CBI2005 PROCEEDINGS
Chem-Bio Informatics in Post Genome Era
Chem-Bio Informatics Society

CONTENTS
Preface
Benefactors
Contributors
Exhibition Companies
CBI Members
Organizer
Program
Abstract for Keynotes and Invited Talks
Abstract for Poster Presentations
Authors' Index

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. The participants of the previous
meeting is approximately 400.

CBI2005 PROCEEDINGS
Edited by Akihiko Konagaya
August 24, 2005
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
The scope of this 25th anniversary international conference is chem-bio informatics
which is basis for pharmaceutical science, agriculture and environmental science.
Established in 1981, the Chem-Bio Informatics Society (CBI) organized many meetings,
seminars, training courses, and symposia for rational drug design targeted towards
researchers in pharmaceutical companies and academia. Recently, the increasing
amount of genome and post-genome data has raised many important issues in pathways
and networks especially from the viewpoints of pharmacogenomics and personalized
medicine.
This conference consists of three keynotes, four invited talks and nine oral presentations
and poster presentations in the field of:(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
The scientific program committee also provides two tutorial talks on bioinformatics,
two adjunct workshops on “Immunoinformatics” and “Genome Medical Informatics”,
and three lunch-on seminars organized by enterprise companies.
As for the keynotes, Richard Friesner will open the conference with his talk titled“Computational Modeling of Protein-Ligand Interactions”, Allen Roses will present“PGx in Early Development and Pharmacovigilance” and lastly, Yuichi Sugiyama will
give his talk on
Role of drug transporter studies in the drug discovery anddevelopment; Molecular multiplicity, substrate specificity and drug-drug interaction”.
We hope all the participants will enjoy this conference by exchanging ideas andexperiences through excellent keynotes, invited talks, oral presentations and poster
presentations.

Akihiko Konagaya
Program ChairCo-organization
RIKEN Genomic Sciences Center (GSC)
Japan Association of Medical Informatics

Benefactors
Information Processing Society of Japan
Japan Association for Medical Informatics
Japanese Society for Bioinformatics
The Biophysical Society of JapanThe Chemical Society of JapanThe Japanese Society for Artificial Intelligence
The Japanese Society for the Study of Xenobiotics
The Molecular Biology Society of JapanThe Pharmaceutical Society

Contributors
Accelrys Inc.
Daiichi Pharmaceutical Co.Ltd.
Eisai Co.Ltd.
IBM Japan, Ltd.
Initiative for Parallel Bioformatics (IPAB)
Mochida Pharmaceutical Co.,Ltd.
Taiho Pharmaceutical Co.,Ltd.
Taisho Pharmaceutical Co.,Ltd.

Exhibition & Lunch on Seminor Companies
AdvanceSoft Corporation
Affymetrix Japan K.K.
Cheminfonavi
CONFLEX Corporation
FUJITSU LIMITED
Infocom Corp.oration
Mathematical Systems, Inc.
NABE International Corporation
NEC Informatec Systems,Ltd.
Nittetsu Hitachi Systems Engineering,Inc.
Patcore Inc.
Ryoka Systems Inc.
SGI Japan, Ltd.
Sumisho Computer Systems Corporation
Tomy Digital Biology Co., Ltd.
Wavefunction, Inc.

CBI Members
Accelrys Inc.
AdIn Research Inc.
AdvanceSoft Corporation
Ajinomoto Co. Ltd.
Astellas Pharma Inc.
Banyu Pharmaceutical Co.,Ltd.
CAC Corp.
Chugai Pharmaceutical Co.,Ltd.
Daiichi Pharmaceutical Co.,Ltd.
Dainippon Pharmaceutical Co.,Ltd.
DNA Chip Research Inc.
Eisai Co. Ltd.
Fuji Film Co. Ltd.
Fujitsu Ltd.
GlaxoSmithKline K.K.
Hitachi Co. Ltd.
IBM Japan, Ltd.
Infocom Corp.
MediBIC
Mitsui Chemical Co.,Ltd.
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
Chairman
Akihiko Konagaya (RIKEN GSC)

ChairsTsuguchika Kaminuma (Hiroshima University)
Yukio Tada (Taiho Pharmaceutical Co., Ltd)
Takatoshi Kawai (Eisai Co.Ltd.)
Hiroshi Tanaka (Tokyo Medical & Dental University)
Hiroshi Mizushima (National Cancer Research Institute)
Fumikazu Konishi (RIKEN GSC)
Masami Uebayasi (AIST)
Yoshiro Nakata(Gunma Univaersity)
Christian Schönbach (Riken GSC)
Jun Nakaya (Kobe University Graduate School of Medicine)
Tomokazu Matsusue (Mochida Pharmaceutical Co. Ltd.)
Masanori Arita (AIST)
Takashi Mizuma (Tokyo University)
Yoshihiro Takamoto (MediBIC)

24th(Wed.)
10:00

Tutorial in Japanese
S1

New Opportunities and Challenging Topics in Informatics and Computing in Life Science
Tsuguchika Kaminuma (Hiroshima University)

S2

Bioinformatics for drug discovery in pharmaceutical company: current status and issues
Hiroki Shirai(Astellas Pharma Inc.)

12:00
Luncheon Seminor
INFOCOM CORPORATION
13:20

Opening

13:30

Keynote Address
Novel Methods for the Prediction of Structure and Binding Affinities of Protein-Ligand
Complexes; Towards a New Theory and Practice of Molecular Recognition
Richard Friesner (Columbia Univ., USA )

S3

14:30

Technical Session
S4

S5

17:10
18:30

Molecular Computation and Molecular Recognition

Invited Talk
Computer-Assisted Drug Design (CADD) at Bristol-Myers Squibb
Roy S. Kimura (Bristol-Myers Squibb Company)
Invited Talk
From Genome Sequence to Protein Structure and Drug Discovery:
computational pipeline with bioinformatics and supercomputing
Yutaka Akiyama (CBRC, AIST)

P7

Molecular Dynamics Simulations of HIV-1 Proteases - Insight into the Multi-drug Resistanceconferred by Non-active Site Mutation L90M
Hirotaka Ode1, Sabrou Neya1, Masayuki Hata1, Wataru Sugiura2, ○Tyuji Hoshino1,3
Graduate School of Pharmaceutical Sciences, Chiba University1, National Institute of
Infectious Diseases2 , JST3

P51

Protein-protein interaction sites prediction and protein-protein docking by the methods ofMD, grid scoring and the pair-wise interaction potential in CAPRI Rounds 3-5○ Genki Terashi, Mayuko Takeda-Shitaka, Daisuke Takaya, Katsuichiro Komatsu,
Kazuhiko Kanou, Mitsuo Iwadate, Hideaki Umeyama
School of Pharmaceutical Sciences, Kitasato University

P64

Structural analysis by molecular dynamics simulations:Protein-protein interactionsof Ras-Raf and Ras-RalGDS complexes○Noriyuki Futatsugi1, Mikako Shirouzu 2, Atsushi Suenaga1, Noriaki Okimoto1, Tetsu
Narumi1, Toshikazu Ebisuzaki3, Shigeyuki Yokoyama2, Akihiko Konagaya1, Makoto Taiji1
Computational and Experimental Systems Biology Group, RIKEN Genomic Sciences Center 1,
Protein Research Group, RIKEN Genomic Sciences Center 2, Ebisuzaki Computational
Astrophysics Laboratory, RIKEN 3
Poster Session

