD simulation which was proposed as a molecular simulation
method to yield conformational transition in short time [5]. As a result, we found a conformationaltransition between two states.
Second, we analyzed the relationship between the hydrogen bond network composed of tightly
bound water around Ras protein and the state transition using informational entropy. From theseanalyses, we have found the hydration waters and hydrogen bonds which are associated with thestate transition.
[1] Geyer M, Schweins T, et al., Conformational transitions in p21ras and in its complexes with theeffector protein Raf-RBD and the GTPase activating protein GAP, Biochemistry 35, 10308, 1996.
[2] Shima F, Ijiri Y, et al., Structural Basis for Conformational Dynamics of GTP-bound Ras Protein, J.
Biol. Chem. 285, 22696, 2010.
[3] Kobayashi C, Saito S, Relation between the conformational heterogeneity and reaction cycle ofRas: Molecular simulation of Ras, Biophys. J. 99, 3726, 2010.
[4] Parakash P, Sayyed-Ahmad A, et al., The Role of Conserved Waters in Conformational Transitionsof Q61H K-ras, PLoS Computational Biology. 8, e1002394, 2012.
[5] Harada R, Kitao A, Parallel cascade selection molecular dynamics (PaCS -MD) to generate
conformational transition pathway, J. Chem. Phys. 139, 035103, 2013.

P1-30Electronic-Structure Data Mining for
Optimization of Anti-Tuberculosis Drugs
Manabu Sugimoto 1,2,3

sugimoto@kumamoto-u.ac.jp

1

2

3

Takayuki Kurogi 1

112t1710@st.kumamoto-u.ac.jp

Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami,
Chuo-ku, Kumamoto 860-8555, Japan
Institute for Molecular Science, National Institutes for Natural Sciences (NINS), 38
Nishigo-Naka, Myodaiji, Okazaki 444-8585, Japan
Research Center for Advanced Science and Technology (RCAST), University of Tokyo, 4-6-1
Komaba, Meguro-ku, Tokyo 153-8904, Japan

Keywords: Electronic structure, Data mining, Drug, Tuberculosis
Chemoinformatics tools based on electronic-structure
calculations are developed and applied to investigate importantelectronic characteristics of anti-tuberculosis drugs. Herein we
focus on pyridinamides (Fig. 1) which have been extensively
studied. The electronic descriptors are molecular orbital energy,
excitation energy, dipole moment, ionization energy, electron
affinity, oscillator strength, and transition dipole moment. The
electronic structure calculations were carried out by using the
Fig. 1. Pyrimidine derivatives
density functional theory (DFT) method with the CAM-B3LYP
functional. The 6-31G(d,p) basis set was used in all the
calculations. Molecular structures were fully optimized without any constraints. The spin-singlet
excited states were calculated by using the time-dependent DFT (TDDFT) method. In thiscalculation, the low-lying 30 excited states were calculated.
We carried out the data-mining analysis in which exhaustive search of observing correlation
between each of the calculated electronic descriptors and experimentally measured biologicalactivity data. Firstly we applied singlet regression analysis using various mathematical functions.
No good correlations were observed in this analysis. Therefore, we tried to apply the linear
multiple regression analysis with respect to the electronic descriptors. We were successful toobtain a function reasonably reproducing the experimental IC 50 values for inhibitory activity
related to membrane affinity of pyramidines. In this analysis, it was found that the molecularorbital localized on the benzene moiety should be an important factor, suggesting that its chemical
modification would be effective to improve the pharmacological action.
[1] Doležal, M., Z. Osička, Z. Zitko, J., Kuneš, J., Jampílek, J., Vejsová, M., Buchta, V., and Kráľová,K., Proceedings of the 14th Int. Electron. Conf. Synth. Org. Chem., 14, b006(2010).

P1-31
Sonification of Ligand-Protein Docking Structures:
Quantification of 3D Topological Similarity
Takafumi Inoue 1

112t1710@st.kumamoto-u.ac.jp

1

2

3

4

Manabu Sugimoto 1,2,3,4

sugimoto@kumamoto-u.ac.jp

Faculty of Engineering, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, Kumamoto
860-8555, Japan
Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami,
Chuo-ku, Kumamoto 860-8555, Japan
Institute for Molecular Science, National Institutes for Natural Sciences (NINS), 38
Nishigo-Naka, Myodaiji, Okazaki 444-8585, Japan
Research Center for Advanced Science and Technology (RCAST), University of Tokyo, 4-6-1
Komaba, Meguro-ku, Tokyo 153-8904, Japan

Keywords: Chemoinformatics, Sonification, Protein, Ligand, Docking, Similarity
In Structure-based drug design (SBDD), one of the important issues is to specify topologicalcharacteristics of docking structures. The information is essential to understand structure-function
relationship in various biological phenomena. Herein we propose a new chemoinformatics methodby which three-dimensional topology is represented by sonification. This is a technology of
expressing information by "sound". By expressing the docking structure by sound detected at abinding site of a ligand, the topological information essentially becomes one dimension. This
provides an advantage for description and comparison with other ligands.
Sonification is carried out by defining a unique sound for each amino acid. Its intensity is
modulated to reflect the distance from the target ligand. By implementing the Fourier Transform,
the synthesized sound is converted into the frequency spectrum. Spectral comparison with areference compound provides information about how much the local environment is similar witheach other.
In this presentation, we report an application of the presently developed sofinication method to
the 2VT4 protein [1] which is one of the G protein-coupled receptors. We evaluate similarity inlocal environment in some docked structure with the ligands of P32, NA, Sog, and D10 given in [1].
For there ligands, there are 4, 4, 12, and 2 equivalent docking sites in the 2VT4 protein. It is shownthat our sonification method is successful to distinguish similarity and difference of the ligands
and the binding sites.
[1] Beuming, T. and Sherman, W., Current Assessment of Docking into GPCR Crystal Structuresand Homology Models: Successes, Challenges, and Guidelines, Journal of Chemical
Information and Modeling, 52: 3263-3277, 2012.

P1-32
Interation analysis of cell adhesion inhibitor
by fragment molecular oribital method.
Norihito KAWASHITA 1,2
kawashita@gen-info.osaka-u.ac.jp

Yu-Shi TIAN 3
yushi-tian@ist.osaka-u.ac.jp

Tatsuya TAKAGI 1,2
ttakagi@phs.osaka-u.ac.jp

1

2

3

Graduate School of Pharmaceutical Sciences, Osaka University, 1 -6 Yamadaoka, Suita, Osaka
565-0871, Japan
Research of Institute for Microbial Diseases, Osaka University, 3 -1 Yamadaoka, Suita, Osaka
565-0871, Japan
Graduate School of Information Science and Technology, Osaka University, 1 -5 Yamadaoka,
Suita, Osaka 565-0871, Japan

Keywords: Cell adhesion, Fragment molecular orbital (FMO) method, Inter -fragment interactionenergy (IFIE)
FimH is a cell adhesion factor in adherent-invasive Escherichia coli (AIEC) and uropathogenic
Escherichia coli (UPEC), and it is triggerd cell adhesion by the interaction with mannose in their
host cell. Therefore, compounds inhibiting the interaction become a drug candidate for these
infections and some mannose derivatives are reported. [1, 2] For the drug design of these inhibitors,
we calculated inter-fragment interaction energy (IFIE) by fragment molecular orbital (FMO)
method [3] and determined the strength of a correlation by compared with the experimental value.
In this study, twelve complexes of the FimH and mannose derivatives were used for FMO
calculation. The complexed were prepared by MOE (CCG Inc.) . One crystallization watermolecule was included in the structures. For FMO calculation, ABINIT-MP6.0+ was used forsoftware and the calculation level was MP2/6-31g*. Then we calculated the correlation between
IFIE and the enthalpy change of ligands. The e xperimental data for comparison with the calculated
value were used from the results of Wallens et al. [1]From the result of the calculations, correlation between IFIE of twelve complexes and enthalpy
change ΔH was low and correlation coefficient R was 0.51. However, when we removed threeoutliers and then recalculated the correlation coefficient only using 9 complexes, the coefficient R
was improved 0.90. It was good correlation between IFIE and the enthalpy change. The results
suggested that slight differences of complex structures affected IFIE.
This work is a part of Construction of platform of FMO-based drug discovery using HPCI systemand this research used computational resources of the K computer provided by the RIKEN
Advanced Institute for Computational Science through the HPCI System Research project (Project
ID: hp150160).
[1] Wallens, A., et al., Biochemistry, 51: 4790-4799, 2012.
[2] Brument, S. et al., J. Med. Chem., 56: 5395-5406, 2013.
[3] Kitaura, K., et al., Chem. Phys. Lett., 312: 319-324, 1999.

P2-01
SBDD project database management: sharing theunderlying wisdom behind the data using MOE
Howard Feldman 2 , Akitoshi Okada 1 , Ryoichi Kataoka 1 , Junichi Goto 1 , Paul Labute 2
okada@rsi.co.jp
1

2

Ryoka Systems Inc., Science & Technology Systems Division, Tokyo S kytree East Tower, 1-1-2
Oshiage, Sumida-ku, Tokyo 131-0045, Japan
Chemical Computing Group Inc., 1010 Sherbrooke St. West, Suite #910, Montreal QC, Canada
H3A 2R7

Keywords: Data organization, Data Collection, Project data management, SBDD
In recent years, Structure based drug design (SBDD) research has become more specialized
allowing it to be divided into a research area and perhaps more streamlined. This allows SBDD
research to be carried out more efficiently. However, it can also be noted that the large amount ofdata obtained from one particular research area maybe looked at very differently by the people
from other research area. This difference in perspective may be very meaningful in the SBDD
research. However, as the research area divides, it becomes more difficult to share the information
among the research group. Moreover, the data needs to be pre-processed in a meaningful way(standardized) so that when any people from any research area looks at the data, the data ismeaningful at the same level of standard.
Molecular Operating Environment 1 (MOE) employs database in its system and also carries many
functions that can perform the necessary pre-processing of the structural data (e.g. superposition,hydrogen addition etc.). In the latest version of MOE (MOE 2014.09), new function named MOE
Project was implemented that gathers and pre-processes all sorts of SBDD-related data into one
database. Below diagram outlines the data accumulation MOE performs ( Figure 1). The output is asingle database where all the data is stored.

Figure 1. Automation process MOE project enables in project data accumulation The resulting database contains
crystal structures and physical/experimental properties that are gathered from different types of data.

MOE Project allows sharing the latest and standardized data within the research group very easily.
Data accumulation becomes automated, allowing one to gather the data rapidly. The pre-processingof the data is also carried out as the data is accumulated. The resulting database can be searched byrelevant keywords or values so the necessary data can be easily retrieved. The “data” now becomes“information” allowing the research group to come together and step forward to decision making
with the same level of information provided.
[1] Molecular Operating Environment (MOE), 2014.09; Chemical Computing Group Inc., 1010
Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2014.

P2-02
Target-based Drug Repositioning Using Large-scale
Chemical-protein Interactome Data
Ryusuke Sawada 1
rsawada@bioreg.kyushu-u.ac.jp

Hiroaki Iwata 1
iwata@bioreg.kyushu-u.ac.jp

Yoshihiro Yamanishi 1,2
yamanishi@bioreg.kyushu-u.ac.jp

1

2

Division of System Cohort, Multi-scale Research Center for Medical Science, Medical Instituteof Bioreg-ulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka
812-8582, Japan
Institute for Advanced Study, Kyushu University, 6-10-1, Hakozaki, Higashi-ku, Fukuoka,
Fukuoka 812-8581, Japan

Keywords: drug reposting, drug target protein, drug indication, compound-protein interaction
In recent years, the development of new drugs based on conventional drug discovery processes
has become increasingly difficult [1], as reflected by a decrease in the number of newly approved drugs
worldwide. Drug repositioning is the identification of new drug indications for known drugs, and it has
been recognized as a useful strategy for drug discovery [2], because with drug repositioning, some of
the information on known drugs can be reused. Most drugs may interact not only with the therapeutictarget proteins but also with additional proteins (hereafter referred to as off-targets), resulting in
unexpected side effects [3]. Drug side effects derived from off-targets are undesired, but they may
occasionally be beneficial for new therapeutic indications. Therefore, there is a strong incentive todevelop computational methods to predict both drug targets and drug indications for systematic drug
repositioning [4].
In this study, we developed novel computational methods to predict potential drug targets andnew drug indications for systematic drug repositioning using large-scale chemical-protein interactome
data. We explored the target space of drugs (including primary targets and off-targets) based on
chemical structure similarity and phenotypic effect similarity by making optimal use of millions ofcompound-protein interactions (mainly in ChEMBL [5]). Based on the target profiles of drugs, weconstructed statistical models to predict new drug indications for a wide range of diseases with various
molecular features of diseases (e.g., disease-causing genes, diagnostic markers, disease-related
pathways, and environmental factors). The proposed method outperformed previous methods in termsof interpretability, applicability, and accuracy. Finally, we conducted a comprehensive prediction of the
drug-target-disease association network for 8,270 drugs and 1,401 diseases, and showed biologicallyand clinically meaningful examples of newly predicted drug targets and drug indications. The
predictive model is useful to understand the mechanisms of the predicted drug indications.

[1] DiMasi, J. A. et al., The Price of Innovation: New Estimates of Drug Development Costs,
Journal of health economics, 22:151-185.
[2] Ashburn, T. T. and Thor, K. B., Drug Repositioning: Identifying and Developing New Uses for
Existing Drugs, Nature reviews Drug discovery, 3:673-683[3] Blagg, Julian, Structure-activity Relationships for in Vitro and in Vivo Toxicity, Annual
Reports in Medicinal Chemistry, 41: 353-368[4] Terstappen, G. C. and Reggiani, A., in Silico Research in Drug Discovery, Trends inpharmacological sciences, 22:23-26[5] Gaulton, A. et al., ChEMBL: a Large-scale Bioactivity Database for Drug Discovery, Nucleic
acids research, 40: D1100-D1107

P2-03
A New Ligand-based Virtual Screening Method that uses
an Exhaustive Shape-based Ligand Alignment
Hideyuki Sato 1

hsato@eyesopen.com

Paul C.D. Hawkins 2

phawkins@eyesopen.com

Robert W. Tolbert 2
bob@eyesopen.com

1

2

OpenEye Japan Co. Ltd., AIOS Toranomon 904, 1-6-12 Nishishimbashi, Minato-ku, Tokyo
105-0003, Japan
OpenEye Scientific Software Inc., 9 Bisbee Court, St.D, Santa Fe, NM, 87508, USA

Keywords: virtual screening, in silico screening, ligand-based virtual screening, shape-basedalignment, shape-based pose prediction
Ligand-based Virtual Screening (LBVS) is a widely used computational method in drug discovery
for identifying hit compounds against a target molecule of interest. LBVS techniques use the 3D
as well as 2D structure of ligands to search molecule databases. We developed ROCS [1] , whichuses shape and chemical features of molecules when comparing molecules. ROCS aligns a target
molecule against a query molecule, initially using the center of mass (CoM) of both molecules, andoptimizes the overlap using a smooth Gaussian function. The shape and chemical similarity between
the molecules can be calculated, which are used to score and rank the target molecules. ROCS
performs very fast virtual screenings against huge chemical databases. Many successes using thisLBVS method in drug discovery have been reported.
We have developed a new ligand-based virtual screening method; subROCS. The new method uses
an exhaustive molecular alignment method based on shape and chemical features of ligand
molecules. subROCS starts finding the best overlay of a target molecule against a query moleculefrom many starting points, not only from the CoM used in standard ROCS. These points are basedon all heavy atoms of the input molecules, in order to exhaustively explore the best overlay between
the molecules. Using the Directory of Useful Decoys, Enhanced (DUD-E), which consists of 102
drug targets, [2] we present both virtual screening performance of subROCS against this standard
virtual screening database; and its pose prediction performance improved by the exhaustive shapebased ligand alignment.
[1] Hawkins, P.C.D., Skillman, A.G., Nicholls, A., Comparison of Shape-Matching and Docking as
Virtual Screening Tools. J. Med. Chem., 50: 74-82, 2007.
[2] Mysinger, M.M., Carchia, M., Irwin, J.J., Shoichet, B.K., Directory of Useful Decoys, Enhanced(DUD-E): Better Ligands and Decoys for Better Benchmarking., J. Med. Chem., 55: 6582-6594,
2012.

P2-04
Rapid Technique for New Scaffold Generation
Tim Cheeseright 1

Marina Takahashi 2

tim@cresset-group.com

Giovanna Tedesco 1
giovanna@cresset-group.com

Sumie Tajima 2

m_takahashi@hulinks.co.jp

Susana Tomásio 1
susana@cresset-group.com

tajima@hulinks.co.jp

Paolo Tosco 1
paolo@cresset-group.com

Mark Mackey 1
mark@cresset-group.com

1
2

Cresset, New Cambridge House, Bassingbourn Road, Litlington, Cambridgeshire, SG8 0SS, UK
HULINKS Inc., 5-14 Nihonbashi Hakozaki-cho, Chuo-ku, Tokyo, 103-0015, Japan

Keywords: Drug design, Scaffold hopping, BioisostereScaffold hopping remains a central task in medicinal chemistry for generating and protecting
intellectual property. We have previously presented a technique for rapidly generating reasonable
yet novel scaffold replacements using molecular fields which has been extended to include
R-group replacement. The approach uses a database of molecule fragments or available reagents tosuggest replacements that maintain the shape and electrostatic character of a known active
molecule.
However, for both of scaffold hopping and R -group replacement, activity is not the only
requirement for any suggested replacement. To be useful they must be synthetically access ible andmust fall within the window of acceptable physicochemical properties for the project. The task ofranking scaffold hops or bioisosteric replacements is thus one of multi -parameter optimisation,
where several often-competing requirements have to be considered simultaneously.
In this poster we suggest methods to address these issues with reference to case studies of bothscaffold hops and R-group replacements[1]. Synthetic accessibility can be handled by tying thebioisostere search to addressable chemistry space, utilising the chemist’s knowledge of what
synthetic routes are feasible to guide the search. The best guide of novelty in a scaffold -hopping
situation is the experience of the user: what other scaffold s are known in the literature or inpatents? To this end, we suggest a clear and minimal user interface to allow rapid triage of large
result lists. Finally, assessing results in the light of physicochemical and predicted ADMET
requirements can be achieved through a configurable radar plot givin g clear visual feedback on
how close any suggested replacement is to the ideal.
[1] http://www.cresset-group.com/sparkP2-05Predicting Regioselectivity and Lability of Cytochrome
P450 Metabolism using Quantum Mechanical Simulations
Jonathan Tyzack 1
jon@optibrium.com

1
2

Sumie Tajima 2
tajima@hulinks.co.jp

Marina Takahashi 2
m_takahashi@hulinks.co.jp

Peter Hunt 1

Matthew Segall 1

Nick Foster 1

peter@optibrium.com

matt@optibrium.com

nick@optibrium.com

Optibrium Ltd., 7221 Cambridge Research Park, Beach Drive, Cambridge, CB25 9TL, UK
Hulinks Inc., 5-14 Nihonbashi Hakozaki-cho, Chuo-ku, Tokyo 103-0015, Japan

Keywords: Predicting P450 metabolism, Quantum mechanical simulation, Quantitative estimate
Many computational methods have been developed that predict the regioselectivity of metabolism
by drug metabolising isoforms of the Cytochrome P450 class of enzymes (P450) [1 -5]. Here we
describe recent developments to a method for predicting P450 metabolism that combines quantum
mechanical (QM) simulations to estimate the reactivity of potential sites of metabolism on acompound with a ligand-based approach to account for the effects of orientation and stericconstraints due to the binding pockets of different P450 isoforms. These new developments
include modelling reaction pathways for epoxidation and developing mode ls for an extended rangeof P450 isoforms. The resulting models achieve accuracies of 85 -90% on independent test sets.
While valuable, predicting the relative proportion of metabolite formation at different sites on acompound is only a partial solution to designing more stable compounds. The advantage of aquantum mechanical approach is that it provides a quantitative estimate of the reactivity of eachsite, from which additional information can be derived regarding the vulnerability of each site tometabolism in absolute terms. One such measurement is the site lability, as calculated by
StarDrop™ [6], which is a measure of the efficiency of the product formation step. This is an
important factor influencing the rate of metabolism and we will illustrate how this provides
valuable guidance regarding the potential to redesign compounds to overcome issues due to rapid
P450 metabolism.
[1] J.P. Jones et al. (2002) Drug Metab. Dispos. 30(1): 7 -12[2] G. Cruciani et al. (2005) J. Med. Chem. 48(22): 6970 -6979[3] M. Hennemann et al. (2009) ChemMedChem 4(4): 657 -669
[4] P. Rydberg et al. (2010) ACS Med. Chem. Lett. 1(3): 96 -100
[5] J. Zaretzki et al. (2011) J. Chem. Inf. Model. 51(7): 1667 -1689[6] StarDrop, Optibrium Ltd, Cambridge, UK, www.optibrium.com/stardrop

P2-06
Ligand-Guided Approach to Validate
Computational Hydrophobic Binding Pocket Modelsof Lipid GPCRs
Misa Sayama 1

Tomohiko Ohwada 1

4884487850@mail.ecc.u-tokyo.ac.jp

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

Takatsugu Hirokawa 2
t-hirokawa@aist.go.jp

Sho Nakamura 1 , Sejin Jung 1 , Masaya Ikubo 1 , Yuko Otani 1
1

2

Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo,
Bunkyo-ku, Tokyo 113-0033, Japan
National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi,
Koto-ku, Tokyo 135-0064, Japan

Keywords: G-protein-coupled receptor, lysophosphatidylserine, structure-activity relationship,
ligand docking
Several G-protein-coupled receptors (GPCRs) have been reported to be activated by lipid or
lipid-derived molecules. Lysophosphatidylserine (LysoPS) has been proposed to be one of such
lipid ligands of GPCRs. 1,2 LysoPS is an amphipathic molecule, generated by enzymatic hydrolysisof phosphatidylserine, which is a component of membrane bilayers. In order to understand how
lipid ligands activate GPCRs, we have synthesized various LysoPS analogues. 3 This project is alsoof interest in terms of medicinal chemistry because LysoPS and its receptors have been proposed to
be involved in regulation of the immune system.
We found that the fatty acid moiety of LysoPS contributes significantly to receptor activation
potency and subtype-selectivity 3 , however their molecular basis is unknown. In order to consider
significance of the fatty acid moiety in the viewpoint of receptor-ligand interactions, weconstructed binding models of some of the LysoPS analogues containing non-lipid fatty acidsurrogates and homology models of LysoPS receptors. Robustness of these binding models was
confirmed by using molecular dynamics simulation and then we designed and synthesizedcompounds that are predicted to be active according to the models. Our models are consistent withthe significant roles of the fatty acid terminal.
LysoPS'(18:1)Endogeneous*pan6agonist*
O

OH

HO

O
NH 2

Phosphoserine*

P
O

O
O

O
OH

Glycerol*Fa-y*acid*[1] Sugo, T., et al., Identification of a lysophosphatidylserine receptor on mast cells, Biochem.
Biophys. Res. Commn., 341:1078-1087, 2006.
[2] Inoue, A., et al., TGFα shedding assay: an accurate and versatile method for detecting GPCR
activation, Nat. Methods, 9:1021-1029, 2012.
[3] Ikubo, M., et al., Structure-Activity Relationships of Lysophosphatidylserine Analogs as
Agonists of G-Protein-Coupled Receptors GPR34, P2Y10, and GPR174, J. Med. Chem.,
58:4204-4219, 2015.

P2-07
Fragment-Based Drug Discovery Applied to the
Antimicrobial Target CapF from Staphylococcus aureus
Takeru Chigira 1

Koichiro Nakano 2

8120661875@mail.ecc.u-tokyo.ac.jp

koi.nakano@so.taisho.co.jp

Takamitsu Miyafusa 3,4
t-miyafusa@aist.go.jp

1

2

3

4

5

Satoru Nagatoishi 5 _nagatoishi@bioeng.t.u-tokyo.ac.jp

Jose Caaveiro 5

Kouhei Tsumoto 1,3,5

jose@bioeng.t.u-tokyo.ac.jp

tsumoto@bioeng.t.u-tokyo.ac.jp

Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo,
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
Department of Medical Genome Sciences, Graduate School of Frontier Sciences, The
University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
Institute of Medical Science, The University of Tokyo, 4-6-1, Shirokanedai, Minato-ku, Tokyo
108-8639, Japan
Biomedical Research Institute, National Institute of Advanced Industrial Science andTechnology, Central 6, 1-1-1 Higashi, Tsukuba, Ibaraki 305-8566, Japan
Department of Bioengineering, School of Engineering, The University of Tokyo, 7-3-1 Hongo,
Bunkyo-ku, Tokyo 113-8656, Japan

Keywords: Fragment-based drug discovery, Structure-activity relationship, Calorimetry
The capsular polysaccharide (CP) surrounding Staphylococcus aureus (S. aureus) is an important
pathogenic factor during infections caused by this gram -positive bacterium [1]. The bi-functional
enzyme CapF, catalyzing two key reactions for the synthesis of a CP precursor, is a suitable target
to develop novel antibacterial agents [2]. A fragment-based screen was performed to identify novel
CapF inhibitors. A hit compound of the family of natural tropolones inactivates the first reaction
catalyzed by the enzyme at the Zn 2+ binding pocket of its cupin domain. Calorimetry and X -ray
crystallography were employed to clarify the mechanism of inhibition. The novel inhibitor binds
exothermically (ΔH° < 0) to the Zn 2+ ion using two coordination bonds, blocking access of thesubstrate to the active site, and suggesting that the hit compound binds to CapF employing specificinteractions [3]. Based on structure-activity relationship analysis from other natural tropolones andour own structural data, the compound is amenable to optimization (to increase affinity andspecificity) using rational structural-based ligand design methodologies, such as computer-aided
drug discovery and lead optimization methodologies for the design of antibiotic agents. We suggestthat extension of the ring at the C3-position of the tropolone ring will yield compounds ofincreasing potency. In summary, we propose tropolones as candidates to develop novel
antibacterial agents.
[1] Foster, T. J. Immune evasion by Staphylococci. Nat. Rev. Microbiol. 3:948-958, 2005.
[2] Miyafusa, T., Caaveiro, J. M. M., Tanaka, Y. & Tsumoto, K. Crystal structure of the enzyme
CapF of Staphylococcus aureus reveals a unique architecture composed of two functional
domains. Biochem. J. 443:671-680, 2012.
[3] Kobe, A., Caaveiro, J. M. M., Tashiro, S., Kajihara, D., Kikkawa, M., Mitani, T. & Tsumoto, K.
Incorporation of rapid thermodynamic data in fragment -based drug discovery. J. Med. Chem.
56:2155-2159, 2013.

