CBI 2006
Chem-Bio Informatics Society
情 報 計 算 化 学 生 物 学 会
CBI 2006 PROCEEDINGS

Integration of Chem-Bio-Pharma Informatics for Rational Drug
Development and e-ADMETHuman SULT1A1 complexed with PAP and Estradiol

July 24-26, 2006
Komaba Eminence, Tokyo

CBI 2006 PROCEEDINGS
Integration of Chem-Bio-Pharma Informatics
for Rational Drug Development and e-ADMETChem-Bio Informatics Society

CONTENTS･･･････････････････････････････････････････････

1

Benefactors ･･ ･･････････････････････････････････････････

2

Contributors

2

Preface･･･････････････････････････････････････････

Exhibition & Luncheon Seminor Companies･･･････････

2････････････････････････････

3

Organizer･･････････････････････････････････････････････

4

Program･･･････････････････････････････････････････････

5

CBI Consortium Members

Venue Layout･････････････････････････････････････････････････････････････････････････

13･･･････････････････････････････････

35

Keynote & Invited Talks
Poster Presentations･･････････

119･･････････････････････････････････････････

129

Let's Share and Solve our Common Problems
Author Index

10

What's CBIThe CBI Association, established in 1981, sponsored nearly two hundred
meetings, such as lectures, seminars, and symposia on chemical computing,molecular biology, and informatics during the past two decades. All of these
activities were financially supported by members of the CBI Company
association, which included a wide range of leading Japanese companies in thefields of pharmaceuticals, chemicals and computers as well as informatics.
Hitherto, this association stimulated Japanese academic and industrial activities
related to computer-aided drug design, protein engineering, structure-based
biology, biochemical (DNA and protein) chips, biocomputing, applications ofpattern recognition, artificial intelligence and the Internet, computational
toxicology, and genomic technologies. In April 2000, the CBI Association
changed its structure and name to become the CBI Society with a new mandate
to enhance its activities on the basis of the previous association and to evolve
towards the new era of biotechnology in the 21st century. The executive team of
the CBI Society comprises leading scientists from universities, industrial
laboratories, and national research institutes.

CBI 2006 PROCEEDINGS
Edited by Takashi Mizuma
July 24, 2006
Chem-Bio Informatics Society(CBI)
Office: Iida Bldg., Room 301
4-3-16, Yoga, Setagaya-ku
Tokyo, 158-0097, JAPAN
Tel: +81-3-5491-5423 Fax: +81-3-5491-5462
Email: cbistaff@cbi.or.jp

Preface
Welcome to the 2006 CBI Annual Meeting entitled
Integration of Chem-Bio-Pharma Informatics for Rational Drug Development ande-ADMETIn May 2005, our CBI Society held a seminar entitled "Toward development of
e-ADMET." The fact that so many attended the workshop made us realize again that CBIsociety members and non-members alike are very interested in this issue. ADMET, which
represents both pharmacokinetics and toxicity, has had a key role in drug development andusage; and the recognition of its importance has been continuing to increase more and more.
Nevertheless, there are many problems with ADMET, such as its cost, speed, and so on, to besolved to further expand its use for drug development.
To overcome these problems, we need to establish a novel method. One way to do thisis to develop e-ADMET, by which we can enable advancements in drug development by relying
less on "wet" approaches (experimentation), and more on "dry" approaches (informatics andcomputational science) that can efficiently utilize the knowledge accumulated by ourpredecessors. Once we establish this approach, the way of carrying out drug development
becomes changed, resulting in monetary savings and more efficient use of personnel, materials,
and time. Moreover, issues regarding the use of experimental animals and human tissues
become less related to drug development.
I believe that for rational drug development to be successful, it is necessary to know
chemistry and biology, and to understand phenomena and their mechanisms derived frominteractions between chemicals and the body. Pharmaceutical science is a study integrating theseissues. Medicines (drugs) are not just about chemicals. Medicines accompanied by information
is "Medicine." The members of CBI society are multidisciplinary; that is, our fields coverchemistry, biology, informatics, computational science, pharmaceutical science, and their "wet"and "dry" studies. This means that our society can provide the best platform for a brainstorming
project about rational drug development.
On the basis of this current background, the 2006 annual meeting of the CBI society isentitled "Integration of Chem-Bio-Pharma Informatics for Rational Drug Development ande-ADMET." I hope that the 2006 annual meeting, which consists of thematic sessions as well as
regular sessions, becomes an indispensable opportunity for attendees to get something new andinnovative for advancing rational drug development and e-ADMET.

Takashi Mizuma, Ph.D. Chairman of the 2006 annual meeting(Tokyo University of Pharmacy and Life Science)Host
Chem-Bio Informatics Society

Benefactors
The Chemical Society of JapanThe Pharmaceutical Society of JapanThe Japanese Society for Artificial Intelligence
The Biophysical Society of JapanThe Japanese Society for the Study of Xenobiotics
Molecular Biology Society of Japan
Japanese Society for Bioinformatics
Protein Science Society of JapanThe Japan Society of Drug Delivery System
The Academy of Pharmaceutical Science and Technology, Japan
Information Processing Society of Japan
Japan Association for Medical Informatics

Contributors
AdIn Research, Inc.
Daiichi Pharmaceutical Co.,Ltd.
Eisai Co.,Ltd.
Mitsui Chemicals Inc.
Mochida Pharmaceutical Co.,Ltd.
Taisho Pharmaceutical Co.,Ltd.
Taiho Pharmaceutical Co.,Ltd.
Tokyo University of Pharmacy and Life Science

Exhibition & Luncheon Seminor Companies
AdvanceSoft Corporation
Cerep Japan
CTC Laboratory Systems Corporation
Fujitsu Ltd.
Hitachi, Ltd.
IBM Japan, Ltd.
Infocom Corporation
Mathematical Systems, Inc.
NABE International Corporation
NalaPro Technologies, Inc.
PharmaDesign, Inc.
Ryoka Systems Inc.
Sumisho Computer Systems Corporation
SGI Japan, Ltd.

Talous Inc.
Cybernet Systems Co.,Ltd.
Wavefunction, Inc.

CBI Consortium Members
Accelrys Inc.
AdIn Research, Inc.
AdvanceSoft Corporation
Ajinomoto Co.,Inc.
Astellas Pharma Inc.
Banyu Pharmaceutical Co.,Ltd.
CAC Corp.
Chugai Pharmaceutical Co.,Ltd.
CTC Laboratory Systems Corporation
Daiichi Pharmaceutical Co.,Ltd.
Dainippon Sumitomo Pharma Co.,Ltd.
DNA Chip Research Inc.
Eisai Co. Ltd.
Fuji Photo Film Co. Ltd.
Fujitsu Ltd.
GlaxoSmithKline K.K.
Hitachi, Ltd.
IBM Japan, Ltd.
Infocom Corp.
Mitsui Chemicals Inc.
Mizuho Information & Research Institute, Inc.
Mochida Pharmaceutical Co.,Ltd.
NEC Corporation
Otsuka Pharmaceutical Co.,Ltd.
Sankyo Co.,Ltd
Shionogi & Co.,Ltd.
Sony Corp.
Sumisho Computer Systems Corporation
Taiho Pharmaceutical Co.,Ltd.
Taisho Pharmaceutical Co.,Ltd.
Tanabe Seiyaku Co.,Ltd.
Teijin Pharma Ltd.
Toray Industries Inc.
Ryoka Systems Inc.
Yokogawa Electric Corporation

Organizer
ChairmanTakashi Mizuma (Tokyo University of Pharmacy and Life Science)

ChairsYukio Tada (Taiho Pharmaceutical Co., Ltd)
Tsuguchika Kaminuma (Tokyo Medical and Dental University)
Masami Uebayasi (AIST)
Yuji Takaoka (Taisho Pharmaceutical Co., Ltd.)
Akihiko Konagaya (RIKEN Genomic Science Center)
Kazuro Shimokawa (RIKEN Genomic Science Center)
Akihiko Hirose (National Institute of Health Sciences)
Hiroshi Tanaka (Tokyo Medical and Dental University)
Yoshinori Harada (Life Science Group, Hitachi,Ltd.)
Yoshiro Nakata (Gunma University)

Program

Program for CBI2006
July 24th (Mon.)
Opening Remarks for Japanese Session<Let's Share and Solve our Common Problems>
Takashi Mizuma (Chairman of The 2006 Annual Meeting)
10:05<Problem A>
Proposal：Hiroyuki Kusuhara((Univ. of Tokyo)
Answer： Motohiro Kato(Chugai Pharmaceutical Co. Ltd.)
10:35<Problem B>
Proposal：Eiichiro Ichiishi(Tohoku University)
Answer： Shigeki Mitaku(Nagoya University)
Answer： Tomokazu Konishi(Akita Prefectural University)
11:30<Problem C>
Proposal：Makoto Tsuruoka(Tokyo University of Technology)
11:35<Problem D>
Proposal：Yoshihiro Mori(Okayama University of Science)
Opening Remarks
Yukio Tada (President of The CBI Society)

10:00

11:50
12:00

Luncheon Seminar by AdvanceSoft Corporation

13:15

Opening Speech
Takashi Mizuma (Chairman of The 2006 Annual Meeting)

13:30

Regular Session 1: Area 1 & 2 <Keynote & Invited Talks>
13:30-14:15
S01

Kazuo Kitaura (Research Institute for Computational Sciences, AIST)"Quantum Chemical Calculations of Binding Energy between Protein andLigand"Chairman: Tatsuya Nakano (National Institute of Health Sciences)
14:15-15:00

S02

Masaki Tomimoto (SGX Pharmaceuticals Inc.)"Fragment-Based Drug Discovery: Concept and Example"
Chairman: Yuji Takaoka (Taisho pharmaceutical Co., Ltd.)
15:00-15:30 Break
15:30-16:30

S03

Ruben Abagyan (The Scripps Research Institute)"Computational Structural Proteomics and Drug Discovery"
Chairman: Mayuko Takeda-Shitaka (Kitasato Univ.)

16:30

Poster Oral Session (Poster ID: P1-03, P1-06, P2-01, P2-02, P3-03, P3-09)

17:00
18:00

Poster Session

July 25th (Tue.)
Annual theme session 1 (Wet study of ADMET) <Keynote & Invited Talks>

9:30

9:30-10:30
S04

Svein Oie (College of Pharmacy The Univ. of Georgia)“Optimizing use of Pharmacokinetics and Toxicokinetics in Drug Discovery”
Chairman: Takashi Mizuma (Tokyo Univ. of Pharmacy and Life Science)
10:30-11:15

S05

Mitsuru Hashida (Kyoto Univ.)"Development of DDS: Analysis and Control of Drug Disposition in the
Body"
Chairman: Toshiharu Horie (Chiba Univ.)
11:15-12:00

S06

Atsushi Ono (National Institute of Biomedical Innovation)"Current topics of toxicogenomics - Challenges on issues in Toxicogenomics
Project -"Chairman: Akihiko Hirose (National Institute of Health Sciences)

12:00

Luncheon Seminar by Fujitsu Ltd.

13:30

Regular Session 2: Area 3 & 4 <Keynote & Invited Talks>
13:30-14:30
S07

Kyoung Tai No (Yonsei Univ.)"Introduction of Integrated e-Drug Discovery System, Lead Explore"
Chairman: Akihiko Konagaya (RIKEN, Genomic Science Center)
14:30-15:15

S08

Shigehiko Kanaya (Nara Institute of Science and Technology)"KNApSAcK: Secondary metabolite database related between metabolites
and species"
Chairman: Akihiko Konagaya (RIKEN, Genomic Science Center)
15:15-15:45 Break
15:45-16:30

S09

Sumi Yoshikawa (RIKEN, Genomic Science Center)"Pharmaco-Ontologies and their Roles"
Chairman: Akihiko Konagaya (RIKEN, Genomic Science Center)

16:30

Poster Oral Session (Poster ID: P3-11, P4-02, P4-04, P5-07, P5-11, P5-16)

17:00

Poster Session

18:00

Poster Award

July 26th (Wed.)
Annual theme session 2 (Dry study of ADMET) <Keynote & Invited Talks>

9:30

9:30-10:30
S10

Sean Ekins (Arizona Commodity Traders, LLC&Univ. of Maryland)“Combining Quantitative Structure Activity Relationships and Systems
Biology Approaches for in silico ADME/Tox”
Chairman: Yasushi Okazaki (Saitama Med. Schl. Res. Ctr. for Genomic Medicine)
10:30-11:15

S11

Edward H. Kerns (Wyeth Res.)“Current Strategies for ADMET Integration to Enhance Efficiency andQuality in Drug Discovery”
Chairman: Fumiyoshi Yamashita (Kyoto Univ.)
11:15-12:00

S12

Kazuya Maeda (Univ. of Tokyo)“In silico database, simulation and prediction for the optimization ofpharmacokinetic properties of drug candidates”
Chairman: Takashi Mizuma (Tokyo Univ. of Pharmacy and Life Science)

12:00

Luncheon Seminar by Cerep Japan

13:30

Regular Session 3: Area 6 & 7 <Keynote & Invited Talks>
13:30-14:20
S13

Akihiko Takano (National Institute of Informatics)“Information Access based on Association”
Chairman: Yoshinori Harada (Hitachi, Ltd.)
14:20-15:10

S14

Asako Koike (Hitachi, Ltd.)“Biomedical text mining: state of the art and future direction”
Chairman: Yoshinori Harada (Hitachi, Ltd.)
15:10-15:25 Break
15:25-16:15

S15

Teruhiko Yoshida (National Cancer Center)“Cancer Genomics: Chip, SNP, Database”
Chairman: Hiroshi Tanaka (Tokyo Med. Dent. Univ.)
16:15-17:05

S16

Kunio Shiota (Univ. of Tokyo)“Application of Epigenetics on Development of Drugs”
Chairman: Takatoshi Kawai (Eisai Co.,Ltd.)

17:10
17:30

Closing Remarks

Venue Layout

1st Floor<Diamond Room>

1st Floor<Main Hall>

to Ikejiri-Ohashi
Station

1

6

2

7

4

Main
Hall

Smoking
Area

PosterSession

3
5

1

down

internet LAN

2

3

4

Stage

8
7
6
5

Registration
desk

Main
Entrance

EV

Front
desk
Restaurant

Hotel
Entrance

to Komaba Todaimae
StationExhibition/ Luncheon Seminor/ Advertisement Companies

Companies

Exhibition

Luncheon
SeminorAdvertisement

DiamondRoom
Cybernet Systems Co.,Ltd.

1○

Talous Inc.

2○

CTC Laboratory Systems Corporation3○

Hitachi, Ltd.

4○

PharmaDesign, Inc.

5○

SGI Japan, Ltd.

6○

Wavefunction, Inc.

7○

Mathematical Systems Inc.

8○

Main Hall
Foyer
Infocom Corporation

1○

Sumisho Computer Systems Corporation

2○

Ryoka Systems Inc.

3○

Fujitsu Ltd.

4

NABE International Corporation

5

AdvanceSoft Corporation

6

NalaPro Technologies, Inc.

7

Cerep Japan

July 25○○

July 24○○

July 26○

Notice
Poster Awards
•
•

•
•

Participants are requested to vote for the best poster.
The Best Poster Awards will be given for five excellent presentations
and posted on the display board on July 25th, 18:00 in Diamond
Room.
Winners of the Poster Prize will be decided by the CBI Journal
Publishing Committee based on the votes of conference participants.
The winners will receive the awards plus a privilege of exception from
submission charges to the CBI Journal. The winners will also be invited
to attend the Dining Party (only one person per poster).

Dining Party (Buffet style)••

Date: July 25th, 18:00 ~ 20:00
Place: "Diamond Room"Keynote & Invited
Talks

Keynote and Invited Talks
S01

Quantum Chemical Calculations of Binding Energy Between
Protein and Ligand
Kazuo Kitaura
Research Institute for Computational Sciences (RICS)
National Institute of Advanced Industrial Science and Technology (AIST)

S02

Fragment-Based Drug Discovery: Concept and Example
Masaki Tomimoto
Associate Director, Computational Chemistry, SGX Pharmaceuticals, Inc.

S03

Computational Structural Proteomics and Drug Discovery
Ruben Abagyan
The Scripps Research Institute

S04

Optimizing use of Pharmacokinetics and Toxicokinetics in Drug
Discovery
Svein ØieCollege of Pharmacy, University of Georgia

S05

Development of DDS:Analysis and Control of Drug Disposition
In the Body
Mitsuru Hashida
Graduate School of Pharmaceutical Sciences, Kyoto University

S06

Current topics of toxicogenomics– Challenges on issues in
Toxicogenomics Project –Atsushi Ono
Toxicogenomics Project, National Institute of Biomedical Innovation

S07

Introduction of Integrated e-Drug Discovery System, Lead Explore
Kyoung Tai No
Department of Biotechnology, Yonsei University
Bioinformatics and Molecular Design Research Center (BMDRC)

S08

KNApSAcK: Secondary metabolite database related between
metabolites and species
Shigehiko Kanaya
Graduate school of Information Science, Nara Institute of Science and Technology

S09

Pharmaco-Ontologies and their Roles
Sumi Yoshikawa
RIKEN Genomic Science Center

S10

Combining Quantitative Structure Activity Relationships andSystems Biology Approaches for in silico ADME/Tox
Sean Ekins
ACT LLC and Department of Pharmaceutical Sciences, University of Maryland

S11

Current Strategies for ADMET Integration to Enhance
Efficiency and Quality in Drug Discovery
Edward H. Kerns
Wyeth Research

S12

In silico database, simulation and prediction for the optimizationof pharmacokinetic properties of drug candidates

Kazuya Maeda1, Hiroyuki Kusuhara1, Yoshihisa Shitara2, Motohiro Kato3, Yuichi Sugiyama1
1
Graduate School of Pharmaceutical Sciences, The University of Tokyo, 2Graduate School of
Pharmaceutical Sciences, Chiba University, 3Chugai Pharmaceutical Co., Ltd.

S13

Information Access based on Association
Akihiko Takano
National Institute of Informatics, Tokyo

S14

Biomedical text mining: state of the art and future direction
Asako Koike
Central Research Laboratory, Hitachi Ltd.

S15

Cancer Genomics; Chip, SNP, Database
Teruhiko Yoshida, Kazuhiko Aoyagi, Hiroki Sasaki and Hiromi Sakamoto
Genetics Division, National Cancer Center Research Institute

S16

Application of Epigenetics on Development of Drugs
Kunio Shiota
Laboratory of Cellular Biochemistry, Animal Resource Sciences, The Universityof Tokyo

S01
Quantum Chemical Calculations of Binding Energy Between Protein andLigandKazuo Kitaura
Research Institute for Computational Sciences (RICS)
National Institute of Advanced Industrial Science and Technology (AIST)
We have developed the fragment molecular orbital (FMO) method1 for quantum
chemical calculations of very large molecules. In the method a molecule is divided intofragments and ab initio MO calculations are performed on the fragmnets, their dimers
and optionally trimers to obtain the total enegy and other properties of the whole
molecule. The FMO method reproduces regular ab initio properties with good accuracy.
Various FMO-based correlation methods have been developed combining with thedensity functional theory(DFT), the 2nd order Møller-Plesset perturbation theory(MP2),
the coupled cluster theory(CC), and MCSCF. The multilayer FMO method2 treats theimportant part of the system with high accuracy and the rest with low level of theory.
Very recently the polarisable continuum model (PCM) was interfaced with the FMO
method3, allowing one to treat solvent effects of real size proteins. These methods havebeen interfaced with the GAMESS program package4.
We applied the method to four FK506 binding protein (FKBP) complexes (denoted bytheir PDB codes 1fkb, 1fkf, 1fkg, and 1fki)5. The geometries of the ligands in thecomplexes were optimized at the restricted Hartree-Fock (FMO-RHF) level using the
3-21G basis set. The binding energies were refined at a higher level of theory (2nd order
Møller-Plesset perturbation theory FMO-MP2 with the 6-31G* basis set). Solvent
effects on the protein-ligand binding were estimated using the FMO/PCM method. The
important interactions between the protein residues and the ligand were revealed basedon the FMO pair interaction analysis.

[1] D. G. Fedorov and K. Kitaura, Theoretical development of the fragment molecularorbital (FMO) method, in “Modern methods for theoretical physical chemistry andbiopolymers”, edited by E. Starikow, S. Tanaka and J. Lewis, Elsevier (2006).
[2] D.G. Fedorov, T. Ishida, K. Kitaura, J. Phys Chem. A , 109, 2638-2646 (2005).
[3] D.G. Fedorov and K. Kitaura, J. Comp. Chem., 27, 976-985 (2006).
[4] GAMESS, http://www.msg.ameslab.gov/GAMESS/GAMESS.html[5] I. Nakanishi, D.G. Fedorov, K. Kitaura, submitted.

S02
Fragment-Based Drug Discovery: Concept and Example
Masaki Tomimoto
Associate Director, Computational Chemistry, SGX Pharmaceuticals, Inc.
Fragment-based drug discovery (FBDD) is already generating a lot of attention. FBDD
has been applied to various targets not only in biotech companies but big pharmas, andit generates lead compounds and drug candidates. However, there is no standard recipefor FBDD so far. Each company develops its own FBDD strategy suited for its
technological advantage. SGX Pharmaceuticals, Inc. has developed a FBDD platformthat utilizes high-throughput X-ray crystallography for lead identification/optimization.
The proprietary FAST™ (Fragments of Active STructures) exploits crystallographic
screening to detect, visualize, and identify fragments (molecular weight 150-200,
typical hit rates 1-5%) that are bound to the target protein. Each member of the FAST™
fragment library is amenable to rapid chemical elaboration at two or three points ofchemical diversity using high-throughput organic synthesis. Initial lead optimization
involves using knowledge of the co-crystal structure of the target-fragment complex andadvanced computational chemistry tools to guide synthesis of small focused linear(one-dimensional) libraries. These linearly elaborated fragments are then evaluated within vitro biochemical assays and crystallography. Thereafter, optimal variations at eachpoint of chemical diversity are combined to synthesize focused combinatorial (two- or
three-dimensional) libraries that are again examined with biochemical assays andcrystallography. Lead compound series are prioritized for further medicinal chemistry
and compound development efforts using the results of cellular and animal model
assays, in vitro and in vivo ADME and in vitro toxicology studies in concert withstructural information. Basic concept and application of the FAST･ fragment-based
lead generation process to various oncology targets will be presented.

S03
Computational Structural Proteomics and Drug Discovery
Ruben Abagyan
The Scripps Research Institute
Abstract
Computational prediction of protein structure, protein function, and ligand binding is an areaof critical importance considering the rapidly growing number of protein structures inProtein Data Bank. We focused on several related computational tasks:
(i)
(ii)
(iii)
(iv)
(v)

predicting druggable binding sites from a single structure;
predicting protein-protein interaction patches from a single structure
predicting conformations of protein loops in models by homology with high accuracy
accurate docking of flexible ligands to flexible protein pockets and virtual screening
protein docking and refinement of flexible protein interfaces

We demonstrated that the small molecule binding pockets can be predicted with a certaintransformation of the Lennard Jones potential [1]. This algorithm is useful in predicting
new or allosteric binding sites or the feasibility of inhibiting protein-protein interaction witha small molecule. Predicting transient protein-protein interaction interfaces without knowing
its partner
was also proposed and validated on a large benchmark[2].
Including the receptor pocket flexibility into account in ligand docking remains to be a task
highly specific to the nature of the receptor. We attempted to formulate a docking protocol
which is relatively general and includes both the side-chain sampling and loop movements[3]. The ICM docking protocol led to the discovery of novel inhibitors against a number ofbiomedical targets, including de novo discovery of antagonists of RAR and thyroid hormone
receptor ([4]), discovery of inhibitors of alpha 1-antitrypsin amyloid formation, discovery of
new antimalarial agents, and novel antagonists of the androgen receptor.
The CAPRI protein docking experiment provides a good platform for evaluation of protein
docking algorithms. While a fully reliable prediction of the association geometry given two
uncomplexed structures remains unattainable, considerable progress has been made in thelast few years [5]. The difficulty of the problem is a function of the scale of the induced
conformational changes upon association. We demonstrate that at least in some cases thesechanges can be correctly predicted. A new way of exchanging and browsing the chemicaland structural data is also presented [6].
References and Footnotes
1. An, J., Totrov, M. and Abagyan, R. Pocketome: Comprehensive Identification andClassification of Ligand-Binding Envelopes. Mol Cell Proteomics 4, 752-61 (2005).
2. Fernandez-Recio, J., Totrov, M., Skorodumov, C. and Abagyan, A.Optimal docking area:
a new method for predicting protein-protein interaction sites. Proteins, 58,134-143 (2005)

3. Cavasotto, C.N. and Abagyan, R. Protein flexibility in ligand docking and virtual
screening to protein kinases J Mol Biol, 337, 209-225, (2004).
4. Schapira, M., Raaka, B.M., Das, S., Fan, L., Totrov, M., Zhou, Z., Wilson, S.R., Abagyan,R., Samuels, H.H. Discovery of diverse thyroid hormone receptor antagonists by highthroughput docking. PNAS USA, 2003, 100,7354-9.
5. Fernandez-Recio, J., Totrov, M. and Abagyan, R. ICM-DISCO docking by global energy
optimization with fully flexible side-chains Proteins, 52.113-117, (2003).
6. Abagyan, R., Lee, W.H., Raush, E., Budagyan, L., Totrov, M., Sundstrom, M., Marsden,
B.D.
Disseminating structural genomics data to the public: from a data dump to an animated story.
Trends Biochem Sci. (2006), 31, 76-8

S04
Optimizing use of Pharmacokinetics and Toxicokinetics in Drug Discovery
Svein ØieCollege of Pharmacy, University of Georgia
Pharmacokinetics (absorption, distribution, metabolism and elimination – ADME) is
extremely important for developing optimal dosing regimens. Predictable and desirable
plasma concentrations, compliance and optimal response depend upon thepharmacokinetic characteristics of the drug.
As cost of discovering and developing new drug entities has spiraled out of control, theuse of computer design and high throughput screening of chemical libraries has become
an integral part drug discovery. Although a larger number of lead compounds can begenerated, most compounds still fail during clinical studies. In 1998 it was reported thatat least 50 % of the drugs failed in clinical trials due to pharmacokinetics and animal
toxicity. To reduce the number of failings due to pharmacokinetics and toxicity, anumber of cellular, in vitro and in silico screening techniques have been adopted. In
silico screening has been particular successful in identifying “drugable” chemical
entities with better bioavailability, and membrane permeability.
Although these approaches have reduced the number of clinical failures due to poor
absorption, they come at a cost. The current methods do not adequately consider factors
such as active transport, modification of ADME through formulation and chemical
modification, and that “optimal” ADME is not necessary for all compounds to beeffective.
Drugs can be classified as 1, “curing” diseases or conditions, 2, “controlling” diseases
and conditions or 3, temporarily “relieving” symptoms. Because these classes of drugs
vary in what stricture we set to their pharmacokinetics, toxicity, drug interactions andpharmacogenetic variability, our approach to “discovering” drugs in each of the classes
will also vary.
For drugs that “cures” (e.g., antivirals, antibiotics, anticancer, genetic modifiers, etc.)
the treatment period is usually short and effect long-term, allowing us to toleratemoderate side effects, accept less than optimal pharmacokinetics, and making costusually less of an issue.
For drugs that control diseases or conditions (e.g., diabetes, high blood pressure,cholesterol, etc.) the treatment period is usually long-term requiring more optimal
pharmacokinetics, low potential for drug-drug interactions and side-effects and low cost.
This is an area where in silico screening methods are particular useful but is still in needof improvements.
For, drugs that temporarily relieve symptoms, the treatment period is usually short, butalso frequently repetitive (e.g., seasonal allergies, migraines, etc.) and optimal
pharmacokinetics is again less critical but requires low potential for drug-druginteractions and side-effects.
By understanding when pharmacokinetics and toxicity play major or minor roles in
effective drug treatment and classifying drugs appropriately, we will be able to makefurther advances in rational drug design and drug discovery.

S05
Development of DDS:
Analysis and Control of Drug Disposition in the Body
Mitsuru Hashida
Graduate School of Pharmaceutical Sciences, Kyoto University
Drug delivery system (DDS) is defined as a technology for a precise control of invivo behavior of a drug aiming at optimization of its therapeutic efficacy and various
applications such as controlled release, targeting, and absorption enhancement havebeen developed being indispensable especially for protein and gene medicines. Drugtargeting, an approach to concentrate the drug on the specific site of the body throughmanipulation of its biodistribution profile attracts great interest and many
macromolecular or particulate carriers have been developed:
The use of polymer can
be a formidable tool because of their high diversity and multiple functions, but
successful application of a polymer carrier requires understanding of thepharmacokinetics of the polymer at whole body, organ, cellular and subcellular levels in
relation to their physicochemical and biological characteristics.
In a series of our investigations, biodistribution of proteins and other types ofmacromolecules have been characterized based on pharmacokinetic analysis and astrategy for controlling their disposition profiles through chemical modification was
proposed. The fundamental properties such as a molecular weight and electric charge
are revealed to determine the disposition patterns of macromolecules. To achieve cell
specific delivery of proteins, receptors existing on the surface of cells can be utilized
and we have developed several methods such as galactosylation, mannosylation or
succinylation, which lead proteins to be recognized by special cell types in the liver.
Cell-specific delivery and resultant therapeutic effects of antioxidant enzymes,
recombinant human superoxide dismutase (SOD) and bovine liver catalase (CAT), withchemical modifications were demonstrated. Similar approaches have been applied togene delivery, and specific delivery of plasmid DNA and subsequent protein expression
were observed in hepatocytes, macrophages, etc.
Successful results have been
observed in application of these systems to DNA vaccination.
In parallel to these approaches, we have interested in application of informatics insuch as dosage form design and in silico ADME-Tox predication.
With a recent
progress in computer technology, we become able to apply sophisticated computation
algorithms to QSAR and a better performance in QSAR modeling have been realized.