25th(Thu.)
9:45

Immunoinformatics Workshop
S6
S7
S8

S9

Strategies for Improved Vaccine Design
Nikolai Petrovsky (Flinders Medical Centre, Australia)
Beyond Epitope Mapping: Making Vaccines and Modifying Therapeutics
Anne S DeGroot (EpiVax Inc., USA)
Automatic Construction of Specialized Databases, Exemplified by the VBASE2 Database,an Integrative Database for Immunoglobulin Variable Genes
Werner Müller (German Research Centre for Biotechnology, Germany)
Transcriptional and Translational Variation from an Immunomics Point of View
Christian Schönbach (RIKEN GSC, Japan)

12:00
Luncheon Seminor
RYOKA SYSTEMS INC.
Interactive Cheminformatics: Workflow meets Visualization
13:30
S10

14:40

17:10
18:30

Keynote Address
Pipeline pharmacogenetics: Phase I, Phase II, & Phase III & Surveillance
Allen Roses (Glaxo Smith Kline)
Technical Session Bioinformatics and Genome-wide Experimental Data Analysis

S11

Invited Talk
High throughput biology: from genome diversity to transcriptional regulation
Hiroyuki Aburatani (Univ. of Tokyo)

S12

Invited Talk
System-level Identification of Mammalian Circadian Clocks
Hiroki Ueda (RIKEN CDB)

P28

Amino acid sequence analysis of Saccharomyces cerevisiae proteome in terms of electriccharge distribution○Runcong Ke , Shigeki Mitaku
Department of Applied Physics, Graduate School of Engineering, Nagoya University

P35

Semi-automatic interpretatio of experimental results based on conceptual network○Asako Koike 1,2 and Toshihisa Takagi 1
Graduate School of Frontier Science, The University of Tokyo1, Central ResearchLaboratory, Hitachi Ltd. 2

P48

In silico analyses of biosynthesis rate limiting factor in cell-free system○Naoya Fujita1, Motoaki Seki2, Kazuki Shinozaki2, Kengo Kinoshita1,
Tatsuya Sawasaki3, Yaeta Endo3, Kenta Nakai1Inst. of Med. Sci., Univ. of Tokyo1, GSC, RIKEN2, CSTRC, Ehime Univ.3
Poster Session

26th(Fri.)
10:00

Medical Genome Informatics Workshop
S14

S15

S13

Key Note Omics-based systems approach to diseases-A new paradigm for clinical medicine in the post-genomic eraHiroshi Tanaka (Tokyo Medical & Dental University)
Invited Talk Future Vision: A possibility of Genome Information toward
the Personalized Medicine and Personalized Healther
Hiroshi Mizushima (National Cancer Research Institute)
Invited Talk MC's Comment
The Translational Research Informatics: A bridge toward the Genome Medicine
Jun Nakaya (Kobe Univ.)
Discussion: The Goal, the Hurdles, and the Path of the Genome Medical Informatics?
Hiroshi Tanaka, Hiroshi Mizushima, Jun Nakaya (MC)

12:00
Luncheon Seminor
AFFYMETRIX JAPANAffymetrix Gene ChipⓇ Technology and its Applications in Clinical Research
13:30
S16

14:30

15:30

Keynote Address
Role of drug transporter studies in the drug discovery and development;
Molecular multiplicity, substrate specificity and drug-drug interaction
Yuichi Sugiyama (Univ. of Tokyo)
Technical Session Drug Design and Toxicology, Disease Mechanism and Control Model

P19

Extraction of Information on Chemicals-CYP3A4 Interactions from Literature○Chunlai Feng, Fumiyoshi Yamashita, Mitsuru Hashida
Graduate School of Pharmaceutical Sciences Kyoto University

P72

A Grid-based Information Integration System for Drug Discovery by using Freely
Accessible Compound Databases○Gen Kawamura1, 2, Masato Kitajima1,3, Takahiro Kosaka1, Susumu Date1,
Shinji Shimojo4, Hideo Matsuda1
Graduate School of Information Science and Technology, Osaka University1, Aztec-System Co.,
Ltd.2, Fujitsu Kyushu System Engineering Limited3, Cybermedia Center, Osaka University4

P14

Evaluation of flexible molecular docking programs for virtual screening○Kenji Onodera, Kazuhito Satou, Hiroshi Hirota
Riken Genomic Sciences Center
Closing
Ceremony for poster awards

Keynotes and Invited Talks
S1

New Opportunities and Challenging Topics in Informatics andComputing in Life Science
Tsuguchika Kaminuma(Center for Quantum Life Science, Hiroshima University)

S2

Bioinformatics for Drug Discovery in Pharmaceutical Company:
current status and issues
Hiroki Shirai(Astellas Pharma Inc.)

S3

Novel Methods for the Prediction of Structure and Binding
Affinities of Protein-Ligand Complexes; Towards a New Theory
and Practice of Molecular Recognition
Richard Friesner(Columbia University, USA)

S4

Computer-Assisted Drug Design (CADD) at Bristol-Myers
SquibbS. Roy Kimura(Bristol-Myers Squibb Company)

S5

From Genome Sequence to Protein Structure and Drug Discovery: computational pipeline with bioinformatics and supercomputing
Yutaka Akiyama(Director, Computational Biology Research Center (CBRC) National Institute of Advanced Industrial Science and Technology (AIST), Japan)

S6

Strategies for Improved Vaccine Design
Nikolai Petrovsky(Australian Centre for Adjuvant Research, Flinders Medical Centre)

S7

Beyond Epitope Mapping:
Therapeutics

Making Vaccines and Modifying

Annie De Groot, Bill Martin, Luisa Marcon, Paul Knopf, Betty Bishop, Julie Mcmurry,
Dan Rivera, Michelle Klutzer, Christine Boren, Claire Rodriguez-annon, David Weiner(TB/HIV Research Laboratory, University of Pennsylvania, and EpiVax, Inc.)

S8

Automatic construction of specialized databases, exemplified by
the VBASE2 database, an integrative database for Immunoglobulin Variable Genes
Werner Müller( Department of Experimental Immunology, GBF German Research Centre for Biotechnology, Braunschweig, Germany)

S9Transcriptional and translational variation from an immunomics
point of view
Christian Schönbach(Immunoinformatics Team, Advanced Genome Information Technology Group RIKEN
Genomic Sciences Center, Yokohama, Kanagawa 230-0045, Japan)

S10

Pipeline pharmacogenetics: Phase I, Phase II, & Phase III &Surveillance
Allen D. Roses(GlaxoSmithKline)

S11

High throughput biology: from genome diversity to transcriptional regulation
Hiroyuki Aburatani( Genome Science Division, Laboratory for Systems Biology and Medicine, ResearchCenter for Advanced Science and Technology, University of Tokyo)

S12

System-level identification of Mammalian Circadian Clocks.
Hiroki R. Ueda1,2,3(Team Leader, Laboratory for Systems Biology and Subunit Leader, Functional Genomics Subunit, Center for Developmental Biology, RIKEN, Kobe Japan. 1 , Visiting
Professor, Tohoku University.2 , Visiting Professor, Tokushima University.3 )S13MC’s Comment: The Translational Research Informatics: A
bridge toward the Genome Medicine
Jun Nakaya(Associate Professor, Clinical Genome Informatics Center, School of Medicine, Kobe
University)

S14

Omics-based systems approach to diseases - A new paradigm forclinical medicine in the post-genomic era Hiroshi Tanaka(Department of Bioinformatics, Tokyo Medical and Dental University)

S15

Future Vision: A possibility of Genome Information toward the
Personalized Medicine and Personalized Healthcare.
Hiroshi Mizushima(Head of Bioinformatics Section. Center for Medical Genomics. National Cancer Center
Research Institute.)