P2-08Analyses of Fragment-Subsite Interactionsin Protein-Ligand Complexes
Noriyuki Yamaotsu 1
yamaotsun@pharm.kitasato-u.ac.jp

1

Shuichi Hirono 1
hironos@pharm.kitasato-u.ac.jp

School of Pharmacy, Kitasato University
5-9-1 Shirokane, Minato-ku, Tokyo 108-8641, Japan

Keywords: Fragment, Ligand, Binding site
We analyzed interactions between fragments of ligands and subsites of the binding site onproteins. In order to obtain fragment-subsite pairs, we used the database of 3D-structuresof protein-ligand complexes (PDBbind v.2013) [1-3]. The core set of PDBbind was chosenfrom 38,918 complexes in PDB according to known binding data, resolutions of X -ray data,clustering of protein sequence similarities and sampling of binding constants. The cor e set
was composed of 195 protein-ligand complexes in 65 protein clusters and thus its diversity
was considered to be suitable for this study. The ligands in the database were divided intofragments using Fragmenter of JChem 6.3.0 (ChemAxon Ltd., Budapest, Hungary, 2014).
The RingChainRecap.xml configuration file in Fragmenter was used as a rule for the
fragmentation. This rule cut non-ring single bonds starting from a ring atom, and thus amolecule split into the ring systems and the connecting chains. When cutting makes the
single atoms, they will be attached to the rings. A set of residues within 4Å of eachfragment was defined as the fragment-subsite using SYBYL-X 2.1.1 (Certara, L.P.,
Princeton, NJ, USA, 2013). Finally, 644 fragment-subsite pairs were obtained as thefragment-subsite set. For comparison, we computed subsite- and fragment-similarities.
The similarities of subsites were calculated on the basis of their 3D coordinates andphysicochemical properties using our SUPERPOSE_SITE program [4]. The chemicalsimilarities of the fragments were calculated as Tanimoto similarities between 64 -bit
fingerprints of 3D distances of atom pairs using Canvas 2.4 in Small -molecule Drug
Discovery Suite 2015-2 (Schrödinger, LLC., New York, NY, USA, 2015). R 3.1.3 w as used asa statistical software (The R Foundation for Statistical Computing, 2015) [5].
We found that the subsite-similarities correlated well with fragment-similarities. T he Pearson
correlation coefficient was very good and the Spearman rank correlation coefficient wasmoderate when larger subsites were used (n= 2766, r p = 0.900, r s = 0.564). Using largest
subsites, the both coefficients were very good (n= 620, r p = 0.869, r s = 0.910). These results
implied that similar fragments bind to similar subsites independent of types of proteins.

[1] Wang, R., Fang, X., Lu, Y. and Wang, S., The PDBbind Database: Collection of Binding
Affinities for Protein-Ligand Complexes with Known Three-Dimensional Structures, Journal of
Medicinal Chemistry, 47:2977-2980, 2004.
[2] Wang, R., Fang, X., Lu, Y., Yang, C.-Y. and Wang, S., The PDBbind Database: Methodologies
and Updates, Journal of Medicinal Chemistry, 48:4111-4119, 2005.
[3] Cheng, T., Li, X., Li, Y., Liu, Z. and Wang, R., Comparative Assessment of Scoring Functions
on a Diverse Test Set, Journal of Chemical Information and Modeling, 49:1079-1093, 2009.
[4] Iwase, K. and Hirono, S., Estimation of active conformations of drugs by a new molecularsuperposing procedure, Journal of Computer-Aided Molecular Design, 13:499-512, 1999.
[5] https://www.r-project.org/

P2-09Prediction of Residues with Key Interactions with Ligands
Based on Receptor Environmental Properties
Daisuke Takaya 1
daisuke.takaya@riken.jp
1

Teruki Honma 1
honma.teruki@riken.jp

RIKEN Center for Life Science Technologies, 1-7-22 Suehiro-cho, Tsurumi,
Yokohama 230-0045, Japan.

Keywords: SBDD, Protein function prediction, Amino Acid Property, Machine learning
In SBDD, protein-ligand docking is a powerful method to identify its inhibitors. Generally,
docking programs for that purpose worked rapidly because of the force-field-based empiricalscoring functions. They give a great advantage for in-silico screening to dock huge amount ofmolecules in commercially available compound databases (e.g. ZINC 1 ) to a drug discovery target
protein. However, because the scoring functions employed many approximations and limited
training data, the prediction performance is still not enough to find all potential inhibitors in theupper rank every time. Moreover, a correlation coefficient between the experimental value (e.g.
IC50, Ki) and the calculated values by the docking methods is not so high. For these reasons, thehighly scored compounds by the scoring functions have to be narrowed down by rescoring or
filtering using other kinds of properties such as interaction fingerprint . For example, mutation
analysis of ligand binding involved residues, interaction fingerprints of docked structures,
similarity to the known inhibitors or medicinal chemist’s visual inspections for scaffolds of dockedcompounds were widely used for this purpose. From this point of view, we focused on residues
involving in key interaction(s) such as H-bonds, ionic bonds, tight hydrophobic contact withligands. If a prediction method for importance measure of the binding site residues is given
beforehand, we could use the rank for the do rescoring and filtering. In this study, we newly set a
purpose to develop prediction methods to quantify the importance of residues with the key
interaction(s) around the pockets.
Firstly, experimental data such as X-ray crystal structures were extracted from Protein Data
Bank2 and a definition of the residues with key interactions was introduced to distinguish positive
and negative data. Secondly, probe molecules to estimate protein ligand interactions weregenerated around the binding pockets and surrounding amino acid properties were calculated forthe residues to obtain descriptors. Using the descriptors, machine learning methods were applied toconstruct prediction models and estimated the performance of the constructed models. In the future,
we will try to construct prediction models to quantitatively predict importance of the ligand
binding.

[1] Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S., and Coleman, R. G. ZINC: A Free
Tool to Discover Chemistry for Biology J. Chem. Inf. Model., 52 (7), 1757–1768, 2012[2] Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov,
I.N., and Bourne, P.E. The Protein Data Bank Nucleic Acids Research , 28, 235-242, 2000

P2-10Adverse effect prediction by random forests model using
polypharmacological profile of a small compound

1

Tomohiro Sato 1

Teruki Honma 1

tomohiro.sato@riken.jp

honma.teruki@riken.jp

Center for Life Science Techn ologies, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama
City, Kanagawa, 230-0045, Japan

Keywords: adverse effect, polypharmacology, machine learning, random forests
Recently, polypharmacology caught the attention to investigate both efficacy and side-effects ofdrugs. With increasing data of 3D structure of proteins and biological activities of small
compounds, several efforts to predict adverse drug reactions (ADRs) considering
polypharmacology were reported.[1,2] In this study, a novel method to predict ADRs based onmachine leaning of polypharmacological profile of a small molecule was developed. Employing
random forests, machine learning models for 326 proteins in ChEMBL database were built, andused to predict pharmacological profile of a compound. Then, the pharmacological profile wasused as the explanatory variables to build the learning models for 129 ADRs registered in SIDER2
drug side effect database (Figure 1). In the cross validation experiments about 129 ADRs, theprediction based on polypharmacology profile showed ROC value of 0.826 on average, andsuccessfully exceed those predicted using conventional molecular fingerprints (Figure 2).
Furthermore, leave-cluster-out validation showed that the proposed method could maintain higher
prediction accuracy than the conventional prediction models directly using molecular fingerprint,
even when compounds in training set and test set had less structural similarity.
[1] LaBute M. X., Zhang X., Lenderman J., Bennion B. J., Wong S. E., Lightstone F. C. Adverse
drug reaction prediction using scores produced by large-scale drug-protein target docking on
high-performance computing machines. PLoS One, 9:e106298, 2014[2] Yamanishi Y., Pauwels E., Kotera M. Drug side-effect prediction based on the integration ofchemical and biological spaces. J. Chem. Inf. Model., 52, 2012, 3284 -3292

Figure 1. Procedure to build the machinelearning models for 129 ADRs.

Figure 2. Box plot showing ROC values of themachine learning models for 129 ADRs

P2-11
Pharmacophore based search for novel STING activators
as vaccine adjuvants
Yuko Tsuchiya 1

Philip Prathipati 2

tsuchiya@protein.osaka-u.ac.jp

philip@nibiohn.go.jp

Kenji Mizuguchi 2
kenji@nibiohn.go.jp

12Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki,
Osaka 567-0085, Japan

Keywords: Pharmacophore modeling, molecular dynamics simulations, vaccine adjuvants
STING (stimulator of interferon gene) is an essential adaptor protein in innate immunity. It senses
cytosolic DNA and induces IFN-β production when a cell is infected. A better understanding of thedownstream signaling mechanism, triggered by ligand binding, is essential for the development ofanti-cancer drugs or vaccine adjuvants.
In this study, we attempt to find novel STING activators based on structural bioinformatics
analyses and pharmacophore modeling, in combination with the screening of candidate compounds
by molecular dynamics simulations.

P2-12
Fragment screening of WNK1 inhibitor
Nae Saito 1
nae-saito@mol.f.u-tokyo.ac.jp

Yukio Tada 2
tada.y.af@m.titech.ac.jp

Takayoshi Okabe 1

Tetsuo Nagano 1

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

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

1

Drug Discovery Initiative, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033,
Japan
2
Interdisciplinary Graduate School of Sience, Tokyo Institute of Technology, 3-3-6 Shibaura,
Minato-ku, Tokyo 108-1123, Japan

Keywords: Fragment screening, WNK1, SPR
Pseudohypoaldosteronism type II has been known as a rare autosomal dominant disorderallegedly triggered by overexpression of WNK [with no lysine(K)]1 or mutations in WNK4. Thusfar, there have been only a few reports regarding the specific inhibitor for WNK1 or WNK4. We
therefore explored the lead compounds for WNK1 from a mixed fragment-based drug discovery
whereby a competition experiment using surface plasmon resonance (SPR) allows for selecting
better lead compounds in the early stages.
The 9,000 fragments were examined in order to select hit compounds by the mobility shift assay,
by which an inhibitory activity for phosphorylation of WNK1 is assessed. Using SPR, we thenevaluated binding abilities of the hit compounds against WNK1. A competition assay by SPR was
carried out using the candidate compounds as well.
The WNK1 possesses characteristics where, due to the lack of normal lysine residue insubdomain II, Lys45 in subdomain I shows the activity instead, and there are two cavities
associated with inhibition activity in WNK1 in the ATP binding sites and back pocket just behind.
Given these unique points, we selected the lead compounds using both SPR and docking study.

P2-13
Prediction of Protein-ligand Binding Affinities Using
Molecular

Mechanics

andQuantum

Mechanics

Calculations
Noriaki Okimoto 1
okimoto@riken.jp

Takao Otsuka 1

Yoshinori Hirano 1

totsuka@riken.jp

hirano@riken.jp

Makoto Taiji 1
taiji@riken.jp
1

QBiC, RIKEN, 1-6-5 Minatojima-minami, Chuo-ku, Kobe, Hyogo 650-0047, Japan

Keywords: affinity prediction, molecular docking, molecular mechanics calculation, quantum
chemical calculation
Accurate computation of the protein-ligand binding affinity is of significant importance for lead
optimization in drug development research. Various methods have been proposed to predict theprotein-ligand binding affinity. There are more accurate free energy calculation techniques basedon molecular dynamics (MD) simulations. These include linear interaction energy methods, themolecular mechanics/Poisson-Boltzmann and surface area (MM-PB/SA) method, alchemical freeenergy calculations, and various other methods. However, these methods employing molecular
dynamics simulations are limited by the use of classical molecular mechanics (MM) force fields,
because the preparation of appropriate parameters for organic compounds is difficult.
Protein-ligand systems exhibit various non-bonded interactions, such as π-stacking, chargetransfer, polarization effects, and dispersion, as well as the usual and weak hydrogen bonds, asevidenced by structural studies 1 . These non-bonded interactions cannot be understood with
sufficient accuracy by using classical MM force fields. On the other hand, application of quantum
mechanical (QM) calculations considering such non-bonded interactions can significantly improve
the accuracy of the MM force fields.
In this study, we tried to predict binding affinities of protein-ligand systems using approaches bycombining MM and QM calculations.

References
1.

(a) Raha, K.; Peters, M. B.; Wang, B.; Yu, N.; Wollacott, A. M.; Westerhoff, L. M.; Merz,

K. M., Jr., The role of quantum mechanics in structure-based drug design. Drug Discov Today
2007, 12 (17-18), 725-31; (b) van der Vaart, A.; Merz, K. M., The role of polarization and chargetransfer in the solvation of biomolecules. J. Am. Chem. Soc. 1999, 121 (39), 9182-9190.

P2-14Computational analysis of a conformational epitope of abroadly neutralizing antibody in influenza A virus
hemagglutinin
Manabu Igarashi 1

Masakazu Sekijima 2

Nobuaki Yasuo 2

igarashi@czc.hokudai.ac.jp

sekijima@gsic.titech.ac.jp

yasuo.n.aa@m.titech.ac.jp

Takashi Abe 3
takaabe@ie.niigata-u.ac.jp

1
2

3
4
5
6

Teruaki Watabe 4

Hiroaki Tokiwa 5

twatabe-mi@umin.ac.jp

tokiwa@rikkyo.ac.jp

Takatsugu Hirokawa 6

Ayato Takada 1

t-hirokawa@aist.go.jp

atakada@czc.hokudai.ac.jp

Hokkaido University Research Center for Zoonosis Control, Sapporo, Hokkaido, Japan
Global Scientific Information and Computing Center, Tokyo Institute of Technology,
Meguro-ku, Tokyo, Japan
Department of Information Engineering, Niigata University, Niigata, Japan
Center of Medical Information Science, Kochi Medical School, Kochi University, Kochi, Japan
Department of Chemistry, Rikkyo University, Toshima-ku, Tokyo, Japan
Molecular Profiling Research Center for Drug Discovery (molprof), National Institute ofAdvanced Industrial Science and Technology (AIST), Koto-ku, Tokyo, Japan

Keywords: Molecular dynamics, Influenza virus, Antibody, Protein modeling
Background
The hemagglutinin (HA) of influenza A viruses is classified into 16 subtypes (H1-H16). It is
generally known that HA-specific antibodies have little cross-neutralizing activity against multiple
HA subtypes. Recently, however, several broadly neutralizing antibodies were reported and haveattracted attention due to their potential application to therapeutics and vaccine design. We havepreviously reported a cross-reactive antibody, designated S139/1, which neutralizes H1, H2, H3,
H13, and H16 subtypes and its crystal structure in complex with the HA of the A/Victoria/3/1975(H3N2) strain. However, detailed structural basis of its cross-neutralizing activity still remain tobe elucidated. In this study, we characterized the S139/1 recognition sites on different HAs using
computational structural biology methods.
Materials and Methods
The HAs from eight strains (subtypes H1, H2, H3, H6, H9, and H13) were analyzed. The structure
models of the HA-S139/1 complex were constructed by homology modeling. Using the structures
as starting points, we performed molecular dynamics (MD) simulations, and then calculated
binding free energies (∆G) between S139/1 and each HA.
Results and Discussions
The ∆G values of the strains neutralized by S139/1 were lower than the other strains tested. We
next investigated the contribution of individual residues on each HA to the interaction with S139/1
and found that amino acids at positions 98, 136, 153, 156, 158, 159, 193, 194, 196, and 226 (H3numbering) on HA strongly contributed to S139/1 binding as for the strains neutralized by S139/1.
Principal component analysis emphasized that the residues at positions 158 and 193 were the mostimportant for S139/1 binding. Indeed, amino acid substitutions at these two positions wereexperimentally observed in the mutant viruses escaping from neutralization by S139/1. Thus, ourcomputational methods identified the amino acid residues critical for the cross-neutralizing
activity of S139/1.

P2-15A Comparative QSAR Analysis and Molecular Docking
Studies of Thiophene Analogues as Protein Tyrosine
Phosphatase 1B (PTP1B) Inhibitors: A Rational Approach to
Antidiabetic Drug Design
Shabana Bibi 1

Katsumi Sakata 1

dazzling.grin@gmail.com

ksakata@maebashi-it.ac.jp

Saima Kalsoom 2
genious_chemist@yahoo.com

1

2

Department of Environment and Life Engineering, Maebashi Institute of Technology, 460-1
Kamisadori-machi, Maebashi, Gunma 371-0816, Japan
Department of Chemistry, Quaid-i-Azam University, Islamabad, Pakistan

Keywords: Diabetes mellitus, PTP1B inhibitor, QSAR, Molecular docking
In this study, we focused to develop the In silico methods to identify potent inhibitor from PTP-1B
target focused library. After the evaluation of drug-likeness profiles and toxicity risk assessment of thetarget focused library, an attempt was made to develop a quantitative structure–activity relationship(2D- and 3D-QSAR) and molecular docking studies on a series of thiophene analogues acting asprotein tyrosine phosphatase 1B (PTP1B) inhibitors. 2D-QSAR was performed using multiple linear
regression (MLR), principal component regression (PCR) and partial least squares regression (PLS)
methods. Among these three methods, the PLS method has comes out with a very promising result (r 2 =0.9063 and q 2 = 0.8412) as compared to other two methods. According to the model generated by PLS
method; antidiabetic activity of thiophene analogues were influenced by physiochemical descriptor(molecular volume and dipole moment), alignment independent descriptor (T_2_S_4 signifies numberof double bonded atoms separated from sulphur atoms by four bond distance) and semi-empiricaldescriptors, (i) SK Most Hydrophobic Hydrophilic Distance signifies distance between mosthydrophobic and hydrophilic point on the vdW surface (by Kellogg method using Slogp), and (ii) XA
Most Hydrophobic signifies the most hydrophobic value on the vdW surface (by Audry method using
Xlogp). These help in understanding the effect of substituent at different position of PTP1B inhibitors.
3D-QSAR study was performed using k-nearest neighbor molecular field analysis (kNN-MFA)
approach for both electrostatic and steric fields. The molecular field analysis was applied for thegeneration of steric and electrostatic descriptors based on aligned structures. The steric andelectrostatic field effects are discussed in the light of contribution plot generated. Finally, moleculardocking analysis was carried out to better understand the interactions between PTP-1B target andinhibitors. Hydrophobic and hydrogen bond interactions lead to identification of key residues involvein the binding sites of PTP1B protein. The present study will be helpful in further optimization and MD
simulation studies and proposed model can be employed to design new derivatives of thiophene withspecific PTP1B inhibitory activity.

P2-16VISCO: visualization and evaluation tool for amino acidsequence conservation of ligand-binding sites
Nobuaki Yasuo 1

yasuo.n.aa@m.titech.ac.jp
12

Masakazu Sekijima 1, 2

sekijima@gsic.titech.ac.jp

Department of computer science, Tokyo Institute of Technology, Meguro, Tokyo 152–8550,
Japan
Global Scientific Information and Computing Center, Tokyo Institute of Technology, Meguro,
Tokyo 152–8550, Japan

Keywords: amino acid sequence conservation, ligand-binding site, visualization, structure-based
drug design (SBDD)
Drug resistance is one of the difficulties for developing antiviral or antimicrobial drugs[1]. We
developed a new application named VISCO to quantitatively evaluate the sequence conservation ofligand-binding sites in order to overcome the drug resistance issue[2]. VISCO integrates
information pertaining to protein structures, ligand-binding sites, and amino acid sequences.
Moreover, VISCO can calculate ConservationScore to find versatile, inappropriate target sites.
These data are visualized onto protein structures via Jmol or PyMOL interface. These visualizationand comparison can facilitate structure-based drug design (SBDD)[3].
Furthermore, we conducted an experiment for validation of visualization and calculation ofConservationScore of four viral proteins and an experiment to visualize the differences amongproteins of the human β adrenergic receptor family. This application is available at
http://www.bio.gsic.titech.ac.jp/visco.html .
[1] Huggins, D. J., Sherman, W. and Tidor, B., Rational approaches to improving selectivity indrug design, Journal of medicinal chemistry, 55(4): 1424–1444, 2012.
[2] Yasuo, N., and Sekijima, M., Application for Evaluating and Visualizing the Sequence
Conservation of Ligand-binding Sites, IPSJ Transactions on Bioinformatics, 8(0): 9-13, 2015.
[3] Kalyaanamoorthy, S. and Chen, Y. P. P., Structure-based drug design to augment hit discovery,
Drug discovery today, 16(17): 831–839, 2011.

P2-17
Accelerating drug discovery for neglected tropical diseases
through free energy perturbation calculations on the
Tsubame supercomputer
Suguru Roy Kimura 1,2

roy.kimura@schrodinger.com

Kazuki Ohno 2,3

ohno@catalyst.jp.net

Daniel Ken Inaoka 5danielken@m.u-tokyo.ac.jp
1

2

3

4

5

Masakazu Sekijima 2,4

sekijima@gsic.titech.ac.jp

Kiyoshi Kita 5
kitak@m.u-tokyo.ac.jp

Schrodinger KK, Marunouchi Trust Tower North 17F, 1-8-1 Marunouchi, Chiyoda-ku, Tokyo
100-0005, JapanTokyo Institute of Technology, Academy of Computational Life Sciences, 2-12-1 O-okayama,
Meguro-ku, Tokyo 152-8552, Japan
Catalyst Inc, Resona Kudan Building 5F KS Floor, 1-5-6 Kudan Minami, Chiyoda-ku, Tokyo
102-0074, JapanTokyo Institute of Technology, Global Scientific Information and Computing Center, 2-12-1
O-okayama, Meguroku, Tokyo 152-8550, JapanTokyo University, Department of Biomedical Chemistry, 7-3-1 Hongo, Bunkyu-ku, Tokyo
113-0033, Japan

Keywords: Docking, Binding free energy prediction, Free energy perturbation, Drug design, Lead
optimization
Free energy perturbation (FEP) is a rigorous physics-based computational method that enables theprediction of small-molecule binding affinities towards their enzyme or receptor targets. Although FEP
has been applied to protein and ligand binding affinity predictions for over 30 years and is considered
the “Gold Standard” in accuracy, its extreme computational cost has prevented it from enjoying
widespread use. However in the last few years, through the development of novel computational
acceleration algorithms and by taking advantage of general purpose Graphics Processing Units (GPUs),
FEP is now becoming a practical tool that is increasingly used in drug discovery research.
We recently ported such an FEP implementation [1] to the Tsubame 2.5 supercomputer at Tokyo
Institute of Technology consisting of over 4000 GPUs. Our preliminary benchmarks on Tsubame
demonstrated calculation rates of approximately 150 to 200 predictions per day using 400 GPUs, andprediction errors within 1.2 kcal/mol root mean square error, consistent with prior published results [1].
Such accuracy enables distinguishing between compounds that exhibit at least a 10-fold activity
difference prior to synthesis of those compounds. Although this level of error is still larger than the
expected errors of experimental assays (which is often estimated to be 2 to 3-fold), we believe that theeffective use of FEP should enable discovery of potent inhibitors at significantly shorter time frames
and lower costs compared to those involved in traditional medicinal chemistry team efforts..
As a preliminary validation on Tsubame, we performed binding affinity predictions for a series ofrecently discovered promising compounds [2] that inhibit dihydroorotate dehydrogenase (DHOD) from
Trypanosoma cruzi, a protozoan causing Chagas’ disease. Co-crystal structures of many of them were
determined [2] prior to prediction and were used in the calculations. The remaining compounds weremanually docked into the protein crystal structures. We observed accuracy consistent with previously
published validations. Through this effort, we aim to accelerate the discovery of potent inhibitors for awide class of tropical disease-causing targets.

[1] Wang, et al., J. Am. Chem. Soc., 2015, 137, 2695.
[2] Inaoka, D., Kita, K., 2015, Manuscript submitted.