S06
Current topics of toxicogenomics- Challenges on issues in Toxicogenomics Project Atsushi Ono
Toxicogenomics Project, National Institute of Biomedical Innovation
Gene expression analysis opens new perspectives in toxicology to identify
mechanisms of toxicity and biological response and to improve toxicity assessment.
However, there is difficulty in biological interpretation of the results obtained from theexpression changes of thousands of genes because of the limitation of knowledge on
relations of gene expression change and biological effect. Toxicogenomics Project(TGP) is a joint national project of National Institute of Health Sciences (NIHS),
National Institute of Biomedical Innovation (NIBIO) and 15 pharmaceutical companies,
which started at 2002. The goal of our project is to construct a large-scale database ofover 150 drugs exposed to rats (in vivo, in vitro) and to human (in vitro), and develops asystem that will forecast the toxicity of new chemical in the early stage, if possible invitro, of drug development. Moreover, the bridging of the interspecies is consideredusing human and rat primary hepatocyte culture system in our project. TGP databaseintegrate gene expression data analyzed by Affymetrix GeneChip with traditional
toxicological endpoints including blood biochemical, hematological, and pathological
data. Additional information about the toxicological, biological and medical effects ofchemicals collected from the literature is stored. Strategic experimental design allows
ensure high quality dataset. All stored data is fully searchable and informatics tools will
aid mechanistic understanding of drug-induced toxicity, and lead to the identification of
new biomarkers relate to toxic phenotypes. In order to develop a reliable systematic
framework for phenotype classification and prediction from gene expression data, firstcritical step of analysis is to find gene sets whose expression is tightly anchored on tosubset of the phenotype. We tried to use various unsupervised and supervised learning
methods. In some in vivo case, such as glutathione depletion, nongenotoxic
carcinogenesis and peroxisome proliferation, etc in the rat liver, we have succeeded inselection of biomarker genes and in classifying samples quantitatively with selectedbiomarker. On the other hand, prediction of in vivo toxicity from in vitro expression data
as well as bridging of the interspecies has some limited by the fact that response ofmany genes is not coincidence in each system from our results. However, there iscluster of genes, which consistently changed in vivo and in vitro, and also in human and
rat in vitro. Therefore, when attempting to use it for specific effects, in vitro data areuseful to predict in vivo effects. The knowledge with our database will make predictive
toxicology possible. The current version of our toxicity forecast system is prototype andfurther validation study and novel informatics to promote it is necessary. In case of its
completion, a new era of toxicology "Systems Toxicology" will arrive.

S07
Introduction of Integrated e-Drug Discovery System, Lead Explore
Kyoung Tai No
Department of Biotechnology, Yonsei University
Bioinformatics and Molecular Design Research Center (BMDRC) www.bmdrc.org

An integrated e-Drug Discovery system, Lead Explore, have been developed since 1997
by BMDRC and it’s collaborators in Korea and also foreign countries for providing
systematic and cost effective drug discovery both for academic and industry researchers.
The following diagram shows outline of Lead Explore and the components introduced.

Target Features
Filtering Engine
Toxicity/ Mutagenecity
Geno, Acute, Calcino
Toxicity, hERG
Clearance/ metabolism
P450-Induction, Inhibition

Drugable
CompoundDB
Focused DB

3D
Conformer

Protein
Str. DB

GenomeDB

Protein Str.
Predictor

GeneFinder

3D Target
Protein
SBDD, LBDD
ADME/Tox

Distribution/ BBB, Organ

PP Binding
Absorption/ Caco2,
MDCK
Properties/ Drug
Likeness
Virtual Library

Virtual Screening
PharmoScanTM
PharmoMapTM
ViSION
in vitro orin vivoi

ChemicalSynthesis

AnimalTest

Lead
CompoundLead Explore consists of library design, chemical properties prediction, chemical
management, ADME/Tox prediction, virtual screening, target protein analysis andpharmacophore define and some graphical interfaces components.

At thepresentation we will describe Lead Explore system in detail and will introduce someresearch results performed with the system.

S08
KNApSAcK:
Secondary metabolite database related between metabolites and species
Shigehiko Kanaya
Graduate school of Information Science, Nara Institute of Science and Technology
Many metabolites have been identified from plants, microbes and other organisms.
Secondary metabolites are highly species specific and play a role for the survival of the
producing organism within its natural habitat. The number of metabolites present in theplant kingdom is estimated to exceed 100,000—an enormous number indicating a large
scale of structured diversity of compounds. Several databases have been made bycollecting metabolite information of various organisms, and provide some chemical
information and biological pathways on metabolites, however, they don’t provide therelationships between metabolites and their biological origins. To systematically andcomprehensively understand species-specific diversity of metabolites, we have designed
a database system called KNApSAcK. This system is useful for obtaining information
on metabolites and their corresponding species, chemical structure and biologicalactivity. In addition, the database has a tool that can be used for analyzing datasets
acquired using Fourier transform ion cyclotron mass spectrometry. We collected
information on 24,604 metabolite-species pairs encompassing 10,181 metabolites and7,362 species from published references (October 11th, 2005). These data have been
stored on a server which is located in Nara Institute of Science and Technology. When
the KNApSAcK system is started, these data are automatically downloaded from theserver. This database system and online manual are freely available at
http://kanaya.naist.jp/KNApSAcK/. This database system is available in Web- andDownload-version. To use the Web-version, get into http://kanaya.naist.jp/KNApSAcK/KNApSAcK.php. If you want to use the Download-version, at the
beginning, you have to install KNApSAcK and Java 1.4.2 on your local computer. In
the CBI coference, some topics concerning species-specific metabolite will be
discussed.

S09
Pharmaco-Ontologies and their Roles
Sumi Yoshikawa
Genomic Sciences Center (GSC), RIKEN
The increasing amount of digital information and the numerous opportunities ofinterdisciplinary activities are raising issues regarding the suitability of conventional
information systems confined to certain domain communities. Ontology as a discipline
is expected to become a solution since it allows to specify explicit semantics andrelations in terms of both human and computer readable manners. “An ontology is a
specification of a conceptualization, in the context of knowledge sharing” according to
Gruber (1993). Historically, it has been investigated in Artificial Intelligent communities,
providing knowledge representation framework theories and tools for inference systems,
information retrieval, mining, and so on.
Today, ontologies are increasingly developed and adopted in various fields including
biomedical domains. For example, Gene Ontology has been developed as a general geneannotation vocabulary system regardless of species differences. Biomedical Ontologies
would be potentially associated with medical information and are expected to contributeto solve clinical problems. Pharmaco-ontologies overlap with biomedical ontologies in a
wider sense, and deal with multi-dimensional aspects of drug functionalities. The
application area of both ontologies will encompass drug discovery and R&D, decision
making in treatment choices including application of pharmacogenetic information,communication of drug information between medical providers and patients / generalpublic, etc. I will refer to some underlying informatics issues specific to thepharmaceutical / alternative medicine domains and how ontologies would contribute to
the solutions.
The development and maintenance of domain-specific ontologies require knowledge
input from domain experts and the user evaluation. I will also address the need for
fostering application domain ontologists and/or ontology coordinators, and that forcollaborative community activities for the development of solution-oriented ontology.

S10
Combining Quantitative Structure Activity Relationships and Systems
Biology Approaches for in silico ADME/Tox
Sean Ekins
ACT LLC and Department of Pharmaceutical Sciences, University of Maryland,
20 Penn Street, Baltimore, MD 21201, USA
The challenge of predicting the ADME/Tox properties of a drug in humans has been
approached independently using multiple experimental technologies and more recently
computational approaches (e.g. computational quantitative structure activity
relationships (QSAR)) and high content data. Understanding the complexity of
biological systems requires a broader perspective rather than focusing on just one
method in isolation for prediction. From experience of developing computationalmodels for specific ADME/Tox targets such as the pregnane X receptor, human
Ether-a-Go-Go-Related Gene potassium channel (hERG), drug transporters andcytochrome P450s to developing a software suite integrating many models, metabolite
prediction, human cell signaling networks, metabolic pathways and and toxicogenomic
data, the potential for understanding toxicity of small molecules computationally has
greatly expanded. Recent work has included applying multiple computational methods
to predict hERG inhibition and understanding the applicability of these methods.
Further work has applied a systems approach, predicting potential small moleculeinteractions and visualizing these molecules as networks that can be compared tomicroarray or other high content data to show the networks of genes that are affected.
This represents an example of how the combination of QSAR models, applicability
methods and systems biology approaches may provide data mining opportunities forboth pharmaceutical and environmental toxicology.

S11
Current Strategies for ADMET Integration to Enhance Efficiency andQuality in Drug Discovery
Edward H. Kerns
Wyeth Research, Princeton, NJ, USA

In the past decade, the increased attention to biopharmaceutical “drug-like” properties indrug discovery has enhanced success and efficiency. Higher quality clinical candidates
are being advanced to development. Several strategies for integration of ADMET into
discovery have proven to be successful. These strategies include the following:•

Diverse ADMET tools (rules, in silico, in vitro, in vivo) have been developed andhave found their most appropriate stages of discovery.

•

Chemical structures, and the role of medicinal chemists in selecting and modifying
structures to optimize properties, are of crucial importance.

•

Potency and properties are evaluated and optimized in parallel.

•

ADMET experts provide consultation on selecting and optimizing lead candidates
with optimal properties, as part of collaborative project teams.

•

ADMET property knowledge has been successfully applied beyond in vivo drugdelivery to optimizing in vitro biological assays.

In silico tools have the opportunity to enhance these ADMET strategies through: a)
predicting properties and compound ranking, b) proactively guiding structure
modifications for medicinal chemists, and c) modeling in vivo processes to assist
visualization by project teams.

S12
In silico database, simulation and prediction for the optimization ofpharmacokinetic properties of drug candidates

Kazuya Maeda1, Hiroyuki Kusuhara1, Yoshihisa Shitara2, Motohiro Kato3, Yuichi Sugiyama1
1
Graduate School of Pharmaceutical Sciences, The University of Tokyo, 2Graduate
School of Pharmaceutical Sciences, Chiba University, 3Chugai Pharmaceutical Co., Ltd.
Recently many metabolic enzymes and transporters responsible for the detoxificationof xenobiotics including drugs have been identified and characterized, and clinicalstudies clearly indicated the importance of these molecules in the pharmacokinetics andsubsequent pharmacological effect of many drugs. At the present, enormous amountof information has been accumulated in the field of pharmacokinetics. However, it isfairly difficult to utilize the whole information effectively for the drug development andmany people are at a loss as to how to mine the valuable information from the piles ofresearch articles and how to make use of the information to accelerate the drug
development. In our laboratory, with the help of in silico technologies, we havedeveloped the useful tools to support the acquisition of necessary information about
drug transporters and the quantitative prediction of transport properties andpharmacokinetics of drugs as shown below.
1) “TP-search” (http://www.TP-Search.jp/): This is a comprehensive Web-based
database for all about drug transporters. We can easily obtain the information
about substrates/inhibitors/inducers, tissue distribution, drug-drug interaction,
genetic polymorphism, pathophysiology, knockout mice etc. with powerful searchfunctions.
The data are obtained from more than 2600 articles and routinely updated.
2) Ligand-based prediction of the pharmacophore of transporters: Based on theassumption that conformation of each substrate which can bind to the transporter is
one of the energetically-stable conformers, we calculated the candidate conformersof each substrate, put “property sphere” representing the local physicochemical
properties of chemical structure on each substrate and searched the common
configulation of pharmacophores by superposing the structure of each substrate.
Subsequent CoMFA analyses revealed the predicted 3D structure of binding site oftransporters.
3) Simulator for drug-drug interaction: This simulator has also a database forpharmacokinetic parameters of substrate and inhibitor drugs.
Usingphysiologically-based pharmacokinetic (PBPK) model, we can simulate thetime-profiles of both substrate and inhibitor drugs with considering thetime-dependent inhibitory effect of inhibitors on metabolic enzymes. This model
was well validated by the reported clinical drug-drug interactions.
4) Whole body PBPK modeling considering the transporter-mediated clearance:
Integrating the kinetic parameters for the transport properties of uptake and efflux
transporters obtained from in vitro analyses into the PBPK model, we simulated thedose-dependent pharmacokinetics of pravastatin, which is taken up by OATP familytransporters and excreted into bile by MRP2. Non-linear pharmacokinetics could
be reproduced by this model.
In my presentation, I want to introduce the concept and overview of these in silico
approaches and discuss their significance for optimizing the pharmacokinetic
properties of drug candidates.

S13
Information Access based on Association
Akihiko Takano
National Institute of Informatics, TokyoGETA (Generic Engine for Transposable Association) is a software that provides
efficient generic computation for association. It enables the quantitative analysis ofvarious proposed methods based on association, such as measuring similarity among
documents or words. Scalable implementation of GETA can handle large corpora oftwenty million documents, and provides the implementation basis for the effective
information access of next generation. Webcat Plus is the book information service forthe union catalogue of one thousand research libraries in Japan, which is a successful
example to show the power and the flexibility of GETA-based computation for
association. It provides the associative search function which find related books basedon similarities between books based on their TOC’s and abstracs. It also provides users
a set of Topic Words which characterizes the search result. This dual mode interface canintuitively inspire the users with further interactions.
The two-stage approach in the associative search, which is the key to its efficiency, also
facilitates the content-based correlation among different databases.
In this talk we overview the basic features of GETA, and introduce the various existing
services using it as their association engines.
Reference:
Webcat Plus: http://webcatplus.nii.ac.jp/en/
Cultural Heritage Online: http://bunka.nii.ac.jp/
Shinsho Map : http://shinshomap.info/
Book Town JIMBOU: http://jimbou.info/

S14
Biomedical text mining: state of the art and future direction
Asako Koike
Central Research Laboratory, Hitachi Ltd.
Dept. of Computational Biology, Graduate School of Frontier Science, The University of Tokyo

Due to the rapid progress of biomedical field and developments in high throughput
technologies, an enormous amount of experimental results has been created and isstored mainly in text. Text-mining techniques have come into the spotlight for two main
reasons. The first is because objective information, which is explicitly written in text,
can be effectively retrieved using information extraction and information retrieval
techniques. The second is because knowledge concealed within multiple sources can be
discovered by connecting fragmented results described in the text. Extracting objective
information is crucial to the interpretation of high-throughput analyses, while thediscovery of knowledge hidden in text is advantageous in searching for unthought of but
plausible implicit relationships between concepts.
To meet these needs, we have developed an information extraction system called“PRIME” and a text mining system called “BioTermNet”. The PRIME has cumulated
protein interactions, gene/protein functions, and gene/protein-disease relationships
extracted by syntactic analysis from MEDLINE abstracts and full papers. Their protein
networks and orthologous pathways can be drawn by a graphic viewer. The BioTermNet
provides mainly two functions for knowledge discovery/hypothesis generation andinterpretation of experimental results and the results are presented by a graphic viewer.
The first function is to connect the explicit relationship and generate the conceptual
network, and search the most appropriate implicit relationship for open (only startconcept is given) and closed (start and end concepts are given) discoveries. The explicit
relationship is calculated with a hybrid method of syntactic analysis and statisticalanalysis. This function is useful in the search for new diseases to apply approved drugs
to, in the search for the dietary effects of foods and in the search for relationships
between genes and phenotypes. The second function of BioTermNet is to cluster
genes/concepts based on document similarities for the interpretation of high-throughput
data. In this presentation, with introduction of the application of this system to theknowledge discovery and interpretation of experimental results, the future direction of
biomedical text mining is also discussed. The BioTermNet and PRIME are respectively
available at http://btn.ontology.ims.u-tokyo.ac.jp/ andhttp://prime.ontology.ims.u-tokyo.ac.jp:8081/ for non-commercial purposes.

S15
Cancer Genomics; Chip, SNP, Database
Teruhiko Yoshida, Kazuhiko Aoyagi, Hiroki Sasaki and Hiromi Sakamoto
Genetics Division, National Cancer Center Research InstituteThis presentation tries to give an overview of the hopes and challenges which the
speaker thinks that the cancer genomics is facing. The 2 words, Chip and SNP, may
characterize the post sequencing era of the human genome research, and they have
rapidly infiltrated many, if not most, fields of the cancer research. The increasing
power and ease of the genome data acquisition have facilitated statistics-driven,hypothesis-free (or hypothesis-generating) research strategies based on the
genome-wide screening of clinical samples. Cancer research has been one of thedriving forces of the genomics, and its unique feature is that 2 types of genomes need to
be analyzed in cancer cases: somatic and germline. In the somatic (cancer) cell
genome analyses, 2 types of chips are now being used widely: array CGH(comparative genomic hybridization) and oligonucleotide microarray for expression
profiling. Our ongoing experience will be presented regarding the application of theAffymetrix GeneChip technology to the class prediction problem of thechemoradiosensitivity of the esophageal cancer. In the germline analyses, SNP
genome scan tool is now in the second generation, the post-HapMap, chip-based
platform at least for the first screening. Our pre-HapMap experience during theMillennium Genome Project, helped by RIKEN, will be presented. With these floodsof the genome data, the 3 obvious vital points are 1) high quality phenotype information,2) sufficiently large number of the samples, and 3) functional database. We havestarted constructing GeMDBJ, Genome Medicine Database of Japan(http://gemdbj.nibio.go.jp), exploring a way for a synthetic analysis to correlate thephenotype data with the multiple types of the omics and their annotation data.

S16
Application of Epigenetics on Development of Drugs
Kunio Shiota
Laboratory of Cellular Biochemistry, Animal Resource Sciences,
The University of Tokyo“Genome projects” of human being and various organisms have shown thatmammals have a much bigger genome than less complex eukaryotes. However, theincreased ‘bio-complexity’ is not reflected by an equivalent increase in the number ofgenes. Epigenetics appears to be one of the most important research fields in the
post-genome sequence era. Epigenetics is defined as “ the study of heritable changes ingene function that occur without a change in the sequence of DNA “ or “the study ofprocesses that produce a heritable phenotype that is not strictly dependent on the DNA
sequence”. DNA methylation is the only chemical modification in mammalian genome
and it occurs in conjunction with histone modifications causing condensation of thechromatin packaging the genome, and results in the repression of genes. The study of
epigenetics focuses on investigating the mechanism by which each cell expresses its
own phenotype using the same genome. We have found tissue-dependent anddifferentially methylated regions (T-DMRs) in CpG-rich or poor unique sequences in rat
and murine normal tissues and cell-types including embryonic stem cells. T-DMRs arewidespread in the euchromatin of the mammalian genome. The complexity of mammals

is likely to depend on the combinational control triggering a vast number of gene
expression pattern. Thus, epigenetic research is anticipated to have implications for
medicine and the understanding of the basic process of the cell fate determination. In
this meeting, I will introduce “Epigenetics” for the various steps of development ofdrugs.

Reviews
1) Shiota K., and Yanagimachi R., Epigenetics by DNA methyaltion for development of
normal and cloned animals. Differentiation 69:162-166 (2002).
2) Shiota K., DNA methylation profiles of CpG islands for cellular differentiation anddevelopment in mammals. Cytogenet Genome Res 105:325-334 (2004).
3) Lieb J. D., et al., Applying whole-genome studies of epigenetic regulation to study
human disease. Cytogenet Genome Res 114:1-15 (2006).

Poster Presentations

Research Topics
1. Molecular Computing
2. Molecular Recognition
3. Bioinformatics and Bio Computing
4. Genome Wide Experimental Data Analyses
5. Information and Computing Infrastructure for Drug Design
and Toxicology
6. Disease Mechanism and Control Models
7. Medical Genome Informatics and New Technology

Poster Presentations
Pn : n=Research Topic No.
P* : Oral Presentation

P1-01

Crystal structure and molecular dynamics simulation of ST0689
from Sulfolobus tokodaii strain7.

Yusuke Miyata1, Takatoshi Arakawa1, Okimasa Okada2, Jyunichi Akutsu11, 3,
Masafumi Yohda1
Department of Biotechnology and Life Sicence,Tokyo, University of Agriculture
And Technology1, Technology and Development, Fuji Xerox Co., Ltd., 2, AIST3

P1-02

Alkylation Mechanism of BPDE with DNA by Quantum
Molecular Dynamics Simulation
Yoshiro Nakata, Wataru Ootani
Department of Biophysics, Faculty of Engineering, Gunma University

P1-03* Enantioselectivity of Candida antarctica Lipase TypeB toward
Secondary Alcohols: Ab initio Fragment Molecular Orbital
Study
Takahisa TANAKA, Yoshihiro MORI, Yoshinobu NAOSHIMA
Faculty of Informatics, Okayama University of Science

P1-04

The Low-Frequency Raman Modes of Crystals of Guanosine
Dihydrate Analyzed from MD Simulation
Shigetaka Yoneda1, Yoko Sugawara1, and Hisako Urabe2
School of Science, Kitasato University1, Tokyo Kasei Gakuin University2

P1-05

Intra- and intermolecular interactions between cyclic-AMP
receptor protein and DNA: Ab initio fragment molecularorbital study

Kaori Fukuzawa1, Yuto Komeiji2, Yuji Mochizuki3, 4, Takeshi Ishikawa3, 4, Akifumi
Kato1, Tatsuya Nakano4, 5, Shigenori Tanaka4, 6
Mizuho Information & Research Institute, Inc. 1, National Institute of Advanced
Industrial Science and Technology2, Rikkyo University3, Japan Science and Technology
Agency, CREST4, National Institute of Health Sciences5, Kobe University6

P1-06* Development of virtual screening method using Solvation Free
Energy Density Model(SFED)
Chang Joon Lee, Se Han Lee, and Kyoung Tai No
Yonsei University

P1-07

Computational Study of Catalytic Reaction of Adenylate Kinase
Kenshu Kamiya, Shinya Morishita and Yoko Sugawara
School of Science, Kitasato University

P1-08NFV-Resistant Mechanism due to Non-active Site Mutation N88S
on Subtype AE HIV-1 PRs

Hirotaka Ode1, Shou Matsuyama1, Saburo Neya1, Masayuki Hata1, Wataru Sugiura2,
Tyuji Hoshino1, 3Graduate School of Pharmaceutical Sciences, Chiba University1,
AIDS Research Center, National Institute of Infectious Diseases2, PRESTO, JST3

P1-09

Evaluation of the binding affinity of antibodies from antigen
binding energy and VH-VL interaction energy calculated by the
MM-GBSA method
Koji Iwamoto1, Saburo Neya1, Tyuji Hoshino1, 2Graduate School of Phamaceutical Science,Chiba University1, PRESTO, JST2

P1-10

Prediction of complex structure comprised of a protein and aglycosaminoglycan using docking simulation and cluster analysis

Tsubasa Takaoka1, Noriaki Okimoto2, Kenichi Mori1, Saburo Neya1, Tyuji Hoshino1, 3Graduate School of Pharmaceutical Sciences, Chiba University1
RIKEN, GSC2, PRESTO, Japan Science and Technology Agency3

P1-11

Catalytic reaction of the enzyme degrading biodegradable
plastics
Y. Sakae1, T. Matsubara1, M. Aida1, H. Kondou2, K. Masaki3, and H. Iefuji3Hiroshima University1, National Institute of Advanced Industrial Science2,
Technology National Research Institute of Brewing3

P2-01* In silico drug screening by docking score index method using
principalcomponent analysis

Sukumaran Murali1, Yoshifumi Fukunishi2, Haruki Nakamura2, 3Japan Biological Information Research Center (JBIRC), Japan Biological Informatics
Consortium (JBIC) 1, National Institute of Advanced Industrial Science and Technology(AIST), Japan2, Institute for Protein Research, Osaka University3P2-02* Solvent Site-Dipole Field Mediating Docking of Biomolecules
Norikazu Takano, Nobuyuki Hamasaki, Junichi HigoTokyo University of Pharmacy and Life Sciences

P2-03

Application of Amino Acid Descriptors for Prediction of
MHC-Peptide Binding Affinity
Tomoko Niwa
Discovery Research Laboratories, Nippon Shinyaku Co., Ltd.

P2-04

Molecular modeling and 3D-QSAR studies of benzothiazol-2ylacetonitrile derivatives as c-Jun N-terminal kinase 3 inhibitors

Abdul Rajjak Shaikh1, Mohamed Ismael1, Hideyuki Tsuboi1, Michihisa Koyama1, Akira
Endou1, Hiromitsu Takaba1, Momoji Kubo1, 2, Carlos A. Del Carpio1, Parasuraman
Selvam1, Kazumi Nishijima3, Tetsuya Terasaki1, Akira Miyamoto1
Tohoku University1, JST-PRESTO2, Mochida Pharmaceutical Company3

P2-05

Bootstrap-based consensus scoring method for high-performanceand robust drug screening by single scoring function
Hiroaki Fukunishi, Reiji Teramoto, Jiro Shimada
Fundamental and Environmental Research Laboratories, NEC corporation

P2-06

P2-07

Introduction of initial binding orientation in protein-protein docking

Jong Young Jung1, Chang Joon Lee1 and Kyoung Tai No1, 2
Yonsei University1, Research Institute of Bioinformatics and Molecular design2

Functions of the key residues in the ligand-binding pocket ofvitamin D receptor : Fragment molecular orbital-interfragmentinteraction energy analysis

Kenji Yamagishi1, 2, Keiko Yamamoto3, Sachiko Yamada3, and Hiroaki Tokiwa4
The University of Tokushima1, CREST-JST2, Tokyo Medical and Dental University3,
Rikkyo University4

P2-08

A Workbench for Selective Nuclear Receptor Modulators

P3-01

Discrimination of β-barrel Membrane Proteins: Comparison
between Statistical Methods and Machine Learning Algorithms

Naomi Komiyama1, Tatsuya Nakano2, Kaori Fukuzawa3, Junpei Komiyama4,
Masumi Yukawa5, Hiroshi Tanaka5, Kotoko Nakata6, Tsuguchika Kaminuma5
Chem-Bio Informatics Society1, National Institute of Health Sciences2,
Mizuho Information and Research Institute3, The University of Tokyo4,
Tokyo Medical and Dental University5, Advance Soft Co./The University of Tokyo6

M. Michael Gromiha, Makiko Suwa
Computational Biology Research Center (CBRC), National Institute of Advanced
Industrial Science and Technology (AIST)

P3-02

SPLITS: an Expanding Program for Split and Intron-ContainingtRNA Prediction

Junichi Sugahara1, 2, Nozomu Yachie1, 3, Yasuhiko Sekine4, Akiko Soma4, Motomu
Matsui1, 2, Masaru Tomita1, 2, 3, Akio Kanai1, 2Institute for Advanced Biosciences, Keio University1, Department of Environmental
Information, Keio University2, Bioinformatics Program, Graduate School of Mediaand Governance, Keio University3, Department of Life Science, College of Science,
Rikkyo University4P3-03* Phylogenetic profiling approach to generate RNA-protein
networks in E. coli

Nozomu Yachie1, 2, Koji Numata1, 2, Yoshiteru Negishi1, 2, Hiroyuki Nakamura1, 3,
Junichi Sugahara1, 3, Rintaro Saito1, 3, Masaru Tomita1, 2, 3
Institute for Advanced Biosciences, Keio University1
Bioinformatics Program, Graduate School of Media and Governance, Keio University2
Department of Environmental Information, Keio University3

P3-04

Development and application of a molecular dynamics
simulation system : myPresto

Satoru Kubota1, Yoshifumi Fukunishi2, Ikuo Fukuda1, Eiji Kanamori1, Katsumi
Omagari1, Haruki Nakamura3
Japan Biological Information Research Center (JBIRC), Japan Biological Informatics
Consortium (JBIC) 1, Biological Information Research Center (BIRC),
National Institute of Advanced Industrial Science and Technology (AIST) 2,
Institute for Protein Research, Osaka University3

P3-05

Automatic Procaryote ORF Finding by Markov Model
Daigo Nakahara, Hiroyuki Nakajima, Yuuichi Tokuda, Atsushi Hanada, Yuuki Shibata,
Koichi Takahashi
Kinki University

P3-06

Prediction of nuclear localization proteins based on charge
periodicity of 28 residues
Noriyuki SAKIYAMA, Runcong KE, Shigeki MITAKU
Department of Applied Physics, Graduate School of Engineering, Nagoya University

P3-07

WinBEST-KIT(Biochemical Reaction Simulator for Analyzing
Metabolic Pathways): Application to the System Analysis of
Acetone-Butanol-Ethanol Fermentation in Clostridium
saccharoperbutylacetonicum
Masahiro Okamoto1, Hideaki Shinto2, Tatsuya Sekiguchi3,Yukihiro Tashiro2,
Mayu Yamashita2, Genta Kobayashi4, Taizo Hanai1, Kenji Sonomoto3
Graduate School of Systems Life Sciences, Kyushu University1, Graduate School of
Bioresource and Bioenvironmental Sciences, Kyushu University2, Department ofInformation Engineering, Maebashi Institute of Technology3, Ariake Sea Research
Project, Saga University4

P3-08

An Integrated Database of Flavonoids
Takashi Kinoshita, Zsolt Lepp, Yoshichika Kawai, Junji Terao, Hiroshi ChumanInstitute of Health Biosciences, The University of Tokushima

P3-09* Estimation of Gene Regulatory Network by means of AdaBoost
Shinya Nabatame, Hitoshi Iba
School of Engineering The University of TokyoP3-10

Theoretical studies of the ATP hydrolysis mechanism of HisP protein

Qiang Pei1, Hideyuki Tsuboi1, Michihisa Koyama1, Akira Endou1, Hiromitsu Takaba1,
Momoji Kubo1, 2, Carlos A. Del Carpio1, Parasuraman Selvam1, Kazumi Nishijima1, 3,
Tetsuya Terasaki1, Akira Miyamoto1
Tohoku University1, JST-PRESTO2, Mochida Pharmaceutical Company3