S16

Role of drug transporter studies in the drug discovery and development; Molecular multiplicity, substrate specificity and drugdrug interaction
Yuichi Sugiyama(Department of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo)

Poster Presentations
Odd Number: 8/24 17:30 – 19:00

P6

Even Number: 8/25 17:30 – 19:00

Structural Analysis and Discrimination of Outer Membrane Proteins
M. Michael Gromiha, Makiko Suwa
(Computational Biology Research Center (CBRC), AIST, 2-42, Aomi, Koto-ku, Tokyo
135-0064, Japan)

P7

Molecular Dynamics Simulations of HIV-1 Proteases - Insight
into the Multi-drug Resistance conferred by Non-active Site Mutation L90M Hirotaka Ode1 , Saburou Neya1 , Masayuki Hata1 , Wataru Sugiura2 , Tyuji Hoshino1,3(Graduate School of Pharmaceutical Sciences, Chiba University1 , National Institute of
Infectious Diseases2 , JST3 )P8

GLYMM: a program for modeling a 3D structure of a carbohydrate chain and a lipid membrane: the application to molecular
dynamics simulations of glycolipid (GM1) membranes
Kenichi Mori1 , Tsubasa Takaoka1 , Koji Iwamoto1 , Masayuki Hata1 , Saburou Neya1 ,Tyuji Hoshino1,2(Graduate School of Pharmaceutical Science, Chiba University1 , PRESTO JST2 )P9Computational Chemistry Platform for Drug Metabolomics
Michihisa Koyama1 , Satoshi Kawagoe1 , Kota Kasahara1 , Hideyuki Tsuboi1 , Akira
Endou2 , Momoji Kubo1,3 , Carlos A. Del Carpio1 , Ewa Broclawik1 , Kazumi
Nishijima4,5 , Tetsuya Terasaki4,6 , Akira Miyamoto4,1(Department of Applied Chemistry, Tohoku University1 , Institute of Fluid Science,
Tohoku University2 , PRESTO, Japan Science and Technology Agency3 , New Industrial
Creation Hatchery Center4 , Development Division, Mochida Pharmaceutical Co. Ltd.5 ,Graduate School of Pharmaceutical Sciences, Tohoku University6 )P10Theoretical Study on the Mechanism of Theophylline
Metabolism by Compound I of Cytochrome P450
Mohamed Ismael1 , Abdul Rajjak Shaikh1 , Hideyuki Tsuboi1 , Michihisa Koyama1 ,Akira Endou2 , Momoji Kubo1,3 , Carlos A. Del Carpio1 , Ewa Broclawik4 , Kazumi
Nishijima4,5 , Tetsuya Terasaki4,6 , Akira Miyamoto1,4(Department of Applied Chemistry, Tohoku University1 , Institute of Fluid Science,
Tohoku University2 , JST-PRESTO3 , New Industry Creation Hatchery Center, Tohoku University4 , Development Division, Mochida Pharmaceutical Co., Ltd.5 , Graduate School of Pharmaceutical Sciences, Tohoku University6 )P11

An in Silico discriminant model constructed for HERG potassium channel inhibitors
Motoi Tobita1,2 , Tetsuo Nishikawa1(Reverse proteomics research institute, 2-6-7 Kazusa-kamatari, Kisarazu-si, Chiba 2920818, Japan,1 , Advanced Research Laboratory, Hitachi, Ltd. 1-280 Higashi-Koigakubo,
Kokubunji-shi, Tokyo, 185-8601, Japan2 )P14

Evaluation of flexible molecular docking programs for virtual
screening
Kenji Onodera, Kazuhito Satou, Hiroshi Hirota(Riken Genomic Sciences Center)

P15

Implementation of a Haplotype Reconstruction Algorithm anda tagSNP Selection Algorithm for Large-scale SNP Information
Processing
Sang Jun Kim, Sang-soo Yeo, Sung Kwon Kim(Chung-Ang University)

P16

Computational study of conformational preference in the tetrahedral intermediates of the acylation step of ester hydrolysis
catalyzed by lipase
Yu Takano1,2 , K. N. Houk2(Institute for Protein Research, Osaka University1 , Department of Chemistry and Biochemistry, University of California Los Angeles2 )P17Computational Analysis of CYP3A4 Mediated Metabolism of
KCNQ2 Potassium Channel Opener
Shaikh Abdul Rajjak1 , Mohamed Ismael1 , Hideyuki Tsuboi1 , Michihisa Koyama1 ,Akira Endou2 , Momoji Kubo1,3 , Carlos A. Del Carpio1 , Ewa Broclawik4 , Kazumi
Nishijima4,5 , Tetsuya Terasaki4,6 , Akira Miyamoto1,4(Department of Applied Chemistry, Tohoku University1 , Institute of Fluid Science,
Tohoku University2 , JST-PRESTO3 , NICHe, Tohoku University4 , Mochida Pharma
Co.,Japan5 , Graduate school of Pharmaceutical Sciences, Tohoku University, Japan6 )P18Computational Study on the Enantioselectivity of Lipase Enzyme toward Non-Natural Organic Compounds
Yoshihiro Mori, Yoichiro Yagi, Yoshinobu Naoshima(Faculty of Informatics, Okayama University of Science)

P19

Extraction of Information on Chemicals-CYP3A4 Interactions
from Literature
Chunlai Feng, Fumiyoshi Yamashita, Mitsuru Hashida(Graduate School of Pharmaceutical Sciences Kyoto University)

P20

Spectral Analysis of the Daily Prescription Variations at a Pharmacy for Infectious and Non-Infectious Diseases
Katsunori Segawa1 , Tatsuya Nakano1 , Kotoko Nakata1 , Kazushige Ijuin2 , Noriko
Hatanaka3 , Yuzuru Hayashi1(National Institute of Health Sciences1 , Tanashi Yakuhin Co. Ltd.2 , Kakunoki Pharmacy3 )P21Computational study on ATP hydrolysis by ABC transporter
subunit HisP
Qiang Pei1 , Hideyuki Tsuboi1 , Michihisa Koyama1 , Akira Endou2 , Momoji Kubo1,3 ,Carlos A. Del Carpio1 , Ewa Broclawik4 , Kazumi Nishijima4,5 , Tetsuya Terasaki4,6 ,Akira Miyamoto1,4(Department of Applied Chemistry, Graduate School of Engineering, Tohoku University1 , Institute of Fluid Science, Tohoku University2 , JST-PRESTO3 , NICHe, Tohoku University4 , Developmen Division, Mochida Pharmaceutical Co. Ltd.5 , Graduate
School of Pharmaceutical science, Tohoku University6 )P22

In silico screening for the human P2Y12 receptor
Yosuke Nonaka, Takeshi Hiramoto, Kiyoshi Baba, Norihisa Fujita(Laboratory of Pharmcoinformatics, Department of Bioinformatics, Ritsumeikan University, Kusatsu, Shiga 525-8577)

P23

Systematic comparisons of consensus scores for computational
ligand-docking
Akifumi Oda1 , Keiichi Tsuchida1 , Tadakazu Takakura1 , Noriyuki Yamaotsu2 , Shuichi
Hirono2(Toyama Chemical Co. Ltd.1 , Kitasato University2 )P24

A Multilocus Simulation Study to Multiple Testing for Association Test
Woosung Yang, Jun Nakaya, Norihiro Sakamoto(Kobe University of Clinical Genome Informatics Center)

P25

Kinetic assessment of lenampicillin absorption using Caco-2 cells:in vitro study aiming for rational design of prodrug
Takashi Mizuma, Sayaka Sakaguchi, Sachie Tanaami, Masahiro Hayashi(Department of Drug Absorption and Pharmacokinetics, School of Pharmacy, Tokyo
University of Pharmacy and Life Science (TUPLS))