P2-18
Protein-ligand Binding Prediction using Supervised
Molecular Dynamics Simulation
Atsushi Ogahara 1
ogahara-atsushi-mk@cb.k.u-tokyo.ac.jp

1

2

3

Chie Motono 2
c-motono@aist.go.jp

Masahiro Gomi 3

Miwa Sato 3

Takatsugu Hirokawa 2

gomi-masahiro@mki.co.jp

satou-miwa@aist.go.jp

t-hirokawa@aist.go.jp

Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi,
Chiba-ken 277-8561, Japan
Molecular Profiling Research Center for Drug Discovery, National Institute of Advanced
Industrial Science and Technology, 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan
Mitsui Knowledge Industry CO., LTD., 2-5-1 Atago, Minato-ku, Tokyo 105-6215, Japan

Keywords: Computer-aided drug design, Structure based drug design
The study of interaction between a chemical and its target protein using molecular dynamics(MD) simulation is of importance for drug discovery. The technique allows us to visualize theprotein-ligand binding pathways as well as the binding structures.
Shan et al. has succeeded to provide a full dynamical description of protein-ligand bindng 1 . It isnecessary to carry out a classical MD simulation in a microsecond time scale, and usually requires
special-purpose computational system for MD simulation.
Sabbadin and Moro has succeeded exploring ligand-protein recognition pathway in a nanosecond
time scale using supervised MD (suMD) simulation 2 . The method allows, however, acceleration ofligand binding simulations only to membrane proteins.
To overcome this limitation, we implemented the suMD method which can be applied to soluble
proteins. From the starting position of a ligand to the neighbourhood of its binding site, the 3D
shortest path is searched 3 and a suMD is carried out guided by the shortest path. After ligand’sreaching the neighborhood of a binding site, the ligand is subjected to a usual suMD without the
shortest path.
In our preliminary evaluation of this approach on chk1-H5K system the ligand H5K reached near
the center of the mass of binding site residues within 32 nanoseconds. The results showed that the
combination of the 3D shortest path search and suMD is helpful to explore the ligand binding event
in applicable time scale, though resulting binding pose of the ligand is different from thecorresponding X-ray crystal structure.
[1] Shan, Y. et al., How does a drug molecule find its binding site? J. Am. Chem. Soc.,
133:9181-9183, 2011.
[2] Sabbadin, D. and Moro, S., Supervised molecular dynamics (SuMD) as a helpful tool to depict
GPCR-ligand recognition pathway in a nanosecond time scale, J.Chem. Inf. Model, 54:372-376,
2014.
[3] Chen, J. and Han, Y., Shortest paths on a polyhedron, Proc. 6th Annual Symposium on
Computational Geometry, 360-369, 1990.

P2-19In silico identification of novel inhibitors of S6K1 kinaseby SBDD and LBDD approach
Shunta Sasaki 2

Hitomi Yuki 1

sh.sasaki@teijin.co.jp

Junko Mikuni 1

Naoko Ogawa 1

mikuni@gsc.riken.jp

naoko.ogawa.aj@riken.jp

Shigeyuki Yokoyama 3
yokoyama@riken.jp

1

2

3

Hideaki Niwa 1

yuki@gsc.riken.jp

niwa@gsc.riken.jp

Akiko Tanaka 1
aktanaka@riken.jp

Teruki Honma 1*

honma@gsc.riken.jp

RIKEN Center for Life Science Technologies, 1 -7-22 Suehiro-cho, Tsurumi, Yokohama
230-0045, Japan
Teijin Institute for Bio-medical Research, Teijin Pharma Ltd., 4-3-2, Asahigaoka, Hino-shi,
Tokyo 191-8512, JapanRIKEN Structural Biology Laboratory, 1 -7-22 Suehiro-cho, Tsurumi, Yokohama 230-0045,
Japan

Keywords: S6K1, kinase, in silico screening, SBDD, LBDD
S6K1 is the member of the serine-threonine kinase family and phosphorylates 70S ribosomal
protein S6. Because S6K1 plays important roles in metabolism, cell growth, and overexpresses atrenal cell carcinoma, S6K1 is expected to be a potential new target of anticancer agent. To findnovel scaffolds as S6K1 kinase inhibitors, we demonstrated two strategies of in silico screening
and inhibition assay of S6K1 kinase supported with X-ray crystal structure analysis.
At the first round of in silico screening, we selected the screening compound sets by
structure-based drug design (SBDD) and ligand-based drug design (LBDD). We demonstrated anefficient in silico screening based on k-PALLAS [1] , which is the semi-automatic optimization
system of docking conditions developed by Honma and co -workers at RIKEN. In parallel with thedocking simulations, 3D similarity searches were performed using the shape-comparison program
ROCS (Rapid Overlay of Chemical Structures), followed by the kinase mobility shift assay. Known
S6K1 ligands and S6K1 X-ray ligands were used as queries for the 3D similarity searches. In thesecond round of in silico screening, 2D similarity searches were performed by using the 2 3 hitswith IC 50 < 10 M as queries, identified in the first round in silico screening. Compound librarywhich were used for the first and second rounds, are supplied by Open Innovation Center for Drug
Discovery at the University of Tokyo.
After the kinase mobility shift assay, five and four compounds with IC 50 <= 500 nM, wereobtained from the first and second rounds respectively. Furthermore, we succeeded in getting five
X-ray complex structures of S6K1 and the hit compounds. From docking simulation approach with
k-PALLAS, we acquired the novel scaffold of S6K inhibitors which are different from known
S6K1 ligands so far.
In this poster session, we will discuss our in silico approach to identify of new scaffolds of S6K1
inhibitors.
[1] Sato T, Watanabe H, Tsuganezawa K, Yuki H, Mikuni J, Yoshikawa S, Kukimoto -Niino M,
Fujimoto T, Terazawa Y, Wakiyama M, Kojima H, Okabe T, Nagano T, Shirouzu M, Yokoyama S,
Tanaka A, Honma T., Identification of novel drug-resistant EGFR mutant inhibitors by in silico
screening using comprehensive assessments of protein structures. Bioorganic Medicinal Chemistry,
20:3756-3767, 2012.

P2-20Computational approaches in structural based drug design

1

Yoshinori Hirano 1

Noriaki Okimoto 1

hirano@riken.jp

okimoto@riken.jp

Takao Otsuka 1

Makoto Taiji 1

totsuka@riken.jp

taiji@riken.jp

Laboratory for Computational Molecular Design, Computational Biology Research Core,
Quantitative Biology Center (QBiC), RIKEN, 1-6-5, Minatojima-minamimachi, Chuo-ku, Kobe,
Hyogo 650-0047, Japan

Keywords: Molecular Modeling, Drug Design, Molecular Screening
Structure-based drug design (SBDD) has become the preferred approach in the field of drug
discovery. Among the various structure-based computational methodologies adopted for compound
screening, the principal one is molecular docking.
Proteins fluctuate dynamically under physiologic conditions, and this flexibility is essential for
ligand binding. Apparent conformational changes upon ligand binding have been observed inseveral pharmacologically relevant proteins. Although the importance of the effects on protein
dynamics and solvent water molecules is widely recognized, most methods for SBDD do not fully
consider related effects. Thus far, there have been considerable efforts to incorporate these effects
on protein flexibility and solvent water molecules into molecular docking. Ensemble docking,which uses multiple receptor conformations, is one of an approach to consider protein flexibility
during molecular docking. However, if conformational change on ligand binding is very large,
conformational selection is extremely difficult.
In this study, we focus on the incorporation of protein flexibility by combining moleculardocking and molecular dynamics (MD) simulation.

References:[1] Otsuka T. et al., JCC accepted.
[2] Okimoto N. et al., PLOS computational biology 5(10) (2009).

P2-21
Open Innovation Approach to Computer-Aided Inhibitor
Identification
Shuntaro Chiba 1

Kazuyoshi Ikeda 2

chiba.s.ac@m.titech.ac.jp

Takashi Ishida 3,1

ikeda@level-five.jp

Kazuki Ohno 4,1

ishida@cs.titech.ac.jp

ohno@catalyst.jp.net

Masakazu Sekijima 5,1
sekijima@gsic.titech.ac.jp

1

2

3

4
5

Education Academy of Computational Life Sciences, Tokyo Institute of Technology, 4259 ,Nagatsutacho, Midori-ku, Kanagawa 226-8501, Japan
Level Five Co. Ltd., Shiodome Shibarikyu Bldg., 1-2-3, Kaigan, Minato-ku, Tokyo 105-0022,
Japan
Department of Computer Science, Tokyo Institute of Technology, 2 -12-1, Ookayama,
Meguro-ku, Tokyo 152-8550, Japan
Catalyst Inc., Risona Kudan Bldg., 1-5-6, Kudan-Minami, Chiyoda-ku, Tokyo 102-0074, Japan
Global Scient ific Information and Computing Center, Tokyo Institute of Technology, 2-12-1,
Ookayama, Meguro-ku, Tokyo 152-8550, Japan

Keywords: Computer-aided drug discovery, Inhibitor identification
It is known that chance of finding potent inhibitors by computer-aided methods varies depending
on a selected targeted biomolecule. 1 We assumed that the more methods we applied to the target
biomolecule, the more inhibitors could be found. In the present study we conducted the open
contest where many individuals and organizations participated in order to suggest compounds IDs
searched from a large compound library. As a result, the compounds suggested by all participants
were diverse. The compounds’ inhibition activity was experimentally assessed and co mpoundswith high activity were found to be diverse, which indicated that having such an open innovation
contest to find various potent inhibitors is valuable.
[1] Sliwoski, G., Kothiwale, S., Meiler, J. & Lowe, E. W., Jr. Computational methods in drug discovery.
Pharmacol Rev 66, 334-395, 2014.

P2-22
Consideration of Protonate Statesin Protein-Ligand Complex for Drug Design
Based on Fragment Molecular Orbital Method
Chiduru Watanabe 1
chiduru.watanabe@riken.jp

1

Teruki Honma 1

honma.teruki@riken.jp

RIKEN Center for Life Science Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama,
Kanagawa 230-0045, Japan

Keywords: Protonate state, MTH1, Fragment molecular orbital (FMO) method
Protonate states of amino acid residues (e.g. Asp, Glu, Lys) on ligand binding pocket are
critically important information to structure-based drug design. Hydrogen atoms including protons
on the amino acid residues, however, are not determined in general PDB entries with resolution ca.
1.0Å ~ 3.0Å. For example, we have generally treated Asp and Glu as deproto nate states at
physiological condition (pH 7.4). There are concerns that ligand binding mode depends on wrong
protonate states of amino acid residues in protein. If we use protein structure with wrong protonate
states, drug design makes misleading by its incorrect ligand binding mode. Therefore, we have to
predict these protonate and tautomer states by pKa calculations of ligand and the visual inspectionof X-ray crystal structures and its electron densities. In addition, we must confirm whether that thepredicted protonate stats of complex are energetically appropriate. Thus, we will attempt toestimate validity of the protonate states on protein by quantum chemical calculations based on
fragment molecular orbital (FMO) method [1].
The MutT Homolog 1 (MTH1) protein, which belongs to the Nudix family of phosphohydrolases,
is reported to be related with cancers caused by the accumulation of oxidized nucleotides in DNA.
The hydrogen bond network among the substrate and the aspartic acids of Asp119 and Asp120 in
MTH1 plays a key role in substrate binding. Svesson et al. [2] have reported that the
protonate/deprotonate states of two aspartic acids would depend on 3D property of a ligand on thebasis of atomic distance between the aspartic acids and the ligand. In this study, we examined theprotonate/deprotonate states of Asp119 and Asp120 in MTH1 and tautomer states of ligands (e.g.
substrate and inhibitors). We performed FMO calculations at MP2/6-31G* level of several
protonate states of the aspartic acids and tautomer stets of ligand. The protonate states wereanalyzed by inter-fragment interaction energies (IFIEs) between protein and ligand. In the poster
session, the relationship of IFIE data and the binding mode candidates will be discussed.
[1] 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.
[2] Svensson, L. M., Jemth, A.-S., Desroses, M., Loseva, O., Helleday, T., Hogbom, M., andStenmark, P., Crystal structure of human MTH1and 8-oxo-dGMP product complex, FEBS Letters,
585:2617-2621, 2011.

P2-23
Construction of research platform in FMO drug design
consortium (FMODD)
Yoshio Okiyama 1

Chiduru Watanabe 1

yoshio.okiyama@riken.jp

chiduru.watanabe@riken.jp

Shigenori Tanaka 3
tanaka2@kobe-u.ac.jp1

2

3

Kaori Fukuzawa 2
fukuzawa.kaori@nihon-u.ac.jp

Teruki Honma 1
honma.teruki@riken.jp

RIKEN Center for Life Science Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama,
Kanagawa 230-0045, Japan
School of Dentistry at Matsudo, Nihon University, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba,271-8571, Japan
Department of Computational Science, Graduate School of System Informatics, Kobe
University, 1-1, Rokkodai, Nada-ku, Kobe, Hyogo 657-8501, Japan

Keywords: Fragment molecular orbital (FMO) calculation, Drug design, Consortium, HPCI
Fragment molecular orbital (FMO) method,which can accurately describe interactionenergies and chemical reactions in biomolecules
based on quantum mechanics, has been thought
to be a promising tool for in silico drug design inthe next generation and increasingly applied toseveral realistic models of bio-systems [1,2]. Despite these successful examples, it seems to be not
usable at ease yet for many researchers except the developers and around.
Now, in order to promote the application and confirmation of FMO still more in the field of drug
design, FMO drug design consortium, abbreviated to FMODD, was established in November 2014as an open community for developing and spreading practical techniques to master FMO-based
analyses with industrial-government-academic cooperation. In this consortium, we will sharecalculated data with its computational know-how through applying FMO method to several
practical themes such asl constructing a database of inter-fragment interaction energies (IFIE) between several proteins
and ligands and screening these ligands with using cluster techniques analyzing the IFIE
patterns in a large scale,l refining molecular coordinates obtained by X-ray crystal structure analysis in cooperation
with FMO electron density, andl simulating dynamics of chemical reaction between proteins and their substrates,by utilizing HPCI resources like a massively parallel supercomputer, K-computer. Additionally,
we plan to not only develop new computational methodologies of FMO, but also form auser-friendly interface supporting to prepare structures, configure calculations, submit jobs, and so
on for more accessibility. We will show the progress of our activities in the session.
Acknowledgment
This work is performed as one of the activities of the FMO drug design consortium (FMODD) andsupported by the “Construction of platform of FMO-based drug discovery using HPCI system” project(hp150160) using K computer.
References[1] Fedorov, D. G., et al., Exploring chemistry with the fragment molecular orbital method, Phys.
Chem. Chem. Phys. 14:7562-7577, 2012.
[2] Tanaka, S., et al., Electron-Correlated Fragment-Molecular-Orbital Calculations for Biomolecular
and Nano Systems, Phys. Chem. Chem. Phys. 16:10310-10344, 2014.

P3-01Whole Cell Migration Modeling reveals How Cells Sense
the Electric Field Gradient even in Noisy Environment
Masato Yasui 1
masato.yasui@riken.jp

Satomi Matsuoka 1s.matsuoka@riken.jp

Masahiro Ueda 2masahiroueda@fbs.osaka-u.ac.jp

12

Quantitative Biology Center, RIKEN, OLABB, 6-2-3, Huruedai, Suita-shi, Osaka, 565-0874,
Japan
Graduate School of Science, Osaka University, 1-1, Machikaneyama, Toyonaka, Osaka,
560-0043, Japan

Keywords: Whole cell modeling, Cell migration, Electro-taxis, Dictyostelium Discoideum
Cell moves toward the attractant molecules or electrode, which is called “taxis.” Taxis are used ina wide range of biological processes including morphogenesis, immunity, neuronal patterning, andwound healing. Dictyostelium discoideum cells shows a chemotactic response to cyclic adenosine
3’,5’-monophosphate (cAMP) [1]. Cells can sense the 0.0033 nM/um cAMP gradient (0.5 nM) [ 2],which imply that cells have the ability to determine the gradient even if the before and after
difference of cAMP binding to the receptors is smaller than the noise of that. How cells sense thegradient even in noisy environment? In our research, to answer this question, we perturbed
phosphatidylinositol lipid system, which determines the direction of the cell migration, by using
genetic engineering. We measured the time-series change of phosphatidylinositol lipid system, cell
migration and taxis of the perturbed cells by live imaging. Then, we modeled thephosphatidylinositol lipid system and cell migration from the quantitative data. Curiously, ourmodel expected that cells sense the attractant gradient in noisy environment better than without
noise. The result of experimental verification also supported our expectation of the model. In thisposter, we will present the mechanism how cells sense the gradient even in noisy environment.
[1] M Ueda, Y Sako, T Tanaka, P Devreotes, T Yanagida , Science, 2001.
[2] L Song et al., European Journal of Cell Biology, 2006.

P3-02Computational Analysis of PM 2.5 by Text Mining
Riho Miyaki 1
puttyo@kwansei.ac.jp

1

Hiroyuki Toh 1	  	 tohhir@kwansei.ac.jp

Bioscience, School of Science and Technology, Kwansei Gakuin University, 2-1 Gakuen,
Sanda, Hyogo, 6691337, Japan

Keywords: Text mining, PM2.5
Recent development of omics technologies has introduced the flood of big data in the molecular
life sciences. Associated with such trends, biological literatures have emerged as a rich resource ofknowledge. The overall perspective of the biological literatures is called "Bibliome", and theknowledge extraction from the literatures by text mining has become one of the important methods
for data analysis in bioinformatics. Actually, there are many applications of text mining to thebibliome. For example, protein-protein interaction network [1] and the association of genes
methylated in diseases [2] have been investigated by text mining.
In this study, we apply the text mining approach to the analysis of PM2.5. PM2.5 is a small particle(under 2.5 µm in diameter) and it has a risk that it causes respiratory illness and cardiovasculardisease [3]. We performed keyword search in PubMed [4] using PM2.5 as a query. 3375 literatures
were detected at 20.8.2015. Then, all the abstracts were downloaded as a set of text data. Then, theset was divided into a unit corresponding to each literature. Next, the text data was preprocessed asfollow. The title, the author information, and the other data rather than the main body of theabstract were removed from each abstract. Furthermore, the characters except for alphabeticaltexts and the title of the sections in the abstract were removed if any. Finally, the stemming was
performed with SnowballC package of R [5]. The, the preprocessed text data were transformed into
a term-document matrix with tm package of R. The term-document matrix was analyzed withseveral R packages for multivariate analysis. Based on the result, we will discuss the association ofelements related to PM2.5.
[1] Shogo Shibutani, Tomoyuki Hiroyasu, Mitsunori Miki, Hisatake Yokouchi, Masato
Yoshimi. 2009 Dec, “Building a protein-protein Interaction Network using Information on
the Web”, JPSJ SIG Technical Report, 2009-BIO-19, 49, pp.1-6.
[2] Arwa Bin Raies, Hicham Mansour, Robert Incitti, Vladimir B.Bajic. 2015 Jan, “DDMGD: thedatabase of text-mined associations between genes methylated in diseases from different species”,
Nucleic Acids Research.
[3] Atkinson RW, Kang S, Anderson HR, et al. 2 0 1 4 A p r , “E p i d e m i o l o g i c a l t i m e s e r i e ss t u d ie s o f P M 2 .5 a n d d a i l y m o r t a l i t y a n d h o s p i t a l a d m i s s i o n s : a s y s t e m a t i c r e v ie w
a n d m e t a - a n a l y s i s ” , T h o r a x , 2 0 1 4 , 6 9 , p p .6 6 0 - 6 6 5 .
[4] http://www.ncbi.nlm.nih.gov/pubmed[5] https://cran.r-project.org/

P3-03
Inferring the mode-of-action of bioactive compounds
using large-scale transcriptome data in LINCS
toward drug repositioning
Michio Iwata 1
m-iwata@bioreg.kyushu-u.ac.jp

Ryusuke Sawada 1

Hiroaki Iwata 1

rsawada@bioreg.kyushu-u.ac.jp

iwata@bioreg.kyushu-u.ac.jp

Yoshihiro Yamanishi 1,2
yamanishi@bioreg.kyushu-u.ac.jp
1

2

Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1
Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, Japan
Institute for Advanced Study, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka,
Fukuoka 812-8582, Japan

Keywords: Drug repositioning, Transcriptome data, Mechanism of action, Bioactive compound,
Drug target
Identifying the mode-of-action of bioactive compounds (including drugs) is a major
challenge in chemical systems biology. In fact, there are many drugs whose mechanisms of actions
remain unknown. Most drugs are chemical compounds that interact with therapeutic target proteins
implicated in a disease of interest. However, drug molecules may interact not only with theprimary target proteins but also with additional proteins (hereafter referred to as off-targets),
resulting in unexpected side effects. Discovering target and off-target effects of drug candidate
compounds is crucial to address the efficacy and safety of compounds in drug development. Side
effects derived from the off-target interactions are basically undesired, but they may occasionallybe beneficial for new therapeutic indications. Therefore, understanding the complex responses of
the human body to the treatments of bioactive compounds is a vital important issue in medical andpharmaceutical research. A promising unbiased approach is genome-wide gene expression
profiling of the transcriptional response to chemical perturbations of human cell lines.
In this study, we developed a novel method to infer the mode-of-action of bioactive
compounds by using large-scale chemically perturbed transcriptome data, which enables to
identify potential target pathways, target proteins, and therapeutic indications of each bioactive
compound. We analyzed chemically induced changes in global patterns of gene expression profiles
in response to the treatments of 20122 bioactive compounds (including most approved drugs) for
68 human cell lines derived from the Library of Integrated Network-based Cellular Signatures. Our
pathway enrichment analyses of up-regulated and down-regulated genes revealed a set of target
pathways that were activated and inactivated by the chemical perturbations, respectively. We
explored the target proteins of compounds (including primary targets and off-targets) with the gene
expression similarity by making the best use of millions of compound-protein interactions. The

cross-validation experiments show that our target prediction method outperforms previous
methods in terms of accuracy, interpretability, and applicability, and the predictive
performance does not depend on compound chemical structures. Based on the target proteinsand pathways, we finally performed a comprehensive prediction of new drug indications for a wide
range of diseases defined in International Classification of Diseases. The proposed method is
expected to be useful for understanding the mode-of-action of bioactive compounds and forpredicting new drug indications toward drug repositioning.
[1] Ashburn, T.T. and Thor, K.B., Drug repositioning: identifying and developing new uses forexisting drugs, Nature Reviews Drug Discovery, 3:673-683, 2004.

P3-04
Exploration of Target Molecules Affecting the Conversionof iPS Cells into Cancer Stem Cells Using ChemicalCompound Library
Takayuki Kudoh 1

kudoh@okayama-u.ac.jp

Takuma Matsumoto 1

pvxn4t5n@s.okayama-u.ac.jp
1

Tomonari Kasai 1

tkasai@okayama-u.ac.jp

Anna Sanchez Calle 1
annasc.nit@gmail.com

Saki Sasada 1

pxa57en3@s.okayama-u.ac.jp

Arun Vaidyanath 1

arunvnath@okayama-u.ac.jp

Kenta Hoshikawa 1

kenta-star@lion.ocn.ne.jp

Masaharu Seno 1

mseno@okayama-u.ac.jp

Graduate School of Natural Science and Technology, Okayama University,
3-1-1Tsushima-Naka, Kita-ku, Okayama 700-8530, Japan

Keywords: Cancer Stem Cell (CSC), iPS, Tumorigenesis
Cancer stem cells (CSCs) are considered as the principal cause of the origin and recurrence oftumor and promising source of curative information for cancer [1]. However, the developmental
processes of CSC are yet to be elucidated, because there are few appropriate model systems for
replicating the events of tumorigenesis. We succeeded in converting iPS cells into CSCs byculturing iPS cells in the conditioned media of cancer cell lines [2]. This process is considered to
be a good model of spontaneous tumorigenesis. Proper analysis of the process would provide us
the underlying mechanism of conversion of normal undifferentiated progenitor cells into CSCs.
In this study, we assessed 75 chemical compounds, which are considered to regulate cellular
proliferation, differentiation and development, for the ability to exhibit the effects on theconversion of normal iPS cells into CSCs by adding each compound in the culture media. The
effect of these chemical compounds on the conversion of iPS cells into CSCs was evaluated after 8days of exposure to each chemical compound by the relative number of spheroids and the GFP
expression compared with the control experiment performed without the chemical compounds [3].
We successfully identified 20 compounds significantly enhanced and 14 compounds suppressed theconversion of iPS cells into CSCs. Searching the target molecules of these compounds throughPubchem database [4], we identified the candidate target proteins, which should be related with theconversion.