P3-11* Proteins with charge periodicity of 28 residues which are coded
in total genomes
Runcong KE, Shigeki MITAKU
Department of Applied Physics, Graduate School of Engineering, Nagoya University

P3-12

Systematic Errors Commonly Found in GeneChip Expression
Data and Effective Compensation Methods thereof

Tomokazu Konishi1, Keishi Hata2, Shigeru Takasaki3, Akihiko Konagaya3
Akita Pref. Univ.1, Akita Research Institute for Food and Brewing2, RIKEN3

P3-13

Protein functional site prediction -DNA binding proteins-

P3-14

HP-Lattice-model Structure Prediction of Proteins using Genetic
Programming

Yukako Sakatsuji1, Ayumi Suzuki2, Ichiro Yamato2, Satoru Miyazaki1
Department of Pharmaceutical Science, Tokyo university of science1,
Department of Biological Science and Technology, Tokyo university of science2

Takahiro Yabuki, Hitoshi Iba
Frontier Informatics, Graduate School of Frontier Sciences, the University of Tokyo

P3-15

Prediction of Human Cytochrome P450 3A4 Substrates using
Statistical analysis
Hyesun Han, Ji Hoon Jung, Kyoung Tai No
Yonsei University

P3-16

Branch And Bound Median Search algorithm for finding asubstrate specific motif at human SLC22 transporters
Takayuki Arakawa, Satoru Miyazaki
Department of Pharmaceutical Science, Tokyo university of science

P3-17

A Molecular Modeling Study on Antigenic Drifts of Influenza
virus Surface Glycoprotein
Manabu Igarashi1, Kimihito Ito1, Hiroshi Kida1, 2, and Ayato Takada1
Department of Global Epidemiology, Research Center for Zoonosis Control1,
Department of Disease Control, Graduate School of Veterinary Medicine2,
Hokkaido University

P3-18Analysis of amino acid sequences of motor protein by physicalfinger print method
Hideki Tanizawa and Shigeki Mitaku
Department of Applied Physics, Graduate School of Engineering, Nagoya University

P3-19

A New Method for Analysis of SNP’s Effect on Protein Functionand Structure
Carlos A. Del Carpio1, Eichiro Ichiishi2, Hideyuki Tsuboi1, Michihisa Koyama1, Akira
Endou1, Hiromitsu Takaba1, Momoji Kubo1, 3, Akira Miyamoto1, 2Graduate School of Engineering, Tohoku University1, NICHe, Tohoku University2,
JST-PREST3

P3-20

Application of Connectivity Matrix Method to Atom-level
Analysis of Metabolic Networks: Calculation of Paths Connecting
Two Metabolites
Jun Ohta
Okayama University

P4-01

Microarray analysis based on Standard Deviation of Effecting ofBiological Factors
Chiaki Handa1, Kayoko Takashima2, Hidetoshi Maruyama2, Naoto Nagasawa1,
Toshiki Honma2, Takeshi Nakabayashi2, Shinji Kikuchi2
Kissei comtec Co., Ltd. 1, Kissei pharmaceutical Co., Ltd. 2

P4-02* Detecting Cell Cycle Regulated genes in S. pombe with
Non-metric Multidimensional Scaling without sinusoidal fittings
Y-h. Tagcuhi
Department of Physics, and Institute for Science and Technology, Chuo University

P4-03

Comprehensive analyses on relationships between
alternative-splicing patterns and developmental stages in mouse
Yasuko Takahashi, Soichi Ogishima, Hiroshi Tanaka
Dept. of Computational Biology, Tokyo Medical and Dental University

P4-04* Genome-wide Prediction of Novel Small Peptides in Escherichia
coli by Using Liquid Chromatography Mass Spectrometry

Motomu Matsui1, 2, Nozomu Yachie1, 3, Kosaku Shinoda4, Naoyuki Sugiyama4,
Fumio Ando1, 2, Yuki Okada1, 3, Martin Robert1, Rintaro Saito1, 2*, Masaru Tomita1, 2, 3, 4
Institute for Advanced Biosciences, Keio University1, Department of Environmental
Information, Keio University2, Bioinformatics Program, Graduate School of Media andGovernance, Keio University3, Human Metabolome Technologies, Inc., c/o Insitute forAdvanced Biosciences, Keio University4P4-05

Transcriptomic approach for understanding the effect of
cryoprotectant, trehalose and DMSO of Saccharomyces
cerevisiae
Yuko Momose, Rena Matsumoto, Mine Odani, Masakazu Yamaoka
The National Institute of Advanced Industrial Science and Technology (AIST)

P4-06

GC contents of nucleotide sequences for proteins with charge
periodicity of 28 residues
Ryusuke Sawada, Runcong Ke, Noriyuki Sakiyama, Masashi Sonoyama
Shigeki Mitaku
Department of Applied Physics, School of Engineering, Nagoya University

P5-01

PBPK-modeling as a tool for interpreting and understanding ofpharmacokinetics
Hiroko Akiyama, CTC LABORATORY SYSTEMS Corporation [Presenter]
S. Willmann, W. Schmitt, Bayer Technology Services GmbH [Authors]

P5-02

P5-03

Scaffold analysis of GPCR ligand datasets from literature

Masami Fujiki1, Yanhai Fan2, Naotaka Ohishi3
Patcore, Inc. 1, Phas Technology, Ltd. 2, Spotfire Japan, K.K. 3

Development of New in silico Method for Predicting Drug
Metabolism

Michihisa Koyama1, Kota Kasahara1, Hideyuki Tsuboi1, Akira Endou1,
Hiromitsu Takaba1, Momoji Kubo1, 2, Carlos A. Del Carpio1, Kazumi Nishijima1, 3,
Tetsuya Terasaki1, Akira Miyamoto1
Tohoku University1, Japan Science and Technology Agency2,
Mochida Pharmaceutical Co.3

P5-04

“CALTA”, a novel structure-property-activity profiling
method: Compound-scaffold profiling and in silico prediction forthe drug-discovery compounds that target membrane proteins
Yoshimasa Kobayashi, Katsuya Akimoto, Hiroshi Kikuchi, and Kenichi Sudo.
Drug Metabolism & Physicochemistry Research Lab., Daiichi Pharmaceutical. Co., Ltd.

P5-05

Virtual Screening Models for Predicting Activities in Various
Therapeutic Areas
Zsolt Lepp, Takashi Kinoshita and Hiroshi ChumanInstitute of Health Biosciences, The University of Tokushima

P5-06

Convergence properties of absolute binding free energy
calculation using BAR method
Yoshiaki Tanida, Masakatsu Ito, Hideaki Fujitani
Fujitsu Laboratories Ltd., Fujitsu Limited

P5-07* Building up an original database of launched drugs for their
ADME and physicochemical HTS profiling data and human PK
data. Rating drugability based on the database and profilingdata for in-house compounds.
Atsuko Kameda, Kyosuke Suzuki, Yoshimasa Kobayashi, Katsuya Akimoto,
Hiroshi Kikuchi and Kenichi Sudo
Drug Metabolism & Physicochemistry Research lab., Daiichi Pharmaceutical. Co., Ltd.

P5-08

Conceptual Design of a Framework for ADME Ontology

P5-09

Acquisition of Extended Information Regarding
Chemicals-CYPs Interactions by Natural Language Processing

Takeshi Arikuma1, Sumi Yoshikawa1, 2, Kentaro Watanabe1*, Kazumi Matsumura1, 2,
Akihiko Konagaya1, 2
Department of Computer Science, Tokyo Institute of Technology1, Genomic Sciences
Center, RIKEN2

Chunlai Feng, Fumiyoshi Yamashita, Mitsuru Hashida
Graduate School of Pharmaceutical Sciences, Kyoto University

P5-10

Ontology Development for RTK-Associated Information
Retrieval System
Sumi Yoshikawa, Kazumi Matsumura, Akihiko Konagaya
Genomic Sciences Center (GSC), RIKEN

P5-11* Physiologically Based Pharmacokinetic (PBPK) model for
Pravastatin - Analysis of transporter-related pharmacokinetics Takao Watanabe, Hiroyuki Kusuhara, Kazuya Maeda, Yuichi Sugiyama
The University of Tokyo

P5-12

A Web-Based Drug Safety Information Community

P5-13

Instant Computing Technology for Molecular Design
Environment for Non-Cluster Experts

Kumiko Sakamoto1, Eiko Nakata Doolin2, Mikio Sasaki3, A.Ammar Ghaibeh3,
Hiroshi Chuman1, Aiko Yamauchi1
Institute of Health Biosciences, The University of Tokushima1,
Department of Pharmacy, NTT-East Kanto Medical Center2, Saila Systems Inc.3

Fumikazu KONISHI1, Toru Yagi2, Ryo Umetsu1, Shingo Ohki1,
Akihiko KONAGAYA1
RIKEN GSC1, BESTSYSTEMS, Inc.2

P5-14

Conformational Analysis and Docking Study of the c-Jun
N-Terminal Kinase Inhibitors Having a Benzothiazol Moiety

Mohamed Ismael1, Abdul Rajjak Shaikh1, Hideyuki Tsuboi1, Michihisa Koyama1,
Akira Endou1, Hiromitsu Takaba1, Momoji Kubo1, 2, Carlos Del Carpio1,
Kazumi Nishijima3, Tetsuya Terasaki1, Akira Miyamoto1
Tohoku University1, JST-PRESTO2, Mochida Pharmaceutical Co.3

P5-15

Development of an Empirical Model for the Activation Energy of
CYP Reaction

Doo Nam Kim1, Won Seok Oh2, Kyoung Tai No1, 2
Bioinformatics & Molecular Design Research Center1, Yonsei University2

P5-16* Prediction of Skin Permeability for Chemical Penetration
Enhancers using Quantitative Structure-Activity RelationshipAnalysis and Classification Methodology
Hyejin Kang1, KyoungTai No1, 2
Yonsei University1, BMDRC 2

P5-17

In Silico Prediction of CYP1A2-mediated Metabolism
Jihoon Jung, Suyeon Kim, Kyoung Tai No
Yonsei University

P5-18

Classification of P-Glycoprotein Substrates and Inhibitors

P5-19

NMR metabolomics 1/2 Multivariate analysis for diagnostic
study of inherited metabolic disorders using human urine

Sokyoung Joo1, Kyoung Tai No 1, 2
Yonsei University1, Research Institute of Bioinformatics and Molecular Design2

Tadashi Nemoto1, Masako Fujiwara1, Itiro Ando1, 3 Kazunori Arifuku2, Tomiko Kuhara4
National Institute of Advanced Industrial Science and Technology1, JEOL DATUM
LTD2, Environmental Research Center Ltd.3, Kanazawa Medical University4

P5-20

NMR metabolomics 2/2 Multivariate analysis for diagnostic
study of streptozotocin-induced diabetes using rat urine

Masako Fujiwara1, Itiro Ando2, 3, Kazunori Arifuku1, Taeko Kataoka2, Kenji Kanazawa2,
Yasuhiro Natori4, Tadashi Nemoto2
JEOL DATUM LTD.1, National Institute of Advanced Industrial Science andTechnology2, Environmental Research Center Ltd.3, Research Institute International
Medical Center of Japan4

P5-21

Development of a database with simulation function for
quantitative prediction of drug-drug interactions

Yoshihisa Shitara1, Toshiharu Horie1, Toshihiko Ikeda1, Masato Kitajima1,
Jose M. Ciloy3, Yuichi Sugiyama4
Chiba University1, Sankyo Co. Ltd. 2, Fujitsu Kyushu System Engineering Ltd. 3,
The University of Tokyo4P5-22

In vitro screening system using a combination of suspensions andmonolayers of Caco-2 cells for detecting drug-drug interactionsin intestinal absorption and conjugative metabolism of drugs
Marie Tanaka, Takashi Mizuma, Masahiro Hayashi
School of Pharmacy, Tokyo University of Pharmacy and Life Science

P5-23

Transport and metabolism of ampicillin prodrugs and theirInteractions with P-glycoprotein: in vitro study on successful
prodrugs using Caco-2 cell monolayers
Sachie Tanaami, Sayaka Sakaguchi, Takashi Mizuma, Masahiro Hayashi
School of Pharmacy, Tokyo University of Pharmacy and Life Science

P6-01

Mathematical modeling of gene expression associated with Wnt
signaling pathway in colorectal carcinoma
Yoshimi Naruo, Kaoru Mogushi, Hiroshi Tanaka
Department of Bioinformatics, Tokyo Medical and Dental University

P6-02

Modeling of extracellular matrix degradation processes in cancer
metastasis using covariance structure analysis
Akiko Hatano, Kaoru Mogushi, Hiroshi Tanaka
Department of Bioinformatics, Tokyo Medical and Dental University

P6-03

Comprehensive analysis of drug-specific mutation patterns inprotease gene of HIV-1 subtype B using decision tree
Kaoru Mogushi, Fengrong Ren, Naoki Hasegawa, Hiroshi Tanaka
Department of Bioinformatics, Tokyo Medical and Dental University

P7-01

Tutorial and Training Courses on Some Areas of Interest of the
CBI Society
Tsuguchika Kaminuma1, Masumi Yukawa1, Naomi Komiyama2, Yoshitomo Tanaka1,
Takako Takai-Igarashi1, Hiroshi Tanaka1
Tokyo Medical and Dental University1, Chem-Bio Informatics Society2

Crystal structure and molecular dynamics simulation of ST0689
from Sulfolobus tokodaii strain7.
Yusuke Miyata1, Takatoshi Arakawa1, Okimasa Okada2, Jyunichi Akutsu1,3,
Masafumi Yohda1
Department of Biotechnology and Life Sicence,Tokyo, University of Agriculture andTechnology1, Technology and Development, Fuji Xerox Co., Ltd., 2, AIST3
ST0689 of the thermoacidophilic archaeon, Sulfolobus tokodaii, is a protein of 122
amino acids which has not been functionally annotated. It exhibits high homology with
HEPN domain (higher eukaryotes and prokaryotes nucleotide-binding domain). HEPN
domain exists in various chaperons or enzymes, but its functional role has not been
elucidated. In this study, we resolved the structure of ST0689 by X-ray crystallography,
and analyzed the dynamic structural change by MD simulation.
The crystal structure has shown that ST0689 is composed of a bundle of four helixes
with two loops exposing on one side of it. RYPD box, the most conservative sequencemotif of HEPN proteins, located in the loop. We performed MD simulations ofmonomer and dimer of ST0689 to grasp the behavior of the loop containing the motif.
During the MD simulation of the monomer, we observed large fluctuation of RYPD box
at 300K. However, at higher temperature, it became small and large fluctuation region
has shifted to the downstream in the loop. We also observed the change of the structure
at each temperature. The loop around RYPD box and the above site were moving during
1 ns at 300 K and 1 ns at 350 K. However, the structure was stable during further 2 ns at
350K. On the other hand, in dimer, the fluctuation of not only RYPD box but also all
residues was small even at high temperature. Because X-ray crystal structure
determination has shown the B factor around RYPD box is large, we thought that
ST0689 exists as monomer with the structure in the state after 1 ns at 350 K during the
MD simulation.
Reference
Grynberg M, Erlandsen H, Godzik A, “HEPN: a common domain in bacterial drug
resistance and human neurodegenerative proteins”, Trends Biochem Sci, 28,
224-226(2003).

Alkylation Mechanism of BPDE with DNA by
Quantum Molecular Dynamics Simulation
Yoshiro Nakata , Wataru Ootani
Department of Biophysics, Faculty of Engineering, Gunma University
Benzo[a]pyrene and structurally-related polycyclic aromatic hydrocarbon
environmental pollutants are metabolically activated in cellular environments into
highly reactive and genotoxic diol epoxide derivatives. These molecules can attack
DNA to form bulky covalently- linked molecules called carcinogen-DNA adducts1).
Quantum Molecular Dynamics calculations were performed to simulate thealkylation process of benzo[a]pyrene diol epoxide(BPDE) with DNA. The starting
structures of the simulation are the 1AVX in Protein Data Bank 2), and the intercalated
model structure by MD simulation and several intermediate structures between theformer and the latter.
Quantum-MD simulations were carried out with PEACH Ver.5.8 Windows
Version3) using the parallel computing with MPICH Ver1.2.54). PEACH5),6) is capableof quantum simulation and minimization by use of Fragment Molecular Orbital method(FMO)7).

The results of Quantum MD simulations show the process of alkylation and theactivation barrier in the alkylation.
References
1) Meehan, T. and Straub, K., Nature, 277, 410-412(1979).
2) http://www.rcsb.org/pdb/
3) Nakata, Y., Takizawa, T., Ogino, M., Uno, M., J.Comput.Chem.Jpn, Vol.2, No.4,
149-154(2003).
4) http://www- unix.mcs.anl.gov/~ashton/mpich.nt/References
5) Komeiji, Y., Nakano, T., Fukuzawa, K., Ueno, Y., Inadomi, Y., Nemoto, T., Uebayasi,
M., Fedorov, D. G., Kitaura, K., Chem. Phys. Lett., 372, 342-347 (2003).
6) Komeiji, Y., Haraguchi, M., Nagashima, U., Parallel Computing 27, 977-987(2001).
7) Nakano, T., Kaminuma, T., Sato, T., Fukuzawa, K. Akiyama, Y., Uebayasi, M.,
Kitaura, K., Chem. Phys. Lett. 351, 475-480 (2002).

Enantioselectivity of Candida antarctica Lipase TypeB toward
Secondary Alcohols: Ab initio Fragment Molecular Orbital Study
Takahisa TANAKA, Yoshihiro MORI, Yoshinobu NAOSHIMA
Faculty of Informatics, Okayama University of Science
Lipases, the most widely studied enzymes in the fields of biotechnology, are now
routinely used as an environmentally friendly and efficient biocatalyst for obtaining
enantiomerically pure and biologically important compounds in the fields of organic
synthesis. As part of our continuous works toward conducting biocatalytic reactions
with lipases,1) we are aimed at understanding the enantioselectivity of Pseudomonas
cepacia lipase (PCL) and Candida antarctica lipase typeB (CALB) toward non-naturalorganic compounds.2) Although a large amount of experimental data is available forthese two popular lipases, the mechanism of their enantioselectivity is still notcompletely elucidated. In this work the ab initio fragment molecular orbital (FMO)
calculations were carried out for the CALB complexed with chiral alcohol esters.
The PDB X-ray crystallographic structure of CALB (1LBS) was optimized byenergy minimization using OPLS-AA force field implemented in MacroModel. Chiralligands, including aromatic and aliphatic alcohol esters, were prepared by employing a
combination of molecular mechanics and MD simulated annealing technique and thesubsequent Hartree-Fock 6-31G** calculations. The flexible docking simulations of
CALB and chiral esters were performed through the Glide four-stage minimization. The
CALB-chiral ester complexes thus obtained were subjected to an ab initio FMO
calculation at MP2/STO-3G level by using BioStation package, on a cluster system ofIntel Itanium2 processors. Similar FMO calculations were carried out toward CALB
enzyme and chiral esters, respectively. The FMO calculations indicated that the binding
energy between CALB and chiral esters is larger for the fast reacting (R)-enantiomer
than for the slow reacting (S)-enantiomer. We also found that the hydrogen bond
network between Thr40 and chiral esters is significant in the formation of each CALB
complex. It is expected that the difference of the binding energy makes it possible topredict the enantiopreference of CALB toward a variety of secondary alcohols. FMO
calculations on the tetrahedron intermediate in the ester hydrolysis are under way.
REFERENCES
1) Y. Naoshima, T. Kimura, Y. Mori, and M. Kamezawa, Recent Res. Devel. Org. Bioorg.
Chem., 6, 1-9 (2004). 2) Y. Mori, Y. Yagi, and Y. Naoshima, Proceedings of 25th
Anniversary International CBI Conference, 53 (2005).

The Low-Frequency Raman Modes of
Crystals of Guanosine Dihydrate Analyzed from
MD Simulation
Shigetaka Yoneda1, Yoko Sugawara1, and Hisako Urabe2
School of Science, Kitasato University1, Tokyo Kasei Gakuin University2
Molecular dynamics simulation was applied to a new region of calculational analysisof organic crystal. In the previous study, we reported MD simulation of a crystal of
guanosine dihydrate and discussed the mobility of the crystal water molecules.1 In thisstudy, we present analysis of low-frequency Raman active phonon modes.
In low-frequency Raman spectra of DNA, one characteristic vibration has been
observed around 20 cm-1,2 which is called S-mode. There exist similar modes inRaman spectra of nucleoside and nucleotide crystals in which base moieties are stacked
along one direction. Frequency shift of the S-mode was observed in the A-B transition
of DNA and in the humidity-induced phase transition between the guanosine dihydrate
and the anhydrous states.3
In order to determine the origin of the S-mode, we performed MD simulation for acrystal of guanosine dyhdrate made of 4×5×8 unit cells with the AMBER6 program,using the Particle Mesh Ewald method. The molecular trajectory of guanine bases in the
MD simulation was Fourier transformed in time and in molecular number. Among
optically active modes, one significant peak was found for the origin of the S-modes.
Phase distribution of vibrational waves in the crystal of guanosine dihydrate isdisplayed with graphics.

References
1) S. Yoneda, Y. Sugawara, and H. Urabe, "Crystal Water Dynamics of Guanosine
Dihydrate: Analysis of Atomic Displacement Parameters, Time Profile of
Hydrogen-Bonding Probability, and Translocation of Water by MD Simulation", J. Phys.
Chem. B, 109, 1304-1312 (2005).
2) H. Urabe and Y. Tominaga, " Low-lying collective modes of DNA double helix by
Raman spectroscopy Biopolymers", Biopolymers, 21, 2477(1982)
3) H. Urabe, Y. Sugawara, and T. Kasuya, " Humidity-dependent structural transition of
guanosine and disodium adenosine 5'-triphosphate crystals studied by low-frequency
Raman spectroscopy", Phys. Rev. B, 51, 5666-5672(1995).

Intra- and intermolecular interactions between cyclic-AMP receptor protein
and DNA: Ab initio fragment molecular orbital study
Kaori Fukuzawa1, Yuto Komeiji2, Yuji Mochizuki3,4, Takeshi Ishikawa3,4, Akifumi Kato1,
Tatsuya Nakano4,5, Shigenori Tanaka4,6Mizuho Information & Research Institute, Inc.1, National Institute of Advanced
Industrial Science and Technology 2, Rikkyo University3, Japan Science and Technology
Agency, CREST4, National Institute of Health Sciences5, Kobe University6
The ab initio fragment molecular orbital (FMO) calculations were performed for thecAMP receptor protein (CRP) complexed with a cAMP and DNA duplex to elucidate
their sequence-specific binding and the stability of the DNA duplex, as determined byanalysis of their inter- and intramolecular interactions. Calculations were performed
with the AMBER94 force field and at the HF and MP2 levels with several basis sets.
The interfragment interaction energies (IFIEs) were analyzed for interactions of
CRP-cAMP with each base pair, DNA duplex with each amino acid residue, and eachbase pair with each residue. In addition, base-base interactions were analyzed includinghydrogen bonding and stacking of DNA. In the interaction between DNA andCRP-cAMP, there was a significant charge transfer (CT) from the DNA to CRP, and thisCT interaction played an important role as well as the electrostatic interactions. It isnecessary to apply a quantum mechanical approach beyond the “classical” force-fieldapproach in order to describe the sequence specificity. In the DNA intramolecularinteraction, the dispersion interactions dominated the stabilization of the base-pairstacking interactions. Strong, attractive 1,2-stacking interactions and weak, repulsive
1,3-stacking interactions were observed. Comparison of the intramolecular interactionsof free and complexed DNA revealed that the base-pairing interactions were stronger,
and the stacking interactions were weaker, in the complexed structure. Therefore, theDNA duplex stability appears to change due to both the electrostatic and the CT
interactions that take place under conditions of DNA-CRP binding.
Reference
1) K. Fukuzawa et.al., J. Comp. Chem. 27, 948-960 (2006).

Development of virtual screening method using Solvation Free
Energy Density Model(SFED)
Chang Joon Lee1, Se Han Lee1, and Kyoung Tai No1
Yonsei University 1
Solvation plays an important role in protein-ligand association and has a strong
impact on comparisons binding energies for dissimilar molecules.
In Solvation Free Energy Density Model(SFED), the solvation free energy of a solute
molecule can be calculated by summing the product of the linear combination
coefficients and the basis interaction functions for the solute. Especially in solvatedsystem, hydrophobicity is an important factor in molecular recognition and the accurate
prediction of the binding modes of ligand to proteins in aqueous solvent, which is uesful
for ligand docking and drug design. SFED maps of each protein and ligand could be
calculated by using the Solvation Free Energy Density (SFED) model. The density map
is understood that interaction field between a solute and solvent at specific three
dimensional space.
The basic concept of density is to extract the information present in 3D moleculardensity map into 3D vectors which are easy to understand and to interpret. Thus, 3D
vectors matching is applied to fast automate filtering of structure-based screening.
Accordingly, we provide SFED model which is essential for successful virtual
screening.

References
1) K.T No. and H.A Scheraga, “Determination of Net Atomic Charges Using a Modified
Partial Equalization of Orbital Electronegativity Method. I~V” J. phys. Chem,
1990,1993,1995
2) YoungYong. In, Han Ha Chai and K.T No, “A Partition Coefficient Calculation
Method with the SFED Model”, J. ACS, 2005

Computational Study of Catalytic Reaction of Adenylate Kinase
Kenshu Kamiya, Shinya Morishita and Yoko Sugawara
School of Science, Kitasato University
Adenylate kinase catalyzes the following reaction:
ATP + AMP + Mg2+  ADP + ADP + Mg2+
In order to elucidate the nature of the catalytic reaction of adenylate kinase, we
carried out model calculations using molecular mechanics and quantum chemistry.
We constructed the complex structure of enzyme and substrates, ATP and AMP
with Mg ion, based on the PDB data 2AKY, complex of enzyme and inhibitor
AP5. After an extra PO3 group was removed from AP5 to make ATP and AMP
molecules, the whole molecular structure was optimized except for the main chain
atoms with the force field of AMBER. The molecular dynamics calculation in 300K
was performed to investigate the role of water molecules and for the structuralrefinement to the model structure trancated within 10 A from substrate molecules.
The quantum chemical calculations were carried out for the model structure of the
enzyme, the phosphate groups of substrates, Mg2+ and water molecules coordinating
the ion, and some molecules which model the side chain atoms of surrounding aminoacids. The reactant, product and transition state structures were optimized within
partial degrees of freedom of the model.
The transition structure accompanied with the phosphate group inversion likethe SN2 reaction was determined using partial optimization of model structure using
ONIOM (HF/6-31G(d):PM3) method without the surrounding protein molecule.
However, more quantitative calculations using DFT method or extended basis sets,
or with surrounding amino acid residues, showed that these effects were quite large.
Also, molecular dynamics calculations revealed the possibility that some watermolecules participated in the reaction mechanism.

NFV-Resistant Mechanism due to Non-active Site Mutation N88S on
Subtype AE HIV-1 PRs
Hirotaka Ode1, Shou Matsuyama1, Saburo Neya1, Masayuki Hata1,
Wataru Sugiura2, Tyuji Hoshino1,3
Graduate School of Pharmaceutical Sciences, Chiba University1,
AIDS Research Center, National Institute of Infectious Diseases2, PRESTO, JST3
Abstract
Human immunodeficiency virus type 1 protease (HIV-1 PR) is one of the proteins thatseveral anti-HIV-1 drugs are targeting on. NFV is a currently available HIV-1 PR
inhibitor (PI). However, the drug efficacy of NFV is reduced because of the appearanceof resistant mutations in HIV-1 PRs. Ariyoshi et al. reported that patients failing intreatment with NFV predominantly carry D30N mutant of HIV-1 PRs if they were
infected with the subtype B virus1. On the other hand, N88S mutant of HIV-1 PRs
predominantly emerges in patients of treatment failure with NFV and of carrier of thesubtype AE virus1. Both D30N and N88S are related to the resistance against NFV. We
previously reported that an active site mutation D30N on subtype B PR conferred
resistance by canceling direct hydrogen bonds between the 30th residue and NFV2. In
contrast, there still remains a question why N88S mutation on subtype AE confers
resistance against NFV, because N88S mutation occurs at the non-active site of PR. The
non-active site residues do not have any direct contacts with PIs. In this study, we haveexecuted molecular dynamics (MD) simulations of subtype AE reference PR and N88S
mutant in complex with NFV. The simulations suggest that N88S mutation on subtype
AE PR affects binding with NFV indirectly. N88S mutation creates a new hydrogenbond between mutated side chain of S88 and side chain of D30. The formation of thishydrogen bond dislocates D30 to make a distance from NFV and NFV and reduces
interaction between D30 and NFV. As shown in our previous report2, D30 is one of themost important residues with which NFV interacts. This is the reason why N88S
mutation confers resistance against NFV.
References
1) K. Ariyoshi et al., “Patterns of Point Mutations Associated With Antiretroviral Drug
Treatment Failure in CRF01_AE (Subtype E) Infection Differ From Subtype B
Infection”, JAIDS, 33, 336-342 (2003).
2) H. Ode et al., “Resistant Mechanism against Nelfinavir of Human Immunodeficiency
Virus Type 1 Proteases”, J. Phys. Chem. B. 109, 565-575 (2005).