P26

Database and analysis of binding site residues and ligands inmembrane protein-ligand complexes
M. Xavier Suresh, M. Michael Gromiha, Makiko Suwa
(Computational Biology Research Center (CBRC), AIST, 2-42, Aomi, Koto-ku, Tokyo
135-0064, Japan)

P27

GeneT2D: A database for the genetic variation in type II diabetes
Yu Lin, Jun Nakaya, Norihiro Sakamoto(Kobe University Graduate School of Medicine, 7-5-2 Kusunokicho, Chuo-ku, Kobe,
Hyogo 650-0017, JAPAN1)

P28

Amino acid sequence analysis of Saccharomyces cerevisiae proteome in terms of electric charge distribution
Runcong Ke, Shigeki Mitaku(Department of Applied Physics, Graduate School of Engineering, Nagoya University)

P29

Folding dynamics of 10 residues protein, chignolin
Atsushi Suenaga, Tetsu Narumi, Noriyuki Futatsugi, Ryoko Yanai, Yousuke Ohno,
Noriaki Okimoto, Makoto Taiji(Computational and Experimental System Biology Group, RIKEN Genomic Sciences
Center)P30

Massively parallel calculation of binding free energy forbiomolecules
Masakatsu Ito1 , Yoshiaki Tanida1 , Hideaki Fujitani1 , Michael R. Shirts2 , Guha
Jayachandran2 , Christopher D. Snow2 , Vijay S. Pande2(Fujitsu Laboratories Ltd.1 , Chemistry Department, Stanford University2 )P31Construction of the Web-Accessible comprehensive transporter
database for drug discovery and development, ”TP-Search”(Part II)
Takafumi Ochiai1 , Naoki Ozawa1,2 , Yoshimasa Hama1 , Katsuhiko Taki3 , Toshiyuki
Ueda4 , Noriko Sugawara4 , Yukiko Abe4 , Hitoshi Matsui4 , Kiyomi Ito5 , Kazuya
Maeda6 , Hiroyuki Kusuhara6 , Yuichi Sugiyama6(Advanced Research Institute for Science and Engineering, Waseda University1 , Wyeth
K.K.2 , Nihon Visual Science.Inc3 , CAC Corporation4 , Hoshi University5 , Graduate
School of Pharmaceutical Sciences, the University of Tokyo6 )P32Particle simulation approach for subcellular spatio-temporal
eventsRyuzo Azuma1 , Tetsuji Kitagawa2 , Tomoyuki Yamamoto3 , Takeshi Arikuma2 , Hiroshi
Kobayashi4 , Akihiko Konagaya1,2(Genomic Sciences Center, RIKEN1 , Mathematics and Computing Sciences, Tokyo
Institute of Technology2 , School of knowledge Sci. JAIST3 , Graduate Sch. of pharmaceutical Sciences, Chiba Univ.4 )P33

Spontaneous Docking MD Simulations of Aspartate Racemase Roles of Amino Acid Residues in Docking Process Okimasa Okada1 , Tomohiro Seko1 , Masafumi Yohda2(Fuji Xerox Co., Ltd.1 , Tokyo University of Agriculture and Technology2 )P34

Transplantation of the PEACH5.8 from UNIX to Windows System, and Practice in the Simulation of TNA
Yoshiro Nakata, Wataru Ootani, Noriaki Saitoh(Department of Biophysics, Faculty of Engineering, Gunma University)

P35

Semi-automatic interpretation of experimental results based on
conceptual network
Asako Koike1,2 , Toshihisa Takagi1(Dept. of Computational Biology, Graduate School of Frontier Science, The Universityof Tokyo, Kiban-3A1(CB01) 5-1-5, Kashiwanoha Kashiwa, Chiba, 277-8561, Japan1 ,Central Research Laboratory, Hitachi Ltd. 1-280 Higashi-Koigakubo Kokubunji,
Tokyo, 185-8601, Japan2 )P36

Development of Effective Computer-Aided Drug Design Strategy
Noriaki Okimoto, Atsushi Suenaga, Noriyuki Futatsugi, Ryoko Yanai, Tetsu Narumi,
Yosuke Ohno, Makoto Taiji(Computational and Experimental Systems Biology Group, RIKEN Genomic Sciences
Center)P37

Novel Type of Secondary Structure Breaker in Soluble Proteins
Kenichiro Imai, Shigeki Mitaku(Nagoya University, Graduate School of Engineering, Department of Applied Physics)

P38

A solvent site-dipole field mediating DNA-protein binding
Nobuyuki Hamasaki1 , Daisuke Mitomo1 , Junichi Higo1 , Akihiko Yamagishi1 , Hiroh
Miyagawa2(School of Life Science, Tokyo University of Pharmacy and Life Science1 , Taisyo Pharmaceutical co., Ltd2 )P39

Development of a Three-Dimensional-Structure Database of
Natural Metabolites (3DMET) and Problems about Conversion
from 2D to 3DStructures.
Miki Maeda, Hisataka Numa
(Genome Research Department, National Institute of Agrobiological Sciences)

P40

How effective for fold recognition is a potential of mean force
that includes relative orientations between contacting residues
in proteins?
Sanzo Miyazawa1 , Robert L. Jernigan2(Faculty of Technology, Gunma University, Japan1 , L. H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, USA2 )P41

MOLWORKS+G: Integrated Workbench for the Chimo-Bio
Molecular Design by Grid Computing
Fumikazu Konishi1 , Toru Yagi2 , Akihiko Konagaya1(RIKEN GSC, Advanced Genome Information Technology Research Group1 , BestSystems Inc.2 )P42A mutation in a cuticle collagen causes hypersensitivity tobisphenol A in Caenorhabditis elegans
Masahito Watanabe1 , Nanako Mitani1 , Naoaki Ishii2 , Keizaburo Miki3(Japanese Institute of Pearl Science.1 , Department of Molecular Life Science, Tokai
University School of Medicine2 , BIOS Inc.3 )P43

Ki Bank, major new developments and status in 2005
Junwei Zhang1 , Masahiro Aizawa1 , Shinji Amari1 , Yoshio Iwasawa2 , Tatsuya Nakano3 ,Kotoko Nakata3(Collaborative Research Center of Frontier Simulation Software for Industrial Science,
Institute of Industrial Science, University of Tokyo1 , AdvanceSoft Corporation2 , Division of Safety Information on Drug, Food and Chemicals, National Institute of Health
Sciences3 )P45

Software system for the prediction of mitochondria localizationon the basis of physicochemical profiles in amino terminal segments
Toshiyuki Tsuji1,2 , Shigeki Mitaku1(Department of Applied Physics Graduate School of Engineering Nagoya University1 ,21st Century COE program ”Frontiers of Computational Science” Nagoya University2 )P46Comparative Analyses for Selecting Effective siRNA Target Sequences
Shigeru Takasaki, Akihiko Konagaya(Advanced Genome Information Technology Group, RIKEN Genomic Sciences Center)

P47

Development of a Workbench for Selective Nuclear Receptor
Modulator
Naomi Komiyama1 , Tatsuya Nakano2 , Kaori Fukuzawa3 , Junpei Komiyama4 , Masumi
Yukawa5 , Kotoko Nakata6 , Tsuguchika Kaminuma7(Chem-Bio Informatics Society1 , The National Institute of Health Sciences2 , Mizuho
Research Institute3 , The University of Tokyo4 , Tokyo Medical and Dental University5 ,IIS, University of Tokyo / Advance Soft Co6 , Hiroshima University7 )P48