[1] Visvader, J. E., Cells of origin in cancer, Nature 2011, 469, 314–322.
[2] Chen, L., Kasai T., Li Y. G., Sugii Y., Jin G. L., Okada M., Vaidyanath A., Mizutani A., SatohA., Kudoh T., Hendrix M. J. C., Salomon D. S., Fu L., and Seno M., A Model of Cancer StemCells Derived from Mouse Induced Pluripotent Stem Cells, Plos One 2012, 7, e33544.
[3] Japanese Patent Application No. 2014/246457[4] https://pubchem.ncbi.nlm.nih.gov/

P3-05
A database for protein-ligand complexes
in enzymatic reactions
Yoichi Murakami 1

yoichi@nibiohn.go.jp

Satoshi Omori 2

omori@sb.ecei.tohoku.ac.jp

Kengo Kinoshita 2,3,4

kengo@ecei.tohoku.ac.jp

1

2
3
4

Center for Drug Design Research, National Institutes of Biomedical Innovation, Health andNutrition, Japan
Graduate School of Information Sciences, Tohoku University, Japan
Institute of Development, Aging and Cancer, Tohoku University, Japan
Tohoku Medical Megabank Organization, Japan

Keywords: Database, Protein-ligand interactions, Protein modeling, enzymatic reaction
NLDB (Natural Ligand DataBase; URL: http://nldb.hgc.jp) is a database of automaticallycorrected and predicted 3D protein-ligand interactions in the enzymatic reactions of various
metabolic pathways [1]. Information about these non-covalent interactions is important, not only
for studying the molecular functions of specific proteins but also for enzyme-targeted drug
discovery. Therefore, in order to complement the structural information about the reactions, wepredict 3D protein-ligand interactions using state-of-the-art programs if their structures are
unknown, and then construct a database of the 3D interactions in various reactions. NLDB
produces three different types of data resources; the natural complexes are experimentally
determined protein-ligand complex structures in the PDB [2], the analog complexes are predictedbased on known protein structures in a complex with a similar ligand [3,4], and the ab initio
complexes are predicted by docking a ligand to predicted [5] or high confidence ligand-bindingsites of a protein. Furthermore, the database has a flexible search function, based on various types
of keywords, and an enrichment analysis function based on a set of KEGG compound IDs. NLDB
will be a valuable resource for experimental biologists studying protein-ligand interactions in
specific reactions, and for theoretical researchers wishing to undertake more precise simulations ofinteractions for drug discovery purposes.
[1] Kanehisa, M. and Goto, S., KEGG: Kyoto encyclopedia of genes and genomes, Nucleic Acids
Res, 28, 27-30, 2000.
[2] Berman, H., Henrick, K. and Nakamura, H., Announcing the worldwide Protein Data Bank,
Nature Struct. Biol, 10, 980, 2003.
[3] Kawabata, T., Build-up algorithm for atomic correspondence between chemical structures, J.
Chem. Inf. Model, 51, 1775-1787, 2011.
[4] Trott, O. and Olson, A. J., AutoDock Vina: improving the speed and accuracy of docking witha new scoring function, efficient optimization, and multithreading. J. Comput. Chem, 31,455-461, 2010.
[5] Kasahara, K., Kinoshita, K. and Takagi, T., Ligand-binding site prediction of proteins based on
known fragment-fragment interactions, Bioinformatics, 26, 1493-1499, 2010.

P3-06
Epigenetic Study between Normal and Cancer Cell Using
NGS data from Bile Duct Cancer Patients
Bharata Kalbuaji *Konagaya Akihiko *kalbuaji.b.aa@m.titech.ac.jp

kona@dis.titech.ac.jp*

Department of Computational Intelligence and Systems Science

Interdisciplinary graduate school of Science and Engineering
Tokyo Institute of Technology

Keywords: RNA sequencing, bile duct cancer, gene regulatory network
Next Generation Sequencing (NGS) is the latest method for DNA or RNA sequencing. NGS cansequence many type of sample depend on the purpose, for example RNA-seq for sequencing
mRNA to measure the gene expression level, Exome -seq for sequencing the protein coding regionof the DNA, and BS-Seq for measuring the level of methylation. NGS is widely used for cancer
research now because it gives a complete picture of human genome.
This research use publicly available database from Bile Duct Cancer patients from NCBI G EO
with accession number GSE63420. The data consist of 7 pair normal -cancer cell for RNA-seq and8 pair normal-cancer cell for Exome-seq. The objective of this research is to understand the causeof differentially expressed (DE) genes in cancer cell.
The method being used to investigate the cause of DE genes is gene regulatory network (GRN).
GRN will be used to visualize the interaction of target gene with its transcription factor (TF). The
correlation of TF and target gene will be calculated to see wh ether the DE genes are caused by the
TF or not.
[1] Blais A., Dynlacht B.D, Constructing transcriptional regulatory networks. Genes Dev.
2005;19:1499-1511.
[2] Koboldt, Daniel C., et al., VarScan 2: somatic mutation and copy number alteration discovery
in cancer by exome sequencing, Genome research 22.3 568 -576, 2012
[3] Love MI, Huber W and Anders S., Moderated estimation of fold change and dispersion for
RNA-seq data with DESeq2, Genome Biology 15(12):550, 2014.
[4] Sia D, Losic B, Moeini A, Cabellos L et al. Massive parallel sequencing uncovers actionable
FGFR2-PPHLN1 fusion and ARAF mutations in intrahepatic cholangiocarcinoma. Nat
Commun 2015 Jan 22;6:6087[5] http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63420

P3-07Development of the Structural Feature Analysis System
based on the Motif Combination Pattern of Proteins
Sota Azuma

Hiroaki Kato

azuma @ mbi.cs.tut.ac.jp

kato @ cs.tut.ac.jp

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

Keywords: protein motif, combination pattern, structural feature, co-occurrence relationship,calcium-binding protein, zinc protease, PROSITE
It is well known that the amino acid sequence of a protein is closely related to its structure andfunction. This is especially true for particular structural features called motifs, and they are
considered to be well-reserved sites in the genomic sequences. It is known that some proteins
contain several types of motif. For example, S100 proteins have two different types ofcalcium-binding sites: a low affinity one with a special structure and a normal EF-hand type highaffinity site [1]. In the present work, the authors have focused the co-occurrence relationshipbetween the known motif patterns, and developed the structural feature analysis tools for proteins.
We have prepared the target dataset taken from NCBI RefSeq (Rel 61 ) which contained 35,665
entries (the total number of residues: 19,864,982) for human proteins. For 1,308 motif patterns
that are available on the PROSITE database (Rel 20.87) [2], 310 patterns were found in the target
dataset [3] which hit from 10 to 1,000 protein entries. They were used to make the motif–protein
matrix diagram. The fraction function, s = c / min(a, b), was employed as the measurement of theco-occurrence relationship. Where there are a hits in motif A, b hits in motif B, and c hits
common to both A and B. The sequence distance for a pair of motifs was also calculated. For
example, (a) 377 proteins hit in the EF-hand motif (PS00018) search, (b) 27 proteins in
S100_CABP motif (PS00303), and (c) 23 proteins were also common to both EF-hand and S100.
It was confirmed that S100 motif pattern spans the region of the EF -hand high affinity site.
In the matrix diagram, the zinc protease motif [4] was often co-occurred with other motif
patterns. For example, the zinc protease (PS00142) and cysteine switch (PS00546) have acharacteristic co-occurrence pattern. A cysteine switch motif has been found in the certain typeof zinc proteases, such as matrix metalloproteinases (MMP) [5]. In the sequence pattern, zincprotease motif was always followed by the cysteine switch motif with the gap of about 120 aminoacids. Structural arrangement for a pair of motifs was also identified using the corresponding
PDB files. These results show the potential applicability of the present approach for structuralfeature analysis of proteins.

References:[1] Kligman D. & Hilt D.C., Trends Biochem. Sci., 13, 437-443 (1988).
[2] Sigrist C.J.A., et al., Nucleic Acids Res., 41, 344-347 (2013).
[3] Shoji J. & Kato H., 2013 Annual Conference of JSBi, 35-36 (2013).
[4] Jongeneel C.V., et al., FEBS Lett., 242, 211-214 (1989).
[5] Woessner J.F. Jr., FASEB J., 5, 2145-2154 (1991).

P3-08The integration of biological data and its application
to drug discovery
Yi-An Chen 1

Lokesh P. Tripathi 1chenyian@nibiohn.go.jp

lokesh@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: Data warehouse, Drug discovery, Proteomics, Systems biology, Bioinformatics
Effective analysis of complex biological data is a key and challenging objective in systems
biology and drug discovery. Thus, bioinformatics tools and approaches that can translate the vast
amounts of biological data into actionable research are essential to new discoveries. Biological
data leveraged from different sources provide deeper insights into gene function underlying thebiological processes under study. Therefore, biological data integration coupled with analysis andvisualisation of different data types are necessary to expand our understanding of biologicalsystems for knowledge discovery. We have previously developed TargetMine, an integrated data
warehouse optimised for target discovery and prioritisation of candidate genes. Here we describe
new developments of TargetMine, including newer data types and data models and a host ofinteractive data analysis tools to enhance the scope of TargetMine as a broad-based data analysis
and knowledge discovery platform.
[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] http://targetmine.mizuguchilab.org

P3-09
A comprehensive genomic analysis reveals the genetic
landscape of mitochondrial respiratory chain complex
defi ciencies
Masakazu Kohda 1 mkoda@saitama-med.ac.jp
Kei Murayama 2 kmuraya@mri.biglobe.ne.jp
Akira Ohtake 3 akira_oh@saitama-med.ac.jp
Yasushi Okazaki 1,4 okazaki@saitama-med.ac.jp
1

Division of Translational Research, Research Center for Genomic Medicine, Saitama Medical
University
2
Department of Metabolism, Chiba Children's Hospital3
Department of Pediatrics, Saitama Medical University
4
Division of Functional Genomics & Systems Medicine, Research Center for Genomic Medicine,
Saitama Medical University

Keywords: Mendelian genetic disorders, Metablic disorders, Mitochondria
We present the fi rst report of a comprehensive genomic analysis of a cohort of patients withmitochondrial respiratory chain defi ciency. Using high-throughput sequencing and chromosomal
aberration analysis, we identifi ed 3 mitochondria-related genes as novel disease causative genes,
and 37 novel mutations in genes linked to mitochondrial disorders including recently reported
NDUFB11, COQ4, GTPBP3 genes. We also identifi ed 2 non-mitochondria-related disease genes(MECP2, and TNNI3) and 3 chromosomal aberrations (6q24.3-q25.1, 22q11.21, and 17p12) in thiscohort.
Using comprehensive genomic analyses, we achieved fi rm genetic diagnoses in 49 of 142
patients (34.5%), which is higher than the general diagnosis rate of approximately 25%.
Comprehensive genomic analyses also enabled us to identify closely related disorders such as
idiopathic cardiomyopathy and Rett syndrome. Identifi cation of closely related disorders isimportant for patient care and application of an effi cient drug repositioning. We demonstrated that
extensive clinical heterogeneity exists in this disorder, and comprehensive genomic analyses could
reveal its clinical and genetic heterogeneity for better understanding of this complex disorder.

P3-10Classification of Mitochondrial DNA
with using deep learning
Yuhei Kaneshita

Kanako Sugiyamak-yuhei@bpe.es.osaka-u.ac.jp

k.sugiyama@bpe.es.osaka-u.ac.jp

Satoshi Asatani

Hirohiko Niioka

s.asatani@bpe.es.osaka-u.ac.jp

niioka@bpe.es.osaka-u.ac.jp

Jun Miyake
Jun_miyake@bpe.es.osaka-u.ac.jp

Graduate School of Engineering Science, Osaka University, 1 -3 Machikaneyama-cho, Toyonaka,
Osaka, 560-8531, Japan

Keywords: Deep Learning, Auto Encoder, Mitochondrial DNA, DNA analysis
A mechanical learning method "deep learning", proposed by Hinton, opened a promissing
way to artificial intelligence [1]. Deep learning extended its application range for example s peech
recognition [2], generic object detection [3] and molecular biology. Analysis on alternative DNA
splicing was also proposed [4].
The application to the field of information processing of biological system could give a
breakthrough on biology and medicine. We should like to examine "deep learning" method incomparison with PCA. In this research, we focused on mitochondrial DNAs.
There are many researches about human classification of haplogroups. Many indexes wereused, for example blood type, virus, Y-chromosome. Mitochondrial DNA (mtDNA) is one of themost popular substance for the analysis. It is regarded as a key factor to study human evolution [5].
Some gene regions in mtDNA have been selected as research indexes. There are many factors toconsider in these approaches as the evolution rates are not uniform among sequences.
We tried whole sequence mtDNA-based classification (The number of bases:16560, The
number of data:375) with deep learning. We plotted the results compressed by deep auto encoder in
2 or 3 dimension. The program of deep learning and a computer system with GPU ware designed in
our laboratory [6].
Spatial dispersion and correlation distances were obtained in 2D to 4D space by theencoder. The dimension compression results suggest that mtDNA sequences should be classified insome groups. The meanings of the spacial dispersion and the correlation distances are discussedwith reference to the divergence of human haplogroups.
[1] G. E. Hinton and R. R. Salakhutdinov, Reducing the Dimensi onality of Data with Neural
Networks, Science, 313,504-507, 2006.
[2] Frank Seide, Gang Li, and Dong Yu, Conversational Speech Transcription Using
Context-Dependent Deep Neural Networks, INTERSPEECH, 437-440, 2011.
[3] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton., ImageNet Classification with Deep
Convolutional Neural Networks, NIPS Proceedings, 2012.
[4] Michael K. K. Leung, Hui Yuan Xiong, Leo J. Lee and Brendan J. Frey, Deep learning of thetissue-regulated splicing code, Bioinformatics, 30, i121-i129, 2014.
[5] Rebecca L. Cann; Mark Stoneking and Allan C. Wilson., Mitochondrial DNA and human
evolution, Nature, 325, 31-36, 1987.
[6] S. Asatani, S. Fukushima, H. Niioka, J. Miyake, CBI Conference, 2015.

P3-11
Application of Deep Learning for
Analysis of Biological Information
Satoshi Asatani

Shoichiro Fukushimas.asatani@bpe.es.osaka-u.ac.jp

syou_fukushima@sml.me.es.osaka-u.ac.jp

Hirohiko Niioka
niioka@bpe.es.osaka-u.ac.jp

Jun Miyake
jun_miyake@bpe.es.osaka-u.ac.jp

Graduate School of Engineering Science, Osaka University,
1-3 Machikaneyama-cho, Toyonaka, Osaka,560-8531, Japan

Keywords: Deep Learning, Biological network analysis,
Deep Learning (DL) that is a kind of Neural Network gives us useful considerations for sequence
data and representation of big data. DL architectures have been developed for computer vision andvoice recognition, and produced a successful result. In recent years, DL begun to be used for
Bioinformatics [1] as an analyzing method that uses many big databases. DL can use not only
extracting the features and tendency of big data but also making models of prediction. In previous
research, Brendan J Frey predicted the sequence specificities of DNA- and RNA-binding proteins byusing DL [2].
Recent DL techniques include many practical tricks (dropout [3], rectified linear function [4],momentum [5], etc.) that make it easier and quickly to train the networks. Some of these tricks are notunderstood completely though these bring successful result. We should research the cause of goodeffect.
We developed a new framework of DL and practical tricks for the application to Biology. To
satisfy the huge steps of calculation, a parallel co mputing system is desired with GPU. A computer
system was prepared in our laboratory with an Intel Core i7 CPU and GTX980Ti GPU operated under
5.63TFLOPS.
The system was used for the analysis of cells and DNAs. A convolutional neural network
structure was prepared because it has a high precision for cell identification. Human / mammalian cells
appeared in microscopy images were sharply classified into distinctive two kinds as: 2 -digits (A = 00,
T = 01, G = 10, C = 11) or 4-digits (A = 1000, T = 0100, G = 0010, C = 0001). The latter set gave stable
results. The divergence of mitochondrial DNA on Deep Autoencoder compression gave suggestive
results on the divergence of human haploid group.

[1] Michael K. K. Leung, Hui Yuan Xiong, Leo J. Lee, and Brendan J. F rey., Deep learning of thetissue-regulated splicing code. International Society for Computational Biology , i121-i129,
2014.
[2] Babak Alipanahi, Andrew Dlong, Matthew T Weirauch & Brendan J. Fray., Predicting thesequence specificities of DNA- and RNA-binding proteins by deep learning. Nature
Biotechnology, 33, 831-838, 2015.
[3] N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Aalakhutdinov. Dropout: A
simple way to prevent neural networks from overfitting. Journal of Machine Learning Research,
15:1929-1958, 2014.
[4] V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In Proc.
International Conference on Machine Learning, 2010.
[5] G. E. Hinton, S. Osindero, and Y. The. A fast learning logarithm for deep belief nets. NeuralComputation, 18:1527-1544, 2006.

P3-12
Classification of C2C12 cells before and after
differentiation with using deep learning
Hirohiko Niioka
niioka@bpe.es.osaka-u.ac.jp

	
  	
 

Satoshi Asatanis.asatani@bpe.es.osaka-u.ac.jp

Aina Yoshimura
aina.3am@bpe.es.osaka-u.ac.jp

Jun Miyake
jun_miyake@bpe.es.osaka-u.ac.jp	  Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho,
Toyonaka, Osaka, 560-8531, Japan

Keywords: cell differentiation, deep learning, image processing
Regenerative medicine industry was accelerated by development of iPS cells that will be
useful for tissue/organ regeneration as well as drug discovery. To this date, well-skilled humans
have to check the cellular differentiations. However, to realize the industry, numerous numbers ofcells are necessary and must be treated correctly, rapidly and automatically. Especially fortissue/organ regeneration, non-invasive method is required for transplant and the cells should not
be stained by fluorescent markers. We utilized deep learning (DL) technology to classify the levelof differentiation of cells with bright-field images of cellular morphologies.
DL is emerging in image recognition field [1,2]. In 2012 DL got a lot of attention because
DL won the first prize in a competition (ImageNet Large Scale Visual Recognition Challenge(ILSVRC)) with a huge lead. Recently, it is starting to apply DL for recognition of cellularconditions [3,4].
In this study C2C12 cells (mouse myoblast) were used as samples and the cells were
cultured in glass base dishes. The differentiation of C2C12 myoblasts into myotubes was inducedby changing the culture medium (defined as day 0). In the differentiation cellular morphology
changed from round shape to elongated tubular shape due to fusion of cells (day 0 to 5). We
developed DL program based on Convolutional Neural Networks (CNN) [2,5] and self-build PCs
for the classification of the differentiating cells.
It was possible to classify C2C12 before and after differentiation. The features generated
by the DL showed both round and elongated shape cellular images. We expect that DL will classify
differentiation of other cell species, cellular species e.g. cancer cells and so on.
[1] G. E. Hinton and R. R. Salakhutdinov, Reducing the Dimensionality of Data with Neural
Networks, Science, 313, 504, 2006.
[2] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, ImageNet Classification with Deep
Convolutional Neural Networks, NIPS Proceedings, 2012.
[3] Angel Alfonso Cruz-Roa, John Edison Arevalo Ovalle, Anant Madabhushi, Fabio Augusto
González Osorio, A Deep Learning Architecture for Image Representation, VisualInterpretability and Automated Basal-Cell Carcinoma Cancer Detection, Lecture Notes in
Computer Science, 8150, 403, 2013.
[4] Ting Chen and Christophe Chefd'hotel, Deep Learning Based Automatic Immune Cell
Detection for Immunohistochemistry Images, Lecture Notes in Computer Science, 8679, 14,
2014.
[5] S. Asatani, S. Fukushima, H. Niioka, J. Miyake, CBI Conference, 2015.

P3-13
Analysis of serum microRNA expression and cytokine production profiles
for understanding febrile episodes after vaccination

Yayoi Natsume-Kitatani 1

Koji Ozasa 1

Tetsuo Nakayama 2

natsume@nibiohn.go.jp

ozasa_183cm@nibiohn.go.jp

tetsuo-n@lisci.kitasato-u.ac.jp

1

2

3

Ken J. Ishii 1,3

Kenji Mizuguchi 1

kenishii@biken.osaka-u.ac.jp

kenji@nibiohn.go.jp

National Institutes of Biomedical Innovation, Health and Nutrition
7-6-8 Asagi Saito Ibaraki-city Osaka 567-0085, JAPANKitasato Institute for Life Sciences, Kitasato University,
Shirokane 5-9-1, Minato-ku, Tokyo 108-8641, Japan.
Immunology Frontier Research Center, Research Building 6F, Osaka University,
3-1 Yamada-oka, Suita-city, Osaka 565-0871, Japan

Keywords: vaccination, microRNA, WGCNA
Vaccination for children is performed world wide to protect from infectious diseases. One of the
major side effects of vaccination is a fever, the molecular basis of which remains to be elucidated.
Here, we report the results of microRNA microarray analysis and cytokine multiplex assays forserum samples collected after simultaneous inoculation of 33 children (fever(+) = 24, fever(-) = 9).
We performed weighted gene co-expression network analysis (WGCNA) [1,2] to link the
microRNA expression and cytokine production. The result showed that microRNAs that were
up-regulated in fever(+) children were divided into two modules and each of them wassignificantly (module-trait relationship p-value = 0.002) correlated with cytokine production. The
result of transcription factor binding sites (TFBS) search [3] in microRNA promoter regions
implied that the combination of activated transcription factors (TFs) differed in fever(+) andfever(-) children, and that TFs related with cell proliferation were activated in fever(+) children.
[1] Zhang, B. and Horvath, S., A general framework for weighted gene co-expression network
analysis, Statistical Applications in Genetics and Molecular Biology, 4.1, 2005.
[2] Langfelder, P. and Horvath, S., WGCNA: an R package for weighted correlation network
analysis, BMC Bioinformatics, 9:559, 2008.
[3] Yang, J.H., Li, J.H., Jiang, S., and Qu, L.H., ChIPBase: a database for decoding thetranscriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data,
Nucleic Acids Research, 41(D1), D177-D187, 2013.

P4-01The drug discovery and the development in academia:
the identification and the development of positive
allosteric modulators of PGI 2 receptor for the treatmentof pulmonary arterial hypertension
Hiroyuki Miyachi 1
miyachi@pharm.okayama-u.ac.jp

Toshifumi Suzuki 2
tsfm.suzuki@gmail.com

Takayoshi Okabe 3
tokabe@mol.f.u-tokyo.ac.jp

Kohki Yamamoto 1
psdq45rm@s.okayama-u.ac.jp

Riyo Imamura 3
riyo-ima@mol.f.u-tokyo.ac.jp

Tetsuo Nagano 3
tlong@mol.f.u-tokyo.ac.jp

1

Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University.
1-1-1, Tsushima-Naka, Kita-ku, Okayama 700-8530, Japan
2 Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7 -3-1 Hongo,
Bunkyo-ku, Tokyo 113-0033, Japan
3 Drug Discovery Initiative, The University of Tokyo , 7-3-1 Hongo, Bunkyo-ku, Tokyo
113-0033, Japan

Keywords: Pulmonary arterial hypertension, positive allosteric modulator, HTS screen
Pulmonary arterial hypertension (PAH), is a generic term for conditions that allow an increase inpulmonary arterial pressure. Rise of long-term pulmonary artery pressure causes right heart failure
is a rare and incurable disease of life prognosis average three years. Only continuous infusion
therapy of PGI 2 derivative is the choice for patients placed in class III or IV PAH. However, theneed for equipment mounting, adaptation method, side effects, decrease QOL of the patients. PGI 2
production amount in PAH patients is reduced to half of the healthy individuals. The authors
speculated that if it is possible to increase the sensitivity of the PGI 2 receptor (IP) additionally, it
is considered possible to utilize the remaining PGI 2 for the treatment of late stage PAH. Therefore,
the authors focused on the search to identify positive allosteric modulators of PGI2 receptor(IPPAM).
In contrast to the competitive compounds (orthosteric modulators), allosteric modulators interact
with binding sites that are topographically distinct from the binding site of the endogenous ligand.
Positive allosteric modulators do not activate receptors on their own , and in the presence of theendogenous ligand, allosteric modulators enhance the natural physiological activity of the receptor.
Consequently, allosteric modulators can exert their effects while preserving normal physiologicalsignaling patterns.
The authors performed HTS screen of 140,000 University Tokyo (UT) compound libraries, andfound only one hit compound, i.e., a tetrazole derivative.
In this poster presentation, the authors would like to disclose the design, synthesis, in vitro
IPPAM activity and structure-activity relationship (SAR) of a series of tetrazole derivatives.
The synthetic route to the optically active tetrazole derivatives and the stereochemistry -IPPAM
activity relationship of a series are also discussed.

P4-02The site of CYP3A4 metabolism prediction of
tolterodine using MD and its application to compounddesignAtsuko Sato 1
atsuko.sato@kyowa-kirin.co.jp

Chiduru Watanabe 2
chiduru.watanabe@riken.jp

Hitomi Yuki 2
hitomi.yuki@riken.jp

Jun-ichi Saito 1
jun.saito@kyowa-kirin.co.jp

Teruki Homma 2
honma.teruki@riken.jp

1

2

Kyowa Hakko Kirin Co.,Ltd., Research Core Function Laboratories, Research functions Unit, R&D Division, 1188, Shimotogari, Nagaizumi-cho, Sunto-gun, Shizuoka, 411-8731, JapanRIKEN Center for Life Science Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama,
Kanagawa 230-0045, Japan

Keywords: Cytochrome P450 (CYP), tolterodine, Sites of metabolism (SOM), Molecular
dynamics simulation, Multiple initial structures
CYP3A4 contributes to more than 50% of clinical drug metabolism . Therefore, optimization of
drug candidates considering the CYP3A4 metabolism is critically important. It is
enormously challenging to precisely predict CYP3A4-ligand complex due to variableconformational possibilities, with its numerous binding substrates. Our purpose of this research is
to establish methods for metabolically stable drug design. In our previous study, we have examined
the prediction for sites of metabolism(SOM) on carbamazepine (CBZ) , which
included docking and molecular dynamics
simulation from multiple initial structures [1].
In this presentation, we constructed a complex
model of CYP3A4 and tolterodine, which was amore flexible substrate than CBZ, by applying asimilar method (Figure 1). With this process, we
compared clustering of docking poses by PLIF
(Protein Ligand Interaction Fingerprint) and
RMSD for efficient selection of MD initialstructures. Following the MD simulation, we
calculated the binding energy of tolterodine andits accessibility to the heme iron by each pose in
the simulation and estimated the binding pose of
tolterodine in CYP3A4. Based on this model,
some compound designs to improve metabolicstability would be proposed.
[1] Yuki, Hitomi; Honma, Teruki; Hata, Masayuki; et al. Prediction of sites of metabolism in a
substrate molecule, instanced by carbamazepine oxidation by CYP3A 4, Bioorg. & Med. Chem.
20: 775-783, 2012.