Evaluation of the binding affinity of antibodies
from antigen binding energy and VH-VL interaction energy
calculated by the MM-GBSA method
Koji Iwamoto1, Saburo Neya1, Tyuji Hoshino1,2 ,Graduate School of Phamaceutical Science,Chiba University1, PRESTO,JST2,
Abstract
Because antibodies are a kind of proteins having high affinity and specificity for
antigen, they are expected to be suitable candidates for drugs which have more efficientand less side effect than conventional chemical drugs. In fact, the increasing number ofbiopharmaceutical drugs are now available or on trial. To support the developments ofthese drugs, rapid and highly accurate screening is indispensable. In silico screening is
one of powerful solutions to this purpose.
In this study, we performed molecular dynamics simulation of antibody HyHEL-10
and its antigen white hen egg lysozyme(HEL) to collect the trajectories of binding
structures. After MD simulation, we have executed the molecular mechanicsGeneralized Born surface area(MM-GBSA) method to evaluate the binding affinity
between HyHEL-10 and HEL. Antigen binding energy calculated with the MM-GBSA
method is partially incompatible with the changes in free energy measured byisothermal titration calorimetry(ITC)1,2. The variants mutated in the light chain(LS91A,LS93A,LY50F) show less satisfactory result than the variants mutated in theheavy chain(HD32A,HD96A,HD32AD96A). To resolve the discrepancy between theexperimental and the computational results, we have also calculated interaction energy
between antibody heavy chain variable region flagment(VH)/antibody light chain
variable region flagment(VL) with MM-GBSA. Combination of these two calculated
energy shows better correlation with the experimental results than the respective energy.

References
1) M. Shiroishi, A. Yokota, K. Tsumoto, H. Kondo, Y. Nishiyama, K. Horii, M.
Matsushima, K. Ogasahara, K. Yutani, I. Kumagai, J. Biol. Chem., 276,
23042-23050 (2001)
2) A. Yokota, K. Tsumoto, M. Shiroishi, H. Kondo, and I. Kumagai, J. Biol. Chem.,
278 5410-5418 (2003)

Prediction of complex structure comprised of a protein and aglycosaminoglycan using docking simulation and cluster analysis
Tsubasa Takaoka1, Noriaki Okimoto2 , Kenichi Mori1, Saburo Neya1, Tyuji Hoshino1,3
1
2

Graduate School of Pharmaceutical Sciences, Chiba University

RIKEN, GSC

3PRESTO ,Japan Science and Technology Agency

One of the docking programs, AutoDock3.0, predict the information on the structure ofligand-recepter complexes and their stability as a docking energy. We have developed
an original method combining AutoDock3.0 with a cluster analysis, and succeeded inremodeling the docking structure to be more accurate than the original output of
AutoDock3.0.
At first, we tried to predict a complex structure of basic Fibroblast Growth Factor(bFGF) with heparin, using only AutoDock3.0. Two X-ray crystal structures werealready obtained for bFGF. One is a complex of the protein and a heparin, which is akind of the glycosaminoglycans, and the other is only for a protein, which will becalled simplex structure hereafter. Considering induced fitting, we docked a heparintaken from the complex crystal structure onto the simplex protein, and the energy of thedocked complexes was minimized by AMBER8.
Although the docking energy calculated by AutoDock3.0 could not satisfactorily
predict the correct complex structure, we found out the majority of the models generated
by AutoDock3.0 were similar to the crystal structure. Hence, we utilized only the
structures for the evaluation, and carried out a cluster analysis with them. This
procedure has successfully selected a quite similar structure to the crystal one.
We have tried to predict other heparin-binding proteins, 1E03&1E04, 1G5N&1A8A
using the above approach. Two crystal structures, complex structure and simplex one,are also available for these proteins as well as bFGF. The trials show fine prediction ofbinding structures of heparin and the proteins.
The approach proposed in this study will be effective for docking of any otherbiomolecules with proteins.

References
1) Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ.
“Automated Docking Using a Lamarckian Genetic Algorithm and and Empirical
Binding Free Energy Function”, J Comput Chem. 1998;19:1639–1662.

Catalytic reaction of the enzyme degrading biodegradable plastics
Y. Sakae1, T. Matsubara1, M. Aida1, H. Kondou2, K. Masaki3, and H. Iefuji3Hiroshima University1, National Institute of Advanced Industrial Science2,
Technology National Research Institute of Brewing3
The environmental pollution caused by the disposal of the plastics is quite serious.
Biodegradable plastics that can be decomposed to water and carbon dioxide bymicroorganisms widely distributed in the nature have attracted much attention as“Green Plastics”. Recently, it has been reported that an enzyme, which belongs to theclass of serine proteases, degrades the biodegradable plastics very efficiently throughthe hydrolytic reaction1. The origin of the high catalytic activity of this enzyme derived
from the yeast Cryptococcus sp. Strain S-2 has not been clarified yet. We therefore
theoretically examine the mechanism of the catalytic reaction of this enzyme using themodel molecule by the density functional theory method (B3LYP) with the basis set of
6-31G**.
The catalytic reaction consists of four elementary steps, and the rate-determining stepis the third step, the nucleophilic attack of an H2O molecule to the substrate. We
examine in detail the third step using a larger molecular system of the active site taking
account of the environmental effect of the neighboring amino acid residues. We
replaced Thr17 by Ala17 and examined the contribution of Thr17 (-OH) to the catalysis.
As a result, the activation barrier of the rate-determining step was increased by 3.4
kcal/mol by this replacement, because the transition state is destabilized in energy by
the loss of the hydrogen bond between Thr17 (-OH) and the H2O that attacks thesubstrate. Thus, it is probable that the neighboring amino acid residue plays animportant role on the catalysis.
References
1) K. Masaki et al., “Cutinase-Like Enzyme from the Yeast Cryptococcus sp. Strain S-2
Hydrolyzes Polylactic Acid and Other Biodegradable Plastics”, Applied andEnvironmental Microbiology, 71, 7458-7550(2005).

In silico drug screening by docking score index method using principal
component analysis
Sukumaran Murali,1 Yoshifumi Fukunishi2 and Haruki Nakamura2,3
1

Japan Biological Information Research Center (JBIRC), Japan Biological Informatics
Consortium (JBIC) , 2 National Institute of Advanced Industrial Science andTechnology (AIST), Japan, and 3 Institute for Protein Research, Osaka University

The docking score index (DSI) method is one of in silico drug screening methods
and is a sort of similarity search method based on a protein-compound docking affinity
calculated by a docking software. By the DSI method, the protein-compound docking
scores are converted to the docking score indexes by the principal component analysis(PCA) method and each compound is projected into a PCA space. The DSI method
projects all the compounds onto a subspace of the PCA space to reduce thecomputational noise and selects the compound, which is close to the known active
compound. The database enrichment by the DSI method strongly depends on how to
select the principal components to form the subspace. In this study, we propose an
automatic method to select a set of suitable principal component axes and evaluate thedatabase enrichment by this method for 12 target proteins. This work was supported bya grant from NEDO.
References
1) Y. Fukunishi et al., “Similarity among receptor pockets and among compounds:
Analysis and application to in silico ligand screening”, J. Mol Graph & Modelling, 24,
34-45 (2005).
2) Y. Fukunishi et al.,“Classification of chemical compounds by protein-compound
docking for use in designing a focused library”, J. Med. Chem., 49, 523-533 (2006).
3) Y. Fukunishi et al.,“Multiple target screening method for robust and accurate in silico
screening”, J. Mol Graph & Modelling,25, 61-70 (2005).
4) Y. Fukunishi et al., “A virtual active compound produced from the negative image ofa ligand-binding pocket, and its application to in-silico drug screening”, J. Compt-Aided
Mol Design, (2006) accepted5) Y. Fukunishi et al., “Finding ligands for G-protein coupled receptors based on
the protein-compound affinity matrix”, J. Mol Graph & Modelling, (2006) acceptedSolvent Site-Dipole Field Mediating Docking of Biomolecules
NorikazuTakano1, Nobuyuki Hamasaki1, Junichi Higo1
Tokyo University of Pharmacy and Life Sciences1
Biomolecular interaction occurs in solvent. According to a continuum-solvent theory(e.g. Poisson-Boltzmann equation), the electrostatic interaction between biomolecules
decays rapidly with increasing the inter-biomolecular distance. Thus, we naturally
imagine that the biomolecules interact effectively only when they come close to eachother by chance through diffusive motions.
On the other hand, we have computationally demonstrated that the orientationof water molecules is ordering around biomolecules [1-3]. We called the patterns ofordering ‘a solvent site-dipole filed’. Furthermore, we have computationally observed
the solvent site-dipole field formed in an inter-molecular zone between two small
biomolecules, where the minimum inter-biomolecule distance is 14 Å [4]. The
mechanism of the field formation is as follows: atomic charges on protein surfaces
restrain the orientation of the first-layer water molecules [3]. This first-layer ordering
restrains the orientation of the second-layer water molecules, and so on. The watermolecules visiting the crystal-water sites anchored the solvent site-dipole field. We
have shown the fluctuations of the solvent site-dipole field around a protein, which canbe a trigger of biomolecular interaction [5].
In this study, we did a molecular dynamics simulation of two biomolecules (aprotein and a peptide), which were located apart to each other in an explicit solvent
environment in the initial state of simulation. We observed a process where the two
biomolecules approach close to each other. Then, we analyze the solvent site-dipole
field formed in the inter-biomolecule zone and discuss the biological significance of thesolvent site-dipole field.
References
1) J. Higo, H. Kono, H. Nakamura, A. Sarai, Proteins 40 (2000) 193.
2) J. Higo, M. Sasai, H. Shirai, H. Nakamura, T. Kugimiya, Proc. Natl. Acad. Sci. USA
98 (2001) 5961.
3) J. Higo, M. Nakasako, J. Comp. Chem. 23 (2002) 1323.
4) J. Higo, M. Sasai, H. Shirai, H. Nakamura, T. Kugimiya, Proc. Natl. Acad. Sci. USA
98 (2001) 5961.
5) T. Yokomizo, S. Yagihara, J. Higo, Chem. Phys. Lett. 374 (2003) 453.

Application of Amino Acid Descriptors for Prediction of
MHC-Peptide Binding Affinity
Tomoko Niwa1
Discovery Research Laboratories, Nippon Shinyaku Co., Ltd.1
Prediction of the binding affinities of antigen peptides to major histocompatibility
complex (MHC) molecules is important for not only developing vaccines but also
understanding peptide-protein interactions. Various models have been developed to
predict these binding affinities. However, most models were so complicated that it wasdifficult to understand the specificity of peptide-protein interactions.
Amino acid descriptors such as electronic, steric and hydrophobic ones have beensuccessfully applied to quantitative structure-activity relationship (QSAR) studies on
bioactive peptides.1,2 The objective of this study was to develop “interpretable” QSAR
models for prediction of MHC-peptide binding affinities by using amino aciddescriptors and multiple regression analysis (MRA) method, where relevant descriptors
were selected with a genetic algorithm. Models were derived for HLA-A*0201,3
H2-Db,4 and H2-Kk.4 The predictabilities of our models were comparable to those ofpreviously reported models. Use of amino acid descriptors and MRA enabled us toclarify which amino acids and which properties were important for the binding and tounderstand the specificity of peptide-protein interactions.

References
1) Sotomatsu-Niwa, T.; Ogino, A. Evaluation of the Hydrophobic Parameters of the
Amino Acid Side Chains of Peptides and their Application in QSAR and
Conformational Studies. THEOCHEM 1997, 392, 43-54.
2) Niwa, S. T.; Ogino, A. Multiple Regression Analysis of the Beta-Sheet Propensity of
Amino Acids. THEOCHEM 1997, 419, 155-160.
3) Doytchinova I. A.; Walshe V.; Borrow P.; Flower D. R. J. Towards the Chemometric
Dissection of peptide--HLA-A*0201 binding affinity: comparison of local andglobal QSAR models. Comput. Aided Mol. Des. 2005, 19, 203-12.
4) Hattotuwagama C. K.; Doytchinova I. A.; Flower D. R. In Silico Prediction of
Peptide Binding Affinity to Class I Mouse Major Histocompatibility Complexes: A
Comparative Molecular Similarity Index Analysis (CoMSIA) Study. J. Chem. Inf.
Model. 2005, 45, 1415-23.

Molecular modeling and 3D-QSAR studies of benzothiazol-2-ylacetonitrile
derivatives as c-Jun N-terminal kinase 3 inhibitors
1

Abdul Rajjak Shaikh1, Mohamed Ismael1, Hideyuki Tsuboi1, Michihisa Koyama , Akira
1,2
1
Endou1, Hiromitsu Takaba1, Momoji Kubo , Carlos A. Del Carpio , Parasuraman Selvam1,
3
1
1
Kazumi Nishijima , Tetsuya Terasaki , Akira Miyamoto
Tohoku University, 2JST-PRESTO, 3Mochida Pharmaceutical Company
Abstract
The c-Jun N-terminal kinases (JNKs), also known as stress-activated protein kinases, aremembers of mitogen activated kinase family. They are generally associated with apoptotic
and inflammatory responses induced by a variety of chemical and physical stresses including
oxidative stress, UV light, inflammatory cytokines, and osmotic shock. In the present study,
for the first time, we report molecular modeling and three-dimensional quantitative structure
activity relationship (3D-QSAR) studies performed on 44 JNK 3 inhibitors.1,2 The
3D-QSAR models, which include molecular field analysis (MFA) and receptor surface
analysis (RSA), was employed using 34 compounds as training set and its predictive ability
was assessed using a test set of 10 compounds. The predictive 3D-QSAR models have r2
values of 0.849 and 0.766 while the r2cv values of 0.616 and 0.605 for MFA and RSA,
respectively. Fig.1 depicts the actual and predicted activity of all the studied compounds.
The results of the 3D-QSAR models were further compared with docking studies. On the one
hand, the MFA 3D-QSAR model demonstrate the importance of several electrostatic andsteric properties required for theactivity. On the other hand, theRSM 3D-QSAR necessities theimportance of hydrophobic andhydrogenbonding
property
required for JNK3 activity. Theseobservations further validated withdocking studies and correlated
well with 3D-QSAR models.
References

Fig. 1:The predicted and actual activities of JNK-3.

1) P. Gaillard et al., “Design and synthesis of the first generation of novel potent, selective,
and in vivo active (Benzothiazol-2-yl) acetonitrile inhibitors of the c-Jun N-terminal
kinase”, J. Med. Chem., 48, 4596-4607 (2005).
2) A. R. Shaikh et al., “Three-dimensional quantitative structure activity relationship(3D-QSAR) and docking studies on (benzothiazole-2-yl) acetonitrile derivatives as ac-Jun N-terminal kinase 3 (JNK3) inhibitors”, Bioorg. Med. Chem. Lett. (in press).

Bootstrap-based consensus scoring method for high-performance androbust drug screening by single scoring function
Hiroaki Fukunishi1, Reiji Teramoto1, Jiro Shimada1
Fundamental and Environmental Research Laboratories, NEC corporation1
We developed a bootstrap-based consensus scoring (BBCS), which is inspired frombagging of ensemble learning, to improve performance of the existing scoring function.
In the BBCS, the bootstrap method [1] is employed to increase a training set from one
to two more, which are used for optimizing energy parameter set of the scoring function.
Each training set includes protein-ligand complexes; each complex has X-ray structureand decoy ones. The optimization method, which is employed in each training set,
explores energy parameter set maximizing an average z-score of X-ray structure for allcomplexes. Once two or more different energy parameter sets are generated, by
combining scoring with these parameter sets, consensus scoring can be implemented.
The reason why we reached the idea of the BBCS is that it is difficult to uniquelydetermine energy parameter set give global minimum for binding free energy from one
training data, in particular, when properties of complexes in training set are biased. On
the other hand, a conventional consensus scoring (CS) [2] gets consensus among a few
scoring functions. Judging from published reports [3,4], it seems that it is not easy tofind the best combination of scoring functions because of heuristic search. In contrast,
for the BBCS, the bothersome work such as the heuristic search is not necessary. In thisstudy, we applied BBCS to FlexX scoring function. From training data including 50
complexes, 100 energy parameter sets were generated. Using these parameter sets, we
could get consensus of much more scorings than CS. To estimate performance of the
BBCS, against each of 48 complexes as test data, X-ray structure and 100 decoy ones
were ranked. Compared to original FlexX scoring function and CS by CScore(Sybyl),
improvements of rank of X-ray structure were shown in 61.9% and in 62.5％ of test
data, respectively. In particular, for the complexes binding with mainly hydrophobic
interactions, the BBCS were quite effective.
References
1) Breiman, L., Machine Learning., 24 ,123-140(1996).
2) Charifson, P. S. et al., J. Med. Chem., 42, 5100-5109(1999).
3) Stahl, M., Rarey, M., J. Med. Chem., 44, 1035-1042(2001).
4) Wang, R. et al. J. Med. Chem., 46, 2287-2303(2003).

Introduction of initial binding orientationin protein-protein docking
Jong Young Jung1, Chang Joon Lee1 and Kyoung Tai No1,2
Yonsei University1, Research Institute of Bioinformatics and Molecular design2
In protein-protein complex, the interface of the complex has different characteristics
from other area of surface. Because the phenomena of protein-protein docking occur inthe cellular environment, solvent effect has to be considered in docking. Surely
salvation affects on binding interfaces in each protein to orient each other. In that sense,hydrophobicity is considered to be the one of principal factors to bind proteins in thecorrect sites. We generate hydrophobic field around each protein using SFED (Solvation
Free Energy Density) model. And then, through the process to analyze hydrophobic
fields near protein-protein interface, we determine the hydrophobic standard to guide
proteins to attach each other. But hydrophobicity is not sufficient to determine interfaces
in protein pairs which form a complex. Interface residue information, chemical
characteristics on interface and shape complementarity between interfaces are also
important factors. Using all of these informations, the vector model is constructed,
which represent approximate orientation of protein components that constitute protein
complexes. In comparison with most of the existing methods to simulate protein-protein
docking, initially introduced orientation reduce the computation and scanning time togenerate putative binding complex candidates in initial stage.

Reference.
1) Youngyong In, Han Ha Chai, and Kyoung Tai No, “A partition coefficient
calculation method with the SFED model”, J. Chem, Inf, Model. 45, 254263(2005)

Functions of the key residues in the ligand-binding pocketof vitamin D receptor:
Fragment molecular orbital-interfragment interaction energy analysis
Kenji Yamagishi1,2, Keiko Yamamoto3, Sachiko Yamada3, and Hiroaki Tokiwa4
The University of Tokushima1, CREST-JST 2,Tokyo Medical and Dental University3,
Rikkyo University 4

All electron calculations based on the ab initio Fragment
Molecular Orbital (FMO) method enable to evaluate correctly theinteractions

betweenligand/substrateandresiduesofreceptor/enzyme.
In this study, FMO-interfragment interaction energy calculationsof the vitamin D receptor (VDR)/1α,25-dihydroxyvitamin D3
complex were utilized to assign functions of key residues in theligand-binding pocket (LBP) of the VDR. We have reached thefollowing conclusions. (1) The important residues that formhydrogen bonds to the 1-, 3- and 25-OH groups are Arg274, Tyr143
and His397, respectively. Only one residue forms a significant
interaction with the corresponding hydroxy group of the ligand, although two residues arelocated around each hydroxy group. (2)The degradation of binding affinity for derivatives upon
removal of a hydroxy group is closely related to the trend in the strength of the hydrogen bonds.
(3)Arg274 forms the strongest hydrogen bond with the ligand in the LBP of the VDR. Type II
hereditary rickets due to an Arg274 point mutation is caused by the elimination of the strongest
hydrogen bond between the ligand and the residues in the LBP of the VDR.

References
1) K. Yamamoto, D. Abe, N. Yoshimoto, M. Choi, K. Yamagishi, H. Tokiwa, M. Shimizu,
M. Makishima, S. Yamada, J. Med. Chem. 49 (2006) 1313.
2) K. Yamagishi, K. Yamamoto, S. Yamada, H. Tokiwa, Chem. Phys. Lett. 420 (2006) 465.

A Workbench for Selective Nuclear Receptor Modulators
Naomi Komiyama1, Tatsuya Nakano2, Kaori Fukuzawa3, Junpei Komiyama4,
Masumi Yukawa5, Hiroshi Tanaka5, Kotoko Nakata6, Tsuguchika Kaminuma5
Chem-Bio Informatics Society1, National Institute of Health Sciences2,
Mizuho Information and Research Institute3, The University of Tokyo4,
Tokyo Medical and Dental University5, Advance Soft Co./The University of Tokyo6
The Selective Nuclear Receptor Modulators (SNuRMs) are the chemicals (lignds) thatbind to nuclear receptors to modulate transcriptional and post-transcriptional signaltransduction pathways and networks. Some of these chemicals are drugs already in themarket used for wide range of diseases including classical endocrine hormonal diseases,
inflammation, breast and prostate cancer, and metabolic syndrome. Well-known
examples of SNuRMs are breast cancer drugs, tamoxifen and raloxifene, which areclassical hormone estrogen receptor agonists/antagonists, and is the PPARγ agonist
thiazolidinediones, which is one of the most well known therapeutic agent for diabetes.
Ligands for LXRs, and FXRs are also under intensive investigation from anti-obesity
and reverse insulin resistance viewpoint for improving metabolic syndrome.
However these existing drugs in the market show some adverse effects, andfine-tuning of structures of the existing drugs or finding new structural scaffold drewattention of drug designers in this field. Identifying ligands for receptors, classified astrue orphan receptors at present, is another challenge, for these ligands may betransformed to lead chemicals for new drugs. Except few cases SNuRMs research has
been carried out focusing on particular target or a class of targets in mind rather than on
the whole superfamily members. Eventual goal of our workbench development is toprovide essential data, docking study tools, and graphical analyzing tools for all
members of the family as the infrastructure of SNuRMs research.
We have employed the FMO (fragment molecular orbital) method for energy
calculation, and chose ZINC, PubChem, PDB, and KiBank as the basic molecular
databases, and BioStation as model builder and viewer. Since these component
resources are distributed as free software for nonprofit use, our workbench will also beable to be used freely. The workbench has been developed in parallel with carrying
several docking studies for selective receptors such as FXR.
References
1) T. Kaminuma, CBI Journal, 3, 130-156 (2003)
2) K. Fukuzawa, et al., J. Comp. Chem., 26, 1-10 (2005)

Discrimination of β-barrel Membrane Proteins: Comparison between
Statistical Methods and Machine Learning Algorithms
M. Michael Gromiha and Makiko Suwa
Computational Biology Research Center (CBRC), National Institute of Advanced
Industrial Science and Technology (AIST), 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan
Discriminating β-barrel membrane proteins from other folding types of globular andmembrane proteins is an important task both for identifying β-barrel membrane proteins fromgenomic sequences and for the successful prediction of their secondary and tertiary structures.
We have developed statistical methods and machine learning techniques for discriminating β-barrel membrane proteins using amino acid composition, residue pair preference andmotifs.The information about amino acid composition could correctly identify the β-barrel
membraneproteins at an accuracy of 89% and exclude globular and α-helical membraneproteins at the accuracy level of 80% (1,2). The residue pair preferences and motifs have more
information than amino acid composition and these methods improved the accuracy of morethan 95% in detecting β-barrel membrane proteins (3).
On the other hand, we have used support vector machines and neural networks fordiscriminating β-barrel membrane proteins. These machine learning techniques improved theoverall accuracy to 92%. The sensitivity and specificity are, respectively, 89% and 94%, whichindicate that the machine learning techniques excluded the globular and α-helical membraneproteins at better accuracy than identifying the β-barrel membrane proteins (4). From thecomparison of statistical methods and machine learning techniques we observed that thestatistical methods could identify the β-barrel membrane proteins at high accuracy while an
opposite trend is observed for machine learning techniques, which correctly excluded otherfolding types of globular and membrane proteins.

References:
1. M.M. Gromiha and M. Suwa (2005) Bioinformatics 21, 961-968.
2. M.M. Gromiha et al. (2005) Nucl. Acids Res. 33, W164-167.
3. M.M. Gromiha et al. (2005) Comp. Biol. Chem. 29, 135-142.
4. M.M. Gromiha and M. Suwa (2006) PROTEINS: Structure Function. Bioinf. (in press).

SPLITS: an Expanding Program for Split and Intron-Containing tRNA Prediction
Junichi Sugahara1,2, Nozomu Yachie1,3, Yasuhiko Sekine4, Akiko Soma4, Motomu
Matsui1,2, Masaru Tomita1,2,3, Akio Kanai1,2Institute for Advanced Biosciences, Keio University1, Department of Environmental
Information, Keio University2, Bioinformatics Program, Graduate School of Mediaand Governance, Keio University3, Department of Life Science, College of Science,
Rikkyo University4Transfer RNA (tRNA), one of the large non-coding RNA families, is a small RNA thattransfers a specific amino acid to synthesize polypeptide chain at the ribosomal site
during translation. In eukaryotes and archaea, approximately 15% of tRNAprecursors contain intron(s) not only in anticodon loop region but also in diverse
sites of the gene (intron-containing tRNA or cis-spliced tRNA). Also the parasite
Nanoarchaeum equitans, a member of the Nanoarchaeota kingdom creates functionaltRNA from separate genes; one encoding the 5’-half and the other the 3’-half (splittRNA or trans-spliced tRNA). Although recent researches reported that there arevarious types of tRNA genes, computational methodology for comprehensive
search is yet to be established.
Here, we developed SPLITS, an expanding program for tRNAscan-SE (1) andSplit-tRNA-Search (2) to improved detection of split and intron-containing tRNA
genes. SPLITS initially predicts bulge-helix-bulge splicing motifs, which is well known
structure of pre-tRNA introns, to determine and remove intronic regions of tRNA
genes. The resulting DNA sequences are automatically queried to tRNAscan-SE.
SPLITS can predict known tRNAs with single introns located at unconventional sites
on the genes (100%), tRNAs with double introns (87%), and known split tRNAs(100%). The repeated trials by using random DNA sequence demonstrated highaccuracy of our prediction. Our program will be very useful for identifying noveltRNA genes after genome projects. SPLITS source code is freely downloadable at
http://splits.iab.keio.ac.jp/.
References(1) Todd M. Lowe and Sean R. Eddy. tRNAscan-SE: a program for improved
detection of transfer RNA genes in genomic sequence. Nucleic Acids Research 25,
955-964 (1997).
(2) Lennart Randau, Richard Munch, Michael J. Hohn, Dieter Jahn and Dieter
Soll. Nanoarchaeum equitans creates functional tRNAs from separate genes fortheir 5’- and 3’- halves. Nature 433, 537-541 (2005).

Phylogenetic profiling approach to generate RNA-protein networks in E. coli
Nozomu Yachie1,2, Koji Numata1,2, Yoshiteru Negishi1,2, Hiroyuki Nakamura1,3,
Junichi Sugahara1,3, Rintaro Saito1,3, Masaru Tomita1,2,3
1

Institute for Advanced Biosciences, Keio University
Bioinformatics Program, Graduate School of Media and Governance, Keio University
3
Department of Environmental Information, Keio University
2

Abstract
Recent genome and transcriptome projects have identified many examples of non-coding RNA(ncRNA) genes in various species. NcRNA do not encode protein products but rather encode
structural, regulatory or catalytic RNA molecules. However, cellular functions of these molecules
are still largely unclear except well-known RNA families such as tRNA, rRNA and micro RNA.
Although cell-wide examination of functional linkages between RNAs and proteins are requiredin order to understand cellular roles of RNAs, no bioinformatics or experiments based
methodologies to discover them have been hitherto established.
Here, we developed a computational system to generate whole-cell network of RNA-proteinfunctional linkages in Escherichia coli K12 mainly based on phylogenetic profiling. In the system,
RNA- and protein-encoded regions within E. coli genomic DNA were initially compared withgenome sequences of the other 307 species (264 prokaryotes, 15 eukaryotes and 28 archaeas)using NCBI BLAST program, and homology profiles which consists of 307 homology scores foreach RNA and protein was created. Then every pairs of two molecules were evaluated on thebasis of mutual information calculated using corresponding pair of E-values. The mutual
information for each pair was statistically assessed according to those calculated from shuffledhomology profile and statistically significant pairs were assumed to have a functional linkage.
The functional linkage network (RNA-protein and protein-protein) of E. coli was generated
full-automatically by using ~100 CPUs. The map contained 295 RNA-protein and 2,615 proteinprotein linkages among 92 RNA and 748 protein nodes. Previously reported protein complexes
such as those related to transport systems, transcriptional regulators and enzyme complexes
were clustered in the map. In addition, some previously reported RNA-protein linkages suchthose of ribosomal components (composed of large subunit and small subunit), tRNA and theircorresponding aminoacyl-tRNA synthetase, and RNaseP complex were detected. The generated
linkages were further validated by comparing these with the documented protein-protein
interactions in EcoCyc and DIP databases. The coverage and accuracy were significantly different
from random network and also higher than those generated by previous works using
phylogenetic profiling. We also mapped these links into 111 unique metabolic pathways in KEGG
database and found that the linked proteins and RNAs are likely to participate in the same
pathway. Based on our results and the other genome-scale data, e.g. microarray expression
profiles and results of comprehensive gene knockout experiments, possible function andmetabolisms of ncRNAs are discussed.

Development and application ofa molecular dynamics simulation system : myPresto
Satoru Kubota 1, Yoshifumi Fukunishi 2, Ikuo Fukuda 1, Eiji Kanamori 1,
Katsumi Omagari 1, Haruki Nakamura 3
Japan Biological Information Research Center (JBIRC), Japan Biological Informatics
Consortium (JBIC) 1, Biological Information Research Center (BIRC),
National Institute of Advanced Industrial Science and Technology (AIST) 2,
Institute for Protein Research, Osaka University 3
We have developed a molecular dynamics simulation system: myPresto. The aimof this system is in silico drug screening with general simulation for protein-ligand
complexes. The current version, version 3 was released in March, 2006. In this version
3, several new facilities are provided: generalized ensemble methods, in particular, asimulated tempering method, protein-ligand docking method, methods for constructionof focused libraries, and a 3-D structural database having more than three million
compounds for in silico drug screening.
We have enhanced our original in silico screening methods, multiple targets
screening (MTS) method and docking score index (DSI) method, based on amulti-receptor vs. multi-ligand docking affinity matrix. According to these methods,
compounds in a database were docked to multiple proteins. In the MTS method, thecompounds, which likely bind to the target protein among these proteins, are selected as
the members of the candidate-hit compound group. The DSI method is one of similarity
search methods and utilizes the docking scores of each compound as indexes instead of
1D/2D descriptors by the conventional similarity search method.
In addition, the component programs of myPresto were designed for parallel
computers.
This work was supported by grants from NEDO and METI.
References

1) Y. Fukunishi et al., "The filling potential method: A method for estimating the freeenergy surface for protein-ligand docking",J. Phys. Chem. B. (2003) 107, 13201-13210.
2) J.G. Kim et al., "Multicanonical molecular dynamics algorithm employing adaptive
force-biased iteration scheme", Phys. Rev. E 70, 057103 (2004).
3) Y.Fukunishi et al.,“Similarity among receptor pockets and among compounds:
Analysis and application to in silico ligand screening”, J. Mol Graph & Model, 24,
34-45 (2005).