In silico analysis of biosynthesis rate limiting factor in cell-freesystemNaoya Fujita1 , Motoaki Seki2 , Kazuki Shinozaki2 , Kengo Kinoshita1 , Tatsuya
Sawasaki3 , Yaeta Endo3 , Kenta Nakai1(Inst. of Med. Sci., Univ. of Tokyo1 , GSC, RIKEN2 , CSTRC, Ehime Univ.3 )P49

Cis-Regulatory Machinery Unit (CiRMU) Model: A portablesystem for studying gene transcriptional processes
Masumi Yukawa1 , Yoshitomo Tanaka1 , Hiroshi Tanaka1 , Kotoko Nakata2 , Naomi
Komiyama3 , Tsuguchika Kaminuma4(Tokyo Medical and Dental University1 , IIS, University of Tokyo / Advance Soft Co2 ,Chem-Bioinformatics Society3 , Hiroshima University4 )P50

A Knowledge Environment for Adipocyte and Adipokine Research
Tsuguchika Kaminuma1 , Masumi Yukawa3 , Naomi Komiyama2 , Yoshitomo Tanaka3 ,Hiroshi Tanaka3(Hiroshima University1 , Chem-Bio Informatics Society2 , Tokyo Medical and Dental
University3 )P51

Protein-protein interaction sites prediction and protein-protein
docking by the methods of MD, grid scoring and the pair-wiseinteraction potential in CAPRI Rounds 3-5
Genki Terashi, Mayuko Takeda-shitaka, Daisuke Takaya, Katsuichiro Komatsu,
Kazuhiko Kanou, Mitsuo Iwadate, Hideaki Umeyama(School of Pharmaceutical Sciences, Kitasato University)

P52

Protein Structure Prediction in the 6th round of Critical Assessment of Techniques for Protein Structure Prediction (CASP6)
using CHIMERA and FAMS
Mayuko Takeda-shitaka, Genki Terashi, Daisuke Takaya, Kazuhiko Kanou, Mitsuo Iwadate, Hideaki Umeyama(School of Pharmaceutical Sciences, Kitasato University)

P53

Performance of multi-stage docking simulation with a fast
Poisson-Boltzmann solver and re-scoring methods
Hiroaki Fukunishi1 , Jiro Shimada1 , Daisuke Tokushima1 , Takumi Washio1 , Hiroshi
Kuramochi2(NEC Corporation1 , NIPPON KAYAKU CO., LTD.2 )P54

FAMS Complex: A fully automated homology modeling systemfor protein complex structures
Mayuko Takeda-shitaka1 , Genki Terashi1 , Daisuke Takaya1 , Kazuhiko Kanou1 , Mitsuo
Iwadate1 , Katsuichiro Komatsu2 , Kinji Fuchikami2 , Hideaki Umeyama1(School of Pharmaceutical Sciences, Kitasato University1 , In-Silico Sciences, Inc.2 )P55

Fully automated protein structure predictions at CASP6 using
homology-modeling server SKE-FAMSD
Kazuhiko Kanou, Mitsuo Iwadate, Genki Terashi, Daisuke Takaya, Mayuko Takedashitaka, Hideaki Umeyama(Department of Biomolecular Desig School of Pharmaceutical Sciences, Kitasato University)

P56

Development and application of a molecular dynamics simulation
system : prestoX
Satoru Kubota1 , Yoshifumi Fukunishi2 , Ikuo Fukuda1 , Masaru Horie1 , Katumi
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 )P57

Transcriptome according to a thermodynamic view
Tomokazu Konishi(Bioresource Sciences, Akita Prefectural University)

P58

Automated genotyping of human CYP2C19 SNPs by a novel
SNP typing system and development of a database of it
Yukari Kurebayashi1,2 , Naoko Matsumoto1,2 , Fumiko Kakihara1,2 , Mitsuharu Hirai3 ,Setsuo Hasegawa1 , Masafumi Yohda2(Sekino Clinical Pharmacology Clinic1 , Tokyo University of Agriculture and Technology2 , ARKRAY, Inc.3 )P59Gene expression profiles of the effect of cryoprotectants on thefreeze-thaw damage in yeast
Yuko Momose, Masakazu Yamaoka(The National Institute of Advanced Industrial Science and Technology)

P60

Analysis of drug-resistant mutation patterns of HIV-1 protease
under highly active antiretroviral therapy using Bayesian network modeling
Kaoru Mogushi, Fengrong Ren, Naoki Hasegawa, Hiroshi Tanaka(Department of Bioinformatics, Tokyo Medical and Dental University)

P61

Comparison of the validation methods with linear and non-linear
classifier
Chihoko Yoshimura1 , Taizo Hanai2 , Masahiro Okamoto2(Drug Discovery Lab., Hanno Research Center, Taiho Pharmaceutical Co., LTD1 , Laboratory for Bioinformatics, Graduate School of Systems Life Sciences, Kyushu University2 )P62Computational Proteomic Differential Analysis: i-OPAL
Mitsuhiro Kanazawa1,3 , Hisae Anyoji1 , Hsiao-kun Tu1 , Takao Kawakami1,2 , Toshihide
Nishimura1,2 , Atsushi Ogiwara1,2(Medical ProteoScope Co., Ltd.1 , Clinical Proteome Center, Tokyo Medical University2 , Graduate School of Pure and Applied Sciences, University of Tsukuba3 )P64Structural analysis by molecular dynamics simulations: Proteinprotein interactions of Ras-Raf and Ras-RalGDS complexes
Noriyuki Futatsugi1 , Mikako Shirouzu2 , Atsushi Suenaga1 , Noriaki Okimoto1 , Tetsu
Narumi1 , Toshikazu Ebisuzaki3 , Shigeyuki Yokoyama2 , Akihiko Konagaya1 , Makoto
Taiji1(Computational and Experimental Systems Biology Group, RIKEN Genomic Sciences
Center1 , Protein Research Group, RIKEN Genomic Sciences Center2 , Ebisuzaki Computational Astrophysics Laboratory, RIKEN3 )P66

NMR-based Metabolomics.1/3 1) Newly developed software;”Alice2 for Metabolome”; Integrated NMR spectroscopic andchemomerirc analysis for mixture
Kazunori Arifuku1 , Itiro Ando2 , Masako Fujiwara1(JEOL DATUM LTD1 , Environmental Research Center LTD2 )P63

NMR-based Metabolomics.2/3 2) How to analyze NMR-spectraof mixture samples by chemometric method; in examples of vegetable metabolome
Masako Fujiwara1 , Itiro Ando2 , Kazunori Arifuku1(JEOL DATUM LTD1 , Envioronmental Research Center LTD2 )P65

NMR-based Metabolomics.3/3 3) High-field NMR-spectroscopy
for metabolomic analysis; An application to urine samples frommodel rats
Tadashi Nemoto1 , Taeko Kataoka1 , Itiro Ando2 , Katsuo Asakura3 , Kazunori Arifuku4 ,Kenji Kanazawa1 , Masako Fujiwara4(National Institute of Advanced Industrial Science and Technology (AIST)1 , Environmental Research Center LTD2 , JEOL LTD3 , JEOL DATUM LTD4 )P67

Controlling dissociation and association of amyloid protofibrils
using high pressure
Yuji O. Kamatari1,2,3 ,Akasaka2,6

Shigeyuki Yokoyama2,3,4 ,Hideki Tachibana5 ,Kazuyuki(Gifu University1 , RIKEN Harima Institute2 , Riken Genome Science Center3 , The
University of Tokyo4 , Kobe University5 , Kinki University6 )P68