P4-03
Utilization of UV spectrum prediction for identification of
drug metabolites
Kaori Murakoshi 1

Hideaki Sato 1

murakoshi.kaori@mc.mt-pharma.co.jp

sato.hideaki@ma.mt-pharma.co.jp

Hidefumi Kaji 1
kaji.hidefumi@mh.mt-pharma.co.jp

1

DMPK Research Laboratories, Mitsubishi Tanabe Pharma Corporation, 2-2-50 Kawagishi,
Toda-shi, Saitama 335-8505, Japan

Keywords: UV spectrum prediction, Metabolite identification, Estimation of metabolite amountPrediction of metabolic pathways in animals and humans in the preclinical stage is extremely
important to develop drug candidates with good DMPK profiles. So we obtain the drug metabolism
information using various biological samples such as plasma, urine and the reaction mixtures
obtained from in vitro metabolic studies. From the point of view of drug-drug interaction andtoxicity, it is necessary to estimate the amount of metabolites in human and animal samples. Whenthe authentic sample of metabolite is not available, we generally estimate its amount bycomparison with the UV spectrum of the parent compound. In general, the UV spectra ofmetabolites such as hydroxide and desmethyl form are hardly different from those of the parent.
On the other hand, in the case where the conjugated system is changed or the original structure isseparated in two by hydrolysis, the UV spectra of metabolites are significantly changed. As a result,
it is difficult to estimate the amount of metabolites. Therefore, we thought that if it is possible topredict the UV spectrum from the estimated structure of the metabolite, we can estimate theamount of metabolites with good precision. After several trials with commercially available
software to predict UV spectrum, we concluded that it is difficult to completely predict the λmax
and absorbance of various types of compounds and their metabolites. However, SCIGRESS V2(FUJITSU) is very useful for the prediction of a significant change of the UV spectrum pattern.
The UV spectra of compound A are shown in Figure 1, and those of its metabolite compound B are
shown in Figure 2. Both predicted spectra indicated suitable similarity to the observed spectra. In
this study, we report how we can utilize the software to elucidate the structure of metabolites.

P4-04Comparison of various three-dimensional culture plates
and monolayer culture plates for CYP metabolic activity
and induction with cryopreserved human hepatocytes
Orie Terai 1

Takeshi Izukawa 1

orie.terai@scas.co.jp

takeshi.izukawa@scas.co.jp

Yasushi Tsutsumi 1

1

Kotaro Okajima 1
k.okajima@scas.co.jp

Chiaki Ueda 1

yasushi.tsutsumi@scas.co.jp

c.nishiyama.ueda@scas.co.jp

Yoshihisa Sogame 1

Kazunori Yanagi 1

yoshihisa.sogame@scas.co.jp

kazunori.yanagi@scas.co.jp

Sumika Chemical Analysis Service, Ltd., 1-135, Kasugade-Naka 3-chome,
Konohana-ku, Osaka 554-0022, Japan

Keywords: CYP enzyme activity, CYP induction, 3D culture[Introduction and Purpose]
Hepatocytes form three-dimensional (3D) structure in vivo. Recently, various 3D culture plates
for hepatocytes have come onto the market. These plates have a structure such as scaffolds,
polymer gels, hydrophobic coating and so on,; which support the cell -cell interactions and3D-conformation of hepatocytes so to offer in vivo-like microenvironment. However, there are nocomparable studies among 2D and 3D culture plates for CYP metabolic activity and induction.
We tested the CYP metabolic activity and induction of hepatocytes on various 3D culture plates.
Here we report the results of comparable study to evaluate features of commercial 3D culture
plates.
[Cell Culture]
Cryopreserved human hepatocytes were cultured in humidified atmosphere containing 5% CO 2 at
37 o C. The CYP metabolic activities were measured at 3, 7, 10, 14, 17, and 21 days after seeding
cells. On CYP induction assay, CYP mRNA expression levels were measured at day6.
[CYP Metabolic Activity Analysis]
On each day for CYP metabolic activity assay, culture medium was aspirated and replaced with
KHB buffer including a cocktail of probe substrate for CYPs. After incubation , the supernatant wascollected and pretreated to give an injection sample for the LC/MS/MS system.
[CYP Induction Assay]
Human hepatocytes were cultured with CYP inducer or DMS O for 2days. Total RNA of the cells
was extracted and then reverse transcribed. The expression levels of CYP1A2, 2B6 and 3A4mRNA were evaluated by Real-Time PCR.
[Results and Discussion]
We confirmed that the cryopreserved human hepatocytes formed 3D str ucture at 1 day after
seeding cells. CYP metabolic activities in 2D monolayer culture plates were dramatically reduced
at 7 to 14 days after seeding cells. However, the activit ies in some 3D culture plates weremaintained. CYP induction in 3D culture plates was almost equal to 2D monolayer culture plates.

P4-05Construction of web services to evolve drug like propertiesof ligands
Naoki Arai 1

Shunsuke Yoshikawa 2arai.n.ai@m.titech.ac.jp

yoshikawa@bi.cs.titech.ac.jp

Nobuaki Yasuo 2
yasuo.n.aa@m.titech.ac.jp

1

2

3

Yusuke Nakashima 2
nakashima.y.ac@m.titech.ac.jp

Ryunosuke Yoshino 3

Masakazu Sekijima 2, 3

yoshino.r.aa@m.titech.ac.jp

sekijima@gsic.titech.ac.jp

Department of Computer Science, School of Engineering, Tokyo Institute of Technology,
2-12-1, Ohokayama, Meguro-ku, Tokyo, 152-8550, Japan
Department of Computer Science, Graduate School of Information Science and Engineering,Tokyo Institute of Technology, 2-12-1, Ohokayama, Meguro-ku, Tokyo, 152-8550, Japan
Global Scientific Information and Computing Center, Tokyo Institute of Te chnology, 2-12-1,
Ohokayama, Meguro-ku, Tokyo, 152-8550, Japan

Keywords: Drug discovery, Virtual compounds library, Web server
In Structure Based Drug Discovery (SBDD), molecular docking is widely used to search hit
compounds. After obtaining of them, their properties are optimized for lead compound.
In this study, we attempted to evolve properties of ligands by applying synthetic rules involved in
the improving Hydrogen Bonding Donor (HBD), Hydrogen Bonding Acceptor (HBA) and stability
in multiple stages. In addition, we implement it as web services. After the user find hit compounds,
our services make it possible for user to obtain virtual libraries of drug -like compounds than hit
compounds while checking used virtual chemical reactions interactively.
In response to the compound data input, our services simulate virtual chemical reactions
accordingly to generate digital data of new chemical compounds, thereby compiling virtualcompound libraries. By imparting purposes to the virtual chemical reactions, the server completes
the construction of libraries including compounds with properties as desired.
[1] http://www.rdkit.org/[2] https://www.djangoproject.com/

P4-06
Evaluation of Applicability of Human iPS cell-derived
Hepatocyte to Pharmacokinetic Studies
Seiichi Ishida

Shinichiro Horiuchi

Takashi Kubo

Yukie Kuroda

ishida@nihs.go.jp

Riho Ishida

Shoko Uchida

Su-Ryang Kim

Yuko Sekino

Division of Pharmacology, National Institute of Health Science,
1-18-1 Kamiyoga, Setagaya-ku Tkyo 1588501, Japan

Keywords: iPS cell-derived hepatocyte, pharmacokinetics, functional evaluation
Pharmacokinetic studies of drug candidates conducted at early stage of drug development areimportant for securing their safety in human administration. Today, basic data are provided fromexperiments using the human primary hepatocytes. However, problems are inherited in them, suchas differences among lots based on donor variations and limited supply of every lots. To
circumvent these problems, hepatocytes derived from human iPS cells (hiPSC -hepatocytes) are
expected as an alternative cell source.
We have been evaluating their drug metabolizing enzyme activities and induction abilities bychemical inducers, and the results were reported at last year’s meeting. In this meeting, we willreport results obtained from a new version of commercially available hiPSC-hepatocytes. For
evaluating hiPSC-hepatocytes, we measured metabolic activities of CYP1A2, CYP2D6, CYP2C9,CYP2C19 and CYP3A4 by LC-MS/MS (Sumika Chemical Analysis Service, Ltd.), and expressionsof CYP1A1, CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP3A4, CYP3A5, CYP3A7, GSTA2,UGT1A1, ABCB1, ABCC2, PXR, CAR, AhR, CEPBα, and HNF4α by qPCR (Life Technologies) in
hiPSC-hepatocytes from three vendors (Takara Bio Europe AB, Cellular Dynamics International,
ReproCELL). We also evaluated induction of CYP1A2, CYP2B6, and CYP3A4 by typical inducers(omeprazole, phenobarbital and rifampicin, respectively) in mRNA level.
CYP enzyme activities in hiPSC-hepatocytes from two venders showed close to HepaRG cells,
which was used as a control hepatocyte cell line. Induction of CYP1A2 and CYP3A4 was observedin hiPSC-hepatocytes from one vender. Preliminary results for the improvement of CYP
expressions and inducibility will be also presented.

P4-07High-Performance Prediction for Estrogenic Compounds
based on the Tox21 10K Compound Library
Yuki Asako
y121007@std.my-pharm.ac.jp

Yoshihiro Uesawa
uesawa@my-pharm.ac.jp

Meiji Pharmaceutical University, 2-12-11, Noshio, Kiyose, Tokyo 204-8588, Japan

Keywords: Random forest, Estrogen receptor, Tox21data challenge 2014
Introduction: Compounds related with estrogen receptor (ER) activation can disrupt humanendocrine processes, severely affecting human reproduction and growth. Our prediction model for
ER-ligand binding domain activation compounds performed well in the Tox21 data challenge 2014
[1,2], a competition organized by the National Institute of Health. In this study, we review themodel construction process to improve the prediction performance.
Methods: Datasets published in the Tox21 data challenge 2014 and based on the Tox21 10K
compound library [3] were used in the present analysis. Several properties were computed using
the MOE, Marvin, and Dragon software packages. The random forest method [4] included in the
JMP Pro, SAS statistical software was adopted for the model construction. The optimum model
was selected from a lot of random forest models with numerous combinations of thehyperparameters. Prediction abilities of the models were validated with area under ROC(ROC_AUC) in the external test set and the final evaluation set.
Results and discussion: Overfitting was observed in the model submitted in the competition.
We were able to improve the prediction model’s discrimination ability for estrogenic compounds
by investigating the optimal combination of hyperparameters used in the random forest algorithm.
Furthermore, the model analyses revealed that certain structural and physicochemical properties
such as the number of phenolic hydroxyl groups contributed to the interactions between ER and thecompounds.
Conclusion: In this study, an optimized random forest model is presented for the prediction of
ER activation compounds. This method is expected to be useful for the screening of toxic
compounds.
[1] Tox21 Data Challenge 2014, https://tripod.nih.gov/tox21/challenge/index.jsp[2] NCATS Announces Tox21 Data Challenge Winners,
http://ncats.nih.gov/news/releases/2015/tox21-challenge-2014-winners[3] Hsieh, J.H., Sedykh, A., Huang, R., Xia, M., Tice, R.R., A data analysis pipeline accounting for
artifacts in Tox21 quantitative high-throughput screening assays, J. Biomol. Screen., 20:887-897,
2015.
[4] Breiman, L., Random forests, Mach. Learn., 45:5-32, 2001.

P4-08Structural Characterization of Compounds with PPARγ
Activation based on the Tox21 10K Compound Library
Masaya Aoki
y121002@std.my-pharm.ac.jp

Yoshihiro Uesawa
uesawa@my-pharm.ac.jp

Meiji Pharmaceutical University, 2-12-11, Noshio, Kiyose, Tokyo 204-8588, Japan

Keywords: Random forest, PPARγ, Tox21data challenge 2014
Introduction: Peroxisome Proliferator-Activated Receptor-γ (PPARγ) shows bioactive functions
such as adiponectin production and inflammatory cytokine production inhibition. PPARγ activation
compounds are valuable antidiabetics and analgesics [1]. In this study, discrimination models wereconstructed based on information for chemical structures included in the Tox21 10K library [2].
Pattern regression was performed using the random forest algorithm [3] to calculate the importanceindex for descriptors.
Methods: Datasets published in the Tox21 data challenge 2014 [4] were used in the present
analysis. Structural and physicochemical properties were computed using the MOE software
package. The random forest method included in the JMP Pro, SAS statistical software was adopted
for the model construction. The optimum model was selected from a lot of random forest models
with numerous combinations of hyperparameters. Prediction abilities of the models were validatedwith area under ROC (ROC_AUC) in the external test set and the final evaluation set.
Results and Discussion: The discrimination model produced an excellent ROC_AUC value inthe final evaluation set that was used for ranking the competitors in the Tox21 data challenge 2014.
Thus, our improved model was equally ranked among the top 10 in the competition, based on its
prediction ability. Hereafter, the physicochemical properties of the active compounds will be
investigated based on the importance of the descriptors used in the model construction.
Conclusion: The results of the current study are expected to contribute to drug design anddevelopment using PPARγ activation compounds.
[1] McKinnon, B., Bersinger, N.A., Huber, A.W., Kuhn, A., Mueller, M.D., PPAR-γ expression inperitoneal endometriotic lesions correlates with pain experienced by patients, Fertility and
Sterility, 93:293-296, 2010.
[2] Hsieh, J.H., Sedykh, A., Huang, R., Xia, M., Tice, R.R., A data analysis pipeline accounting for
artifacts in Tox21 quantitative high-throughput screening assays, J. Biomol. Screen., 20:887-897,
2015.
[3] Breiman, L., Random forests, Mach. Learn., 45:5-32, 2001.
[4] Tox21 Data Challenge 2014, https://tripod.nih.gov/tox21/challenge/index.jsp

P4-09Current Status of Population Pharmacokinetic Studies inDrug Development and Their Usefulness in Medical
Practice

1

Megumi Watanabe-Uchida 1

Mamoru Narukawa 1

watanabem@pharm.kitasato-u.ac.jp

narukawam@pharm.kitasato-u.ac.jp

Department of Clinical Medicine (Pharmaceutical Medicine), Graduate School of
Pharmaceutical Sciences, Kitasato University, 5-9-1 Shirokane, Minato-ku, Tokyo 108-8641,
Japan

Keywords: Population pharmacokinetics (PPK), Package Insert (PI), Interview Form (IF)With the publication of the ICH-E7 guideline, the guidance document on population pharmacokinetics(PPK) by the FDA, and the guideline on clinical pharmacokinetic studies in Japan, PPK analys is has
been recognized widely in drug development. Also, a section to describe the result of PPK analysis wasadded in the interview form (IF) preparation manual in 2008, which is a positive movement towardutilizing PPK analysis results in medical practice. In this study, we investigated the recent situation ofutilizing PPK analysis results in new drug applica tion (NDA) and other documents in Japan and the US
and the usefulness of the provided information in medical practice.
Data on PPK analysis results were collected from package insert (PI), IF, review report and common
technical document for Japan, and label and clinical pharmacology review by the FDA for the US, for
new molecular entities approved between Jan 2012 and Dec 2014. As for the usefulness of the provided
information, degree of contribution to the pharmacokinetic parameter estimation of an individual patient
in medical practice was classified into 6 based on the information of pharmacokinetic parameters andcovariates in the PI, IF and label.
The proportion of documents that contain information of PPK analysis results for new molecular
entities approved in 2012, 2013 and 2014 were 43.6% (17/39), 51.9% (14/27) and 61.0% (25/41) in label,
14.8% (9/61), 21.9% (7/32) and 13.3% (6/45) in PI, and 52.5% (32/61), 53.1% (17/32) and 46.7%(21/45) in IF, respectively. As for the usefulness of the provided information, the information of PPKparameters and cause of variation was provided in 92.9% of the label, 50.0% of the PI and 68.6% in theIF.
When the information was compared between label and PI, and between clinical pharmacology review
and review report, the situation of utilizing PPK analysis results still hasn’t progressed in Japan. PPK
analysis is often used to examine the change of pharmacokinetic profiles in patients with hepatic or/and
renal impairment in the US, whereas standard pharmacokinetic studies or stratified analysis are usuallyused in Japan. This is considered as one of the factors to differentiate the situation in the US and Japan.
Result of the examination of usefulness of the provided information suggested that in some cases theinformation was not sufficient to precisely estimate patient pharmacokinetic parameters.

P5-01
Pseudorotaxane formation methods
targeting on nucleic acids
Kazumitsu Onizuka

Takuya Miyashita

onizuka@tagen.tohoku.ac.jp

Takuya Amemiya

Fumi Nagatsugi

nagatugi@tagen.tohoku.ac.jp

Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1
Katahira, Aoba-ku, Sendai, Miyagi, 980-8577, Japan

Keywords: Nucleic acids, Pseudorotaxane, Template reaction, Molecular robotics
Rotaxane is a molecular architecture consisting of a dumbbell shaped molecule which isthreaded through a molecular ring. In the nucleic acid chemistry, unique mechanically interlocked
molecular architectures such as catenane and rotaxane have been constructed by taking advantage
of base pairing. A variety of methods to construct interlocked molecular architectures have beendeveloped for DNA nanotechnology 1 and DNA topological labeling 2 . However, these methods
either require a toxic chemical reagent or an enzyme to form topological structures; thus, it isdifficult to construct them inside the cell.
Currently, we are investigating novel methods to form topological DNA/RNA architectures thatneither require a toxic chemical reagent nor an enzyme. Here we report a method to form a
pseudorotaxane architecture using only a pair of reactive oligodeoxyribonucleotides (ODNs),
which we designed and synthesized, and then performed the pseudorotaxane formation reaction
with both DNA and RNA oligonucleotides. The reaction proceeded smoothly without any extra
reagents at 37 °C and pH 7.2, leading to the formation of a stable complex on a denaturing
polyacrylamide gel 3 . Interestingly, the pseudorotaxane was formed with the cyclized ODNreversibly by the slipping process. This new pseudorotaxane formation represents a promising
method for developing new DNA nanotechnologies and antisense oligonucleotides.

37 o C, pH 7.2[1] D. Ackermann, T. L. Schmidt, J. S. Hannam, C. S. Purohit, A. Heckel, M. Famulok, Nat.
Nanotechnol., 2010, 5, 436-442.
[2] Y. Liu, A. Kuzuya, R. Sha, J. Guillaume, R. Wang, J. W. Canary, N. C. Seeman, J. Am. Chem.
Soc., 2008, 130, 10882-10883.
[3] K. Onizuka, F. Nagatsugi, Y. Ito, H. Abe, J. Am. Chem. Soc., 2014, 136, 7201-7204.

P5-02Modularity analysis of a cascade of DNA circuits andits application to DNA feedback regulator
for molecular robotics
Takashi Nakakuki 1
nakakuki@ces.kyutech.ac.jp

1

Department of Systems Design and Informatics, Kyushu Institute of Technology, 680-4
Kawazu, Iizuka, Fukuoka 820-8502, Japan

Keywords: Molecular robotics, Regulator, Feedback control, Retroactivity, Stability
In this study, we consider a kind of regulation problem for molecular robotics, realized by DNA
reactions. The control objective is to regulate the concentration of a target DNA strand to a desired
level using practical DNA circuits. It is a challenging problem in the architecture of the molecularrobot because of the positiveness, modularity, and finiteness problems. A DNA comparator-based
controller with DNA amplifiers is proposed, and it is shown to successfully achieve the control
objective. The property and stability of the system are evaluated in terms of retroactivity and the
Lyapunov stability theory for a positive second-order system. To our knowledge, this study is thefirst to realize a regulator in a practical DNA reaction system.
[1] Nakakuki, T. and Imura, J., “Molecular Governor: DNA Feedback Regulator for Molecular
Robotics” (submitted).
[2] Nakakuki, T. and Imura, J., “Molecular Governor that regulated DNA reactions”, Proc. of
CCC-SICE2015, Hanghzou, China, July, 2015.

P5-03Structural Instability Conditions of Boolean Networks
Shun-ichi Azuma 1

Takahiro Yoshidasazuma@i.kyoto-u.ac.jp

Toshiharu Sugie 1
sugie@i.kyoto-u.ac.jp
1

Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku,
Kyoto 606-8501, Japan

Keywords: Boolean network, structural instability
Any networked control system is specified by the combination of the network structure and the
dynamics of the components. Meanwhile, one may encounter a situation where the former is known but
the latter is (almost) unknown. In such a case, we cannot apply any method using the full informationof the target system [1]. In this presentation, we introduce our recent results on the structural instability,that is, the instability with unknown component dynamics, for a class of networked control systems. In
particular, we consider here Boolean networks and present a series of (necessary and) sufficientconditions for the structural instability.

[1] S. Azuma, T. Yoshida, and T. Sugie: Structural Monostability of Activation-Inhibition Boolean
Networks, 53rd IEEE Conference on Decision and Control, pp. 1521--1526 (2014)

P5-04
Evaluation of drug-induced CB2 cannabinoid receptor
activity in the CNS using the ERK1/2 phosphorylation
pathway of microglial cells
Yukari Shigemoto-Mogami 1 , Kaoru Sato 1 , Kazue Hoshikawa 1 , Ruri Kikura-Hanajiri 2 ,shigemot@nihs.go.jp

Takashi Hakamatsuka 2 , Yuko Sekino 1
1: Division of Pharmacology, National Institute of Health Science, 1-18-1 Kamiyoga, Setagaya-ku, Tokyo
158-8501, Japan. 2: Division of Pharmacognosy, National Institute of Health Science, 1 -18-1 Kamiyoga,
Setagaya-ku, Tokyo 158-8501, Japan.

Keywords: microglial cell, CB2 receptor, ERK1/2 phosphorylation
Herbal products containing synthetic cannabinoids have caused serious social problems.

If

the compounds found in the products have CB1/CB2 receptor agonist activity, they should becontrolled as “Designated Substances” under the Pharmaceutical and Medical Device Act in japan,
because of their psychoactive and intoxicating effects.

Although it has been considered that only

CB1 receptors are expressed in the central nervous system (CNS), recent studies shows that CB2
receptors are also expressed and causing modulation of neural functions.

In the CNS, microglial

cells are main cells expressing CB2 receptors. CB2 receptors are G i/o -protein-coupled and havebeen reported to activate the extracellular signal-regulated kinase (ERK) pathway, which is one of
mitogen activated protein kinases (MAPK).

So, we examined whether the stimulation of CB2

receptor induced ERK1/2 phosphorylation in microglia using anti-phosphorylated ERK1/2
antibody and anti-total ERK antibody, in order to develop a risk detection system of herbal
products.
First, we analyzed by Western Blotting.
expressed in primary cultured rat microglia.

We confirmed that the CB2 receptor is highly

CB2 receptor selective agonists JWH015 and HU308

induced ERK1/2 phosphorylation of microglial cells at the 10 minutes after treatment with aconcentration-dependent manner.

In addition, its phosphorylation was inhibited by selective CB2

receptor antagonist SR144528, whereas selective CB1 antagonist AM251 had no effect.
analysis by high-throughput ELISA method confirmed the similar results.

The

These results

demonstrate that selective stimulation of CB2 receptor induced ERK1/2 phosphorylation in
microglial cells.

The microglial ERK1/2 phosphorylation pathway through the CB2 receptors

might be a useful tool for the prediction of the potential health damage caused by the herbal
products acting through CB2 receptors in the CNS.