Automatic Procaryote ORF Finding by Markov Model
Daigo Nakahara, Hiroyuki Nakajima, Yuuichi Tokuda,
Atsushi Hanada, Yuuki Shibata, Koichi Takahashi
Kinki University
Gene codes of organisms have acquired their own specific stochastic biases in codons.
Escherichia coli are widely adopted to build parameters of Markov model1. In thecases which belong to the other families than E-coli or newly found ones such as
Nanoarchaeum equitans, ordinary E-coli Markov model is of no use. An automatic
model building method is needed for gene findings for various kinds of genomes.
Audic and Claverier2 have presented a new method for predicting coding regions inprokaryote genomes. In genomes there are seven kinds of stochastically different
regions. They are three direct classes which are based on reading frames, three reverse(complementary) coding classes and a non-coding region. A Markov model withseven kinds of transition probabilities is adopted to find gene candidates.
With the initial randomly selected sequences, repeated improvements of transition
probabilities of Markov model have discriminated gene candidates as shown in Fig.1.
We searched the coding regions for 14 prokaryotes3, in the performance of less than ten
cycles (Fig.1.). After refinements of gene candidates with stop and start codons, i.e.,
ORFs, ca 70% of genes were found.

Fig. 1. Convergence of the iterative
homogeneous Markov model for
Escherichia coli K12

Fig.2. Direct(D), reverse(complementary) coding
regions(R), and non-coding regions(N), of
Bacillus subtilis by inhomogeneous Markov model

References
1) A.V.Lukashin and M.Borodovsky, Nucleic Acids Research, 26, 1107–1115(1998).
2) S.Audic and J.M.Claverie, Proc. Natl. Acad. Sci. USA, 95, 10026-10031(1998).
3) NCBI, http://www.ncbi.nlm.nih.gov/

Prediction of nuclear localization proteins based on charge periodicity of
28 residues
Noriyuki SAKIYAMA, Runcong KE, Shigeki MITAKU
Department of Applied Physics, Graduate School of Engineering, Nagoya University
There is strong correlation between the localization of proteins in a cell andtheir function, and the accurate prediction of the cellular localization ofproteins is very interesting problem from the viewpoint of the annotation of
unknown sequences. Nucleus is the largest organelle and nuclear proteins
have very important biological functions such as the transcription factors. It
has been detected that some DNA binding protein as Zinc Finger Proteins
have charge periodicity of 28 residues by research of Ke et al.. Moreover,myosin and tubulin other than DNA binding proteins also had charge
periodicity of 28 residues. Therefore, in this study, our aim is to focus on
charge periodicity of 28 residues and discriminate of nuclear proteins fromother ones.
The dataset of nuclear proteins and other proteins having charge periodicity of 28
residues were prepared from the SWISS-PROT database release 48.2. We analyzed
these proteins by two steps. The first step was to analyze the average values of eightphysicochemical properties of whole proteins sequences. The eight properties are thedensities of positively charged residues, negatively charged residues, proline, glycine,small polar amino acid residues and aromatic residues, the amphiphilicity [1] and thehydorophobicity. The second step was the analysis of local sequences around the peekof charge symmetry [2]. The discriminant analysis was performed by these two modules.
As the result, nuclear proteins having charge periodicity of 28 residues could be
predicted in the specificity of about 94%.

References
1) S. Mitaku et al., “Amphiphilcity index of polar amino acids as an aid in thecharacterization

of

amino

acid

preferenceatmembrane-waterinterfaces”,

Bioinformatics, 18, 608-616(2002).
2) K. Imai et al., “Secondary structure breakers and hairpin structures in myoglobin andhemoglobin”, Chem-Bio Informatics Journal, 5, 65-77(2005)

WinBEST-KIT(Biochemical Reaction Simulator for Analyzing Metabolic
Pathways): Application to the System Analysis of Acetone-Butanol-Ethanol
Fermentation in Clostridium saccharoperbutylacetonicum

Masahiro Okamoto1, Hideaki Shinto2, Tatsuya Sekiguchi3,Yukihiro Tashiro2,
Mayu Yamashita2, Genta Kobayashi4, Taizo Hanai1, Kenji Sonomoto3
Graduate School of Systems Life Sciences, Kyushu University, Japan1, Graduate School of
Bioresource and Bioenvironmental Sciences, Kyushu University, Japan2, Department ofInformation Engineering, Maebashi Institute of Technology, Japan3, Ariake Sea Research
Project, Saga University, Japan4
We have implemented an efficient, user-friendly biochemical reaction simulator named
web-based BEST-KIT (http://www.best-kit.org) for analyzing large scale nonlinear networks
such as metabolic pathways. The users can easily design and analyze an arbitrary reaction
scheme through the Internet and the efficient graphical user interface without considering themathematical equations. The rate velocity of reactant (dx/dt, where x and t represent reactant andtime, respectively) involves several kinds of reaction terms; some are represented by mass action
law and some are by approximated velocity functions of enzyme kinetics at steady state such as
Michaelis-Menten, competitive inhibition and so on. The problem here is that all the reaction
mechanisms of the actual reaction schemes in vivo have not always been elucidated. It is
desirable for the users to speculate reaction mechanisms and to construct the corresponding
mathematical equations with trial and error in the simulator. For this purpose, we havedeveloped new version of BEST-KIT (Microsoft Windows® version named WinBEST-KIT1) inorder for the users to define and register original mathematical equations and to customize themin user-defined library. In this study, we have applied WinBEST-KIT to the system analysis of
Acetone-Butanol-Ethanol fermentation in Clostridium saccharoperbutylacetonicum N1-4. By
using WinBEST-KIT, we made a suitable mathematical model and estimated the kinetic
parameters followed by the system analysis, based on several experimentally observed
time-course datasets of glucose, acetone, butanol, ethanol, acetate, butyrate and biomass. The
first thing we have to do is to identify “bottleneck reaction process” in order for the productionof the target metabolite (butanol in this case) to be maximized. Using the establishedmathematical kinetic model, we can examine the transient change of flux of each reaction
process time by time (time-sliced metabolic flux analysis) after adding some perturbation to thesystem. In this study, we shall describe on the system analysis of mathematical kinetic model for
Acetone-Butanol-Ethanol fermentation in Clostridium saccharoperbutylacetonicum N1-4.
References
1) T. Sekiguchi and M. Okamoto, “WinBEST-KIT: Windows-based Biochemical Reaction
Simulator for Metabolic Pathways”, J. Bioinformatics and Computational. Biology, (inpress)

An Integrated Database of Flavonoids
Takashi Kinoshita, Zsolt Lepp, Yoshichika Kawai Junji Terao and Hiroshi ChumanInstitute of Health Biosciences, The University of Tokushima

Abstract
Flavonoids are polyphenolic compounds that exist ubiquitously in foods of plant origin.
Flavonoids show various interesting biological activities, such as removal of oxygen
radical, anti-cancer action, improvement of high blood pressure, antibacterial, antibiotic
and anti-allergy actions. So far, over 4000 structurally unique flavonoids have been
isolated from plant sources. Recently, databases of chemicals have been utilized in to
help chemical and biological researches, however the comprehensive database offlavonoids with information about structural, biological and physicochemical properties
not yet available. We have constructed the integrated database of flavonoids fornutrition research. Moreover, prediction of activity against enzyme was performed using
a virtual screening procedure to demonstrate a possible application of the database forpharmaceutical research.

References
1) Lepp Z, Kinoshita T, Chuman H., Screening for new antidepressant leads of multiple
activities by support vector machines. J. Chem. Inf. Model, 46, 158-67, 2006.
2) Kinoshita T, Lepp Z, Kawai Y, Terao J Chuman H., An Integrated Database of
Flavonoids. Biofactors, in press.

Estimation of Gene Regulatory Network by means of AdaBoost
Shinya Nabatame1, Hitoshi Iba1
School of Engineering The University of Tokyo1
The major step in inferring a Gene Regulatory Network (GRN) from the time series
data of gene expression is the estimation of the values of different parameters of asystem of differential equations. In this context, various models such as S-system, linear
model, SDE model have been proposed. To measure the goodness of a model, criteriasuch as Akaike Information Criteria (AIC) and Mean Square Error with heuristics havebeen used. However, the inferred GRN by these models do not agree well with the real
network confirmed with biological experiments. In other words, accuracy is not high.
Additionally, the performance depends on the criteria.

Ensembles of multiple models and information criteria are expected to improve theestimation accuracy of a GRN. Consequently, we use AdaBoost, which is a very
powerful algorithm for integrating multiple estimation methods, in order to improve theaccuracy. In AdaBoost, the decision function uses a weighted majority voting technique.
However, the success of AdaBoost depends on the way of treating the training data andon the base algorithm used as learning machine.
In GRN estimation, each data point of gene expression time series is used as training
data. To be more precise, the loss function is calculated from the difference between thefitting result of learning machine and the observation data. Learning is conducted by
updating weight of each data point from the value of the loss function.
Models and information criteria used as learning machine are as follows: (1) Models:linear model, SDE model, weighted matrix model. (2)Information criteria: AIC, BIC,
Mallows’ statistics, determination coefficient adjusted for the degrees of freedom,
Hannan-Quinn IC, penalized likelihood. Thus, the number of learning machine is 18 (i.e.
3models * 6 criteria).
To validate the effectiveness of proposed method, we estimated a part of S.O.S DNA
repair network of E.coli. The expressed data are the kinetics of main 8 genes, i.e. lexA,
umuD, recA, uvrA, uvrY, ruvA, ruvA, and polB are monitored with 50 instants evenly
spaced every 6 minutes. Our proposal method got the accuracy of 78% Sensitivity and
51% Specificity which are higher than those of almost learning machines.
As a future work, we will apply our method to other networks such as cell-cycle
network of S.cerevisiae.

Theoretical studies of the ATP hydrolysis mechanism of HisP protein
1

Qiang Pei1, Hideyuki Tsuboi1, Michihisa Koyama , Akira Endou1, Hiromitsu Takaba1,
1,2
1
1,3
Momoji Kubo , Carlos A. Del Carpio , Parasuraman Selvam1, Kazumi Nishijima ,1
1
Tetsuya Terasaki , Akira Miyamoto
1

Tohoku University; 2JST-PRESTO; 3Mochida Pharmaceutical Company

Abstract
The reported high-resolution crystal structures of several nucleotide-binding domains (NBD),
e.g., HisP protein - a subunit of ATP-binding-cassette (ABC or traffic ATPase) of the histidine
transporter complex (HisQMP2J), not only offered the first overall view of NBD but also gavea significant breakthrough toward the elucidation of the binding of ATP to the protein.1 On theother hand, how the ATP is exactly hydrolyzed to ADP is currently a matter of considerable
debate, and mainly different models have been proposed.2 In the present investigation, density
functional theory (DFT) method was used to study the ATP hydrolysis reaction in HisP by
considering the ATP binding site, especially the γ-phosphate group, surrounding residues andwater molecules. Since the hydrolysis is favored by thepresence of cations, we have substituted Wat407 with
Wat443
Mg2+. Based on the gas phase DFT calculations, weATP
propose that ATP hydrolysis in an aqueous environmentis initiated by nucleophilic attack of a single water Wat500
molecule (Wat437) on the γ-phosphate; the hydrolysis
1.4Wat437
Pγ Oβ-γ
product ADP acts as leaving group. Further, we
1.8
1.5
OW
2.0 1.5
describe the proton transfer through water surrounding
γ-phosphate. Transition state structure was determined Wat415
2.2 2.2
Gln100
Mg2+
by approximate saddle-point search as shown in Fig. 1.
2.1
Gln100, Asp178 and Glu179 formed a coordinated2.1
complex with both β- and γ-phosphates through theinteraction of Mg2+. The bond distances between Pγ
Glu179-Oβ-γ and between Pγ –Ow are 2.00 and 1.84 Å,
Asp178Fig. 1: Transition state structure ofrespectively. The angle Ow-Pγ -Oβ-γ and the dihedral
angle formed by four atoms of γ-phosphate group were ATP binding site hydrolysis
process of HisP protein.
also analyzed.
References
1) L. Hung et al., “Crystal structure of the ATP-binding subunit of an ABC transporter”,
Nature, 396, 703-707 (1998).
2) C. van der Does et al., “How do ABC transporters drive transport?”, Bio. Chem., 385,
927-933 (2004).

Proteins with charge periodicity of 28 residues which are coded
in total genomes
Runcong KE and Shigeki MITAKU
Department of Applied Physics, Graduate School of Engineering, Nagoya University
The electric charge distribution was recently analyzed for all proteins from total
genomes of several biological organisms (1, 2). We calculated the autocorrelation
function of electric charges of amino acid sequences in eukaryota and prokayota. The
results indicated that the autocorrelation function of electric charges of all amino acidsequences showed significant positive correlation for eukaryota and no correlation forprokaryota. The characteristic length of the positive correlation in the case of eukaryota
was as long as several tens residues. Furthermore, we found recently significant charge
periodicity with 28 residues in the charge autocorrelation function of amino acidsequences from human genome.
In this work, we extracted the proteins, which have 28-residues periodicity, andclassified their functions for revealing the relationship between the charge periodicity
and the protein functions. Many of those proteins are not annotated but almost 80% of
those proteins are similar to known nuclear proteins such as zinc-finger proteins. The
cytoplasmic proteins with same characteristics are mainly contractile and cytoskeleton
proteins such as myosin and tubulin. The nuclear proteins with charge periodicity of 28
residues are about 10% of all nuclear proteins, when data from Swissprot are analyzed.
These findings indicated the relationship between charge periodicity in amino acidsequence and protein function. The charge periodicity of 28 residues may be used forthe prediction of nuclear proteins, combining with the physical finger print method. In
fact, the physical properties of amino acid sequences are different between the nuclear
proteins and the cytoplasmic ones.

References
1) R. Ke and S. Mitaku, “Gaussian analysis of net electric charge of proteins in the
Drosophila melanogaster genome”, Chem-Bio Informatics J., 4, 101-109(2004)
2) R. Ke and S. Mitaku, “Local repulsion in protein structures as revealed by charge
distribution analysis of all amino acid sequences from the Saccharomyces Cerevisiae
genome”, Journal of Physics: Condensed Matter, 17, 2825-2831(2005)

Systematic Errors Commonly Found in GeneChip Expression Dataand Effective Compensation Methods thereof
Tomokazu Konishi1, Keishi Hata2, Shigeru Takasaki3 and Akihiko Konagaya3
Akita Pref. Univ.1, Akita Research Institute for Food and Brewing 2, RIKEN 3
Measurements can never be free from errors. However, errors that have been derived bysystematic reasons can be removed or reduced by calculations, if the causes of the errors
are identified. In this poster, it is introduced that the systematic errors, their possible
causes and compensation methods frequently found in GeneChip expression analysis
arrays (Affymetrix Inc.). By using the methods, one can reduce the effects of noises andimprove reproducibility of measurements.
GeneChip is a well used microarray; it is equipped with the most comprehensible
format available and user-friendly manipulating system. In the meantime, calculation
methods for GeneChip seem to base on arbitral ideas, which spoil objectivity of data
analysis. Indeed, criteria of data evaluations have been altered in many updating of thesoftware. Without rigid criteria, it is difficult to systematize measurement errors, since
stability in analytical results cannot be expected.
By using a suitable analytical technique, the problems caused by the arbitral data
handling methods for GeneChip have been dissolved. Moreover, the systematic errors
are discovered through data analyzing against a parametric model. The model, which
describes statistical distribution of transcriptome data, has been confirmed over many
experimental evidences 3). Additionally, this model bases on a theory that explains
formation of transcriptome in a cell, in the terms of thermodynamics 4). The systematic
errors are found as frequently occurring tendencies in sets of data. Estimations on thecauses of the errors and correction methods are discussed on experimental evidences.
References
1) T. Konishi, “A thermodynamic model of transcriptome formation” Nucleic Acids
Research, 33, 6587-6592 (2005).
2) T. Konishi, “Three-parameter lognormal distribution ubiquitously found in cDNA
microarray data and its application to parametric data treatment”, BMC
Bioinformatics, 5, 5(2004).

Protein functional site prediction -DNA binding proteinsYukako Sakatsuji1, Ayumi Suzuki2, Ichiro Yamato3, Satoru Miyazaki１
Department of Pharmaceutical Science, Tokyo university of science,
2641 Yamazaki, Noda-shi, Chiba 278-8510, Japan1
Department of Biological Science and Technology,

Tokyo university of science, 2641

Yamazaki, Noda-shi, Chiba 278-8510, Japan 3
E-mail：j3106635@ed.noda.tus.ac.jp
Abstract
It is difficult to define functional sites in any proteins, because classification,
characterization or definition of all the functional sites are not yet clear. Recently
useful methods such as fold recognition search and 3D template search are thought out,
but it is not yet easy to detect a functional site in a protein even if we can find thesimilar structures in PDB. To overcome these problems, we developed a novel
algorithm and program to predict a functional site of a protein. Our method,“FCANAL”, is a program used for functional site prediction using the score matrix
obtained from the distances between CA atoms and frequencies of appearance of any
amino acids.
The FCANAL was applied to characterize DNA binding proteins. For ZINC FINGER
C2H2 protein analysis, we constructed the score matrix to identify functional site withhigh accuracy and characterize the functional site. As a matter of course, this function
prediction algorithm, FCANAL, worked well for other DNA binding proteins suchas LEUCINEZIPPER, HOMEOBOX, and HELIX-TURN-HELIX. From these results,
we are going to search for a commonality of functional sites of DNA binding proteins.
We will report these results and will discuss about the commonality of functional sitesof DNA binding proteins.

References
1) A.Suzuki, T.Asaoka, I.Yamato and S.Miyazaki ,CBIJ,3,39-55(2005)
2) T.Asaoka, T.Ando, T.Meguro and I.Yamato, CBIJ,3,96-113(2003)

HP-Lattice-model Structure Prediction of Proteinsusing Genetic Programming
Takahiro Yabuki1, Hitoshi Iba1
Frontier Informatics1, Graduate School of Frontier Sciences, the University of Tokyo
Since folding real-life proteins is extremely complex and demands extensive
resources, a simpler model is often used for benchmark computational purposes. One of
the most commonly used model is Hydrophobic-Polar model (HP-model) , which
involves folding a binary-based genome on a two or three dimensional regular lattice. In
this paper, HP-model in two dimensional regular lattice (2DHP) and three dimensional
regular lattice (3DHP) are used.
HP-model is widely used to simulate protein folding. Several benchmark sequences
and minimum-energy solutions have been described in the literature. HP-model wasconceived by researchers in order to gain insight into the folding process of a protein,and, albeit simple, it exhibits many features of real-life protein folding. HP-model
considers only one force driving protein folding, namely, the hydrophobicity of aminoacids. In HP-model, All amino-acids (20 kinds) which constitute protein sequences areclassified into H(hydrophibic, water-haters) or P (hydrophilic, water-lovers). H aminoacids try form bonds(H-H bonds) with other H amino acids. The more H-H bonds there
are, the better the Structure (result) is.
In this poster, the technique based on GP (Genetic Programming) is introduced
concerning the prediction of structure of proteins. We have extended the traditional
methods through the usage of exhaustive search (LookN) and Counting Backward Tree
(CBT) . LookN performs an exhaustive search of depth N and follows the best pathfound. For example, Look2 examines all possible paths of length 2 and then takes theone that results in maximal energy improvement. CBT creates Trees without LookN
from good results (counts backward tree from results) and raises the average fitness.
In 2DHP Our method managed to find the best-known free energy value in several
cases. Next, we try to extrapolate the great results in 3DHP.
References
1) K.Yue et al., “A test of lattice protein folding algorithms” Chemistry Vol. 92, pp.
325-329, 1995.
2) R.Jakovski et al., “Applying Genetic Programming to the HP Protein folding
Problem” in Proc. of Problem Solving from Nature (PPSN2004), 2004.

Prediction of Human Cytochrome P450 3A4 Substrates using
Statistical analysis
Hyesun Han1, Ji Hoon Jung1, and Kyoung Tai No1
Yonsei University, Seoul, Korea1
Human Cytochrome P450s are considered to be the most important enzyme family indrug metabolism. CYP3A4 is one of the major P450s and plays an important role inmetabolic degradation of approximately 50% of all marketed drugs. Consequently, it isnecessary to develop system for predicting of CYP3A4 substrates. This system will be
used in predicting metabolism profile of novel compounds for drug discovery anddevelopment. To separate CYP3A4 substrates or non-substrates, we used ensembled
statistical methods using logistic regression, linear discriminant analysis (LDA), support
vector machine (SVM) and so on. In present study, we discussed the factors relating
specificity of CYP3A4 enzyme.

References
1) Jan M. Kriegl et al., “A support vector machine approach to classify human
cytochrome P450 3A4 inhibitors”, J. Comp-Aid. Mol. Design, 19, 189-201(2005).

Branch And Bound Median Search algorithm for finding a substratespecific motif at human SLC22 transporters
Takayuki Arakawa1, Satoru Miyazaki1
Department of Pharmaceutical Science, Tokyo university of science, 2641 Yamazaki,
Noda-shi, Chiba 278-8510, Japan1
Email：j3106601@ed.noda.tus.ac.jp
The solute carrier 22 (SLC22) family comprises organic ion and drug transporters ofvarious tissue cells. The study of the correlation between their functions and their active
sites is helpful for pharmacokinetics and a drug development. However, few SLC22
active sites have been identified because their three-dimensional structure analyses are
difficult technically. Therefore, the development of an algorithm for predicting theiractive sites is expected.
We thought that a conserved region of protein sequences whould correlate with anactive site. To find an active site of SLC22 family proteins, as well as other proteins, wedeveloped an amino acid sequence motif finding algorithm which is based on the
Branch And Bound Median Search algorithm (BABMS) for DNA motif findings. Thisalgorithm allows us to evaluate the Hamming distance as an alignment score and finds
the conserved region with a local multiple alignment of amino acid sequences. Thismethod avoids the insertion of involuntary gaps often caused by length differencesamong sequences. By using this, we have analysed orthologous proteins of SLC22 inseveral organisms and these paralogous proteins of human respectively.
We focused on SLC22 family proteins and have searched a substrate specific motif
about these proteins. In this presentation, we will discuss about the derived results.

References
1) NEAL C.JONES AND PAVEL A.PEVZNER, AN INTRODUCTION TO
BIOINFORMATICS ALGORITHMS, 91-115(2000)
2) 遠藤 仁ら, ”有機イオントランスポーターファミリー”, PNE, vol46, No5,
583-604-611(2001)
3) 菅原 秀明ら, あなたにも役立つバイオインフォマティクス,

21-31(2004)

A Molecular Modeling Study on Antigenic Drifts ofInfluenza Virus Surface Glycoprotein
Manabu Igarashi1, Kimihito Ito1, Hiroshi Kida1,2, and Ayato Takada1
Department of Global Epidemiology, Research Center for Zoonosis Control1 andDepartment of Disease Control, Graduate School of Veterinary Medicine2,
Hokkaido University
The hemagglutinin (HA) is the major surface glycoprotein of influenza viruses
and plays an important role in virus entry into host cells. The HA undergoes antigenic
drifts which occur by accumulation of a series of amino acid substitutions. Influenza
viruses that escape from antibody-mediated immune pressure of human populationacquire a new antigenic structure and continuously cause epidemic in the world. Since
the HA is a favorable target for the development of vaccines and antivirals to control
influenza, it is important to understand the structural changes caused by amino acidmutations made in the evolution of influenza viruses.
Three-dimensional (3D) molecular models were built for 15 antigenicallydifferent HAs of the human H3N2 virus strains isolated during 1968 to 2006. Based onthese predicted models, we analyzed the change of electrostatic potential (ESP) on thesurface of HA molecule, which may associate with antigenic property. The 3D
distribution of ESP showed that the charge distribution of HA surface, especially in theglobular head which interact with the receptor, have gradually changed with increased
positive regions. To further examine the biological significance of ESP change, we
performed a retrospective and comprehensive analysis of amino acid sequences of
H3N2 subtypes derived from human, swine, and avian hosts. Amino acid sequence dataof about 1,900 virus strains were collected from the NCBI influenza virus resources,
and used for the calculation of the isoelectric points (pI) of HA molecules. We foundthat the pI of human virus HAs gradually moved to basic pH during 1968 to 1984 and,since then, has been fairly constant. In contrast, avian virus HAs have retained their pI
since 1963. These results suggest that these structural changes of the HA molecule wererequired for the adaptation of avian virus to human populations.

Analysis of amino acid sequences of motor protein by physical finger print
methodHideki Tanizawa and Shigeki Mitaku
Department of Applied Physics, Graduate School of Engineering, Nagoya University
The movement of cells and subcellular structures of eukaryotic cells are generated by
the activities of motor proteins that interact with rigid cytoskeletal fibers. There arethree superfamilies of motor proteins: myosin, kinesin and dynein. The sequencehomology between motor proteins within a superfamily is considerably high, but thesequence homology among three superfamilies is low. They commonly have theATP­binding sites as well as the domain which bind the cytoskeletal fibers. If thephysical mechanism of the coupling between the chemical reaction and the movementof proteins is common among the superfamilies, there have to be common features also
in the physical properties of sequences despite of their very low homology. Therefore,
the purpose of this work is to find out the common clusters of particular physicalproperties around the domains which bind with the skeletal fibers, leading to theprediction of any motor proteins from all amino acid sequences coded in genome
sequences. Here, we adopted the physical fingerprint approach, in which various
sequences of the physical properties are used for the prediction on the assumption thatthe motor proteins function by the common mechanism.
First, we prepared dataset of proteins of three superfamilies. Then, the plots of themoving average of various physical properties were compared around the binding
regions. We found that there are three significant clusters of negative charges. The
amino acid sequences of those regions are highly conserved among superfamilies but
there are no homologous sequences with known function. We developed a method todiscriminate motor proteins using physical fingerprints of three regions including theclusters of negative charges. We used motor proteins as positive dataset and other kinds
of ATP­binding proteins as negative dataset. As a result, the accuracy was as high as
80%. The physical meaning of the three regions with negative clusters will be
discussed.