The evolutionary mechanism of protein-protein interaction network
Takeshi Hase, Soichi Ogishima, So Nakagawa, Hiroshi Tanaka(Department of Bioinformatics, Tokyo Medical and Dental University)

P69

Is there a code for protein-DNA interaction? Yes, there exists
the analog code for protein-DNA interaction.
Tomoki Yoshida1 , Misako Aida1 , Akinori Sarai2(Center for Quantum Life Sciences (QuLiS), and Department of Chemistry, Graduate
School of Science, Hiroshima University1 , Department of Bioscience and Bioinformatics,
Kyushu Institute of Technology (KIT)2 )P70

Investigation of Structural and Dynamic Properties of Importinβ Based on a Molecular Dynamics Study
Yoshinori Hirano1 , Noriaki Okimoto1,2 , Atsushi Suenaga2 , Naoko Imamoto3 , Toshikazu
Ebisuzaki1(Computational Astrophysics Laboratory, RIKEN1 , Bioinformatics Group, Genomic
Sciences Center, RIKEN2 , Cellular Dynamics Laboratory, RIKEN3 )P71

Matlab/GNU Octave as a Platform for Analyses of Metabolic
NetworksJun Ohta(Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences)

P72

A Grid-based Information Integration System for Drug Discovery by using Freely Accessible Compound Databases
Gen Kawamura1,2 , Masato Kitajima1,3 , Takahiro Kosaka1 , Susumu Date1 , Shinji
Shimojo4 , Hideo Matsuda1(Graduate School of Information Science and Technology, Osaka University.1 , AztecSystem Co., Ltd.2 , Fujitsu Kyushu System Engineering Limited.3 , Cybermedia Center,
Osaka University.4 )P73

Conformational analysis of an antimicrobial peptide, Pediocin
PA-1
Hitoshi Goto1 , Tatsunori Matsui1 , Toshiyuki Kamakura1 , Naofumi Nakayama1 , Tatsuya Tominaga2 , Hideo Nakajima2(Toyohashi University of Technology1 , Saitama Industrial Technology Center North
Institute2 )P74

Normal mode analysis of the complex of HIV-1 protease with
inhibitor vibrational behavior
Toshiyuki Kamakura, Hitoshi Goto
(Toyohashi University of Technology)

P75

Genome Functional Analysis based on Systematic Map of Protein Interactions
Carlos A. Del Carpio1 , Pei Qiang1 , Eichiro Ichiishi2 , Michihisa Koyama1 , Momoji
Kubo1,3 , Akira Miyamoto1,2(Department of Applied Chemistry, Tohoku University1 , New Industry Creation Hatchery Center, Tohoku University2 , JST-PRESTO3 )P76

GenoSearch: High Performance Inter-Genomic Sequence Retrieval System using Suffix Array Technique
Aki Hasegawa, Akihiko Konagaya(Informatics Infrastructure Team, Genome Core Technology Facilities RIKEN Genomic
Sciences Center)

P77

Evaluation of polymorphism for an organic molecular crystal
Shigeaki Obata, Hitoshi Goto, Naofumi Nakayama(Toyohashi University of Technology)

P78

Workflow for Open Genome Grid Service
Ryo Umetsu, Shingo Ohki, Akihiko Konagaya(RIKEN GSC, Advanced Genome Information Technology Research Group)

P79

Drug Interaction Ontology (DIO) - OWL formalizations of interaction causal logic Sumi Yoshikawa1,2 , Kentaro Watanabe2 , Kazumi Matsumura1,2 , Akihiko Konagaya1,2(Genomics Science Center (GSC), RIKEN (The Institute of Physical and Chemical
Research)1 , Graduate School of Information Science and Technology, Tokyo Instituteof Technology2 )P80

Transition state of a SH3 domain detected with principle component analysis and a charge-neutralized all-atom protein modelDaisuke Mitomo, Junichi Higo
(School of Life Science, Tokyo University of Pharmacy and Life Science)

P81

CBDB: Cell Behavior Database - Toward understanding highorder level biological phenomena Michiaki Iwazume1 , Hiroyuki Kaneko2 , Marina Dan3
(Brain Science Institute, RIKEN1 , Keio University2 , Hierarchical Biology Lab.3 )P82

Drug discovery based on the structural dynamics of prion
Kazuo Kuwata(Division of Prion Research, Center for Emerging Infectious Diseases, Gifu University,
Gifu 501-1194)

P83

Parametric Normalization for GeneChip and Tiling Array Data
Tomokazu Konishi1 , Midori Katakami2 , Kenya Shibahara2 , Masahiro Hayashi3 ,Takushi Nakajima3(Bioresource Sciences, Akita Prefectural University1 , INTENSITY Co. Ltd.2 , Skylight
Biotech Inc.3 )P84Consensus scores for efficient selection of computational proteinligand docking models
Tadakazu Takakura1 , Akifumi Oda1 , Keiichi Tsuchida1 , Noriyuki Yamaotsu2 , Shuichi
Hirono2(Toyama Chemical Co. Ltd.1 , Kitasato University2 )P85

Molecular dynamics simulation of prion using a coarse-grained
model
Hironori K. Nakamura1 , Mitsunori Takano2 , Kazuo Kuwata1(Division of Prion Research, Center for Emerging Infectious Diseases, Gifu University1 ,Department of Physics, School of Science and Engineering, Waseda University2 )P86

Molecular Interaction Between Estrogen Receptor and Their
Ligands
Kaori Fukuzawa1,2 , Yuji Mochizuki2,3 , Shigenori Tanaka2,4 , Kazuo Kitaura2,5 , Tatsuya
Nakano2,6(Mizuho Information & Research Institute, Inc.1 , JST-CREST2 , Advancesoft3 , Kobe
University4 , National Institute for Advanced Industrial Science and Technology5 , National Institute of Health Sciences6 )S1
New Opportunities and Challenging Topics in Informatics andComputing in Life Science
Tsuguchika Kaminuma
Center for Quantum Life Science, Hiroshima University

Biology and related applied sciences such as medicine is getting into the so called post
genomic era. The role of informatics and computing in these fields is dramaticallyincreasing. This new trend is offering new challenging opportunities for researchers
specialized in theory and computation, including mathematics, theoretical physics,
computational chemistry, information science, and computer science. In this seminar
what are the new opportunities and how to cope with the new challenges are discussed.
Examples of new research topics are introduced, and necessity of educational
reformation is touched. Questions and comments from the participants are mostwelcomed.

S2
Bioinformatics for drug discovery in pharmaceutical company:
current status and issues
Hiroki Shirai
Astellas Pharma Inc.

Bioinformatics is being effectively used for “genomics / proteomics-based” drug
discovery. There are four different roles; I) infrastructure construction <introduction andmaintenance of hardware and software and construction of bioinformatics system anddownload of biological information> and management of routine operation, II) support
for biological experiment <construction of database to be utilized by experimental
researcher> III) analysis <analysis of sequence including genetic statistics / medical
statistics method and a large amount of biological experimental data such as microarraydata> IV) “in silico-driven” research <prediction of gene function and computational
physics>. Current status and issues of each role is discussed.