P5-05Prediction for chemical-induced hepatomegaly
from chemical structure by machine learning methods
Kaori Ambe 1ambek@phar.nagoya-cu.ac.jp

Sorami Tamura 1
c072037@ed.nagoya-cu.ac.jp

Masahiro Tohkin 1
tohkin@phar.nagoya-cu.ac.jp

1

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

Keywords: Hepatomegaly, Machine learning methods, QSAR, Toxicological data
Hepatomegaly, such as hepatocellular hypertrophy or the increased relative liver weight, ishepatic responses to chemicals and is generally observed at non-clinical toxicological study. Since
the chemical structures of causative chemicals show a wide variety, the comprehensive
understanding of the toxicological significance of hepatomegaly has not been elucidated [1]. So,
we have developed the predicting methods for hepatomegaly based on the structure of thechemicals.
In this study, at first, we made the toxicological database from the risk assessment reports ofpesticides, food additives, and veterinary medicinal products that were published by Food Safety
Commission of Japan [2]. Our database consists of the toxicological information from 28-day rat
repeated-dose toxicological study and the chemical data which is linked to PubChem [3]. Then, weconstructed the prediction Quantitative Structure Active Relationship (QSAR) model for thehepatomegaly based on chemical substance’s descriptors by machine learning methods [4] (known
as deep learning, support vector machine, random forest), by using our toxicological database. To
improve the performance of the prediction model, variable selection [5] and applicability domain[6] were determined. As a result, the deep learning shows the highest performance hepatomegaly
prediction QSAR model among all methods, which had the nearly 80% prediction accuracy. Then,the reliability of models was assessed by Y-randomization test [7]. In this research, we havedeveloped the practical QSAR model of the hepatomegaly for chemicals in a wide range with ahigh degree of accuracy by using highly reliable large-scale toxicological data.
[1] A. P. Hall., et al., Liver Hypertrophy: A Review of Adaptive (Adverse and Non-adverse)
Changes, Toxicologic Pathology, 40:971-994, 2012.
[2] http://www.fsc.go.jp/hyouka/[3] https://pubchem.ncbi.nlm.nih.gov/[4] C. M. Bishop., Pattern recognition and machine learning, Springer New York, 2006.
[5] I. Guyon., et al., An Introduction to Variable and Feature Selection, Journal of Machine
Learning Research, 3:1157-1182, 2003.
[6] OECD, ENV/JM/MONO, 2, 2007.
[7] I. V. Tetko., et al., Critical Assessment of QSAR Models of Environmental Toxicity against
Tetrahymena pyriformis, Journal of Chemical Information and Modeling, 48:1733-1746, 2008.

P5-06
Regulation of Dynamics of Oil-Droplets and Molecular
Assemblies at Electrified Interfaces
Takamasa Sagara 1
sagara@nagasaki-u.ac.jp

Tetsuro Morooka 1
bb52315201@cc.nagasaki-u.ac.jp

Hironobu Tahara 1
h-tahara@nagasaki-u.ac.jp

1

Graduate School of Engineering, Nagasaki University, Bunkyo, Nagasaki 852-8521, Japan

Keywords: Au electrode/aqueous solution interface, Adsorption-desorption, phase change
Control over the dynamic self-assembling, reshaping, and movements of water-immiscible liquid
droplets and molecular assemblies at a solid electrode/water interface can be made by the changeof electrode potential. The potential regulates interfacial electric potential distribution, surface
electron density and chemical activity, and solvent molecular orientation in proximity to thesurface. Through these changes, we may realize a highly controllable dynamics of droplets andmolecular assemblies with large amplitudes and short time constants. As an example, apotential-dependent wetting/dewetting transition of hexadecane (HD) macro-droplet on mercury
was previously reported [1]. Such a dynamics may be applicable to both the trigger and driving
force of a prototype molecular robot in mesoscopic and even macroscopic scales in future.
We herein describe in detail the dynamics of HD ranging from its monolayer to a μm-size droplet
on a Au(111) electrode in water and discuss the roles played by coexistent surfactants.
On a Au(111) electrode surface in 0.05 M KClO 4 solution, a monolayer-equivalent amount ofliquid HD splits into many tiny droplets (<50 μm) because of the interfacial tension balance.
Upon addition of an anionic surfactant, sodium dodecyl sulfate, the potential-dependent phasechanges of adsorbed dodecyl sulfate ion (DS – ) became dominant, albeit showing difference ofinterfacial capacitance in the ad-micelle potential region. To reveal the micro-droplet reshaping
from the monolayer amount HD, a zwitterionic surfactant dye Di10ASP-PS as a fluorescent probe
was added in HD. The fluorescence intensity (FI) obtained for a linear cyclic potential sweep inthe coexistence with DS – was closely in line with the change of contact angle of HD droplet (1 L)
on Au(111) electrode (Fig. 1-a), indicating that the change of FI can track the small HD droplet
reshaping. The two-stage decreases of FI at positive potentials were interpreted as the gradualspreading of HD micro-droplets, followed by the phase transition of DS ‒ from hemi-micellar tointerdigitated bilayer state (Fig. 1-b), even in the presence of HD. We are under study of the finecontrol methods with a goal to achieve
an ameba-like motion in mind.
This work was supported by aGrant-in-Aid for Scientific Research on
Innovative Areas “Molecular Robotics”(No. 24104005) from MEXT, Japan.
[1] N. Inošević, V. Žutić, Spreading anddetachment of organic droplets at anelectrified interface, Langmuir, 14:
231-234, 1998.

Fig. 1. HD + DS ‒ adlayer on Au(111) electrode:(a) droplet reshaping, (b) monolayer-level model.

P5-07
Dynamical Properties of Chemical Reaction Networks byusing Strand Displacement reactions
yasuhiro SUZUKI
Graduate School of information science, Nagoya University, Furocho Chikusa Nagoya, 4648603, Japan

Keywords: molecular robotics, strand displacement reaction, chemical reaction networks,
dissipative of information equation
Chemical Reaction Networks, CRNs have been implemented by using strand displacement
reactions of DNA. In such reactions, intermediate substances play key roles [1];,e.g., in implementing the Lotka-Volterra (LV) reaction, the amplification of the substance X i is
implemented as X i + G i à O i , O i + T i à 2X i ; where G i and T i are auxiliary substances and they areprepared before the reaction starts (G i is called “ Gate” and T i , “ Transfer”, respectively), while
O i is not prepared and it is produced and consumed in the reactions between the input substance X iand these auxiliary substances; we call such intermediate substances intermediators.
Although intermediators are not used in the formal LV chemical reaction system; theirmathematical characteristics are different; in the formal LV, there are two equilibriums and in the
LV with intermediators, there are infinite number of equilibriums that include equilibriums of theformal LV.
It has been known that one of the characteristics of dynamical system of the formal LV model (2species) is periodic, and there are no attractors, and the orbits of oscillations are given by the
initial concentration. On the other hand, we found that in the LV with intermediators (implemented
by using strand displacement reactions [1]), even if the initial concentrations are different, every
orbit is attracted near to the equilibrium space.
[1] D. Soloveichik, G. Seeling, E. Winfree, PNAS, vol107, No 12, 5393-5398, 2010

P5-08DNA strand exchange reaction activated by cationic
comb-type copolymers modified with ureido groups
Naohiko Shimada 1

Wei Song 2

nshimada@bio.titech.ac.jp

songwei@ms.ifoc.kyushu-u.ac.jp

Atsushi Maruyama 1
amaruyama@bio.titech.ac.jp
1

School and Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology,
4259 Nagatsuta-cho, Midori-ku, Yokohama-shi, Kanagawa Pref. 226-8501, Japan2
Departments in Graduate School of Engineering, Kyushu University,744, Motooka, Nishi-ku,
Fukuoka, 819-0395, Japan

Keywords: molecular robotics, cationic polymers, DNA strand exchange reaction
Strand exchange reactions (SERs) of nucleic acids are essential for genetic recombination. A
variety of proteins induce strand exchange reactions in vivo. SER has been utilized inbiotechnological applications. There are many examples for DNA nanomachines driven by
SER.[1-4] Recently, DNA logic gates or circuits employing SER have been also built.[5,6]
Nanomachines or logic gates work properly because of sequence specific SER. However, there is
an issue with the response speed of the action. Acceleration of SER is required for the rapid
response of the nanomachines or the logic gates.
We have reported that cationic comb-type copolymers composed of a polycation backbone andhydrophilic graft chains such as dextran and poly(ethylene glycol) greatly accelerate the SER in
vitro[7]. To elucidate structure/function relationships involved in the SER accelerating activityofthecopolymer,
we
modifiedthecopolymerwithureidogroups,poly(allylamine-co-allylurea)-graft-dextran (PAU-g-Dex, Figure 1). The melting temperature of
dsDNA in the presence of the copolymers with ureido groups decreased with increasing ureido
content, suggesting that the ureido groups have a destabilizing effect on the DNA duplex.
Copolymers modified with ureido groups (about
50 mol%) accelerated a SER more strongly
CH CH
CH CH
CH
CH
than unmodified copolymers. Given our previous
CH
CH
CH
n
1-n-p
p
NH
NH
NH
observation that modification of the copolymer
C
O
OH
O
CH
with guanidino groups resulted in enhanced SER
OH
OH
NH
O OH
OH
m
OH
OH
accelerating
activity,
weproposethat
incorporation of chaotropic functional groups
into the copolymer structure results in an
Figure 1 Structural formula of PAU-g-Dex
increase in SER accelerating activity.
2

2

2

2

2

2

2

2

2

[1] Alberti, P. and Mergny, J.-L. Proc. Natl. Acad. Sci. U. S. A.100:1569–1573, 2003.
[2] Yan, H., Zhang, X., Shen, Z. and Seeman, N. C., Nature, 415: 62–65, 2002.
[3] Tian, Y. and Mao, C., J. Am. Chem. Soc., 126:11410–11411, 2004.
[4] A. J. Turberfield, J. C. Mitchell, B. Yurke, A. P. Mills Jr., M. I. Blakey and F. C. Simmel, Phys.
Rev. Lett., 90:118-102, 2003.
[5] Li, W., Yang, Y., Yan, H., and Liu, Y., Nano Lett., 13: 2980–2988, 2013.
[6] Qian, L. and Winfree, E. Science, 332:1196–1201, 2011.
[7] Kim, W. J., Sato, Y., Akaike,T. and Maruyama, A. Nat. Mater., 2: 815–820, 2003.

P5-09Formation of two-dimensional crystalline DNA origami
lattices on lipid membranes
Yuki Suzuki 1

Ma sayuki Endo 2

ysuzuki79@kuchem.kyoto-u.ac.jp

endo@kuchem.kyoto-u.ac.jp

Hiroshi Sugiyama 1,2
hs@kuchem.kyoto-u.ac.jp

12

Department of Chemistry, Graduate School of Science, Kyoto University,
Kitashirakawa-oiwakecho, Sakyo-ku, Kyoto 606-8502, Japan
Institute for Integrated Cell-Material Sciences (WPI-iCeMS), Kyoto University,
Yoshida-ushinomiyacho, Sakyo-ku, Kyoto 606-8501, Japan

Keywords: DNA nanotechnology, DNA origami, Self-assembly, Atomic force microscopy
Self-assembly is a ubiquitous approach to the design and fabrication of novel supramoleculararchitectures. Here, we report a strategy termed ‘lipid -bilayer-assisted self-assembly’ that is used
to assemble DNA origami nanostructures into two -dimensional lattices. DNA origami structures
are electrostatically adsorbed onto a mica-supported zwitterionic lipid bilayer in the presence ofdivalent cations. We demonstrate that the bilayer-adsorbed origami units are mobile on the surface
and self-assembled into large micrometer-sized lattices in their lateral dimensions. Using
high-speed atomic force microscopy imaging (HS -AFM), a variety of dynamic processes involvedin the formation of the lattice, such as fusion, reorganization and defect filling, are successfully
visualized. The surface modifiability of the assembled lattice is also demonstrated by in situ
decoration with streptavidin molecules. Our approach provides a new strategy for preparing
versatile scaffolds for nanofabrication and paves the way for organizing functional nanodevices ina micrometer space.
[1] Suzuki, Y., Endo, M., Yang, Y., and Sugiyama, H., Dynamic assembly/disassembly processes ofphotoresponsive DNA origami nanostructures directly visualized on a lipid membrane surface,
Journal of the American Chemical Society, 136:1714–1717, 2014.
[2] Suzuki, Y. Endo. M. and Sugiyama, H., Lipid-bilayer-assisted two-dimensional self-assemblyof DNA origami nanostructures. Nature Communications, 6:8052, 2015.

P5-10Comparison of the NMDA receptor expression andthe extent of excitotoxicity in human induced
pluripotent stem cell (hiPSC)-derived neurons.
Kanako Takahashi1

Yukari Shigemoto-Mogami1

Hideo Shimizu1

shigemot@nihs.go.jp

hshimizu@nihs.go.jp

ktakahashi@nihs.go.jp

Kaori Chujyo1

Kazue Hoshikawa1

Yohei Okada2

hoshikawa@nihs.go.jp

yohei@aichi-med-u.ac.jp

chu-773@nihs.go.jp

12
3

Hideyuki Okano3

Yuko Sekino1

Kaoru Sato1

hidokano@a2.keio.jp

yukos@nihs.go.jp

kasato@nihs.go.jp

Division of Pharmacology, National Institute of Health Sciences, Setagaya-ku,
Tokyo 158-8501, Japan
Department of Neurology, School of Medicine, Aichi Medical University, Nagakute
Aichi 480-1195, Japan
Department of Physiology, School of Medicine, Keio University, Shinjuku-ku,
Tokyo 160-8582, Japan

Keywords: Human induced pluripotent stem cell-derived neurons, Excitotoxicity, L-glutamate,
NMDA, Toxicity evaluation system
Neurons differentiated from human induced pluripotent stem cells (hiPSC) neurons are expected
to improve predictability of the non-clinical CNS evaluation in the drug development.
‘Excitotoxicity’ is a neuron-specific cell damage, which is caused by excessive stimulation of Lglutamate receptors (L-GluR). Ca2+ influx via NMDA type of L-GluR (NMDAR) is an initial stepof excitotoxicity.
In this study, the functional expression of NMDAR and excitotoxicity of three kinds of hiPSC
neurons, neurons differentiated from 253G1 neurosphere (253G1-EB), iCell neurons(iNeurons), and ReproNeuroTM (ReproDA), were investigated. The NMDAR expression wasstudied by measurements of Ca2+ increases induced by brief bath applications of L-Glu, (100 µM)
with Ca2+imaging method using fura-2 AM, and the excitotoxicity was studied by propidium
iodide (PI)-calcein staining, lactate dehydrogenase (LDH) release, MTT reduction and MAP2
staining. In cultured rodent neurons, a L-Glu treatment (100 µM) causes severe neuronal damage
24 hrs after. We used the treatment to study excitotoxicity of hiPSC neurons.
Ca2+ imaging revealed that 253G1-EB started to response to L-Glu at 10 days after
differentiation, while they did not response to NMDA (100 µM) at 40 days after differentiation.
We did not observe the excitotoxicity in 253G1-EB. Almost of all iNeurons showed rapid increaseof Ca2+ responses to L-Glu at 1 day in vitro (DIV). AP5, a NMDAR antagonist, did not suppress
the L-Glu-induced Ca2+ increase, but in some of iNeurons, AP5 suppressed the L-Glu-induced
Ca2+ increase, namely that functional NMDAR expression was not stable in iNeuons. In
accordance with this, the reproducibility of excitotoxicity was also unstable in iNeurons.
ReproDA started to response to L-Glu at 7 DIV and most of cells with neuron-like shape wereAP5-responsive at 28 DIV. However, the number of neurons damaged by L-Glu was small.
In summary, we found that hiPS-neuron, which had the functional NMDAR expression, failed
to reproduce severe excitotoxicity that was observed in cultured rodent neurons.

P5-11
Accelerated construction of matched molecular pairs -based
dataset on PG-Strom architecture
Satoshi Takahashi 1

Kohei Kaigai 2takahashi@itmol.com

kaigai@ak.jp.nec.com

Takanori Ohgaru 3

Atsushi Yoshimori 1

ohgaru.takanori@mb.mt-pharma.co.jp

yoshimori@itmol.com

1

Institute for Theoretical Medicine, Inc., 4259-3 Nagatsuta-cho, Midori-ku, Yokohama 226-8510,
Japan
2
NEC Business Creation Division, NEC Corporation, 1753, Shimonumabe, Nakahara-ku,
Kawasaki, 211-8666, Japan
3
Chemistry Research Laboratories, Mitsubishi Tanabe Pharma Corporation, 1000,
Kamoshida-cho, Aoba-ku, Yokohama 227-0033, Japan

Keywords: Matched molecular pair (MMP), GPU acceleration, Query acceleration engine
PG-Strom is an extension of PostgreSQL, designed to off-load several CPU intensive query
workloads (scan, join, aggregation and sorting) to GPGPU [1, 2]. It works as a query accelerationengine, and provides cost-effective solution for processing massive amount of records. Itscharacteristics well fits usual workloads of data manipulation in chemoinformatics.

Matched molecular pair analysis (MMPA) has widely used in chemoinformatics area for
extracting promising chemical transformations from massive chemical data [3]. Matched
Molecular pair (MMP) is a pair of molecules that have only a structural change at a single site.
Systematic calculations of activity changes on the basis of MMPs provide rules that affectactivities of interest. However, the systematic calculations are time consuming process.
In this study, we attempted to construct MMPs-based dataset from ChEMBL [4] by use ofstandard SQL queries on PG-Strom architecture. The construction time of the dataset was observed
to be significantly accelerated with PG-Strom. We will discuss about the relationship between the
SQL queries and PG-Strom architecture and its applications to chemoinformatics.
[1] Kaigai K., PG-Strom-GPGPU meets PostgreSQL-, PGconf 2015.
( http://www.slideshare.net/kaigai/pgstrom-gpgpu-meets-postgresql-pgcon2015)[2] https://github.com/pg-strom/devel[3] Griffen E., Leach AG., Robb GR., et al., Matched molecular pairs as a medicina lchemistry tool, Journal of Medicinal Chemistry. 54: 7739–7750, 2011[4] Gaulton A., Bellis L., Chambers J., et al., ChEMBL: A Large -scale Bioactivity Database
For Chemical Biology and Drug Discovery. Nucleic Acids Res. Database Issue, 2012

P5-12Control of DNA Nanostructure Formation by Using a
G-Rich-DNA Binding Artificial Peptide and a Protease
Masayuki Shimooka

Kenji Usui
kusui@center.konan-u.ac.jp

m1561004@s.konan-u.ac.jp

Arisa Okada
m1261001@center.konan-u.ac.jp

Faculty of Frontiers of Innovative Research in Science and Technology (FIRST), Konan
University, Kobe, Hyogo 650-0047, Japan

Keywords: P eptide, P eptide nucleic acid (PNA), Protease, G-quadruplex, G-wire
Recent research has revealed that DNA secondary structures regulate a variety of cellular events.
Guanine-rich (G-rich) DNA sequences can fold into a secondary structure, the G-quadruplex
structure, and this structure regulates cellular events such as transcription and telomerase
elongation, which play roles in various serious diseases and cellular aging [1,2]. In addition,
Guanine-rich DNA can fold into a supramolecular structure called the G-wire. The G-wirepossesses promising characteristics as a functional element for various applications in
nanotechnology and electronics. Therefore, many ligands, which can alter the stability ofG-quadruplex structures by binding to G-rich DNAs, have been investigated. However, thenext-generation ligands should have a greater degree of functionality including on-off-switching.
In this study, we attempted to construct a system to regulate G-quadruplex structure forming withon-off-switching module depending on a particular environment (a particular protease activity inthis study) by using a peptide (a small protein) conjugated with peptide nucleic acid (PNA) [3] .
Then we attempted to apply this system to a nanotechnological field.
At First, a peptide conjugated with nucleic acid bases was designed. It consisted of two parts.
One part was composed of guanine PNA-rich sequences for induction of DNA to form DNA-PNA
hybrid G-quadruplex structures. The other part was a switching module depending on activity of aparticular protease as the environment. Thus, this system would induce DNA to form G-quadruplex
structures when the protease would not exist, and once the protease emerging, the peptide would bedigested and simultaneously loose the induction ability resulting in collapse of a DNA/PNA hybrid
G-quadruplex structure. After synthesis of the peptides by Fmoc chemistry, we checked the
G-quadruplex structure induction by CD spectroscopy, electrophoresis, UV-melting andfluorescence spectroscopy. Then, we demonstrated the switch function depending on the proteaseactivity by UV-melting or fluorescence spectroscopy. Finally, the switching DNA nanosturacure(G-wire <--> particles) by the protease was demonstrated. Throughout this study, we established acontrol system for forming DNA G-wire structures depending on protease activity using designed
small proteins. It is obvious that these small proteins would be able to one of the promising tools
for regulation of nanowire structural formation on various applications including an electronic
circuit toward nano and nanobio technology.
[1] Kobayashi, K., Matsui, N., and Usui, K., Use of a designed Peptide library to screen for binders
to a particular DNA g-quadruplex sequence, Journal of Nucleic Acids, Article ID 572873, 2011.
[2] Okada, A., and Usui, K., Peptides Targeting G-Quadruplex Structures, Chemical Biology of
Nucleic Acids:Fundamentals and Clinical Applications, 459-475, 2014.
[3] Usui, K., Okada, A., Kobayashi, K., and Sugimoto, N., Control of guanine-rich DNA secondary
structures depending on the protease activity using a designed PNA peptide, Org. Biomol. Chem.,
13:2022-2025, 2015.

P5-13
Object Tracking of Multi-crossing Behaviors on Microtubule
Gliding Assay
Bulibuli Mahemuti 1
bulbul.aa@m.titech.ac.jp

Akira Kakugo 2 3
kakugo@sci.hokudai.ac.jp

Daisuke Inoue 2
daisukeinoue@mail.sci.hokudai.ac.jp

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

1 Departement of Computational Intelligence and Systems Science,

Interdisciplinary graduate school of Science and Engineering, Tokyo Institute of Technology
2 Faculty of Science, Hokkaido University
3
Graduate School of Science, Department of Chemistry, Hokkaido University

Keywords: Microtubule gliding assay, image processing, object tracking
Object tracking is one of important issues in bio-imaging necessary to elucidate thedynamics of molecules from video data. In microtubule (MTs) gliding assays, object
tracking becomes non-trivial due to the occurrences of compound objects such as crossing
and snuggling of MTs as well as sudden appearance and disappearance of MTs . There areseveral alternative methods for MTs tracking. Most of the methods have focused on the
movements of MTs heads or several selected points on a MTs . However, these methods
ignore the length and shape information of MTs which turn out to be the most importantfactors in the study of MT dynamics. Continuation of previous work [1], we investigate MT
dynamics based on its morphological information. We also develop easy and useful
workflow of decomposing and tracking. With this algorithm, single MTs, crossing MTs andmulti-crossing MTs can be decomposed and tracked correctly.
We proposed a new method for measuring the swarm behavior of MTs with gliding assay
videos [2]. This method enables us to estimate the degree of swarming with regards to of
the density fluctuation score. An of the density fluctuation score is obtained from theinformation entropy of estimated MTs numbers distributed over a picture frame.
Aggregation scores help us to estimate how MTs swarm behavior is different from random
movement.
[1] B. Mahemuti, Y. Han, D. Inoue, A. Kakugo, A. Konagaya: Automated Microtubule Path
Tracking on Gliding Assay Using Hidden Markov Model, oral presentation in International
Conference on Bioinformatics (InCoB 2014), July 31-Aug. 2, Sydney, Australia (2014).
[2] D. Inoue, B. Mahemuti, A.M.R. Kabir, T.I. Farhana, K. Touraku, K. Sada, A. Konagaya, A. Kakugo:
Depletion force induced collective motion of microtubules driven by kinesin, Nanoscale (2015).

P5-14Going Beyond Real-Time with a 3-D Live controlled
Simulation Environment for Microtubule Swarm Dynamics
Greg Gutmann 1
gutmann.g.aa@m.titech.ac.jp

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

1

Daisuke Inoue 2
daisukeinoue@mail.sci.hokudai.ac.jp

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

Departement of Computational Intelligence and Systems Science,
Tokyo Institute of Technology,Yokohama 226-8502, Japan

2 Faculty of Science, Hokkaido University, Sapporo 060-0810, Japan

Keywords: Microtubule gliding assay, GPGPU, Simulation, Microtubule Dynamics
Microtubule dynamics is now an area of research interest. With the goal of being
able to control the dynamics for use in molecular motors. However gaining a clear
understanding from experiments alone can prove difficult. As a solution to this w e havedeveloped a 3-D live controlled simulation environment for microtubule swarm dynamics,
which is able to run beyond real-time speeds¹. The simulation is able to deal with verylarge test cases while maintaining the desired performance, speed, needed for easy 3-D
viewing. This was accomplished by using GPU cards, for both general purpose computing
and graphical rendering; optimized parallel CUDA algorithms for calculations and DirectX
for rendering. With the use of these technologies the simulation is capable of simulatingtens of thousands microtubules, while allowing for live control by the user. These include
DNA linker interactions between microtubules that can be induced or broken up by the user,a microtubule flow² option and collision interactions handled by a Lennard -Jones potential.
The individual parameters of each of these can be adjusted live for easy tuning. Custom
microtubule placements can also be set up prior to running.
[1] Greg Gutmann, Daisuke Inoue, Akira Kakugo, Akihiko Konagaya: Real -Time 3D
Microtubule Gliding Simulation, Life System Modeling and Simulation, Communications
in Computer and Information Science Volume 461, 2014, pp 13-22.
[2] Vicsek model Vicsek, T. et al: Novel Type of Phase Transition in a System of Self-Driven
Particles, Physical Review Letters, vol. 75, no.6, pp.1226 - (1995)

P5-15
Simulating Dynamic Behaviors in Molecular Arrays
Teijiro Isokawa 1
isokawa@eng.u-hyogo.ac.jp

1

2

3

Ferdinand Peper 2
peper@nict.go.jp

Anirban Bandyopadhyay 3

Nobuyuki Matsui 1

Anirban.BANDYOPADHYAY@nims.go.jp

matsui@eng.u-hyogo.ac.jp

Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo,671-2280, Japan
Brain Networks & Communication Laboratory, National Institute of Information andCommunications Technology, 1-4 Yamadaoka, Suita, Osaka,565-0871, Japan
Surface Characterization Group, Nano Characterization Unit, Advanced Key Technologies
Division, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki, 3-5-0044,
Japan

Keywords: Molecular array, DDQ, Universal computation
The building of computational elements and their circuits at nanometer-scale feature sizes has
gained much attention as fabrication technology has progressed. One promising candidate for acomputing system based on molecules uses 2,3-dichloro-5,6-dicyano-1,4-benzoquinone, shortly
called DDQ molecules [1]. These molecules are uniformly arranged on an Au (111) surface witha honey-comb structure. A DDQ molecule has four distinct states that are distinguished by the
numbers and positions of excess electrons, whereby the state of a DDQ molecule changes as aresult of interactions with its neighboring DDQ molecules. Seven types of rules for transitions
between molecular states have been discovered. Though it has been shown in [1] that Boolean
gates capable of information processing can be constructed based on DDQ molecules on an
Au(111) surface, the construction relies on the averaged behavior of a large number of molecules,
and it is yet unclear how circuits can be built that connect these gates to each other.
This presentation proposes novel constructions of logic gates based on DDQ molecules on an
Au(111) surface. The logic gates use tokens as signals, which are represented as molecules incertain states, and the gates can be connected to each other to form more complex circuits. The
circuits are simulated on a computer by software implementing a Cellular Automaton that mimics
the behavior of DDQ molecular systems on Au(111) surfaces. We also evaluate the potential foruniversal computation by the constructed computational elements through simulations.
[1] Bandyopadhyay, A., Pati, R., Sahu, S., Peper, F., and Fujita, D., Massively parallel computing
on an organic molecular layer, Nature Physics, 6:369-375, 2010.