A New Method for Analysis of SNP’s Effect on Protein Function and
Structure
Carlos A. Del Carpio1, Eichiro Ichiishi2, Hideyuki Tsuboi1, Michihisa Koyama1,
1

1,3

Akira

1,2

Endou , Hiromitsu Takaba1, Momoji Kubo , Akira Miyamoto

Graduate School of Engineering, Tohoku University1, NICHe, Tohoku University2,
JST-PREST3【Introduction】
Single nucleotide polymorphisms (SNPs) are the most frequent type of DNA variation and they are being
used in association studies of gene and protein function distortion as well as genetic diseases in general. To
understand the relationship between genetic and phenotypic variation in particular, it is essential to evaluate
the consequences of the specific non-synonymous mutations in the structure as well as function of therespective proteins they encode.
【Method】
Here, we have mapped

5.5

known distorting mutations

4.5

the

known

4.0

three

dimensional structure of
the wild type p53 which isinactivated in the majority

3.0

1.0

-0.5

conformationalchangesintheHydrophilic→Hydrophobic

1.5

singlecause

Structure Conservation

2.0

0.5

that

Structural Change

2.5

of cancers, mostly throughmutations

Prediction：91.7%

3.5
予測値(kcal/mol)

onto

5.0

UnFold0.0

DNAR248Q R248P R248L R248W R249S R249W R273C R273H R273L R273P A276P R282W
P53のアミノ酸変異

Fig. 1 Prediction of effects of SNP’s in P53

binding core domain of the protein【Results and Discussion】.
Propensities to changes in folding characteristics of proteins caused by cSNP’s on the wild type structure
can be calculated in terms of the variation of the hydrophobicity patterns of the protein surface using theabove exposed scheme. To perform these calculations we have utilized the hydrophobic patch inference
1,2
module of the system for bio-macromolecular interaction MIAX . The study is performed by mapping

mutations dictated by common SNP’s on the wild type structure of P53 and analyzing the changes arisen on
the surface of the protein.The methodology applied to the P53 protein results in a high prediction rate as
shown in Fig. 1 for twelve of the most common SNP’s observed experimentally.
【References】

1)Del Carpio et al. PROTEINS: Structure, Function and Genetics, 48:696-732, 2002

2)Del Carpio et al. Genome Informatics 16-2:148-160,2005

Application of Connectivity Matrix Method to Atom-level Analysis of
Metabolic Networks: Calculation of Paths Connecting Two Metabolites
Jun Ohta1
Okayama University1
E-mail: jo25@md.okayama-u.ac.jp
Connectivity matrix method is an approach for analyses of biological networks.
In this approach, all the connectivities of interest are expressed in a matrix, connectivity
matrix. Each row of connectivity matrix, which corresponds to one connectivity, isformed by combination of three row vectors or numerals indicating two different nodes
and one edge, respectively. Connectivity matrix carries information defining or
characterizing nodes or connectivities. Analyses of networks are performed based on theinformation described in connectivity matrix on Octave and Matlab. Octave and Matlab
are software suitable for manipulation of matrices and simulation. Both can also
manipulate strings and be easily customized using script files and functions, indicating
their utility and flexibility as analytical tools.
Atom-level analysis of metabolic networks is the first application ofconnectivity matrix method. Atom is the smallest node in metabolic networks. The
structure of metabolic networks is defined at atom-level by inter-metabolite connectivity
through enzymatic reactions and intra-metabolite connectivity through chemical bonds.
Calculation of atom-level paths or cycles can be performed by connectivity matrix
method. Whereas atom-level consideration is important, metabolite-level considerationis also important, because metabolic network works through consumption andproduction of metabolites. This presentation shows 2 algorithms for calculation of paths
connecting metabolites. Both algorithms are based on connectivity matrix method. In
the algorithm 1, calculation of metabolite-level path is performed based on
substrate-product relationship extracted from connectivity matrix, whereas, in thealgorithm 2, metabolite-level paths are formed by rearrangement of the calculated
atom-level paths.
References
1) J. Ohta, “Connectivity matrix method for analyses of biological networks and its
application to atom-level analysis of a model network of carbohydrate metabolism”,
submitted.
2) http://www.metabo-info.org

Microarray analysis based on Standard Deviation of Effecting of Biological
Factors
Chiaki Handa1, Kayoko Takashima2, Hidetoshi Maruyama2, Naoto Nagasawa1, Toshiki
Honma2, Takeshi Nakabayashi2, Shinji Kikuchi2
Kissei comtec Co., Ltd.1, Kissei pharmaceutical Co., Ltd.2
Background: The DNA microarray technology is a powerful tool for large-scale gene
expression analysis. The most important step in the analysis of microarray expression
profiling data is the identification of genes that display significant changes in expression
signals between two biological conditions (e.g. vehicle vs treatment). However, nouniversal standard methods exist for the identification of a significant change in
expression signals between two conditions.
Methods: In this study, we propose a method using a computer to identify genes
changed significantly out of the large number of experimental data. We called thismethod SDFC(Fold Change analysis using Standard Deviation), which is combinationof simple fold-change and t-test. The feature of SDFC is adopting not only control datafrom same experiment but also many control data from other experimental data asnormal conditions.
Results: The result of SDFC compared with simple fold-change or t-test indicated that
SDFC is more useful to display genes changed significantly than two methods. This
result suggests that the effect of biological factors in the analysis of DNA microarray isimportant. We will report this result in details.
1000000

100000

10000

1000

100

10

1

simple FC
SDFC
t-testDetecting Cell Cycle Regulated genes in S. pombe with Non-metricMultidimensional Scaling without sinusoidal fittings
Y-h. Tagcuhi1
1Department of Physics, and Institute for Science andTechnology, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan,1Microarray is versatile method to depict relative amount of transcripted material under
several experimental conditions. It can measure relative amounts for several thousandsof genes simultaneously. However, this advantage can be a weak point on the otherhand. Even if only limited number of genes are significant under some experimental
conditions, measurements are performed for almost all genes. Since we do not know
which genes are significant since this is what we would like to know, this forbids us toobserve only the significant genes. Thus, it is hoped that some method can depict what
the important genes are without any preknowledge.
In this poster, we would like to propose non-metric multidimensional scaling method(nMDS)1-3) in order to check which genes are worth while investigated.
Schizosaccharomyces pombe's cell division cycle experiments by several groups areanalyzed by nMDS and compared with each other. By requiring consistency among
those performed by distinct research groups, we can pick up two sets of so-called “cellcycle regulated genes” without assuming sinusoidal behavior which are frequently
assumed. Polar angle obtained by the circular distributions of genes in the embedded
space turns out to be cell cycle phases estimated by sinusoidal fittings in the proceedingstudies in spite that we do not assume sinusoidal fittings. Biological meanings of two
sets of genes (by gene ontology) as well as the problem in assuming sinusoidal
behavior will be discussed.
References
1) Y.-h. Taguchi and Y. Oono, “Relational patterns of gene expression via non-metricmultidimensional scaling analysis” Bioinformatics, 21, 730-740 (2005) .
2) Y-h. Taguchi, “Detecting Cell Cycle Regulated genes in S. pombe with Non-metricMultidimensional Scaling without sinusoidal fittings” IPSJ SIG Technical Report,
2005-BIO-3, 59-66 (2005).
3) Y-h. Taguchi and Satwik Rajaram, “Noise reduction procedure for microarrayexperiments with non-metric multidimensional scaling method” IPSJ SIG Technical
Report, 2006-BIO-4, 9-16 (2006).

Comprehensive analyses on relationships between alternative-splicing
patterns and developmental stages in mouse
Yasuko Takahashi, Soichi Ogishima, Hiroshi Tanaka
Dept. of Computational Biology, Tokyo Medical and Dental University.
“Human genome has almost 22,000 genes (, whereas, for example, worm
genomes have almost 19,000 genes)”. It is an astonishing finding of the human genome
project. On the other hand, recent studies showed that RNAs from approximately
40-79% of multi-exon human genes undergo alternative splicing. These findings suggestthat alternative splicing is considered to be necessary for achieving complexity of higher
eukaryotes. Preceding studies have demonstrated that alternative splicing has key roles
in temporal and spatial regulation of development and differentiation inhigher-multicellular organisms; tissue-specific splicing variants have been reported, forexample, mitochondorial ATP synthase gamma-subunit (F1 gamma) gene undergoalternative splicing in mouse muscle development.
However, the overall picture of alternative splicing in development has notbeen obtained. Here we show the overall relationships between alternative-splicing
patterns and developmental stages in mouse by employing comprehensive analyses onmouse full-length cDNAs. We built a splicing-variant dataset which consists of 4,850
transcriptional units (TUs), based on over 102,801 FANTOM3 full-length cDNA clones.
To reveal the occurrences of alternative splicing and their patterns at eachdevelopmental stage, we classified the splicing variants by their patterns and examined
the relationships between these patterns and developmental stages. As a result, we foundthat alternative splicing rarely occurs in early developmental stage (from stage 4 tostage 9), though it might be a result of bias in a splicing-variant dataset. We also foundthat rates of sharing common-exons are distributed centered at 0.5. A rate of sharing
common-exons were defined as a rate of numbers of transcribed exons to numbers of
total exons of a transcriptional unit. In this poster presentation, we will also report therelationships between splicing variants and domain-compositional changes which resultin functional complexity.
References
1) Nakai K, et al., Large-scale analyses of human alternative protein isoforms: patternclassification and correlation with subcellular localization signals, Nucleic Acids
Research, 33, 2355-2363 (2005).
2) Tomita M, et al., Inferring alternative splicing patterns in mouse from a full-length
cDNA library and microarray data, Genome Research, 12, 1286-1293 (2002).

Genome-wide Prediction of Novel Small Peptides in Escherichia coli byUsing Liquid Chromatography Mass Spectrometry
Motomu Matsui1,2, Nozomu Yachie1,3, Kosaku Shinoda4, Naoyuki Sugiyama4,
Fumio Ando1,2, Yuki Okada1,3, Martin Robert1, Rintaro Saito1,2,*, Masaru Tomita1,2,3,4
1
2

Institute for Advanced Biosciences, Keio University, Tsuruoka, 997-0035, Japan，Department of Environmental Information, Keio University, Fujisawa, 252-8520,

Japan，3Bioinformatics Program, Graduate School of Media and Governance, Keio
University, Fujisawa, 252-8520, Japan，4Human Metabolome Technologies, Inc., c/o
Insitute for Advanced Biosciences, Keio University, Tsuruoka, 997-0017, Japan
MOTIVATION: uORFs (upstream Open Reading Frames) are small open reading
frames which are located upstream of protein coding region (CDS) and some of themencode small peptides which act as important translational regulators in several
eukaryotes. In prokaryotes, there are similar small open reading frames upstream ofmain CDS, which encode leader peptides although most of them are yet to be identified
and their functions are unclear. Genome-wide predictions and verifications of novel
small ORF candidates are all-important to investigate their cellular roles, which may
provide additional insights into the prokaryotic metabolisms.
RESULTS: Here we introduce a novel method to predict small ORFs from the
Escherichia coli (E. coli) genomic DNA with the combination of Liquid
Chromatography Mass Spectrometry (LC/MS) and MS/MS ion search to verify theirexpressions. All possible longest ORFs in E. coli strain K12 genome were initially
screened by computational approaches, and total protein extractions were analyzed by
LC/MS. Then the computationally predicted ORFs are compared with the peptide
signals by MS/MS ion search. As a result, 201 small peptide candidates with highspecificity and sensitivity were finally extracted. Among them, six had promoter motifs
and two were overlapped with downstream ORFs, and suggested as sORFs. These
results suggest that there are many small peptides than previously thought and some ofthem may have important functions.

Transcriptomic approach for understanding the effect of cryoprotectant,trehalose and DMSO of Saccharomyces cerevisiae
Yuko Momose 1, Rena Matsumoto1, Mine Odani1 , Masakazu Yamaoka1
The National Institute of Advanced Industrial Science and Technology (AIST)1,
Background: In our institute, International patent organism depositary (IPOD),
we should keep various organisms in good states of the preservation. Freezing is a goodand convenient method for the preservation, but some organisms are susceptible tofreeze-thaw stress, which get serious damage and decrease viability after freeze-thaw
treatment. Therefore we tried to improve more efficient cryoprotectants for thosefreeze-sensitive organisms. To understand of molecular mechanism of various
cryoprotectants, and choose more effective combination of those, we analyzed global
gene expression profiles just before freeze using the model eukaryotic organism,
Saccharomyces cerevisiae.
Methods: The yeast Saccharomyces cerevisiae was frozen at -80 oC (coolingrate, 1 oC/min) for 1 day with or without cryoprotectant and thawed at 30 oC for 6min.
After freeze-thaw, we checked viability by flow cytometry using Vybrant Apoptosis
Assay #4 YO-PRO-1 kits (Molecular Probes). We analyzed the gene expression profile
of yeast before freeze-thawing with and without the cryoprotectants, DMSO (dimethyl
sulfoxide) and trehalose and their combination.
Results: From the aspect of the viability after freeze, DMSO (dimethyl
sulfoxide) and trehalose had almost the same level of protect freeze-stress at proper
concentration respectively in yeast, and after we compare the profiles of gene
expression obtained from the yeast treated by DMSO or Trehalose, or their combination(DT), the profiles of those treatments were similar to induce the genes related to protein
synthesis, transporter, energy and stress response categories. But there were somedifference in gene expression. DMSO induced genes of protein synthesis and trehaloseinduced genes related to protein fate more than other treatments. From those results, we
tried to pick up the gene related to survival after freeze in both cases and suggest themore effective combination of cryoprotectants.

GC contents of nucleotide sequences for proteins
with charge periodicity of 28 residues
Ryusuke Sawada, Runcong Ke, Noriyuki Sakiyama, Masashi Sonoyama
and Shigeki Mitaku
Department of Applied Physics, School of Engineering, Nagoya University
The eukaryotic genomes have different regions with high and low GC contents. Lengthof each type of region is in the order of 100kbp. It is considered that AT-rich regions are
more compact than GC-rich regions and that the housekeeping genes are mainly in
GC-rich region. These facts indicate that GC content is one of the most importantfactors for considering the meaning of genes in terms of the expression in cells.
Recently, we found that proteins with the charge periodicity of 28 residues (PCP28) areabundant in vertebrate genomes, most of which are nuclear proteins. In this work, wefound that the GC content for PCP28 is very low.
We first studied the histogram of GC content of the total ORFs from 9 eukaryotic
genomes, as the control data of the particular type of proteins. The histograms had two
peaks and were fitted well with the combination of two Gaussian distribution. For
human genome, the GC contents of the two peaks were about 0.45 and 0.6. Then, thehistogram of GC content for PCP28 from human genome was plotted, the shape of
which was very different from that for total ORFs, having a single peak at the GC
content of 0.42.
This characteristic distribution of GC content for PCP28 is formed in the process ofevolution under some pressure from the environment. Most codons are degenerated into
smaller number of amino acids, in which the 3rd letter of codon is variable. Therefore,
the GC content of a gene corresponding a protein can vary, even when the amino acidsequence is kept the same. In order to elucidate whether the 3rd letters of codons in
PCP28 occur randomly, we carried out the simulation of random occurrence of the 3rdletters with the amino acid sequence maintained. The result showed that the distributionof the GC content for PCP28 is far from the random occurrence of nucleotides at the 3rd
position of codons. The meaning of the AT-rich character of nucleotide sequences for
PCP28 will be discussed.
References
1) T. Ikemura et al., "Giant G+C% Mosaic structures of the Human genome found byarrangement of GenBank human DNA sequences according to genetic positions",
Genomics, 8, 207-216(1990)
2) B. Alberts et al., molecular biology of the cell, 4th ed., chapter 4, Garland Science.

PBPK-modeling as a tool for interpreting and understanding ofpharmacokinetics
Hiroko Akiyama, CTC LABORATORY SYSTEMS Corporation [Presenter]
S. Willmann, W. Schmitt, Bayer Technology Services GmbH [Authors]
In-silico methods in ADME are mainly seen as tools for property prediction. However,mechanistic computer based simulation techniques as physiology based pharmacokinetic (PBPK) modeling have a much larger potential and can deal as valuable tools to
support the interpretation and understanding of experimental data. By means of different
exemplary case studies it is shown how initially hidden information important for
optimization and development of drug candidates can be revealed from pharmacokinetic
data using PBPK-simulations. Together with the gained knowledge the simulations
build then the basis for reliable predictions of the ADME behavior in experimentally notinvestigated scenarios. Thus PBPK-modeling bears a high potential for accelerating
optimization and development of new drug molecules.
In particular the following topics are discussed:
Bioavailability- Distinguishing between solubility and permeability limitation of oral absorption.
Analysis of the plasma curve form allows to distinguish between the to possible causes
for reduced fraction absorbed.
- Disclosing of active transport in gut wall.
p-Gp-transport of a compound results in a characteristic change of curve form comparedto passively absorbed compound.
Distribution in the body- Organ specific accumulation behavior
Depending on the physicochemical properties of a compound an accumulation in certain
organs may appear that is not apparently reflected by the blood concentration curve.
- Species specifity of distribution kinetics:
Under certain circumstances the distribution kinetics depends on the size of theorganism, leading to significantly different plasma levels in different species.

Scaffold analysis of GPCR ligand datasets from literature.
Masami Fujiki1, Yanhai Fan2, Naotaka Ohishi3
Patcore, Inc.1, Phas Technology, Ltd.2, Spotfire Japan, K.K.3
Maximum Common Substructure (MCS)-based clustering is a highly intuitive method
to categorize chemical datasets by common chemical motifs that exist within a chemical
dataset. Because molecules are subdivided and grouped by chemical substructure orscaffolds, small changes in molecular structure can be easily identified, thereby
facilitating interpretation of large datasets in a chemically intuitive manner. However,
MCS-based clustering methods are computationally more demanding than classicalclustering techniques that exploit chemical fingerprints or molecular keys. In this study
the benefits of conducting MCS-based clustering analysis versus classical clustering
methods is addressed.
For this evaluation, 1) the ability to correlate activity to chemical motifs between
MCS clustering and other classical clustering methods (Tanimoto and Ward’s based
clustering) are evaluated, and 2) scaffolds (substructures) are compared by different
target from datasets collected from patents and journal articles.
The results show that MCS clustering has comparable or better detection power as
methods that use fingerprint based clustering, and it has the potential to find uniquesubstructures larger than the known privileged structures in the set.
scaffolds# of mols
in Neurokinin dataset(12,529 mols)# of mols
in Histamine dataset(10,591 mols)

1,890(15.1%)

823(7.8%)

1,101(8.8%)

1
(0%)

1,020
(8.1%)

0(0%)

978(7.8%)

1,808(17.1%)

2
(0%)

401
(3.8%)

374(3.0%)

378(3.6%)

Development of New in silico Method for Predicting Drug Metabolism
Michihisa Koyama1, Kota Kasahara1, Hideyuki Tsuboi1, Akira Endou1,
Hiromitsu Takaba1, Momoji Kubo1,2, Carlos A. Del Carpio1, Kazumi Nishijima3,1,
Tetsuya Terasaki1, Akira Miyamoto1
Tohoku University1, Japan Science and Technology Agency2,
Mochida Pharmaceutical Co.3
Predicting drug metabolism in silico is of siginificant importance and a greatchallenge. Conventionally, “local” or “global” method was applied to predict drug
metabolism in silico1). “Local” method is typically based on molecular simulation while“global” method utilizes database or knowledgebase. The former method is not capableof predicting macroscopic metabolic characteristics of living body; on the other hand
the latter often faces the predictability issue for newly developed drug molecules. For
these issues, we proposed a new concept for the prediction of metabolism2). In this study,
we developed a new in silico method that bridges “local” and “global” methods.
In the developed method, first ①Interaction energy②bound/unboundbinding of drug molecule was
equilibrium constantDrugunbound+Receptor DrugboundDrugassessed by using Monte Carlo
EboundK=exp(-Ebound/RT)
method as schematically shown in Fig.
③Predict drug-enzyme interaction
1. Based on the binding energy,
Enzyme A rate equation: rA=kaACdrug (1-α)(1-θA)-kdACAθA
Enzyme B rate equation: rB=kaBCdrug (1-α)(1-θB)-kdBCBθB
macroscopic metabolic characteristics
・・・
Mass balance of drug molecule: Cdrugθ=ΣCiθi
are predicted.
Fig. 1 Scheme of new in silico method
We applied the developed method

References
1) B. Testa et al., “Predicting Drug Metabolism-An
Evaluation of the Expert System METEOR”,
Chemical and Biodiversity, 2, 872-885(2005).
2) M. Koyama et al., “Computational Chemistry
Platform for Drug Metabolomics”, CBI2005
Proceedings, p.46(2005).

CYP3A4&2C9

CYP3A4
1.2

Binding ratio [-]for the metabolism of several drug
molecules by CYP3A4 and CYP2C9. Fig. 2 shows
results for Phenytoin, which is metabolized by both
enzymes. Details are discussed at the conference.

1

Drug
0.8
0.6
0.4

CYP3A4

0.2

CYP2C9

0
0

0.2

0.4

0.6

0.8

1

Drug/Enzyme Ratio [-]Fig. 2 Calculated results for
Phenytoin

“CALTA”, a novel structure-property-activity profiling method:
Compound-scaffold profiling and in silico prediction for the drug-discovery
compounds that target membrane proteins
Yoshimasa Kobayashi, Katsuya Akimoto, Hiroshi Kikuchi, and Kenichi Sudo.
Drug Metabolism & Physicochemistry Research Lab., Daiichi Pharmaceutical. Co., Ltd.

Log IC50app

Purpose: When drug-targeted proteins are located near biomembranes, drugs with highlog D are concentrated in the membrane and a high apparent affinity (Kaapp) can beobserved. From the viewpoint of the ADME, however, nonessential high log D must
be avoided for drug-discovery compounds. In this presentation, a novel
structure-property-activity profiling method, here termed “Correlation Analysis for
Lipophilicity and Target Affinity (CALTA)”, has been proposed. This method allows
researchers to “intuitively” find out the intrinsic SAR using only the dataset of Kaappand log D, thus avoiding undesirable ADME properties caused by nonessential highlipophilicity of the compounds.
Data & Theory: (1) Certain activity values (e.g. IC50,app) and the octanol/water (pH 7.4)
partition coefficients (D7.4) were measured for the in-house compounds of several
research projects. (2) The compounds for a project log IC50app
were classified into clusters based on their = - n log D7.4 + log IC50int + C
substructures. Their IC50,int values were estimated fromCALTA based on the following theory: The formulain Fig.1 is derived from the assumption that
nonspecific interaction is positively correlated withdrug lipophilicity.
When a series of active
Log D 7.4
compounds is plotted on a scatter diagram using theirhigh intrinsic activity
D and IC50,app values as shown in Fig.1, producing a
Fig. 1. CALTA-map
1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

0 1 2 3 4 5 6

0 1 2 3 4 5 6

0 1 2 3 4 5 6

0 1 2 3 4 5 6

50
10
5
1
0.5
0.1
0.05

Log (“Activity”)“CALTA-map”, plots of compounds with the same
IC50,int are located around an identical line along theformula. The y-intercept corresponds to their IC50,int.
Results: (1) Each cluster of compounds was locatedaround the corresponding down-sloping line in their
CALTA-maps (Fig.2). The compounds with thelowest IC50,int, around the left-lowest line, were foundto be the most effective in the in vivo studies. (2) The
pharmacophores generated from the IC50,int in silico
predicted the activities of other new compounds moreaccurately than those from the IC50,app.

0.01
50
10
5
1
0.5
0.1
0.05
0.01
50
10
5
1
0.5
0.1
0.05
0.01
50
10
5
1
0.5
0.1
0.05
0.01
0 1 2 3 4 5 6

logD74_and_logD74'

Log D

Fig.2. CALTA-maps for somecompound-groups
ofaproject classified by theirsubstructures

Virtual Screening Models for Predicting Activities in Various
Therapeutic Areas
Zsolt Lepp, Takashi Kinoshita and Hiroshi ChumanInstitute of Health Biosciences, The University of Tokushima.

A very important aim for drug research is to identify the possible side effects ofdrug candidates. A straightforward way to do it would be to predict those therapeuticareas for which the compounds might be effective, directly from the molecular
structures. However there are relatively few software tools that could do this task.
The aim of the work was develop virtual screening models for therapeutic areas, inorder to help the process of new lead design in high throughput fashion. The virtual
screening was done by the means of support vector machines using a cluster of
personal computers. Simple atom type descriptors were used to characterize themolecules.
Altogether 30 general therapeutic areas were included in the study. The datasets
were selected from the MDDR database from MDL. To improve the applicability ofthe method large numbers of molecules were chosen to build the models.
About 50-80% of the positive, and more than 96% of the negative samples of thelarge external test sets could be correctly identified. The method was suitable toassign therapeutic area-likeness properties to large virtual libraries.
Various statistical analyses were also carried out in order to understand therelationship between therapeutic areas.

References
1. Screening for New Antidepressant Leads of Multiple Activities by Support
Vector Machines. Z. Lepp, T. Kinoshita, H. Chuman. J. Chem. Inf and Model
2006; 46(1); 158-167
2. 33rd QSAR Symposium of Chemical Society of Japan, 2005

Convergence properties of absolute binding free energy calculation using
BAR method
Yoshiaki Tanida, Masakatsu Ito, Hideaki Fujitani
Fujitsu Laboratories Ltd., Fujitsu Limited
Recent development of the BAR method1, 2) provides us the capability of thequantitative estimation of the binding free energy for biomolecular docking problem. In
this paper, we examine the influence of the convergence of the interaction energies for
obtaining the absolute binding energy. The procedure of estimating the binding freeenergy of ligand-receptor system is as follows. First, the free energy of the annihilationof ligand from the complex system is calculated, hereafter called ΔGcomplex. Next, thefree energy of the annihilation of ligand from solvated ligand system is calculated,hereafter called ΔGligand. Finally, we obtain the absolute binding energy as the former
value minus the latter one, ΔG= ΔGcomplex-ΔGligand. PME is used to compute theelectrostatic interaction. We also use the switched LJ potential with long range
correction for describing the van der Waals interaction. Figure 1 shows the cutoff
dependency of the electrostatic energy and the van der Waals one for ΔGligand and for
ΔGcomplex in theophylline-RNA system. It is found that ΔGligand gradually decreases as
the cutoff radius is increased as shown in Fig.2. It can be seen that ΔGcomplex has also
the same tendency. Consequently, we find that the cutoff radius of 0.9 nm is enough toobtain the absolute binding free energy.
70

-390

-13

-30

-13.5

-30.5

-14

-31

-14.5

-31.5

-15

-32

LigandComplex

55

Coul

-405

50

Coul

-410

45

-415

VdW

40
0.7 0.8 0.91

1.1 1.2 1.3-420
1.4 1.5

Cutoff radius (nm)

-15.5
0.8

0.9

1

1.1

ΔGcomplex (kcal/mol)

-400

60

ΔGligand (kcal/mol)

-395

VdW

Coul (kcal/mol/nm3)

VdW (kcal/mol/nm3)

65

-32.5
1.2

Cutoff radius (nm)Fig.1 Convergence of interaction

Fig.2 Free energy convergence asenergies

a function of the cutoff radius

References

1) M. R. Shirts, E. Bair, G. Hooker, and V. S. Pande, Phys. Rev. Lett. 91, 140601 (2003).
2) H. Fujitani, Y. Tanida, M. Ito, G. Jayachandran, C. D. Snow, M. R. Shirts, E. J. Sorin, and V. S.
Pande, J. Chem. Phys. 123, 084108 (2005).

Building up an original database of launched drugs for their ADME andphysicochemical HTS profiling data and human PK data.
Rating drugability based on the database and profiling data for in-housecompounds.
Atsuko Kameda, Kyosuke Suzuki, Yoshimasa Kobayashi, Katsuya Akimoto,
Hiroshi Kikuchi and Kenichi Sudo
Drug Metabolism & Physicochemistry Research lab., Daiichi Pharmaceutical. Co., Ltd.
Purpose: An original database of launched drugs was built up to identify the profiles ofthese drugs. The database consists of a wet part; ADME and physicochemical HTS
profiling data, and a dry parts; literature-based human pharmacokinetics data. With thisdatabase, the profiles of in-house compounds were compared with those of the launcheddrugs, and the drugability was evaluated.
Methods: The literature-based information for approximately 1400 launched drugs,
mainly FDA-approved drugs, is compiled with data from Prous Science's Integrity
database, IMSworld drug launches database, GVK's drug database, Technomics's"Asu-no-sin'yaku," and Goodman & Gilman's "The pharmacological basis oftherapeutics: Tenth Edition." The commercially available drugs in that database werepurchased and their ADME and physicochemical parameters were measured. Thosevalues are linked to the literature-based information above. The key statistical values forthe launched drugs and in-house compounds were compared to rate drugablity.
Results: The collected PK data were reviewed from the point of the confirmation ofhealthy human data and the uniformity of units. The original hybrid database, called
KameDB, which consists of both the dry and wet data, was built up. The approximately
170 compounds that had wet data within KameDB and approximately 7000 in-housecompounds were statistically analyzed for the group of drugs and non-drugs,
respectively. Consequently, the following conclusions were obtained. (1) The
distribution coefficient correlates with many ADME parameters. (2) Increasing themolecular weight by 10 and increasing the distribution coefficient by 1 decreases
drugability in the ratios of 0.82 and 0.66, respectively.

Conceptual Design of a Framework for ADME Ontology
Takeshi Arikuma1, Sumi Yoshikawa1, 2, Kentaro Watanabe1*, Kazumi Matsumura1, 2,
Akihiko Konagaya1, 2
Department of Computer Science, Tokyo Institute of Technology1,
Genomic Sciences Center, RIKEN2
Ontology is a science that studies the formal representation of domain knowledge. An
ontological issue in ADME knowledge is the representation of hierarchical biologicalinteractions between drugs and human beings from molecular level to body level. In
addition, we should consider the fact that biological interactions are dynamic anddepend on context such as health conditions and multiple drug administration. In orderto solve the above issues, we propose a framework for ADME Ontology based on the
Triadic Relations of continuants [1].
The framework consists of a class hierarchy of continuants and processes, and sets of
instances of biological interactions represented by Triadic Relations (Fig.1). In a Triadic
Relation, a process is represented by three kinds of participants, i.e. ‘trigger’, ‘situator’and ‘resultant’ [1]. Instances of primitive processes are represented by Triadic Relations
accompanied by instances of continuants as the participants. The originality of theframework is in its use of ‘instance constructors’ and ‘anonymous classes’. Instanceconstructors are inference rules applied to connect Triadic Relations to generate
pathways. Anonymous classes are classes that intentionally have no formal definitions
and do not exist in the class hierarchy of the Ontology. Generated pathways are
considered as instances of anonymousclasses to avoid combinatorial explosion

Class hierarchyof processesof class definitions and instance variations.

Formal representationof processes
Drug A
Drug Body
Body level

We applied this framework to the
ADME Ontology of Irinotecan, an anti
cancer drug. 162 classes and 65 instancesof organs, molecules and molecular level

Drug/Body
Drug

Process

is_a

is_a

Cell

Cell level

Drug/Cell

Drug

BioMolecule(Trigger)(Situator )Instance_of

Class hierarchyof ContinuantsContinuant

Instance_of

Drug/BioMolecule(Resultant)primitive processes are implemented with

Molecular level

OWL-DL and prolog.
Fig.1 ADME Ontology
References
1) Sumi Yoshikawa, Satou Kenji, Akihiko Konagaya. Medinfo, 2004; 11, pp454-458*

Current affiliation: System Development Laboratory, Hitachi Ltd.