S3
Novel Methods for the Prediction of Structure and Binding Affinities
of Protein-Ligand Complexes; Towards a New Theory and Practice ofMolecular Recognition
Richard FriesnerColumbia University, USA
The problem of predicting the structure and binding affinities of protein-ligand
complexes is central to the effective application of computational methods in structurebased drug design. While significant progress has been made in both areas over the past
decade, current approaches are lacking in the accuracy and robustness required to create acompelling technology that will qualitatively impact the drug discovery process. At thesame time, the advent of inexpensive Linux clusters, and practical grid computing
platforms, have offered the possibility of dramatically increasing the computing powerthat is available for these tasks. Thus, the crucial goal is to create new computational
methods and models which exploit the available hardware platforms to deliver reliableanswers that can drive both lead discovery and lead optimization.
Over the past several years, via collaborations between Schrodinger, Inc. andseveral academic research laboratories, we have developed new methods which represent
a significant advance in docking and scoring technology. The major components of theseadvances can be enumerated as follows: (1) a scoring function for rigid receptor docking,based on a new theory of molecular recognition that identifies the key motifs capable ofcreating highly potent binding affinity with a relatively small number of ligand atoms; (2)sampling methods capable of accurately exploring the potential energy surface defined bythis scoring function, which requires substantially higher resolution conformational searchmethods than standard empirical models for binding affinity; (3) coupling of docking andprotein structure prediction algorithms to construct an efficient and robust methodology
for binding mode prediction in cases where protein flexibility must be incorporated into
the docking procedure, i.e. when induced fit effects are important.
Our new theory of molecular recognition is based on a concept which we termhydrophobic enclosure. The central idea is that a group of lipophilic ligand atoms which issurrounded on two sides by lipophilic protein atoms will yield a significantly larger
contribution to binding affinity than is predicted by simply atom-atom pairhydrophobicity models. Examples from the medicinal chemistry literature will be used tographically illustrate this idea. Furthermore, validation of the model has been carried out
by calculating the binding affinity of a large number of protein-ligand complexes in the
PDB, as well as the generation of enrichment curves for a substantial number of diverse,
pharmaceutically interesting receptors. A similar analysis can be applied to hydrogenbonding, and “special” hydrogen bonds, which make exceptionally large contributions tobinding affinity, can be identified. The hydrogen bonding terms require hydrophobic
enclosure of ligand atoms surrounding the hydrogen bonding group. Finally, a set of
empirical rules have been developed which assess when electrostatic interactions favorbinding, based on multiple examples of ligands binding to pharmaceutically relevanttargets with important charge-charge interactions with ligands in the active site.
Finally, our new induced fit methodology will be described and illustrated with acritical mass of examples, and its deployment in the workflow for computationally driven
structure based drug design will be discussed. A number of advanced examples ofapplication, e.g. to modeling of the HERG ion channel and to cytochrome P450, will bepresented.

S4
Computer-Assisted Drug Design (CADD) at Bristol-Myers Squibb
S. Roy Kimura
Bristol-Myers Squibb Company
An overview of CADD methodologies applied to active drug development programs at
BMS will be presented. High-throughput methods routinely used include ligand- andstructure-based virtual screens and QSAR modeling. Lower throughput, higheraccuracy techniques are also often used: these include rational drug design or lead
optimization via construction of detailed binding models. Additional low throughput
methods include quantum chemical analysis of ligands, and relative binding free-energy
calculations (via Linear Response or MMPB/SA). CADD is also involved in more
exploratory applied research; these include, for example, improving the quality andutility of homology models through molecular dynamics simulation and developing
better docking protocols. A brief critique of current methodologies and possible future
directions (e.g., QM/MM methods) is presented in the context of the trade-off between
cost and accuracy.

S5
From Genome Sequence to Protein Structure and Drug Discovery:
computational pipeline with bioinformatics and supercomputing
Yutaka Akiyama
Director, Computational Biology Research Center (CBRC)
National Institute of Advanced Industrial Science and Technology (AIST), JapanThe Computational Biology Research Center (CBRC) is engaged in a variety ofbioinformatics research activities covering such themes as automatic gene finding,expression analysis, gene regulatory network estimation, protein structure prediction,
protein-protein docking, and virtual screening against chemical compound databases.
We are conducting in-depth research using large-scale computing resources such as,
the Magi cluster system (1040 processors, Pentium III 933MHz), the AIST super cluster
system (2048 processors, Opteron 2GHz, plus other 1000 processors), and the Blue
Protein system (8192 processors, Blue Gene p440-based 700MHz, 22TFLOPS peak).
We have applied our large-scale parallel computing techniques in our genome research
including systematic search for human GPCR genes (Suwa et.al) and genome-levelannotation for micro-organism like Aspergillus Oryzae (Asai et.al). Those required
extensive calculation for genome sequence parsing with large hidden Markov models.
One of our major research highlights is the development of FORTE (Tomii et.al), a
system for predicting three dimensional protein structures from amino acid sequencesby applying profile-profile comparisons (Bioinformatics, 20, 4, 2004). This method wasdevised to detect similarities in three-dimensional structures of compared proteins, even
if they have no clear sequence similarities. With this system, CBRC participated in the
Sixth Community Wide Experiment on the Critical Assessment of Techniques forProtein Structure Prediction (CASP6) in 2004. The CBRC-3D team placed the third
position among more than 200 prediction teams in “FR/H (fold recognition)” category.
We have been also participating the CAPRI competition for protein-protein docking
prediction. CBRC obtained top-level results among 37 teams in CAPRI7. A model
selection scheme, similar to the one in CASP, was devised for docking (Hirokawa,et.al).
We have also developed CoLBA (Comparative Ligand Binding Analysis) system for
efficient virtual screening procedure (Hirokawa, et.al). Using this scheme, we canutilize even a theoretical (predicted) protein structure as a docking target, practically.
We believe that bioinformatics techniques are getting matured with the help ofsupercomputing and soon we can build an automatic computational pipeline fromgenome sequence to protein structure and protein-protein or protein-compound docking.

S6
Strategies for Improved Vaccine Design
Nikolai Petrovsky
Australian Centre for Adjuvant Research, Flinders Medical Centre
Many challenges remain in the design of modern day vaccines. These include theidentification of immunogenic and yet safe vaccine antigens, the problem that purerecombinant antigens are generally far less immunogenic than whole killed vaccines
and the time taken for design and validation of new vaccines. This has created a major
need to develop new strategies for vaccine development. Computer-aided design has
potential to transform vaccine research. Similarly, newer more potent adjuvants are
helping increase the immogenicity of subunit vaccines. In light of new vaccine
technologies, adjuvants are needed for use with mucosal vaccines, DNA vaccines,
cancer and autoimmmunity vaccines. Each of these areas are highly specialised with
their own unique needs in respect of suitable adjuvant technology. This paper examines
key areas in particular immunomics and adjuvant design technologies that aretransforming the vaccine development field. Finally it will also address some of theremaining impediments and barriers to development and registration of new human
adjuvants for infectious diseases, allergy, autoimmunity and cancer.