P5-16
Small-molecule screening of fluorinated chemical libraryusing SPR, ITC and 19 F-NMR in FBDDSatoru Nagatoishi 1,2
nagatoishi@bioeng.t.u-tokyo.ac.jp

Keita Kajita 3
kajita@nard.co.jp

Hiroyuki Akiyama 5akiyama@kishida.co.jp

1

2

3
4
5
6
7

Sou Yamaguchi 16711877632@mail.ecc.u-tokyo.ac.jp

Etsuko Katoh 4
ekatoh@nias.affrc.go.jp

Satoru Kanai 6s-kanai@peptidream.com

Toshio Furuya 6

Kouhei Tsumoto 1,2,7

furuya@pharmadesign.co.jp

tsumoto@bioeng.t.u-tokyo.ac.jp

Department of Bioengineering, School of Engineering, The University of Tokyo, 7 -3-1 Hongo,
Bunkyo-ku, Tokyo 113-8656, Japan
Drug Discovery Initiat ive DDI, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo
113-8656, Japan
Nard Institute, Ltd., 5-4-1 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan
National Institute of Agrobiological Sciences NIAS, 2-1-2 Tsukuba, Ibaraki 305-8602, Japan
Kishida Chemical Co.,Ltd., 2-11-8, Shin-ohashi ,Koutou-ku ,Tokyo 135-0007, Japan
PharmaDesign, Inc., 2-19-8, Hatchobori, Chuo-ku, Tokyo 104-0032 Japan, Japan
Institute of Medical Science, The Universit y of Tokyo, 4 -6-1, Shirokanedai, Minato-ku, Tokyo
108-8639, Japan

Keywords: Fluorine, Fragment-based drug discovery, 19 F-nuclear magnetic resonance, Surface
plasmon resonance, Isothermal titration calorimetry
Fluorine compounds have had a profound impact on the development of drugs for the mod ern
pharmaceuticals market [1]. Recently, fluorine NMR-based spectroscopy ( 19 F-NMR) has emerged
as an efficient tool for performing binding assays. The chemical shift or intensity change of fluorine
NMR is especially a powerful marker of the fluorine local env ironment. This method can become auseful tool for hit validation in primary screening of drug discovery.
In the primary screening, fragment-based drug discovery (FBDD) has emerged as one of thesuccessful approach to design high-affinity ligands for biomacromolecules of therapeutic interest.
The interactions between the fragments and a target macromolecule are relatively weak withsubmilimolar affinity. It is expected that these weak binders provide a starting point for thedevelopment of inhibitors with submicromolar affinity. The fragment bindings can be detected using
sensitive biophysical techniques such as NMR, surface plasmon resonance (SPR), or isothermal
titration calorimetry (ITC) [2].
Here, the FBDD was performed using a chemical library containi ng a fluorine atom. We haveused the extracellular signal-regulated kinase 2 (ERK2) as a model target protein. ERK2 binds toan inhibitor (FR180204) and shows binding responses in SPR and ITC. We performed 19 F-NMR,
SPR and ITC analysis to obtain the hit co mpounds binding specifically to ERK2. 19 F-NMR and SPR
are suitable to use in the primary screening due to the high sensitivity in the NMR signal and theexclusion of false positive signals in the SPR response, respectively. ITC was used to validate
thermodynamically the ERK2 binding in hit candidates of NMR and SPR.
[1] E.P. Gillis, K. J. Eastman, M. D. Hill, D. J. Donnelly, N. A. Meanwell, J. Med. Chem. (2015)
DOI: 10.1021/acs.jmedchem.5b00258[2] A. Kobe, J. M. Caaveiro, S. Tashiro, D. Kajihara, M. Kikkawa, T. Mitani, K. Tsumoto, J. Med.
Chem. (2013) 56, 2155-2159.

P5-17
Photoactivation of Thermosensitive Ion Channel by
Plasmonic Nanoparticles
Tatsuya Murakami 1

Hirotaka Nakatsuji 2murakami@icems.kyoto-u.ac.jp

nakatsuji.hirotaka.36z@st.kyoto-u.ac.jp

Tomohiro Numata 3

Nobuhiro Morone 1

numata@fukuoka-u.ac.jp

Shuji Kaneko 4skaneko@pharm.kyoto-u.ac.jp

1

2

3

4

morone@icems.kyoto-u.ac.jp

Yasuo Mori 3
mori@sbchem.kyoto-u.ac.jp

Hiroshi Imahori 1,2
imahori@scl.kyoto-u.ac.jp

Institute for Integrated Cell-Material Sciences (WPI-iCeMS), Kyoto University,
Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan
Department of Molecular Engineering, Graduate School of Engineering, Kyoto University,
Kyotodaigaku-Katsura, Nishikyo-ku, Kyoto 615-8510, Japan
Department of Synthetic Chemistry and Biological Chemistry, Graduate School of Engineering,
Kyoto University, Kyotodaigaku-Katsura, Nishikyo-ku, Kyoto 615-8510, Japan
Department of Molecular Biology, Graduate School of Pharmaceutical Sciences, Kyoto
University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto 606-8501, Japan

Keywords: TRPV1, nociceptive neuron, pain, gold nanorods, high-density lipoprotein
Recently, much attention has been focused on regulation of cellular function by light.
Meanwhile, such methods require genetic modification of cells of interest to introduce
light-sensitive targets, which limits their diverse applications. One of the strategies toovercome this limitation is targeted cellular delivery of light-sensitive materials. Goldnanorods (AuNRs) are a photothermal conversion nanomaterial capable of absorbing
minimally invasive near-infrared (NIR) light. In this study, we focused on photoactivation of athermosensitive cation channel, TRPV1, in the plasma membrane of mammalian cells by using
highly cell adhesive AuNRs.
Electrostatic interaction was utilized for plasma membrane targeting of AuNRs because cellsurface is negatively charged. Plasma membrane-targeted AuNRs (pm-AuNRs) were preparedaccording to the surface chemistry we developed with a cationized form of high-density
lipoprotein [1]. Control AuNRs were also prepared by a layer-by-layer assembly method withconventional cationic polymers.
Among the cationized AuNRs prepared, pm-AuNRs were found to bind most adequately tothe plasma membrane with the weakest cytotoxicity. Upon NIR illumination of
TRPV1-expressing cells bearing pm-AuNRs on the plasma membrane, Ca 2+ influx was
observed. This influx was dependent all on TRPV1 expression, NIR illumination, andpm-AuNRs treatment, clearly demonstrating photothermal activation of TRPV1 by AuNRs.
Importantly, similar illumination of the control AuNRs resulted in the plasma membrane
disruption with no signs of TRPV1 activation. Our method using pm-AuNRs is an
unprecedented cell engineering means for thermosensitive ion channel activation by light [2].

[1] Murakami, T. et al., Mesoscopic metal nanoparticles doubly functionalized with natural andengineered lipidic dispersants for therapeutics, ACS Nano, 8:7370–7376, 2014.
[2] Nakatsuji, H. et al., Thermosensitive ion channel activation in single neuronal cells by using
surface-engineered
plasmonic
nanoparticles,
Angew.
Chem.
Int.
Ed.,
DOI:
10.1002/anie.201505534.

P5-18Construction of a DNA Generation Circuit
for a Molecular Robot
Ken Komiya 1

Chizuru Noda 1

komiya@dis.titech.ac.jp

Masayuki Yamamura 1
my@dis.titech.ac.jp

1

Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute ofTechnology, 4259, Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8503,
Japan

Keywords: DNA-based state machine, Physiological temperature, Molecular robot
We had developed a DNA-based state machine that allows single-molecule programming [1],and later expanded it into a DNA-responsive DNA-based state machine that implements signaldependent computation at a physiological temperature [2]. Our state machine requires sequential
addition of distinct DNA species for triggering each computational step. We then constructed asingle-stranded (ss) DNA generation reaction system that generates distinct ssDNA species [3], forachieving an intelligent molecular robotic system by combining it with the state machine. In
contrast to the enzyme-free DNA logic circuits that require elaborate preparation of DNA
complexes containing DNA strands as signals [4], DNA generation circuits utilizing DNA
polymerase might allow easy preparation and fast operation. Although our ssDNA generation
reaction system enables some functions via cascading, including signal amplification at aphysiological temperature, it seems infeasible to solely apply it as a control circuit to a molecularrobot. For directing our DNA-responsive state machine or other DNA-responsive DNA nanomachines [5], there remain various problems to be solved. In the present study, we report anexperimental investigation of the characteristics of DNA generation behavior of our reaction
system. And then, we discuss the requirements for integrating our reaction system with
DNA-responsive nanomachines. The proposed intelligent molecular system would contribute to anovel means for controlling biochemical process and automation of the common procedure in
medical treatment, as a controller embedded in a solution.
[1] Komiya, K., Sakamoto, K., Kameda, A., Yamamoto, M., Ohuchi, A., Kiga, D., Yokoyama, S.,
and Hagiya, M., DNA polymerase programmed with a hairpin DNA incorporates amultiple-instruction architecture into molecular computing, BioSystems, 83:18-25, 2006.
[2] Komiya, K., Yamamura, M., and Rose, J. A., Experimental Validation and Optimization of
Signal Dependent Operation in Whiplash PCR, Natural Computing, 9:207-218, 2010.
[3] Komiya, K. and Yamamura, M., Cascading DNA Generation Reaction for Sequential Control of
DNA Nanomachines at a Physiological Temperature, New Generation Computing, 33:213-229,
2015.
[4] Seelig, G., Soloveichik, D., Zhang, D. Y., and Winfree E., Enzyme -free nucleic acid logic
circuits, Science, 314:1585-1588, 2006.
[5] Bath, J., and Turberfield, A. J., DNA nanomachines, Nature Nanotechnology, 2:275-284, 2007

P5-19Efficient Universal Computation by Molecular
Co-transcript ional Folding
Cody Geary 1

cody@dna.caltech.edu

Nicolas Scabanel 3

nicolas.schabanel@cnrs.fr

1
2
3
4

Pierre-Etienne Meunier2

pierre-etienne.meunier@aalto.fi

Shinnosuke Seki 4s.seki@uec.ac.jp

California Institute of Technology, Pasadena, CA 91125, USA
Department of Computer Science, Aalto University, P.O.Box 15400, FI -00076, Aalto, Finland
CNRS – Universite Paris Diderot (LIAFA), France and IXXI – Universite de Lyon, France
Department of Communication Engineering and Informatics, Uni versity ofElectro-Communications, 1-5-1, Chofugaoka, Chofu, Tokyo, 1828585, Japan

Keywords: Nano-engineering, Self-Assembly, Co-transcriptional folding, Turing completenessSelf-assembly is an omnipresent phenomenon in which
relatively-simple objects (atoms, molecules, cells, etc.)
interact with each other locally so as to build up a complex
structure without being controlled externally. It has been
intensively applied for manufacturing structures at nanoscale including logic circuits and molecular robots. It al readybecame possible to design molecules (e.g. square -like DNA
tiles) so that they are guaranteed to autonomously combine
and assemble a single target nanoscale structure at atomic
precision, even in a well-mixed chaotic chemical soup. At
this scale, programming matters becomes a significant
cost-effective alternative to industrial tools (easier, cheaper,and more powerful).
Figure 1. RNA origami [1]. RNA
Theory of self-assembly has been intensively studied
polymerase binds to a DNA
template (circular arc), scans it,
mainly in the abstract tile self-assembly model (aTAM)
and transcribes it. The productproposed by Winfree [2] in 1998. It is the self-cohesion of
RNA single-stranded sequence
DNA tiles that is studied in aTAM. We have just launched theco-transcriptionally folds into acollaborative research of self-assembly based on anrectangular RNA tile.
essentially different phenomenon: cotranscriptional folding.
Transcription is a process in which RNA polymer ase binds to a DNA sequence and produces its RNA
copy sequence nucleotide by nucleotide while scanning the DNA template from one end towards theother. Being unstable if being single -stranded, the product RNA sequence starts folding into itself assoon as a few of its nucleotides are transcribed. Using this phenomenon called the cotranscriptional
folding,
Geary, Rothemund, and Anderse n [1] developed an experimental technique called RNA
origami (see Figure 1) to manufacture nanoscale structures autonomously. In this work, we propose amathematical model of computation by cotranscriptional folding called oritatami system, and provethat it can compute all computable functions efficiently.

[1] Geary, C., Rothemund, P. W. K., and Andersen, E. S. , A Single-Stranded Architecture for
Cotranscriptional Folding of RNA Nanostructures, Science, 345:799-804, 2014.

[2] Winfree, E., Algorithm self-assembly of DNA. Ph.D. thesis, California Institute of Technology,
June 1998.

P5-20
Toward Implementation of Cellular Automaton by
Encapsulated Chemical Reaction Network
Ibuki Kawamata 1

Takuto Hosoya 1

Satoru Yoshizawa 1

kawamata@molbot.mech.tohoku.ac.jp hosoya@molbot.mech.tohoku.ac.jp yoshizawa@molbot.mech.tohoku.ac.jp

Fumi Takabatake 1

Ken Sugawara 2

Satoshi Murata 1

takabatake@molbot.mech.tohoku.ac.jp sugawara@cs.tohoku-gakuin.ac.jp murata@molbot.mech.tohoku.ac.jp
1
2

Tohoku University, Aramakiazaaoba 6-6-01, Aobaku, Sendai, Miyagi, 980-8579, Japan
Tohoku Gakuin University, Tenjinzawa 2-1-1, Izumiku, Sendai, Miyagi 981-3105, Japan

Keywords: DNA-enzyme chemical reaction network, cellular automaton, molecular robotics
We propose a theoretical reaction-diffusion model that can emulate simple maze-solving cellular
automaton [1]. The model is based on experimentally well-characterized bistable switch that isdriven by DNA and enzymatic reactions [2]. Assuming a space discretized by hydrogel is our novelidea to realize state transitions of cellular automaton by spatio-temporal chemical reaction system.
Since our model employs realistic parameters such as rate constan ts and diffusion coefficients, it isreasonable to assume that the model can be implemented as real chemical experiment. In the poster
presentation, we show in silico simulation results and further discuss about preliminary
experiments using basic DNA logic gates and alginate hydrogel. Our results indicate that it ispossible to program a dynamic chemical reaction system capable of evolving both in space andtime. Our programmable spatio-temporal chemical reaction system using DNA has a potential to be areaction field for molecular robots that may contain sensors and actuators [3 ].
[1] B. A. Nayfeh, Cellular Automata for Solving Mazes, Dr. Dobb's Journal, 1993, 18, 32-38[2] A. Padirac, T. Fujii, Y. Rondelez, Bottom-up construction of in vitro switchable memories,
Proceedings of the National Academy of Sciences, 2012, 109, E3212 -E3220[3] S. Murata, A. Konagaya, S. Kobayashi, H. Saito, M. Hagiya , Molecular Robotics: A New
Paradigm for Artifacts, New Generation Computing, 2013, 31, 27-45

P5-21The development of photochemical DNA toolbox toward
construction of intelligence on DNA robotics
Shigetaka Nakamura

S7kmr4gtk6a@jaist.ac.jp

	
  	
 

Kenzo Fujimoto

kenzo@jaist.ac.jp

School of Materials science, Japan Advanced Institute of Science and Technology, 1-1
Asahi-dai, Nomi, Ishikawa, 923-1292, Japan

Keywords: DNA robot, photochemical DNA manipulation, DNA strand displacement
The specificity of the A-T and G-C hydrogen bonded Watson-Crick interaction is an emerging
scientific discipline that seeks to engineer nanoscale system created out of DNA strand. Based on
this DNA specificity, various nanoscale DNA device and DNA structure have been reported.
Hence, the research toward construction of DNA robot is done actively all over the world.
The aim of construction of DNA robot is the design and fabrication of dynamic DNA
nanostructure that perform specific tasks via a series of state changes. In previous report, dynamic
DNA nantechnology often uses toehold-mediated strand displacement and enzymatic method forcontrolling reaction kinetics.
Attempts to design and implement chemical reaction circuits to serve as computational components in
DNA robots. However, this approach is not always appropriate, given the physical properties of theactual chemical reactions. In order to introduce intelligence in molecular robots, it is important to
explore various chemical reactions that might be more suitable for DNA robotics.
In our laboratory, various type DNA photochemical manipulations were reported. In particular, We
previously reported an artificial nucleoside in which a nucleobase was reported with
3-cyanovinylcarbazole moiety ( CNV K) 1 . An oligodeoxynucleotide containing CNV K canphoto-cross-link to a pyrimidine base in complementary DNA or RNA molecules within a few
seconds of photoirradiation 2 . This reversible photo-cross-linking is applicable for antisense
strategies 3 , photo-induced chemical shift change 4 , and the construction and stabilization ofnanostructed DNA 5 . Now, we study construction of intelligence toward DNA robotics based on
photochemical DNA manipulation.
[1] Yoshimura, Y., Fujimoto, K., Ultrafast reversible photocrosslinking reaction: toward in situ
DNA manipulation, Org. Lett., 10, 3227-3230, 2008.
[2] Fujimoto, K., Yamada, A., Yoshimura, Y., Tsukaguchi, T., Sakamoto, T., Details of theultra-fast DNA photocrosslinking reaction of 3-cyanovinylcarbazole nucleoside; Cis-transisomeric effect and the application for SNP based genotyping, J. Am. Chem. Soc., 89, 1095-1099,
2013.
[3] Sakamoto, T., Shigeno, A., Ohtaki, Y., Fujimoto, K., Photo-regulation of constitutive gene
expression in living cells by using ultrafast photo-cross-linking oligonucleotides, Biomaterials
Science, 2, 1154-1157, 2014.
[4] Nakamura, S., Fujimoto, L., Photo-cross-linking using trifluorothymidine and3-cyanovinylcarbazole induced large shifted 19F MR signal, Chem. Commun., 52, 11765-11768,
2015.
[5] Nakamura, S., Fujimoto, K., Creation of DNA array structure equipped with heat resistance byultrafast photocrosslinking, J. Chem. Technol. Biotechnol., 89, 1089-1090, 2015.

P5-22
A Loosely-stabilizing Algorithm for Leader Election
on Arbitrary Graphs in Population Protocols

1

2

Yuichi Sudo 1

Fukuhito Ooshita 2

y-sudou@ist.osaka-u.ac.jp

f-oosita@is.naist.jp

Hirotsugu Kakugawa 1

Toshimitsu Masuzawa 1

kakugawa@ist.osaka-u.ac.jp

masuzawa@ist.osaka-u.ac.jp

Graduate School of Information Science and Technology, Osaka University
Yamadaoka 1-5, Suita, Osaka 565-0871, Japan
Graduate School of Information Science, Nara Institute of Science and Technology
Takayama 8916-5, Ikoma, Nara 630-0192, Japan

Keywords: Distributed algorithm, Loose-stabilization, Population protocols, Leader election
Recently molecular robotics, which uses molecules as robot components, has received a lot of
attention as a new paradigm for artifacts [1]. To realize molecular robots, it is necessary toassemble a large number of molecular devices in a self-organizing manner. That is, each moleculardevice operates autonomously based on local information (obtained with their sensors), but themolecular devices as a whole should work cooperatively as a robot. This behavior is similar to thatof distributed systems, where computing devices operate based on local information but they work
cooperatively as a system. For this reason, it is natural to apply distributed algorithms (i.e.,algorithms for distributed systems) to molecular robotics.
As a model to design distributed algorithms for molecular robotics, we focus on the population
protocol (PP) model [2]. In the PP model, devices are passively moved, and two devices
communicate with each other only when they come sufficiently close to each other. The PP model
is suitable to molecular robotics because typical molecular devices are not fixed and they can sense
only nearby devices.
In our previous work, we proposed a loosely-stabilizing leader election algorithm on arbitrary
graphs in the PP model [3]. The algorithm ensures that, starting from any initial configuration,
exactly one device is elected as a leader within a relatively short time; after that, it is kept as theleader for a sufficiently long time. Such an algorithm is highly reliable because, even if states ofdevices are changed due to some fault, one device is elected as a leader within a relatively shorttime. Since molecular devices can fail, high reliability of algorithms is important. However, thisalgorithm assumes that each device has a unique identifier or can use random numbers.
In this poster, we remove both of the assumptions, that is, we present a loosely-stabilizing leader
election algorithm on arbitrary graphs in the PP model without identifiers nor random numbers.
That is, our new algorithm can be applied to devices with more restricted capability.
[1] Murata, S., Konagaya, A., Kobayashi, S., Saito, H., and Hagiya, M., Molecular robotics: A newparadigm for artifacts, New Generation Computing, 31, 27-45, 2013.
[2] Angluin, D., Aspnes, J., Diamadi, Z., Fischer, M. J., and Peralta, R., Computation in networksof passively mobile finite-state sensors, Distributed Computing, 18(4), 235-253, 2006.
[3] Sudo, Y., Ooshita, F., Kakugawa, H., and Masuzawa, T., Loosely-stabilizing leader election on
arbitrary graphs in population protocols, Proc. of OPODIS, 339-354, 2014.

P5-23
Formation Problems by Autonomous Mobile Robots in theThree Dimensional Euclidean Space
Yukiko Yamauchi

yamauchi@inf.kyushu-u.ac.jp

Shuji Kijima

kijima@inf.kyushu-u.ac.jp

Taichi Ueharataichi.uehara@inf.kyushu-u.ac.jp

Masafumi Yamashita

mak@inf.kyushu-u.ac.jp

Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan

Keywords: Swarm of mobile robots, rotation groups, symmetry, formation problem
We consider a swarm of autonomous mobile robots each of which is anonymous and oblivious(memory-less), synchronously executes the same algorithm, and moves in the three-dimensional
Euclidean space (3D-space). The plane formation problem requires the robots to land on a common
plane without forming any multiplicity from a given initial configuration and the pattern formation
problem requires the robots to from a given target pattern from a given initial configuration.
The pattern formation problem in the two dimensional Euclidean space (2D-space) has been
investigated by Yamashita et al. [2] and Fujinaga et al. [1]. They characterized the patternformation problem by using the notion of symmetricity of an initial configuration that shows thesymmetry that the robots can never break. The symmetricity of an initial configuration is
intuitively the order of the cyclic group of the positions of robots. It has been shown that oblivious
fully-synchronous (FSYNC) robots can form a target pattern from an initial configuration if andonly if the symmetricity of the initial configuration is a subgroup of the symmetricity of the target
pattern.
We extend the notion of symmetricity to 3D-space by using the rotation groups that is defined bya set of rotation axes and their arrangement. We define the symmetricity of positions of robots in
3D-space as the set of rotation groups formed by rotation axes that the robots can never eliminate.
We show the following necessary and sufficient condition for the pattern formation problem which
is a natural extension of the results in 2D-space: Oblivious FSYNC robots in 3D-space can form atarget pattern from an initial configuration if and only if the symmetricity of the initialconfiguration is a subset of the symmetricity of the target pattern. As a corollary, we can rephrasea necessary and sufficient condition for the robots to form a plane shown in [3] as follows:
Oblivious FSYNC robots can from a plane if and only if the symmetricity of an initialconfiguration consists of cyclic groups and dihedral groups. We then show a pattern formation
algorithm and a plane formation algorithm for oblivious FSYNC robots.
[1] Nao Fujinaga, Yukiko Yamauchi, Hirotaka Ono, Shuji Kijima, and Masafumi Yamashita,
Pattern formation by oblivious asynchronous mobile robots, SIAM Journal on Computing, 44(3),pp.740-785 (2015).
[2] Masafumi Yamashita and Ichiro Suzuki, Characterizing geometric patterns formable byoblivious anonymous mobile robots, Theoretical Computer Science, 411(26-28), pp.2433-2453(2010).
[3] Yukiko Yamauchi, Taichi Uehara, Shuji Kijima, and Masafumi Yamashita, Plane formation bysynchronous mobile robots in the three dimensional Euclidean space, 29th International
Symposium on Distributed Computing (to appear).