Acquisition of Extended Information Regarding Chemicals-CYPs
Interactions by Natural Language Processing
Chunlai Feng 1, Fumiyoshi Yamashita 1, Mitsuru Hashida 1
Graduate School of Pharmaceutical Sciences, Kyoto University1
Cytochrome P450s (CYPs) are a superfamily of enzymes that are responsible foroxidative metabolism of a wide variety of drugs. Drug-drug interaction that arises due
to CYP metabolism becomes a critical problem in clinical usage of many drugs.
Therefore, it is an important issue to predict the interaction of new chemical entities
with CYPs in drug discovery and development. In the CBI 2005 meeting, we reported anatural language processing system for extracting information on chemicals-CYP3A4
interactions from literature. This system implements the rules that identify chemicalnames and extract interaction in a context-based manner, in addition to a chemical namedictionary. Information extraction is carried by the following steps: identification ofchemical and enzyme names in the text, transformation of sentences into multiple
simple clauses containing single event, and pattern matching of keyword sequences
inside the clauses. It gave a high performance on extraction of chemical names (0.842
recall, 0.939 precision) and chemical-enzyme interaction relationships (0.868 recall,
0.877 precision). In the present study, we applied the system to extract interaction
information between chemicals and other metabolic enzymes, including CYP1A2,
CYP2C9, CYP2C19, CYP2D6 and CYP2E1. Using PubMed database, 1600 records oninteraction with 570 chemicals for CYP1A2, 900 records (300 chemicals) for CYP2C9,
850 records (260 chemicals) for CYP2C19, 1670 records (620 chemicals) for CYP2E1,
and 1790 records (450 chemicals) for CYP2D6 were obtained. A corpus of randomly
selected one hundred CYP2D6 abstracts was used for validation of the text mining
system used. It achieved 83.6% recall and 89.1% precision in information extraction ofchemicals-CYP2D6 interactions. The recall and precision were comparable to thosewith CYP3A4.
References
1) Guengerich FP, “Role of cytochrome P450 enzymes in drug-drug interactions”,
Advances in pharmacology, 43:7-35(1997)
2) Ono T. et al., “Automated extraction of information on protein-protein interactions
from the biological literature”, Bioinformatics, 17(2), 155-61(2001)

Ontology Development for RTK-Associated Information Retrieval System
Sumi Yoshikawa, Kazumi Matsumura, Akihiko Konagaya
Genomic Sciences Center (GSC), RIKEN
RTK families have a relatively long history of investigation and are known to be
involved in various basic biological processes such as cellular proliferation,differentiation and tumor progression. RTK-related literature information iscontinuously increasing in number and an efficient information retrieval system isdemanded. Although literature search through PubMed is convenient and efficient when
the MeSH terminology system is relevant to users’ searching scope and purposes, thisgeneral purpose biomedical terminology system is not always sufficient when users
intend to focus on a specific biological domain in depth. In case of RTK families, forexample, search results are often so large in number that users tend to spend too muchtime screening out really needed ones (specificity problem). On the other hand, they
may be overlooked due to the fact that relevant gene names and synonyms are not fully
incorporated (sensitivity problem).
In order to solve these problems, we developed a prototype of RTK-specific literature
retrieval system, which is a kind of filtering tool for PubMed search. Our approach is
focused on the following two methods:
1) Collecting and structuring RTK-specific terminologies, including names of biologicalsubstances, roles and events. These RTK-terminology systems are incorporated as an
ontology. The terms are scanned on the result of user’s free term search on PubMed,
followed by showing the distribution pattern in a hierarchy tree, as well as showing
markings on the abstract.
2) Preparation of a gene-MeSH terms matrix which provides numbers of relevant papers
in each cell and a direct (one-click) access to the abstracts through PubMed.
To collect RTK-specific terms, we examined terms in Gene Ontologies and otherexisting terminology systems, and currently, we have collected 1, 063 concept entries(no duplicate of synonyms), comprising 804 gene entries and 259 process oriented
terms. Among these 804, 143 were extracted from Gene Ontology annotation, andothers were manually collected with the aid of a commercially available tool for
extracting co-occurrence of gene names in the literature. Future work will be done tofurther develop the terminology system, accompanied by the development of an
efficiency evaluation method, as well as constructing links to another RTK-information
system under development in GSC, RIKEN.

Physiologically Based Pharmacokinetic (PBPK) model for pravastatin- Analysis of transporter-related pharmacokinetics Takao Watanabe, Hiroyuki Kusuhara, Kazuya Maeda, Yuichi Sugiyama
The University of Tokyo
Cumulative studies have elucidated importance of drug transporters in thepharmacokinetics of drugs (hepatobiliary and renal transport, intestinal absorption, andtissue barriers). Recently, many reports on genetic polymorphisms in drug transporters
and transporter-related drug-drug interactions have been published, and these are part ofmechanisms of interindividual difference in drug response. It is important to predict
such interindividual difference at early stage of drug development. The purpose of thepresent study is to establish a PBPK model that includes the transporter-mediatedsaturable membrane transport processes and to investigate the effect of changes intransporter function on the pharmacokinetics and, ultimately, the pharmacologicaland/or toxicological effects.
In this study, pravastatin, the HMG-CoA reductase inhibitor, was used as a modelcompound, because cumulative studies have demonstrated the importance oftransporters in the pharmacokinetics (hepatobiliary and renal transport) of pravastatin.
We constructed a PBPK model which consists of compartments such as the liver, kidney,muscle and brain and includes physiological and biochemical parameters, and simulated
the plasma and tissue concentrations in rats at various doses. The parameters ofmembrane transport were taken from previous studies.
The simulated data were comparable to the observed data that exhibited non-linear
pharmacokinetics because of the saturation of transporter-mediated membrane transport
and/or metabolism. We also performed sensitivity analysis to understand the effect of
the changes in hepatic transporter function on the plasma and liver concentration. Thechanges of transporter activity affected the plasma and liver concentration in a different
manner. This PBPK model can be thus applied to predict changes of drug concentrationin plasma and target organs caused by changes in transporter function and expression
level.
References
1) M. Yamazaki et al., “Na+-independent multispecific anion transporter mediates
active transport of pravastatin into rat liver”, American Journal of Physiology, 264,
G36-G44 (1993).
2) M. Yamazaki et al., “Biliary excretion of pravastatin in rats: contribution of theexcretion pathway mediated by canalicular multispecific organic anion transporter(cMOAT)”, Drug Metabolism and Disposition, 25, 1123-1129 (1997).

A Web-Based Drug Safety Information Community
Kumiko Sakamoto1, Eiko Nakata Doolin2, Mikio Sasaki3, A.Ammar Ghaibeh3,
Hiroshi Chuman1, Aiko Yamauchi1
Institute of Health Biosciences, The University of Tokushima1,
Department of Pharmacy, NTT-East Kanto Medical Center2, Saila Systems Inc. 3
Progress of the recent science and technology are revolutionary. It is important thatsuch technologies are used to support the safe society and safe citizen’s life. And
conversely, the information from society and citizens can be fed back to the progress of
Science and Technology. Since the thalidomide catastrophe occurred in the beginning of
the 1960's, many people have assumed that all medicines have generally someteratogenicity. Thus the drug-induced birth defect seems to be over-assessed and fearedexcessively by the pregnant woman who had taken medicines without noticing the
pregnancy. In addition, the information about drug teratogenicity is beneficial for thepharmaceutical scientists in the development of new drugs. We have pursued a
research/development project, “Construction of a Drug Safety Information Community”.
The objective of this research is to develop a drug safety information community for
teratogenic agents as a social system. This information community system allows aneffective circulation of drug safety information among health care consumers, healthcare professionals, and drug discovery researchers through a shared knowledge on theInternet.
The integrated bi-directional database system has three classes of information;
1) Documentary information of the medicinal drugs on teratogenicity from package
inserts, scientific papers and the summary basis for approval of new drugs
2) Prediction of teratogenicity for a given compound by the structural similarity searchof drugs with teratogenicity.
3) Information of the clinical case reports registered by the general public.
The information provided by each community member will contribute to the practice ofevidence-based medicine, an efficient drug research and development, and training for“the informational pharmacist” who will play important roles in risk communication in
the area of clinical pharmacy. The database is going to be partially released soon.

Acknowledgement: This research is supported by Research Institute of Science andTechnology for Society (RISTEX), Japan Science and Technology Agency.

Instant Computing Technology for Molecular Design Environment for
Non-Cluster Experts
Fumikazu KONISHI1, Toru Yagi2, Ryo Umetsu1,
Shingo Ohki1, Akihiko KONAGAYA1
RIKEN GSC1, BESTSYSTEMS, Inc 2
Abstract
We have designed and implemented a new portable system (Knoppix for Molworks) thatcan rapidly construct a computer environment where high-throughput research
applications can be performed instantly. The Knoppix is a collection of GNU/Linux andfeatures one CD live file system (iso9660) that can be customized as a full-featured
portable computer system. This paper presents an approach for instant computing
using Knoppix technology that can allow even a non-computer specialist to easily
construct and operate a Beowulf cluster.
In order to show an actual example using a reference application, we have chosen
MolWorks while is an integrated software tool for molecular design. The MolWorks
supports a molecular builder and pre/post interface to quantum mechanics calculation
software, such as Gaussian, GAMESS, Q-Chem and MOPAC. MolWorks also estimatesproperties of the molecules.
Build the Structure of Molecules
It is very easy to build up your own molecules in MolWorks. You can draw themolecular structure within "Molecule Window". MolWorks also provides "Optimizer" forthe purpose to relax the structure. It is so easy to change element type and bond orderwith the selection of each atoms and bonds. Currently, MolWorks has the capability todisplay the molecular structure with wire, and ball & stick.
Quantum Mechanics Calculation Assistant
BSI migrate the interface for well-known quantum mechanics programs, like Gaussian,GAMESS, Q-Chem and MOPAC. You can select the keywords and options forcalculation from the menu. After you save the input file for calculation, it is very easy to
submit the calculation on your own PC. MolWorks also provide CNDO/2 calculationengine, so you can calculate charge distribution and display molecular orbital with thegraph of energy level.
Instant Build High Throughput Computing Environment
Knoppix for MolWorks can be booting up from CD-ROM image and be automatically
setup up for a high-performance cluster computing system. Therefore, all of thenecessary troublesome installation work isn't necessary by researcher.

Conformational Analysis and Docking Study of the c-Jun N-Terminal
Kinase Inhibitors Having a Benzothiazol Moiety
Mohamed Ismael1 , Abdul Rajjak Shaikh1 , Hideyuki Tsuboi1 , Michihisa Koyama1 ,Akira Endou1 , Hiromitsu Takaba1 , Momoji Kubo1,2, Carlos Del Carpio1 ,Kazumi Nishijima3 , Tetsuya Terasaki1 , Akira Miyamoto1
Tohoku University1 , JST-PRESTO 2 , Mochida Pharmaceutical Co.3
Stress activated protein kinases or c-Jun N-terminal kinases (JNKs) belong to themitogen-activated protein kinase (MAPK) family, which regulate signal transduction in
response to environmental stress. Several evidences support the hypothesis that JNKs
plays a critical role in a wide range of diseases (e.g. cell death). In this study we appliedcomputational calculation methods to inve stigate the activities of well characterized
JNKs inhibitors using docking and molecular dynamics simulation techniques.
Selected (Benzothiazol-2-yl)acetonitrile inhibitors were docked to the active site of thec-JUN protein1 . According to these results the ligands showed three main binding
modes depending in the inhibitor size. Our
Glu147
Met146classical molecular dynamics simulation(New-Ryudo) program was used to perform
Lys93
adynamics
equilibration
forthe
Met149LigandLeu206(protein/ligand) complex models at constanttemperature (300K) for 100 ps to provide
additional relaxation. All the geometric alproperties converged after 50 ps of
Asn152
simulation. Our results show that the ligands
bind to the active site pocket and form two
Gly71
types of hydrogen bonds with the carbonyl
Gln155of Met149 and another with the amine of the
Gln155. Fig. 1 shows the conformation of Fig. 1. Overlay of the docked orientation forthe most active inhibitor (ligand) in thethe most active compound (ligand) in
active site pocket.
the active site of c-jun kinase.
Reference
1) P. Gaillard et al., “Design and synthesis of the first generation of novel potent,
selective, and in vivo active (benzothiazol-2-yl)acetonitrile inhibitors of the c-jun
n-terminal kinase” J. Med. Chem., 48 (14), 4596 -4607(2005).

Development of an Empirical Model for the Activation Energy of
CYP Reaction
Doo Nam Kim1, Won Seok Oh2, Kyoung Tai No1,2
Bioinformatics & Molecular Design Research Center1, Yonsei University2
Human cytochrome P450 (CYP450) is the most abundant enzyme in the human liverand is responsible for the metabolism of nearly over 90% of the drugs on the market.
Accordingly, it is necessary to determine regioselectivity which shows what site is
metabolized preferentially for the purpose of reducing time and resources of drug
discovery and development. However, the lack of quantitative experimental metabolic
data and the fact that CYP system depends on both steric effect and electronic reactivity
indicate that a determining regioselectivity is difficult.
Here, we present newly developed empirical model for prediction of activationenergy of CYP reactions for the purpose of estimating electronic reactivity. We used
AM1 quantum mechanics calculation to collect values of activation energies of various
molecules with para-nitrosophenoxy radical as a surrogate oxygenating species ofCompound I which is responsible for CYP catalytic property, Modified Partial Electron
Orbital Equalization (MPEOE) program to see atomic charge, polarizibility, electron
negativity of each atom, and derived empirical model for activation energies of eachatoms of a given molecule by statistical method. This model explains aliphatichydroxylation, aromatic hydroxylation, and dealkylation which altogether constitutemost reactions mediated by CYP and also is valuable regarding that further research will
enable to know activation energy of not only metabolism reactions but also of generalmolecules.

Prediction of Skin Permeability for Chemical Penetration Enhancers using
Quantitative Structure-Activity Relationship Analysis
and Classification Methodology
Hyejin Kang1, KyoungTai No1,2
Yonsei Univ1, BMDRC 2
Skin penetration enhancers are chemicals used to help drug sufficiently permeable toskin. Making mathematical model for skin penetration enhancers is required to predict
new compounds. Recursive partitioning (RP) and Quantitative-Structure Activity
Relationship are useful tools to classify enhancers into fluidizers and extractors whichhave been considered main types of mechanism of stratum corneum perturbation, and tocorrelate structure of each categorical enhancers with permeability. In this study, almost
all kinds of enhancer containing anionic, cationic, zwitterionic and nonionic surfactant,fatty acid, ester and amine, terpene and terpenoid, pyrrolidone derivatives and azones
have been studied. The resulting RP models show classification rate is sufficiently high,which is about 93.7% . Regression results show that fluidizers with more hydrophobic
part and carbonyl group tend to have high potency. In case of extractors we further
classified them into two groups based on the sphericity. Molecules with round shapeshows correlation with 3 topological distance. On the other hand, permeability of longshape extractors have linear relationship with molecular weight and long distance
topological distances. This classification based on the shape is proved appropriate byunsupervised learning, SOM (self organizing map), which shows similar resultsthat extractors can be grouped into further classes based on the shape. Accordingly, thisstudy supports more accurate means of predicting the permeability potential ofclassified enhancers.

References
1) P. Karande et al., “Design principles of chemical penetration enhancers fortransdermal drug delivery ”, PNAS, 102, 4688-4693(2005).

In Silico Prediction of CYP1A2-mediated Metabolism
Jihoon Jung1, Suyeon Kim1, Kyoungtai No1
Yonsei University, Seoul, Korea1
Human Cytochrome P450 (CYP) enzymes are membrane-associated heme proteins
involved in phase I metabolism of many drugs. Cytochrome P450 1A2 is a member of
the CYP1 family that is responsible for the metabolism of a series of planar conjugatedcompounds, such as caffeine. To develop more efficient and effective drugs, it will be
important to estimate quantitatively drug metabolism. Therefore, a model for theprediction of the regioselectivity of substrates for CYP1A2 enzyme was constructed
through various computational methods. It mainly consists of homology modeling of
human CYP1A2 enzyme and docking trials of selected chemicals into the binding siteof protein. In this study, we will discuss the details of enzyme-substrate binding
complex and their binding affinity.

References
1) D.F.V. Lewis et al., “Homology modelling of human CYP1A2 based on the
CYP2C5 crystallographic template structure”, Xenobiotica, 33, 239-254(2003).
2) J.J. Lozano et al., “Three-dimensional modelling of human cytochrome P450 1A2
and its interaction with caffeine and MeIQ”, Journal of Computer-Aided Molecular
Design, 11, 395–408(1997).

Classification of P-Glycoprotein Substrates and Inhibitors
Sokyoung Joo1, KyoungTai No1, 2
Yonsei University1, Research Institute of Bioinformatics and Molecular Design 2
P-glycoprotein (P-gp), which belongs to a superfamily of ATP-binding cassettetransporters, has been discovered in various resistant tumor cells as well as many
normal tissues. The effect of P-gp-mediated drug efflux limits intestinal absorption
and oral bioavailability of drugs. There is a great interest in anticipating whether drugcandidates are P-gp substrates or inhibitors. Extensive research has been conducted touncover the molecular features required for the substrates and inhibitors of P-gp.
However, accurate classification is difficult due to broad substrate specificity, multiple
binding sites and different modulator mechanisms.
Herein, a data set of diverse compounds taken from ADME database andexperimental literatures were explored. Various different computational modelingmethods have been set up using a set of predefined descriptors encoding the key
molecular properties capable of discerning a substrate from an inhibitor. The
performance was compared using various different descriptor sets based on a wide
range of different properties and physicochemical descriptors.

References
1) Wang et al., “Classification of Substrates and Inhibitors of P-Glycoprotein Using
Unsupervised Machine Learning Approach”, J. Chem. Inf. Model, 45,
750-757(2005).

NMR metabolomics 1/2
Multivariate analysis for diagnostic study of inherited metabolic disorders
using human urine
Tadashi Nemoto1, Masako Fujiwara2, Itiro Ando1,3 Kazunori Arifuku2, andTomiko Kuhara4
National Institute of Advanced Industrial Science and Technology 1,
JEOL DATUM LTD 2, Environmental Research Center Ltd.3,
Kanazawa Medical University4
Metabolic disorders in new born babies are serious when it is overlooked, howeveradequate treatment cure at each early stage can mask the disease onset and ensuresnormal life for the patient. In Japan, only 6 kinds have been selected and does mass
screening test with blood. On the other hand, gas chromatographic-mass spectrometry(GC/MS) technique using urine has been established for clinical diagnosis and revealed
over 130 kinds of metabolic disorders1). Using Urine is, non-invasive, merit having
potential to detect wide range of such disorders including multiple complexities.
We have proposed NMR metabolic profiling (NMR-MP) method using 1H NMR
spectra pattern recognition analysis. Seventeen urine samples including several
disorders and controls already diagnosed by GC-MS, were prepared by our method
previously reported2). ECA-500 (500MHz, JEOL) was used for 1D-1H-NMR
measurements and FIDs were directly processed by Alice2 for Matabolome software(JEOL). SIMCA (Soft Independent modeling of Class Analogy) method, which is anadvanced multivariate analysis based on Principal component analysis (PCA) resulted
in clear discrimination between disease and controls and in addition, detection of
disease status, such as onset or remission, or so on. Our method of NMR-MP using
urine would have promising scope for clinical diagnosis.
References:
1) T. Kuhara. Mass Spec. Rev. 2005, 24, 8142) M. Fujiwara, K. Arifuku, I. Ando, and T. Nemoto, Anal. Sci. 2005, 21, 1259.

NMR metabolomics 2/2
Multivariate analysis for diagnostic study of streptozotocin-induced
diabetes using rat urine
Masako Fujiwara1, Itiro Ando2, 3, Kazunori Arifuku1, Taeko Kataoka2,
Kenji Kanazawa2, Yasuhiro Natori4 and Tadashi Nemoto2
JEOL DATUM LTD.1, National Institute of Advanced Industrial Science andTechnology 2, Environmental Research Center Ltd 3, Research Institute International
Medical Center of Japan4
Assessment and evaluation of physical condition by non-invasive way is highly
required to detect complications in a diabetes patient. We tried very early detection ofnephritis in diabetes model rats by using NMR metabolic profiling method (NMR-MP).
Twenty male Wistar rats (11 week old) were used for the study and their left kidneys
were surgically removed and then critical amount of streptozotocin was injected as
citrate buffer solution and fed separately in metabolic cages under controlled condition.
Urine samples were collected every week and kept in a freezer. For NMR-MP
measurements, urine samples were prepared by our method previously reported １ ).
ECA-500 (500MHz, JEOL) with automated sample changer was used for 1D-1H-NMR
measurements and FIDs were directly processed by Alice2 for Matabolome software(JEOL). After first stage analysis by principal component analysis (PCA), we found
four outliers, those were found to be insufficient rats to be a diabetes model. In second
stage analysis, we removed outliers and combine additional diagnostic and biochemical
information and re-run multivariate analysis with the method named SIMCA to find apotential to very early detection of albuminuria before its starts.
References:1) M. Fujiwara, K. Arifuku, I. Ando, and T. Nemoto, Anal. Sci. 2005, 21, 1259.

Development of a database with simulation function for quantitative
prediction of drug-drug interactions
Yoshihisa Shitara1, Toshiharu Horie1, Toshihiko Ikeda2 , Masato Kitajima3,
Jose M. Ciloy3, Yuichi Sugiyama4
Chiba University1, Sankyo Co. Ltd.2, Fujitsu Kyushu System Engineering Ltd.3,
The University of Tokyo4
Abstract
Most drugs are cleared from the body by P450-mediated metabolism andtransporter-mediated hepatobiliary elimination. In clinical situations, a number ofdrugs are administered concomitantly. A coadministered drug (inhibitor) often inhibits
the metabolism and/or hepatobiliary transport of another drug (substrate), resulting in a
drug-drug interaction. The ratio of the unbound inhibitor concentration (Iu) to theinhibition constants (Ki) is used as an index for predicting drug-drug interactions.
However, this often makes a false positive prediction. Thus, in the present study, we
predicted pharmacokinetic alterations by simulation analysis to aim a quantitatively
accurate prediction. For the accurate prediction, we estimated Ki values based on invivo data (in vivo Ki). To estimate it, the plasma concentration-time profiles ofsubstrates with coadministration of inhibitors were fitted to physiologically-based
pharmacokinetic (PBPK) model. These in vivo Ki values were compared with thereported in vitro Ki values. The accuracy for predicting drug-drug interactions with theuse of in vivo Ki values was compared with that obtained by in vitro 1 + Iu/Ki. In thepresent study, we obtained in vivo Ki values of 11 inhibitors. The prediction from the
PBPK model using in vivo Ki values was more accurate than the prediction obtained by
1 + Iu/Ki. But there were false negative predictions of cytochrome P450 3A4
substrates possibly due to inhibition of intestinal metabolism. By taking the inhibitionof intestinal metabolism into consideration, more accurate predictions were obtained.
We are now trying to make accurate predictions of drug-drug interactions caused bytransporter inhibition and mechanism-based inhibition of metabolic enzymes.
References
1) K. Ito et al., Pharmacol. Rev. 50, 387-411, 1998
2) M. Kato et al., Drug Metab. Pharmacokinet. 18, 121-127, 2003
3) Y. Shitara et al., Annu. Rev. Pharmacol. Toxicol. 45, 689-723, 2005
4) Y. Shitara and Y. Sugiyama, Pharmacol. Ther. in pressIn vitro screening system using a combination of suspensions andmonolayers of Caco-2 cells for detecting drug-drug interactions in
intestinal absorption and conjugative metabolism of drugs
Marie Tanaka, Takashi Mizuma, Masahiro Hayashi
School of Pharmacy, Tokyo University of Pharmacy and Life Science
We have previously reported on the kinetic impact of intestinal conjugative
metabolism on oral bioavailability in humans using reported data [1]. For example,
sequential analysis showed that the intestinal availability of salbutamol, which ismetabolized by sulfate conjugation, was 0.700, whereas hepatic availability and fractionof drug absorbed were 0.893 and 0.802, respectively. The objective of this study was todetect drug-drug interactions in conjugative metabolism and the transport of salbutamolusing Caco-2 cells.
Caco-2 cells were seeded onto Transwell inserts, and used for transport (absorption)and metabolism experiments. Metabolism experiments were done in Caco-2 cellsuspensions as well. Salbutamol and salbutamol sulfate conjugate were determined by
HPLC.
Salbutamol was metabolized to sulfate conjugate in Caco-2 cell suspensions. The
sulfation metabolism of salbutamol was inhibited by the presence of fenoterol,
isoproterenol, or acetaminophen. After salbutamol was added onto the apical side of
Caco-2 cell monolayers, salbutamol appeared on the basal side and its amount increased
over time. Salbutamol sulfate conjugate also appeared on the basal side and its amountincreased over time. The sulfation metabolism rate of salbutamol across Caco-2 cellmonolayers was decreased by the presence of fenoterol or isoproterenol. In contrast, theappearance rate of salbutamol on the basal side was unchanged by the presence offenoterol or isoproterenol, suggesting that salbutamol interacts with fenoterol orisoproterenol not only in metabolism but also in membrane transport.
In conclusion, Caco-2 cell monolayers and suspensions are useful for the study ofintestinal conjugative metabolism and transport. Even when assessing the intestinal
metabolic impact on absorption, transport studies are indispensable for the prevention of
misunderstandings about drug-drug interactions in intestinal absorption.
Reference
1. T. Mizuma et al., J. Pharm. Sci., 94, 571-575 (2005).

Transport and metabolism of ampicillin prodrugs and their interactions
with P-glycoprotein: in vitro study on successful prodrugs using Caco-2cell monolayers
Sachie Tanaami, Sayaka Sakaguchi, Takashi Mizuma, Masahiro Hayashi
School of Pharmacy, Tokyo University of Pharmacy and Life Science
Prodrugs have been developed to overcome the faults of active drugs. One typicalexample is to improve intestinal absorption by increasing lipophilicity of active drugs.
Lenampicillin, bacampicillin, and talampicillin are prodrugs of antibiotics such as
ampicillin. Nevertheless, the absorption process of these prodrugs has not beencharacterized in detail. In addition, it has been reported that a prodrug failed to improvedrug absorption [1]. These tell us that it is important to verify the absorption process of
prodrug. Therefore, we have previously characterized transport and metabolism oflenampicillin using Caco-2 cells [2]. In the present paper we report on the transport andmetabolism of bacampicillin and talampicillin.
Bacampicillin and ampicillin appeared on the basal side after apical dosing of
bacampicillin (AtoB transport). The amount of ampicillin on the basal side was muchlarger than that of bacampicillin. After basal dosing of bacampicillin, it and ampicillin
also appeared on the apical side (BtoA transport). The amount of ampicillin on theapical side was much larger than that of bacampicillin as well. The sum of the transport
clearances of ampicillin and bacampicillin in a direction of the AtoB transport was
similar to that of the BtoA transport, and these clearances were much higher than thetransport clearance of ampicillin, suggesting a high permeability of bacampicillin bypassive transport. Similar results were observed in the transport of talampicillin. The
transport of rhodamine 123, a substrate for efflux transporter of P-glycoprotein, fromthe basal to the apical sides was significantly inhibited by the presence of bacampicillin
or talampicillin. The inhibitory effect of lenampicillin on the rhodamine 123 transport
was observed in our previous study as well [2]. In contrast, ampicillin did not inhibit therhodamine 123 transport. Therefore, the reason for the success of these ampicillin
prodrugs is that the high membrane permeability of these prodrugs and no involvementof P-glycoprotein in ampicillin transport resulted in improvement of intestinal
absorption, although these ampicillin prodrugs interacted with P-glycoprotein.
References. 1) N. Okudaira et al., J. Pharmacol. Exp. Therp., 295, 717 (2000). 2) T.
Mizuma et al., Proceedings of the 2005 annual meeting of CBI society, Yokohama,24-26 Aug., pp. 60 (2005).

Mathematical modeling of gene expression associated with Wnt signaling
pathway in colorectal carcinoma
Yoshimi Naruo, Kaoru Mogushi, Hiroshi Tanaka
Department of Bioinformatics, Tokyo Medical and Dental University
Colorectal carcinoma (CRC) is one of the most common causes of cancer death inEurope and the United States. In Japan, according to dietary habit change to Western
style food, CRC is thought to increase in the future. In cancer cells of CRC, β-catenin is
known to accumulate in nucleus and cytoplasm because of aberration of Wnt signaling
pathway. The well-known target genes of β-catenin are cyclin D1 and c-myc; the former
is a cell cycle-related gene, the latter is an oncogene which controlls cell proliferation
and apoptosis. Expression levels of these genes are upregulated by accumulation of
β-catenin. Thus, β-catenin is one of the most important molecules of Wnt signaling.
Futhermore, recent researches in CRC show that CpG islands within the AXIN2(axis inhibitor 2) promoter are methylated in CRCs with microsatellite instability (MSI)
and that CPG islands within the APC (adenomatous polyposis coli gene) promoter are
methylated in CRCs１). Such epigenetic mutations decrease the mRNA expression andcause a downregulation of the protein expression.
In this research, we developed mathematical model about the relation of selected
genes in Wnt signaling pathway. Using genes such as APC, AXIN2, and CTNNB1(encoding β-catenin) associated with Wnt signaling pathway, we expressed the relationof these mRNA expression with differential equations based on reaction kinetics.
Moreover, we evaluated their dynamics using mRNA expression obtained by microarrayexperiments. We used public microarray dataset of CRC specimen materials containing
10 MSI+ samples and 10 MSI- samples. The dataset is published on Gene Expression
Omnibus with accession number GSE2138 1,2). We set default values at constant offset
from equilibrium point and ran a simulation by MATLAB software. As a result, MSI+
samples exhibited more β-catenin remained without ubiquitination, compared to MSIsamples. This is consistent with the report that β-catenin accumulated in nucleus andcytoplasm in cancer cells in MSI+ CRC 3).
References:
1) K Koinuma et al., “Epigenetic silencing of AXIN2 in colorectal carcinoma withmicrosatellite instability”, Oncogene, 25(1), 139-46(2005).
2) Gene Expression Omnibus website (URL:http://www.nubi.nlm.nih.gov/geo)
3) Y Shimizu et al, “Frequent alterations in the Wnt signaling pathway in colorectal
cancer with microsatellite instability”, Genes, Chromosomes & Cancer
33:73-81(2002).