P5-24
Stimulus-Responsive Control of Peptide Structure and its
Functions Directed toward Molecular Robotics
Shin Mizukami 1,2
smizukami@mls.eng.osaka-u.ac.jp

Masayoshi Kashibe 1
kashibe@molpro.mls.eng.osaka-u.ac.jp

Kazuya Kikuchi 1,2
kkikuchi@mls.eng.osaka-u.ac.jp

12

Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871,
Japan
Immunology Frontier Research Center, Osaka University, 2-1 Yamadaoka, Suita, Osaka
565-0871, Japan

Keywords: peptide, photoswitch, molecular robot
Recently, lots of attempts to develop a molecular robot based on self-assembly andself-organization have attracted increasing attentions. In order to create a high-performancemolecular robot, it is important to develop the individual elements such as sensor, actuator, etc.
Therefore, we focused on the development of a molecular robot element, an actuator that shows
one of the most basic operation, reversible catch and release. We are developing artificial peptides
that reversibly catch and release their specific target molecules in response to external stimulation.
As the external input, light was chosen because light enables the precise control of irradiation
area and time, wavelength, repetitive frequency, intensity, and so on. In addition, light is
non-invasive if correctly applied. To achieve the synchronized movement of the actuator with thelight irradiation, a photochromic compound was utilized. In this study, we are aiming at
development of peptide aptamers that reversibly control their a ffinity to the target molecule.
At the first step, we hypothesized that the affinity of peptide aptamers toward target molecules
can be controlled by the structural changes in the scaffold structure induced by light irradiation. As
the scaffold, we chose the artificial twisted β-turn structure stabilized by tryptophan (Trp) zipper.
It is reported that Trp zipper structure is very stable (T m = 72 °C), [1] and peptide aptamers based on
Trp zipper bound various target proteins with high affinity (K d : ~50 nM). [2] Their high affinity is
considered to be guaranteed by the scaffold structure. Thus, affinity of the peptide aptamer is
supposed to be dramatically changed by the structural changes of Trp zipper. In this conference, wewill report characteristics of newly synthesized azobenzene derivatives in terms of the kinetic
parameters of photoisomerization, and the structural changes of the artificial peptides when theazobenzene moiety was incorporated into the amino acid sequences of Trp zip.

[1] M. A. Starovanik et al., Proc. Natl. Acad. Sci. USA 2001, 98, 5578.
[2] S. Jon, et al., Angew. Chem. Int. Ed. 2012, 51, 1890.

P5-25
Stability evaluation of DNA origami in living cell
Masahiro Endo 1
endo@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

Tohoku University, 6-6-01, Aoba Aramaki-Aza, Aoba-ku, Sendai, Miyagi, Japan

Keywords: Molecular Robotics, DNA nanotechnology, DNA origami, HeLa cell, FRET
Recently, the research on using DNA to construct nano divices capable of sensing, computation andactuation has been actively conducted [1, 2]. These devices are expected to apply to nanomedicine,such as DNA nanorobot that transports molecular payloads to cell, taking advantages of high accuracy
in nano scale and biocompatibility [3]. Considering the applications as nanomedicine, assessing thestability of DNA device in vivo environment is essentially important. The stability of DNA origami,
which is widely used substrates for DNA devices, in cell lysate has been investigated [4]. However, theevaluation inside living cell has not been reported yet. Here, we propose a way to evaluate thestability of DNA origami in various environments, especially in a living cell. Fluorescentand quencher molecules are attached at the center of origami and are designed to be aparteach other when a part of origami is damaged, by which deformation can be easily detected
as a change of fluorescent intensity.

[1] Murata, S., Konagaya, A., Kobayashi, S., Saito, H., and Hag iya, M., Molecular Robotics: A New
Paradigm for Artifacts, New Generation Computing, 31:27-45, 2013.
[2] Hagiya, M., Konagaya, A., Kobayashi, S., Saito, H., & Murata, S., Molecular Robots with Sensors
and Intelligence. Accounts of Chem. Res., 47:1681-1690, 2014.
[3] Douglas, S., Bachelet, I., and Church, G., A Logic-Gated Nanorobot for Targeted Transport of
Molecular Payloads, Science, 335:831-834, 2012[4] Mei, Q., Wei, X., Su, F., Liu, Y., Youngbull, C., Johnson R., Lindsay, S., Yan, H., and Meldrum,D., Stability of DNA Origami Nanoarrays in Cell lysate, NANO LETTERS, 11:1477 -1482, 2011

P5-26
Toward Efficient Computation of Chemical Equilibria ofInteracting Nucleic Acid Strands Including Pseudoknots
Mizuki Yokoshima 1

Satoshi Kobayashi 1
kobayashi.satoshi@uec.ac.jp

1

Department of Communication Engineering and Informatics ,Graduate School of Informatics and Engineering,
University of Electro-Communications,
1 – 5 – 1, Chofugaoka, Chofu, Tokyo 182-8585, Japan

Keywords: Computing chemical equilibria, Interacting nucleic acid strands,
The analysis of chemical equilibria of interacting nucleic acid strands is important for the designof chemical reaction circuits using DNAs. Dirks, et al., proposed a method for computing chemical
equilibria of interacting nucleic acid strands with pseudoknot -free secondary structures ([1]).
However, their method uses a dynamic programming algorithm in couple with a concave
programming, which runs in exponential with respect to the maximum length and the maximum
number of nucleic acid strands. Kobayashi proposed a new approach, called symmetric
enumeration method (SEM), to computing chemical equilibria of complex chemical reaction
system ([2] [3]). SEM has been applied to the equilibrium analysis of one -dimensional tileassembly systems ([4]), interacting nucleic acid strands ([5]), and also to kinetic simulation of
RNA folding process ([6]). In [5], Kobayashi and Kawakami considered the problem of computing
chemical equilibrium of interacting nucleic acid strands under the restriction that the strands will
form only linear secondary structures. They showed that the problem can be reduced to a concave
programming problem with a set of variables whose cardinality is polynomial with respect to the
maximum length and the maximum number of strands. In this poster presentation, we will
challenge to the problem of computing chemical equilibria of interacting nucleic acid strands
under the restriction that the strands will form on ly secondary structures which can be generated
by simple linear tree adjoining grammars ([7]).
[1] R. M. Dirks, J. S. Bois, J. M. Schaeffer, E. Winfree, and N. A. Pierce, Thermodynamic Analysis
of Interacting Nucleic Acid Strands, SIAM Rev., 49(1), pp.65–88, 2007.
[2] S. Kobayashi, A New Approach to Computing Equilibrium State of Combinatorial
Hybridization Reaction Systems, Proc. of Workshop on Computing and Communications from
Biological Systems: Theory and Applications, paper No.2376, CD-ROM, 2007.
[3] S. Kobayashi, Enumeration Approach to Computing Chemical Equilibria., TheoreticalComputer Science, 499, pp.51-87, 2013.
[4] S. Kobayashi, Applying Symmetric Enumeration Method to One -dimensional Assembly of
Rotatable Tiles, in Algorithmic Bioprocesses, Natural Computing Series, Springer, pp.159-183,
2012.
[5] S. Kobayashi and T. Kawakami, Enumeration Approach to the Analysis of Interacting Nucleic
Acid Strands, in Biomolecular Information Processing --- From Logic Systems to Smart Sensors
and Actulators, Wiley-VCH, pp.225-244, 2012.
[6] T. Tanigawa and S. Kobayashi Smith, T.F. and Waterman, M.S., Efficient and Approximate
Simulation Algorithm of Kinetic Folding of an RNA Molecule, Proc. of The 2011 International
Conference on Parallel and Distributed Processing Techniques and Applications , pp.706-712,
2011.
[7] Y. Uemura, A. Hasegawa, S. Kobayashi, T. Yokomori , Tree Adjoining Grammars for RNA
Structure Prediction, Theoretical Computer Science, 210:277-303, 1999.

P5-27
Toward Design and Analysis of Analog Computing
Chemical Reaction Circuits
Daiki Matsuwaki 1

Satoshi Kobayashi 1
kobayashi.satoshi@uec.ac.jp

1

Department of Communication Engineering and Informatics ,Graduate School of Informatics and Engineerin g,
University of Electro-Communications,
1 – 5 – 1, Chofugaoka, Chofu, Tokyo 182-8585, Japan

Keywords: DNA computing, Molecular robotics, Analog circuit
Since Adleman’s seminal work on the DNA solution to directed Hamiltonian path problem ([1]),there has been proposed many DNA computing devices and circuits ([2] [3] [4] [5], etc.). Amongthem, Qian’s seesaw gate is one of the promising DNA computing devices toward the scalable
DNA computing ([3]).
In order to build up molecular robots, one of the mos t important properties required for thecomputing devices is time-responsiveness , where we say that a circuit system is time-responsive if,
when inputs to the circuit change after the initial computation, the output is re -computed to reflect
the new inputs ([4]). Kobayashi, et al., proposed a time -responsive analog computing device using
DNA strand displacement reaction ([6]), where they proposed time-responsive thresholding
devices and comparators, etc., and showed their behavior by numerical simulation . However, it is
not so easy to find the parameters (i.e., concentrations of dominating strand complexes , andtoehold length of DNA strand exchange reactions) which make the circuits work in the intended
manner. In this poster presentation, we will consider a problem of designing time-responsive
analog computing circuits based on the idea proposed in [6], but do not restrict our attention only
to DNA strand displacement reactions, but also to any type of general chemical reactions. We thentry to design a more complex chemical reaction circuit.
[1] L. Adleman, Molecular Computation of Solutions to Combinatorial Problems. Science 266,
pp.1021-1024, 1994.
[2] M. N. Stojanovic and D. Stefanovic, A Deoxyribozyme based Molecular Automaton. Nature
Biotechnology, 21(9):1069-1075,
2003.
[3] L. Qian, E. Winfree, Scaling Up Digital Circuit Computation with DNA Strand Displacement
Cascades, Science, 332:1196-1201, 2011.
[4] S.E.Chiniforooshan, D.Doty, L.Kari, S.Seki, Scalable, time -responsive, digital,energy-efficient molecular circuits using DNA strand displacement. Proc. of International
Conference on DNA Computing and Molecular programming, DNA16, In Lecture Notes in
Computer Science, LNCS, 6518, Y.Sakakibara, Y.MI, Eds., 25 -36, 2011.
[5] A. J. Genot , J. Bath , and A. J. Turberfield, Reversible Logic Circuits Made of DNA. J. Am.
Chem. Soc., 133 (50):20080-20083, 2011.
[6] S. Kobayashi, K. Yanagibashi, K. Komiya, K. Fujimoto and M. Hagiya, Analog DNA
Computing Devices Toward the Control of Molecular Robots. Proc. of Wo rkshop on
Self-organization in Swarm of Robotics, 11pages, (invited talk) 2014.

P5-28Reconfiguration of molecular device on lipid membrane
Koichiro Katayama1
katayama@molbot.mech.tohoku.ac.jp

Shinichiro M. Nomura1
nomura@molbot.mech.tohoku.ac.jp

Ibuki Kawamata1
kawamata@molbot.mech.tohoku.ac.jp
1

Satoshi Murata1
murata@molbot.mech.tohoku.ac.jp

Department of Bioengineering and Robotics, Graduate School of Engineering, Tohoku University, Sendai, Miyagi 980-8579, Japan

Keywords: DNA nano-engineering, Molecular robotics, Liposome, DNA origami
Controlling cell membrane is fundamentally important for living organisms because the membrane is related tovarious kinds of rolls like signal transduction and cell reduction. In nature, membrane proteins have various
functions from molecular transportation through membrane to mechanical deformation of membrane. There is thefield called the artificial cell engineering which realize such functions by synthesized molecules. Various artificial
channels made of DNA origami nano-structures have been reported.
Here we propose a novel DNA origami device capable of deforming membrane by external signal. The device
attaches on lipid membrane via cholesterol moieties. By adding single stranded DNA as an input, the device
transforms from open to close state, which can pull and deform lipid bilayer. In the poster presentation, we will
discuss preliminary experimental data observed by atomic force microscopy.
Artificial membrane deformation is essential function to reproduce natural behaviors of the cell.
We would be able to control cell membrane as desired in the future. Another application is DNA
sequence-responsive liposome. If the molecular devices can break the membrane by its
transformation, the liposome release its payload. This expands the possibilities of DDS using
liposome and DNA nanostructure.
[1]Yuki, S., et al., Dynamic Assembly/Disassembly Processes of Photoresponsive DNA Origami Nanostructures
Directly Visualized on a Lipid Membrane Surface, JAC S, 136 (5), 1714–1717, 2014.
[2]Martin, L., et al., Synthetic Lipid Membrane Channels Formed by Designed DNA Nanostructures, Science,
338:932-936, 2012.
[3] M, Endo., et al., DNA Prism Structures Constructed by Folding of Multiple Rectangular Arms, JACS., 131(43), 15570–15571, 2009.
[4] Aleksander, C., et al., Amphipathic DNA Origami Nanoparticles to Scaffold and Deform Lipid Membrane
Vesicles, Angewandte Chemie International Edition, Vol.54, Issue 22, 6501–6505, 2015.

P5-29Construction and functional analysis of DNA origami base
DNA-RNAP hybrid nanomachine
Takeya Masubuchi 1
kk147326@mgs.u-tokyo.ac.jp

Hisashi Tadakuma 1
tadakuma.hisashi.8s@kyoto-u.ac.jp

Masayuki Endo 2
endo@kuchem.kyoto-u.ac.jp

Yoshie Harada 2
harada.yoshie.4r@kyoto-u.ac.jp

1

2

Hiroshi Sugiyama 2
hs@kuchem.kyoto-u.ac.jp

Takuya Ueda 1
ueda@k.u-tokyo.ac.jp

Graduate School of Frontier Science, The University of Tokyo, 5-1-5
Kashiwanoha, Kashiwa-shi, Chiba 277-8562, Japan
Institute for Integrated Cell-Material Sciences (iCeMS), Kyoto University,
Yoshida-Ushinomiyacho, Sakyo-Ku, Kyoto 606-8302, Japan

Keywords: Molecular robotics, DNA nanostructure, RNA polymerase, gene expression
In the cell, gene expression is highly controlled. To create biologically inspired nanoscale
device enabling the control of gene expression, we made h ybrid nanomachine (T7-tile)
using DNA origami tile as the skeletal structure and T7 RNA polymerase (T7-RNAP) as thefunctional module (Miyazono et al., EMBO J , 2010). T7-tile hybrid allowed us to evaluate
the effects of intermolecular distance of enzyme (T 7-RNAP) and substrate (target genecontaining T7 promoter). We will show our recent achievements.

[1] Miyazono Y., Hayashi M., Karagiannis P., Harada Y., Tadakuma H. Strain through the necklinker ensures processive runs: a DNA-kinesin hybrid nanomachine study. EMBO J. 29:93 – 106.
2010.

P5-30Synthesis of Cell-Sized Liposomes and Droplets
by Centrifugal Capillary Based Microfluidic Device
Masamune Morita 1
morita.m.ah@m.titech.ac.jp

1

	
  	
 

Masahiro Takinoue 1

takinoue.m.aa@m.titech.ac.jp

Tokyo Institute of Technology, G3-53, 4259, Nagatsuta, Midori-ku, Yokohama, Kanagawa
226-8503, Japan

Keywords: Liposome, Water-in-oil Droplets, Microfluidic device	  We developed a simple generation method of cell-sized liposomes and water-in-oil (W/O)
droplets using a centrifugal capillary-based microfluidic device in a tabletop mini-centrifuge [1-3].
Cell-sized liposomes and W/O droplets have been used in a wide range of fields in biochemistry
and biophysics. In recent years, microfluidic techniques have been developed to generatecell-sized liposomes and W/O droplets. However, some difficulties hamper the use of a microchannels, such as complicated microfabrication processes to make the micro channels, precise
control of the liquid flow in the micro channels using a syringe pumps, and requirement of a large
amount of samples into the syringe pumps. Our device was constructed from a microtube, a glass
capillary, and a polyacetal holder for the glass capillary. This device does not need complicatedmicrofabrication processes. We demonstrated the production of monodisperse liposomes and W/O
droplets from a small sample volume (0.5-5 µL). To test the applicability of our method, we
encapsulated a cell-free gene expression system into liposomes and W/O droplets. We believe that
this method will assist biochemical and biophysical experiments.
[1] Morita, M., Onoe, H., Yanagisawa, M., Ito, H., Ichikawa, M., Fujiwara, K., Saito, H., Takinoue,
M. Droplet-Shooting and Size-Filtration (DSSF) Method for Synthesis of Cell-Sized Liposomes
withControlled
Lipid
Compositions,
ChemBioChem,
inpress,
2015.
DOI:
10.1002/cbic.201500354[2] Yamashita, H., Morita, M., Sugiura, H., Fujiwara, K., Onoe, H., Takinoue, M. Generation of
Monodisperse Cell-Sized Microdroplets using a Centrifuge-Based Axisymmetric Co-Flowing
Microfluidic Device, J. Biosci. Bioeng., 119(4):492-495, 2015.
[3] Morita, M., Yamashita, H., Hayakawa, M., Onoe, H., Takinoue, M. Capillary-based Centrifugal
Microfluidic Device for Size-controllable Formation of Monodisperse Microdroplets, Journal of
Visualized Experiments, in press, 2015.

P5-31
Building molecular robot made of biological molecularmotor and DNA-origami
Hiroyuki Fujino 1

Ibuki Kawamata 1

fujino@molbot.mech.tohoku.ac.jp

kawamata@molbot.mech.tohoku.ac.jp

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

Shoichi Toyabe 2
toyabe@m.tohoku.ac.jp

Satoshi Murata 1
murata@molbot.mech.tohoku.ac.jp
12

Department of Bioengineering and Robotics, Graduate School of Engineering,Tohoku University, Aramaki-aza-aoba 6-6-01, Aoba-ku, Sendai, Miyagi 980-8579, Japan
Department of Applied Physics, Graduate School of Engineering, Tohoku University
Aramaki-aza-aoba 6-6-05, Aoba-ku, Sendai, Miyagi 980-8579, Japan

Keywords: Molecular robotics, DNA-origami, F 1 -ATPase, Self-Assembly
Realizing autonomous molecular robots is one of the goal of nanotechnology. We will build themicro-scale vehicle composed of the body, the motor, and the helical shaped propeller. The body isthe colloidal particle coated Ni 2+ ion. The motor is the F 1 -ATPase which is a rotational molecularmotor working in biological cells. The propeller is made of the DNA-origami which is thenanostructures build by programmed self-assembly of a long DNA strand and many short DNA
strands [1].
To create the helical shaped propeller, we designed the linked DNA-origami structures. The
structures are the bend [2] and twisted [3] cylindrical DNA-origami and connected each other bycomplement bases. The cylindrical DNA-origami is composed by bundling some helixes of DNA.
When you change the number of bases of the helixes partly and adequately, the pressure occurs inthe structure so that the cylindrical DNA-origami are bent and twisted. The curvature and thetorsion of the propeller are controlled by changing the number of bases added or removed withrespect to the cylindrical DNA-origami.
The micro-scale vehicle is assembled by using the selective binding between biotin andstreptavidin II, and also Ni 2+ ion and Histidine-tag. Biotin is modified to the top of the rotator shaftof the F 1 -ATPase and the DNA-origami. Histidine-tag is modified to the bottom of the stator of theF 1 -ATPase. To demonstrate the assembly, we observed the complex of the colloidal particle and theF 1 -ATPase and the streptavidin coated beads, and also the rotation of the proved DNA-origami
connected to the F 1 -ATPase.
[1] Rothemund, P. W. K. Nature 2006, 440, 297–302.
[2] Hendrik Dietz, Shawn M. Douglas, William M. Shih, Science 2009, 325, 725-730.
[3] Yang Y, Zhao Z, Zhang F, Nangreave J, Liu Y, Yan H. Nano Lett, 2013, 13 (4), 1862–1866

P5-32
Random Matrix Theory of Rigidity in Protein
Masanori Yamanaka 1
yamanaka@phys.cst.nihon-u.ac.jp
1

Department of Physics, College of Science and Technology, Nihon University, 1-8
Kanda-Surugadai, Chiyoda-ku, Tokyo 101-8308, Japan

Keywords: Protein, Molecular Dynamics, Random Matrix, Variance-Covariance Matrix, Rigidity,
Softness, Time Series
The softness or rigidity is defined by the Young modulus or the Poisson ratio to a macroscopic
object. The definition is also valid in microscopic levels such as a macromolecule. Although themeasurement is extremely difficult, there have been several approaches to measur e the Youngmodulus of proteins [1,2,3,4]. When we consider the dynamics and fluctuation of a molecule, suchdefinition of the softness of a single molecule or a fraction may be ambiguous in the true sense,
because we have to consider dynamics in atomic level. In protein , the dynamics and the softness inthis sense are believed to play an important role in folding, molecular recognition, and struct uralchange. One of the definitions of the softness in microscopic level is the root mean squaredeviation (RMSD) of the normal mode and this has been extensively investigated.
We study the rigidity of protein [5] in microscopic level using the theory of characteristic scale ofcoupling formation developed in random matrix theory [6]. The eigenvalues and the eigenvectorsof the time-dependent variance-covariance matrices, which are calculated from the time series dataof the atomic coordinates of a protein produced by the molecular dynamics, are analyzed. We
demonstraight the method to a lysozyme, PDBID:1AKI as an example. We find that there are atleast three different time scales involved in the coupling formation of correlated sectors of atoms
and at least two different time scales for the size of the correlated sectors. In short time scale, there
were many atom sectors in which the atoms had a strong correlation inside the sector but almost no
correlation among sectors. When making an analogy with classical elastic theory, the protein
exhibits a type of plasticity. In long time scales, there are a lot of large sectors of atoms in which
the correlation of atoms extends over the whole protein. These features occur simultaneously andtheir nature is very complicated.
[1] H. Kojima, A. Ishijima, and T. Yanagida, Proc. Natl. Acad. Sci. U.S.A. 91, 12962 (1994).
[2] H. Suda, M. Sugimoto, M. Chiba, and C. Uemura, Biochem. Biophys. Res. Commun. 211, 219(1995).
[3] M. Radmacher, M. Fritz, J. P. Cleveland, D. A. Wal ters, and P. K. Hansma, Langmuir 10, 3809
(1994).
[4] M. Tachibana, H. Koizumi, and K. Kojima, Phys. Rev. E 69, 051921 (2004).
[5] M. Yamanaka, J. Phys. Soc. Jpn. 84, 063801 (2015).
[6] M. L. Mehta, Random Matrices (Academic Press, New York, 1991).

P5-33
Reaction Diffusion Model to Understand Slime-mold
Morphological Reconfiguration
Masahiro Shimizu 1
shimizu@sys.es.osaka-u.ac.jp

1

Koh Hosoda 1
hosoda@sys.es.osaka-u.ac.jp

Department of Systems Innovation, Graduate School of Engineering Science, Osaka University,
1-3 Machikaneyama-machi, Toyonaka, Osaka, 560-8531, Japan

Keywords: Reaction diffusion model, Slime-mold, Morphological reconfiguration, Molecular
Robotics
The paper deals with reaction diffusion model to understand slime-mold morphologicalreconfiguration. We specifically modeled by modular robots and discusses a control method bywhich fluidity of a modular robot is enhanced. A living true slime-mold utilizes “sol-gel
transition” so that fluidity is encouraging enough to make “protoplasmic streaming” inside thebody. Based on this fact, we so far advanced the modular robot which has decentralized control
system consisting of coupled oscillators [1]. It is shown from simulation results that as fluidity ismade higher, the modular robot is enhanced in its abilities. Therefore when attention is anew paid
to the fact that the control system and mechanical system as direct objectives for design are in
existence on the origin of the interaction dynamics, it might be intrinsically proper enough forthem to be dealt with as having the equalized weight. We believe that above consideration isindispensable for development methodology for molecular robotics [2] [3].

Acknowledgements. The authors would like to thank Prof. Akio Ishiguro, Tohoku University, forstimulating, and helpful suggestions. This work was supported partially by Grant-in-Aid for Scientific
Research on 15H02763 and Innovative Areas “Molecular Robotics”(No. 15H00820) from the Ministryof Education, Culture, Sports, Science and Technology of Japan.
[1] M. Shimizu, A. Ishiguro: “Amoeboid Locomotion Having High Fluidity by a Modular Robot”,
International Journal of Unconventional Computing, Vol.6, No.2, 145-161, 2010.
[2] Hagiya M, Konagaya A, Kobayashi S, Saito H, Murata S. Molecular robots with sensors andintelligence. Acc Chem Res. 47(6), 1681-1690, 2014.
[3] Murata S, Konagaya A, Kobayashi S, Saito H, Hagiya M. Molecular Robotics: A New Paradigm
for Artifacts. New Generation Computing, 31, 27-45, 2013. Ohmsha, Ltd. and Springer, 2013.