Modeling of extracellular matrix degradation processes in cancer
metastasis using covariance structure analysis
Akiko Hatano1, Kaoru Mogushi1, Hiroshi Tanaka1
Department of Bioinformatics, Tokyo Medical and Dental University1
Cancer metastasis is comprised of subsequent processes such as release of cancer cells
from a primary focus, invasion in blood vessels and adhesion to target tissues. Various
molecules are involved in each process including overexpression of extracellular matrix(ECM) degrading proteases and abnormality of cell adhesion molecules. Because cancer
metastasis greatly affects prognosis of patients, it would be useful to clarify themechanism of metastasis using expression information at a molecular level.
Matrix metalloproteinases (MMPs) and a disintegrin and metalloproteinases (ADAMs)
are ECM degrading enzymes which relate to cancer metastasis, while tissue inhibitor of
metalloproteinases (TIMPs) are their natural inhibitors.1,2 Excessive degradation of
ECM by loosing balance between such molecules is a critical cause for cancer
metastasis. Cancer cells obtain metastatic ability through consecutive transformations,but it is difficult to observe temporal processes of such transformation events.
Covariance structure analysis (CSA) is a sophisticated method to estimate
unobservable phenomena. CSA is one of the graphical modeling methods and has someproperties of traditional multivariate analysis such as causal analysis, regression
analysis and pass analysis. CSA can also be used to validate the hypothesis models. It isable to extract hidden characteristics behind observable phenomena, and to measure thedegree of causality between such features.
In this study, we focused on the process of ECM degradation and vascular / lymphatic
invasion. We analyzed a microarray dataset with CSA using mRNA expression levels of
MMPs, ADAMs and TIMPs. We used an oral cancer dataset registered in Gene
Expression Omnibus (GEO) with accession NO.GDS10623. The dataset contains 22
samples including 14 samples with lymph node metastasis and 8 samples with nometastasis. We hypothesized a model of the balance between these enzymes and various
factors in its destruction under metastasis progression.
References
1)M.D.Sternlicht et al., Annu. Rev. Cell Dev. Biol, 17, 463-516 (2001)
2)K.Reiss et al., Pharmacol. Ther, (2006)3)R.K.O’Donnell et al., Oncogene, 24,1244-1251 (2005)

Comprehensive analysis of drug-specific mutation patterns in protease geneof HIV-1 subtype B using decision tree
Kaoru Mogushi 1, Fengrong Ren1, Naoki Hasegawa1, Hiroshi Tanaka1
Department of Bioinformatics, Tokyo Medical and Dental University1
The emergence of anti-HIV drug resistance has been the largest obstacle in thetreatment of AIDS in recent decades. Characterization of drug-specific mutation
patterns of HIV is crucial to the challenge of anti-HIV drug resistance acquisition. In
this study, we employed C4.5 decision tree1, a classification method which represents
successive rules to assign each sample to a particular class, for characterizing thepatterns of resistant mutations induced by protease inhibitors (PIs) and applied it to alarge dataset with more than 10,100 HIV-1 protease genes downloaded from HIV Drug
Resistance Database2.
Prior to the analysis, we chose 3,902 isolates from the dataset with following criteria:(1) a specific drug regimen (including no PI treatment) had been received by a patient
over 15 weeks, (2) the patient had never experienced any terminations of PIs other thanthose in the last regimen, (3) a complete PI drug history was known for the patient, and(4) protease mutation had no ambiguity.
Using these isolates, we constructed a decision tree to characterize specific mutation
patterns in amino acid sequence of protease caused by PIs treatment. The part ofamprenavir (APV) and atazanavir (ATV) treated isolates clearly separated by mutationsof I50V and I50L, respectively. Furthermore, D30N mutation was typically found for
nelfinavir (NFV) treatment. Occurrence of these mutations for APV, ATV and NFV wasconsistent with previous reports3, 4. We also found that mutation of V82A in conjunction
with I84V was particular for isolates treated by ritonavir (RTV) and saquinavir (SQV)
in combination. Such drug-specific mutation patterns extracted by decision tree can be
helpful to understand the mechanisms of the drug resistance acquisition of HIV-1 underPIs treatment.
References:
1. J. R. Quinlan, C4.5: Programs for Machine Learning, San Francisco, Morgan
Kaufmann Publishers, 1993.
2. HIV Drug Resistance Database. URL: http://hivdb.stanford.edu/
3. R. W. Shafer., Clin. Microbiol. Rev. 15:247-277 (2002).
4. R. J. Colonno et al., Antimicrob. Agents Chemother., 47:1324-1333 (2003).

Tutorial and Training Courses on Some Areas of Interest of the CBI Society
Tsuguchika Kaminuma1, Masumi Yukawa1, Naomi Komiyama2,
Yoshitomo Tanaka1, Takako Takai-Igarashi1, Hiroshi Tanaka1
Tokyo Medical and Dental University1, Chem-Bio Informatics Society2
Quarter century have passed since the Chem-Bio Informatics (CBI) Society had
started its activities. In these years the on-line journal and annual meetings werelaunched in addition to the monthly-based seminars. However now the Society is understrong pressure to change in order to adapt to the rapid transformation of biomedical
science in the (post) genomic era. This transformation is embodied by the new axis ofresearch of biomedicine namely Genome-Omics-Pathway/Network. Chemical andbiological informatics and computing, the basic knowledge body and GOP/N
methodologies of the Society, must grow and expand so that they can be relevant for thetransforming biomedicine. Biomedical research in the post genomic era requires new
type of informatics and computing specialists who not only understand basic molecularbiology for structure and function of genes and proteins but also physiologies andpathologies. We considered that developing tutorial and skill up training courses thatcan convert present informatics and computing specialists to the new type ofinterdisciplinary researchers is one of the most urgent activities addressed to theSociety.
As a starting effort we chose several cutting edge subjects and have designed courses
and materials collaborating particularly with those who are operating special graduatelevel educational programs sponsored by Japan Science and Technology Agency. These
subjects are (1) computer-aided drug design, (2) genome-omics-pathway/network, (3)pathway/network to disease (systems pathology), and (4) nuclear receptors andmetabolic syndrome (NR-MS). Each course consists of classroom lectures, reading
assignments, web-based skill-up problems, and report writings. The courses rely heavily
on the Web technology, open resources on the Web, full text search system,computational modeling and simulation. Prototype system is build on the CBI andTokyo Medical and Dental University servers and have been used by the assigned
course participants.
In biomedical field discrepancy between rapidly advancing basic research and slow tochange medical education is ever enlarging. We expect that our lectures and thematerials or at least part of them will be put on open use and/or be adapted to regular
graduate level courses at other academic institutions and even industry within few years.

Let's Share and Solve
our Common Problems

Problem A: Proposal
Prediction of the distribution of plasma concentration profile of drugs inJapanese population : interindividual variation in drug disposition
Hiroyuki Kusuhara
Graduate School of Pharmaceutical Sciences, the University of Tokyo
Drug response is governed by pharmacokinetic factors as well as
pharmacodynamic ones. And thus, it is necessary to select candidate compounds withproper pharmacokinetic properties to obtain sufficient pharmacological effect in vivo.
Physiologically based pharmacokinetic model allows prediction of the average plasma
ant tissue concentration time profiles in animals and even in human from in vitro studies.
It is well known that the pharmacokinetic profiles of drugs in human exhibit
interindividual variation, eventually leading to the interindividual variation in thepharmacological and toxicological action of drugs. Such interindividual variation in
drug disposition is ascribed to the difference in the functional activity of enzymes andtransporters responsible as well as physiologic parameters, such as blood-flow rate andtissue size. For example, CYP3A4, one of major drug metabolic enzymes involved in
the metabolism of a variety of drugs, is well known enzyme the activity of which varies
greatly. In addition, many reports on genetic polymorphisms in the promoter andcoding region of drug metabolizing enzymes and transporters have been published, andthe genetic polymorphisms affecting the enzyme- and transporter function have beenfound, resulting in a prolongation of plasma elimination half lives of drugs, or increased
oral bioavailability. The frequencies of genetic polymorphisms depend on the ethnic
group, and varied from rare one to frequent one. Prediction of the distribution ofpharmacokinetic profiles of compounds at early stage of drug development will be
important to avert the failure due to ineffectiveness and adverse effect at the clinicalstage. Question is, when metabolic enzymes and transporters responsible for
disposition of selected compounds are identified, how the distribution of plasma
concentration in human can be predicted?, and what kind software is available for thisprediction?

Problem A: Answer
Prediction of interindividual differences in pharmacokinetics
Motohiro Kato1, Takashi Ito2, Toshiko Koue3, Kouji Chiba4, Yuichi Sugiyama5
Chugai Pharmaceutical Co. Ltd.1, Daiichi Pharmaceutical Co. Ltd2., Otsuka
Pharmaceutical Co. Ltd.3, Pfizer Japan Inc.4, The University of Tokyo5
Abstract
Interindividual difference in the efficacy and toxicity of drugs is generally
known. Sensitivity and exposure of a drug for a target tissue in each person varies.
Factors including genetic polymorphism of metabolizing enzymes, body weight, sex,age, lifestyle habits such as smoking, and so on produce individual difference in thepharmacokinetics of drugs. We have been constructing a method for prediction ofindividual difference in pharmacokinetics using a Monte Carlo simulation of suchfactors.
The mean and standard deviation values of various physiological andbiochemical parameters that affect pharmacokinetics were collected from the literature.
A tube model for liver was used as the mathematical model. The coefficient ofvariation (CV) of the amount of CYP3A4 in human microsomes ranged from 33% to99%. AUC data of CYP3A4 substrates were collected to compare actual CV values of
AUC with the simulated results. Simulation results using a CV of 33% reflected theactual values in cases of both intravenous and oral administration. These results
suggest that 33% is a suitable CV value for CYP3A4. The simulation showed that low
bioavailability drugs exhibited large interindividual difference and reproduced results
similar to actual results. We predicted the mean and SD values of clearance formidazolam in Japanese and European Americans. In all cases, the simulated mean andSD values reflected the observed values in human. We predicted the clearance of
midazolam in each person from his or her body weight. Ninety-five percent (38/40)
were in the 95% confidence interval. The predicted probability agreed with theobserved probability. Individual differences in the AUC of CYP3A4 substrates may
be predictable. This model would be most helpful in the design of Phase II and III
studies. The ultimate goal is the prediction of clinical efficacy from virtual clinical
trials, which would reduce the scale of clinical studies, especially in Phases II and III
and thus reduce cost and time.

Problem B: Proposal
The status quo and problems of OMICS study
Eiichiro Ichiishi
Team Leader, Biomedical Engineering Research Organization, Tohoku University

In a situation of a doctor in the front hospital, a situation of a wet researcher, asituation of bio-informatician, I think that there are several problems about the statusquo and problems of OMICS study in each field. I am going to have discussions about
future medical care and the problem by OMICS study that we should get over inconcerning the most wisdom "Two heads are better than one".
Even for several diseases, there are considerably real differences in approaching
methods and problems in its own way on OMICS study. My approaches in OMICS
study about "pollinosis" and "artificial dialysis" attract attention of the news in Nikkei
biotechnology and winning each society prizes, a special feature magazine. In thissession, I have a presentation about" The status quo and problems of an OMICS

study" from my experience and situation as a proposer
As a clinical doctor of internal medicine, I want to aim at "clinical OMICS study". In
comparison with standard "OMICS study", "clinical OMICS study" will be a little
different in a purpose and a method and an item; may be approached. In other words I
want to challenge "OMICS study from an exit". I think that it is to be important to pushforward a study by "an inductive method" from the domain that is near to an exit, aclinical result.
I assume that various "OMICS study" is exhibited this time, but want to do various
arguments to realize practical, useful "OMICS study" together.

Problem B: Answer 1
Analysis of Genome Scale Amino Acid Sequenceswith the “Physical Fingerprint” Method
Shigeki Mitaku
Nagoya University, Graduate School of Engineering, Department of Applied Physics
A protein is folded by the balance of several types of physical interactions. Thus, itwould be desirable to predict the structure of all proteins with the use of molecular
dynamics simulations that take these physical interactions into account. However,structure prediction by this approach is not possible even for a single protein ofmoderate size because of the problems of the computational time required and thecomplicated energy landscape. Therefore, a completely different approach is necessary
for the analysis of amino acid sequences on the genome scale. We developed a ‘physicalfingerprint’ method in which all amino acid sequences are classified byphysicochemical parameters such as hydrophobicity, electric charge, amphiphilicity, anddensity of proline. It is expected that, when the appropriate parameters are selected forclassification, the prediction of unknown sequences will be as accurate as that of known
sequences.
The basic idea behind this method can be summarized in two points. (1) Each aminoacid has various kinds of physicochemical properties, and we obtain various sequencesof the indices of these properties from a single amino acid sequence. The idea here isthat a phenomenon occurring on a protein is in principle caused by the combination of
the sequences of various physical parameters. (2) As a transmembrane helix is formed
by a cluster of hydrophobic residues, various other characteristics of protein structures
may be formed by the particular distribution of physical properties. This idea leads tothe coarse graining approach to amino acid sequence analysis.
The first system that employed the ‘physical fingerprint’ method was the membraneprotein prediction system, SOSUI, in which the characteristic length of the coarse
graining was the thickness of the lipid membrane, and the essential factors forprediction were the indices of amphiphilicity and hydrophobicity. Other highperformance systems are SOSUIsignal (a signal peptide prediction system),
SOSUIdumbbell (a dumbbell type protein prediction system) and SOSUIbreaker (asystem for predicting the breaking points of secondary structures), which are available
at the Internet site: http://bp.nuap.nagoya-u.ac.jp/sosui/. Several systems for cellular
localization to organella and classification of GPCR are now under construction.

Problem B: Answer 2
Estimating Transcriptome from Genome: a Thermodynamic Approach
Tomokazu Konishi
Akita Prefectural University
Decoding genomic information is routine work for a cell, but is a challenging theme forresearchers. Indeed, although genome sequences have been determined for manyorganisms, and transcriptomes has been measured under various conditions, genomes
cannot be interpreted by means of transcriptomes, and transcriptomes are still notpredictable from genome sequences. These problems base on our ignorance on the“grammar” of the genome; i.e., how quantitative information is embodied in it.
Introduced is a theory that describes the grammar. This theory is derived from aninterpretation of biochemical studies using formulas of statistical thermodynamics. The
concentration of each transcript is described according to three energies; each of theenergy indicates the interaction between a protein factor and nucleic acid of the cell.
The appropriateness of this model is verified through tests on hypotheses that have been
deduced from the theory.
Using this theory, we will be able to analyze transcriptome data, find out heritable
quantitative information and simulate transcriptome with minimum deflection between
analysts. Additionally, minimum amounts of calculations will be required to perform
those analyses, since the model is derived from linear equations of thermodynamics.
Furthermore, the approach will have total fidelity to a cell; the theory describes thefunctions of protein factors that transcribe the genome and degrade the transcripts.
Clearly, the theory used in this approach is contrast markedly with previous non-linear
models, which often have been applied adhocery. With the stable basis on the theory, we
can expect objectivity and high reproducibility in the analytical results.
References
1) T. Konishi, “A thermodynamic model of transcriptome formation” Nucleic Acids
Research, 33, 6587-6592 (2005).
2) T. Konishi, “Three-parameter lognormal distribution ubiquitously found in cDNA
microarray data and its application to parametric data treatment”, BMC
Bioinformatics, 5, 5(2004).

Problem C: Proposal
Weak Interaction between Inhibition Peptides and a Soluble Receptor
for the Docking Analysis Using Software
Makoto Tsuruoka
School of Bionics & Engineering, Graduate School,
Tokyo University of Technology
A rapid screening method of ligands or receptors in the liquid phase has been examinedin our laboratory. Interaction with the peptide ligand (SNP-1) to the receptor (BST-1:
bone marrow stromal antigen 1) was measured in liquid phase, and the correlation withdata by a solid phase method was obtained. The affinity of the ligand’s three derivatives
with the receptor was also examined by the single amino acid substitution. The receptor
was stable in liquid phase because it was fused with the Fc region of humanimmunoglobulin G (IgG). SNP-1 is the peptide of 15 amino-acids. BST-1 is one of "GPIanchored membrane receptors", and the amino acid sequence of the active site isunderstood.
We have obtained the data of very weak affinity (Dissociation constant: 7000 nM ormore) between one of the ligand derivatives and the receptor. It would be difficult toevaluate such weak interaction in the experiment using a solid phase method. For
instance, it is considered that the ligand derivative which shows such weak affinity will
give some suggestions to study reducing the side effect of medicines or controlling theirlevels of effect. We would like to compare the experimental data with those of thedocking analysis using software, because it will be more interesting and helpful, if it
becomes possible to explain cause or mechanism of the weak interaction from theviewpoint of molecular structures by the analysis.
[1] Sato A, et al. Inhibitor peptide SNP-1 binds to a soluble form of BST-1/CD157 at a
2:2 stoichiometry. Eur J Biochem. 1999 Sep; 264(2): 439-45. Erratum in: Eur J
Biochem 1999, Dec; 266(3): 1219.

[2] Sato A, et al. Novel peptide inhibitor of ecto-ADP-ribosyl cyclase of bone marrowstromal cell antigen-1 (BST-1/CD157). Biochem J. 1999 Feb 1; 337 (Pt 3): 491-6.

[3] Yamamoto-K. S, et al. Site-directed removal of N-glycosylation sites in BST-1/CD157:
effects on molecular and functional heterogeneity. Biochem J. 2001 Jul 15; 357 (Pt 2):
385-92.
[4] Yamamoto-K. S, et al. Crystallographic studies on human BST-1/CD157 with
ADP-ribosyl cyclase and NAD glycohydrolase activities. J Mol Biol. 2002 Feb 22;316(3): 711-23. Shimizu M, et al. Weak Interaction between Inhibition Peptides
and a Soluble Receptor of Fusion Protein in Liquid Phase., submitted, 2006.
* Katakura 1404-1, Hachiouji, Tokyo, 192-0981 Japan.

E-mail: tsuruoka@bs.teu.ac.jp

Problem D: Proposal
Computations on the Enantioselectivity of Lipase Enzymes
Yoshihiro MORI and Yoshinobu NAOSHIMA
Faculty of Informatics, Okayama University of Science
Protein lipases are enzymes hydrolyzing a variety of alcohol esters and are one of themost widely used biocatalysts in the synthesis of enantiomerically pure chiralcompounds of biological relevance. A number of experimental data (including our
results) are available for the stereopreference and enantioselectivity of Pseudomonas
cepacia and Candida antarctica lipases toward secondary alcohols.1) Recently we havecarried out the docking simulations (Glide) between these two lipases and chiral ligands
and the molecular dynamics calculations (MacroModel) and the quantum chemical
calculations (ab initio fragment molecular orbital method, BioStation) of thelipase-ligand complexes.2) Although we are neither specialists on theoretical chemistry
nor on computational chemistry, we believe that computer simulation can be a usefultechnique for the elucidation of the enantioselectivity of the enzymes and related
biomolecules toward chiral organic compounds.
We do hope that, through this kind of plan, we will be acquiring the knowledge andskill of molecular modeling and simulation more.
Enantioselectivity of Different Lipases toward Secondary Alcohols

REFERENCES
1) Y. Naoshima,
Kamezawa,

M.

T. Kimura,

Candida sp.
Lipases

Pseudomonas sp.
LipasesOH

OH

F. Okimoto, M. Watanabe,
H.

Tachibana,
RecentDevel.

Org.

Chem.,

4, 1-16 (2001) ;Y. Mori,

OH

Res.

OH

OH

MY
AY

RecentRes. Devel. Org.

Bioorg.

Chem., 6, 1-9 (2004).

OH

OH

LIP

OH

CF3
CH3O

OH
CH3(CH2)7

CALA

OH

OH

CF3

PS
AK

OH

O

N

OH

OH

M. Kamezawa,T. Ohtani,

OH

Cl

OH
CH3(CH2)11

H. Tachibana, K. Kohara,
and

OH

OH

Bioorg.

Y. Naoshima, T. Kimura,

CH3(CH2)9CALB

and T.

Ohtani,OH

OH

CH3(CH2)10

OH

OH

OH

CF3

CF3
Cl

OH

OH

OH

OH

CH3(CH2)7

CF3
Br

CF3

S

CF3

S

good to high enantioselectivity2) Y. Mori, Y. Yagi, and Y. Naoshima, Proceedings of 25th Anniversary International CBI
Conference, 53 (2005).

Author Index

A Abagyan Ruben

S03

Hanada Atsushi

P3-05

Aida Misako

P1-11

Hanai Taizo

P3-07

Akimoto Katsuya

P5-04, P5-07

Handa Chiaki

P4-01

Akiyama Hiroko

P5-01

Hasegawa Naoki

P6-03

Akutsu Jyunichi

P1-01

Hashida Mitsuru

S05, P5-09

Ando Fumio

P4-04

Hata Keishi

P3-12

Ando Itiro

P5-19, P5-20

Hata Masayuki

P1-08

Aoyagi Kazuhiko

S15

Hatano Akiko

P6-02

Arakawa Takatoshi

P1-01

Hayashi Masahiro

P5-22, P5-23

Arakawa Takayuki

P3-16

Higo Junichi

P2-02

Arifuku Kazunori

P5-19, P5-20

Honma ToshikiP4-01

Arikuma Takeshi

P5-08

Horie Toshiharu

P5-21

P3-08, P5-05, P5-12

Hoshino Tyuji

P1-08, P1-09, P1-10

Iba Hitoshi

P3-09, P3-14

P3-10, P5-03, P5-14,

Ichiishi Eichiro

P3-19

P2-04, P3-19

Iefuji Haruyuki

P1-11

E Ekins Edward H.

S11

Igarashi Manabu

P3-17

Ekins Sean

S10

Ikeda ToshihikoP5-21

Endou Akira

P2-04, P3-10, P3-19,

Ishikawa Takeshi

P1-05

P5-03, P5-14

Ismael Mohamed

P2-04, P5-14

P5-02

Ito Kimihito

P3-17

Feng Chunlai

P5-09

Ito Masakatsu

P5-06

Fujiki Masami

P5-02

Iwamoto Koji

P1-09

Fujitani Hideaki

P5-06

Joo Sokyoung

P5-18

Fujiwara Masako

P5-19, P5-20

Jung Ji Hoon

P3-15, P5-17

Fukuda Ikuo

P3-04

Jung Jong Young

P2-06

Fukunishi Hiroaki

P2-05

K Kameda Atsuko

P5-07

Fukunishi Yoshifumi

P2-01, P3-04

Kaminuma Tsuguchika P2-08, P7-01

Fukuzawa Kaori

P1-05, P2-08

Kamiya Kenshu

P1-07

G Ghaibeh A.Ammar

P5-12

Kanai Akio

P3-02

Gromiha Michael

P3-01

Kanamori Eiji

P3-04

H Hamasaki Nobuyuki

P2-02

Kanaya Shigehiko

S08

P3-15

Kanazawa Kenji

P5-20

C Chuman Hiroshi
Ciloy JoseD Del Carpio Carlos A.

F Fan Yanhai

Han Hyesun

P5-21

I

J

Kang Hyejin

P5-16

Lepp Zsolt

P3-08, P5-05

Kasahara Kota

P5-03

M Maeda Kazuya

S12, P5-11

Kataoka Taeko

P5-20

Maruyama Hidetoshi

P4-01

Kato Akifumi

P1-05

Masaki Kazuo

P1-11

Kato Motohiro

S12

Matsubara Toshiaki

P1-11

Kawai Yoshichika

P3-08

Matsui Motomu

P3-02, P4-04

Ke Runcong

P3-06, P3-11, P4-06

Matsumoto Rena

P4-05

Kida Hiroshi

P3-17

Matsumura Kazumi

P5-08, P5-10

Kikuchi Hiroshi

P5-04, P5-07

Matsuyama Shou

P1-08

Kikuchi ShinjiP4-01

Mitaku Shigeki

P3-06, P3-11, P3-18,

Kim Doo Nam

P5-15

Kim Suyeon

P5-17

Kinoshita Takashi

P3-08, P5-05

Kitajima Masato

P5-21

Miyata Yusuke

P1-01

Kitaura Kazuo

S01

Miyazaki Satoru

P3-13, P3-16

Kobayashi Genta

P3-07

Mizuma Takashi

P5-22, P5-23

Kobayashi Yoshimasa

P5-04, P5-07

Mochizuki Yuji

P1-05

Koike Asako

S14

Mogushi Kaoru

P6-01, P6-02, P6-03

Komeiji Yuto

P1-05

Momose Yuko

P4-05

Komiyama Junpei

P2-08Mori Kenichi

P1-10

Komiyama Naomi

P2-08, P7-01

Mori Yoshihiro

P1-03

Konagaya AkihikoP3-12, P5-08, P5-10,

Morishita Shinya

P1-07

P5-13

N Nabatame Shinya

P3-09

Kondou Hidemasa

P1-11

Nagasawa Naoto

P4-01

Konishi Fumikazu

P5-13

Nakabayashi Takeshi

P4-01

Konishi Tomokazu

P3-12

Nakahara Daigo

P3-05

Koyama Michihisa

P2-04, P3-10, P5-03

Nakajima Hiroyuki

P3-05

P5-14, P3-19

Nakamura Haruki

P2-01, P3-04

P2-04, P3-10, P3-19,

Nakamura Hiroyuki

P3-03

P5-03, P5-14

Nakano Tatsuya

P1-05, P2-08

Kubota Satoru

P3-04

Nakata EikoP5-12

Kuhara Tomiko

P5-19

Nakata Kotoko

P2-08

Kusuhara Hiroyuki

S14, P5-11

Nakata Yoshiro

P1-02

P1-06, P2-06

Naoshima Yoshinobu

P1-03

P1-06

Naruo Yoshimi

P6-01

Kubo Momoji

L Lee Chang Joon
Lee Se Han

P4-06
Miyamoto Akira

P2-04, P3-10, P3-19,
P5-03, P5-14

Natori Yasuhiro

P5-20

Sakatusji Yukako

P3-13

Negishi Yoshiteru

P3-03

Sakiyama Noriyuki

P3-06, P4-06

Nemoto Tadashi

P5-19, P5-20

Sasaki Hiroki

S15

Neya Saburo

P1-08, P1-09

Sasaki Mikio

P5-12

Nishijima Kazumi

P2-04, P3-10, P5-03,

Sawada Ryusuke

P4-06

P5-14

Schmitt Walter

P5-01

Niwa Tomoko

P2-03

Sekiguchi Tatsuya

P3-07

No Kyoung Tai

S07, P1-06, P2-06,

Sekine YasuhikoP3-02

P3-15, P5-15, P5-16,

Selvam ParasuramanP2-04, P3-10

P5-17, P5-18

Shaikh Abdul Rajjak

P2-04, P5-14

Numata Koji

P3-03

Shibata Yuuki

P3-05

O Odani Mine

P4-05

Shimada Jiro

P2-05

Ode Hirotaka

P1-08

Shinoda Kosaku

P4-04

Ogishima Soichi

P4-03

Shinto Hideaki

P3-07

Oh Won Seok

P5-15

Shiota Kunio

S16

Ohishi Naotaka

P5-02

Shitara Yoshihisa

S12, P5-21

Ohki Shingo

P5-13

Soma Akiko

P3-02

Ohta Jun

P3-20

Sonomoto Kenji

P3-07

Øie Svein

S04

Sonoyama Masashi

P4-06

Okada Okimasa

P1-01

Sudo Kenichi

P5-04, P5-07

Okada Yuki

P4-04

Sugahara Junichi

P3-02, P3-03

Okamoto Masahiro

P3-07

Sugawara Yoko

P1-04, P1-07

Okimoto Noriaki

P1-10

Sugiura Wataru

P1-08

Omagari Katsumi

P3-04

Sugiyama Naoyuki

P4-04

Ono Atsushi

S06

Sugiyama Yuichi

S12, P5-11, P5-21

Ootani Wataru

P1-02

Sukumaran Murali

P2-01

P Pei Qiang

P3-10

Suwa Makiko

P3-01

R Ren Fengrong

P6-03

Suzuki Ayumi

P3-13

Robert Martin

P4-04

Suzuki Kyosuke

P5-07

S Saburo Neya

P1-10

T Taguchi Y-h.

P4-02

Saito Rintaro

P3-03, P4-04

Sakae Yoshitake

P1-11

Sakaguchi Sayaka

P5-23

Takada Ayato

P3-17

Sakamoto Hiromi

S15

Takahashi Koichi

P3-05

Sakamoto KumikoP5-12

Takahashi Yasuko

P4-03

Takaba Hiromitsu

P2-04, P3-10, P3-19,
P5-03, P5-14

Takai-Igarashi Takako

P7-01

Yamagishi Kenji

P2-07

Takano Akihiko

S13

Yamamoto Keiko

P2-07

Takano Norikazu

P2-02

Yamaoka Masakazu

P4-05

Takaoka Tsubasa

P1-10

Yamashita Fumiyoshi

P5-09

Takasaki Shigeru

P3-12

Yamashita Mayu

P3-07

Takashima Kayoko

P4-01

Yamato Ichiro

P3-13

Tanaami Sachie

P5-23

Yamauchi Aiko

P5-12

Tanaka Hiroshi

P2-08, P4-03, P6-01,

Yohda Masafumi

P1-01

P6-02, P6-03, P7-01

Yoneda Shigetaka

P1-04

Tanaka Marie

P5-22

Yoshida Teruhiko

S15

Tanaka Shigenori

P1-05

Yoshikawa Sumi

S09, P5-08, P5-10

Tanaka Takahisa

P1-03

Yukawa Masumi

P2-08, P7-01

Tanaka Yoshitomo

P7-01

Tanida Yoshiaki

P5-06

Tanizawa Hideki

P3-18

Tashiro Yukihiro

P3-07

Teramoto Reiji

P2-05

Terao Junji

P3-08

Terasaki Tetsuya

P2-04, P3-10, P5-03,
P5-14, P2-07

Tokiwa Hiroaki

P2-07

Tokuda Yuuichi

P3-05

Tomimoto Masaki

S02

Tomita Masaru

P3-02, P3-03, P4-04

Tsuboi Hideyuki

P2-04, P3-10, P3-19,
P5-03, P5-14

U Umetsu Ryo

P5-13

Urabe Hisako

P1-04

W Watanabe Kentaro

P5-08

Watanabe Takao

P5-11

Willmann Stefan

P5-01

Y Yabuki Takahiro

P3-14

Yachie Nozomu

P3-02, P3-03, P4-04

Yagi Toru

P5-13

Yamada SachikoP2-07