S7
Beyond Epitope Mapping: Making Vaccines and Modifying
Therapeutics
Annie De Groot,
Bill Martin, Luisa Marcon, Paul Knopf, Betty Bishop, Julie McMurry, Dan Rivera,
Michelle Klutzer, Christine Boren, Claire_Rodriguez-Annon and David Weiner
TB/HIV Research Laboratory, University of Pennsylvania, and EpiVax, Inc.
http:://www.Brown.edu/Research/TB-HIV_Lab; http://www.EpiVax.com

We are in the process of making epitope-driven vaccines for TB, HIV, HPV, EBV, Smallpox,
Tularemia, and Helicobacter pylori vaccines using, as a point of departure, ourimmunoinformatics tools EpiMatrix, ClustiMer, Conservatrix, EpiAssembler, and VaccineCAD.
We are also using these tools to alter protein therapeutics such as alpha interferon and beta
interferon, which are used for the treatment of HCV and multiple sclerosis, respectively.
We believe that this approach to developing vaccines and therapeutics is justified because
immune response to a limited set of epitopes derived from proteins, whether “self” or from aninfectious pathogen, may be sufficient for competent protection. Clearly, this is the case forsome vaccines against pathogens such as Hepatitis B. Immune recognition of every potential
epitope derived from the pathogen’s genome does not appear to be required for protection from
infection and disease, since vaccination with the HbSAg of HBV and not the entire virus, isalmost always protective. Similarly, “immunome-derived vaccines” are based on the conceptthat immune response to a subset of epitopes that interface with host immune system (theimmunome), and not the whole organism (represented by the proteome, or genome) can be
sufficient for protection. Competent immune responses to cancer are also probably restricted to
the neoplasm’s “immunome” although the set of antigens that drive successful immune response
to cancer cells has proven more difficult to uncover.
We have been using bioinformatics sequence analysis tools, epitope-mapping tools, microarrays and high-throughput immunology assays to discover the components of the immunomes,
which are then used to compose new vaccines. We have successfully generated de novo
immune responses to several of our new immunome-derived vaccines in HLA transgenic mice,
and we are currently testing them in challenge models. My presentation will review the work of
my laboratory and the work of other researchers who are applying immunoinformatics tools to
making immunome-derived vaccines.

S8
Automatic construction of specialized databases, exemplified by the
VBASE2 database, an integrative database for Immunoglobulin
Variable Genes
Werner Müller
Department of Experimental Immunology, GBF German Research Centre for
Biotechnology, Braunschweig, Germany

VBASE2 is an integrative database of germ-line V genes from the immunoglobulin lociof human and mouse. It presents V gene sequences extracted from the EMBL nucleotide
sequence database and Ensembl together with links to the respective source sequences.
Based on the properties of the source sequences, V genes are classified into 3 different
classes, namely class 1=genomic and rearranged evidence; class 2=genomic evidence
only and class 3=rearranged evidence only.
References to other immunological databases (KABAT, IMGT/LIGM and VBASE) are
given to provide all public annotation data for each V gene. The VBASE2 database canbe accessed either by the Direct Query interface or by the DNAPLOT Query interface.
The Sequences given by the user are aligned with DNAPLOT against the VBASE2
database. Direct Query allows to enter sequence IDs and names (Field 1), choose
species, locus, V gene family and class (Field 2) or search for 100% sequences (Field 3).
At the DNAPLOT Query, a sequence given by the user is aligned with DNAPLOT
against the VBASE2 database. The DNAPLOT program offers V gene nucleotide
sequence alignment referring to the IMGT V gene unique numbering.
The VBASE2 database was used to annotate the proximal part of the immunoglobulin
heavy chain locus (IgH) of the 129/Sv mouse strain. 117 variable genes (V genes) andpseudo genes have been identified, representing 12 of 15 murine IgH-V gene families.
47 V genes are supposed to be functional. Of that, 27 sequences are described in the
germline configuration for the first time. Furthermore, 2 new D segments have been
identified. The order of D segments in the 129/Sv strain is DFl16.3, DST4.2, DFl16.1,
DSP2.9, DSP2.3, DST4.3, DFl16.2, DSP2.5, DSP2.2, DSP2.11, DSP2.2, DSP2.8,
DSP2.7, DST4, DQ52.
The availability of 1.5 Mb genomic sequence of the IgH locus allows the comparison
between different mouse strains on the level of the coding sequence as well as on thelevel of the locus' structure. The 129/Sv mouse has been assigned to the IgHa haplotype.
Unexpectedly, the comparison of the D region, J region and constant region genes
revealed differences to the BALB/c strain which also has the IgHa haplotye. The
comparison of the two IgHa strains to the C57BL/6 strain of the IgHb haplotype will
give insight to the evolution of immunoglobulin variable gene segments. The genomic
sequences are available from the EMBL database by the accession numbers AJ851868AJ851885.

S9Transcriptional and translational variation froman immunomics point of view
Christian Schönbach
Immunoinformatics Team, Advanced Genome Information Technology Group RIKEN
Genomic Sciences Center, Yokohama, Kanagawa 230-0045, Japan

The immune system is much more complex than the sum of its parts. Computationaland functional genomics approaches have revealed some gene-to-phenotype
connections and how certain parts work together on cellular level. However a large
number of protein-coding and non-coding transcripts in mouse and human are of
unknown functions. Apart from filling the knowledge gaps in the parts list we need toelucidate the mechanisms of interaction and regulation. Alternative splicing, translation
initiation and post-translational mechanisms contribute to the complexity of the immune
system and have relevance for non-mendelian etiologies of diseases. This presentation
will highlight some of the results and potential non-canonical mechanisms oftranscriptional and translational variations in context of increased functional complexity.

S10
Pipeline pharmacogenetics: Phase I, Phase II, & Phase III & Surveillance
Allen D. Roses, MD
GlaxoSmithKline
Careful clinical phenotyping is the basis for differential diagnoses as well as clinical responses totreatment. Quite simply, the patient phenotype is made up of the history, physical exam, and laboratory tests.
Disease diagnosis, disease prognosis, or response of disease to treatment [surrogate markers] are all
fundamentally focused on the patient’s clinical status – with effects of treatments set in that framework. The
term “biomarkers” is generally used to describe laboratory-based measurements related to clinicalphenotypes [diagnostic, prognostic or surrogate biomarkers]. Regulatory agencies are accountable for
evaluating the risk/benefit ratio of new medicines, that is, the safety versus the efficacy of a treatment. Thisis an exercise that is based fundamentally on the most discriminating clinical information.
Chronic Obstructive Pulmonary Disease [COPD] will be used as an example to demonstrate theheterogeneity of clinical phenotypes in evaluating efficacy of medicines. COPD is a syndrome of multiple
phenotypes that are defined by a non-sensitive and non-specific “diagnostic” biomarker called FEV1 [forced
expiratory volume in 1 second] as a measurement of EFL [expiratory flow limitation]. The standard forregistration of a therapy is improvement of patients’ EFL impairment as measured by FEV1. However, there
are major clinical variables segment patient populations into different pathologies, with potentially different
anatomical and physiologic capacities to respond. This heterogeneity is a critical issue, as there can be awide variety of pathologic state associate with impaired FEV1. Patients can have different disease processes,
different severity of the same process, or quite distinct physical capabilities when viewed through othermeasures of phenotype. COPD provides an excellent example of defining efficacy across a variable
syndrome using a response to a non-specific biomarker [FEV1]. Examples of variable phenotypes will beillustrated.
Can diagnostic biomarkers segment the clinical population of patients with impaired expiratory
flow into groups that are more likely to respond to a particular intervention? High resolution CT scanning
represents a diagnostic method that allows characterization of anatomical heterogeneity: having the capacity
to segment a population of COPD patients with destructive anatomical features [“bubble lung”] from thosepatients with preserved lung parenchyma – even if patients have the same impaired FEV1 measurement.
“Response to treatment” also provides prognostic information for COPD patients - those who
respond with benefit to treatment have a better prognosis. Yet we measure therapeutic response forregistration purposes solely on the primary measurement of FEV1, despite the existence of other moresensitive and specific measures of diagnostic and physiologic responses. Specific airway conductance [SAC]is another clinical method that can provide additional information regarding quantitative changes in patient’s
clinical phenotype that are more sensitive than FEV1. This is a specialized technique that is currently being
evaluated as a surrogate endpoint for FEV1 in bioequivalence studies in Japan.
Hyperpolarized 3-Helium MRI represents a relatively simple method to measure lung functions
both anatomically and physiologically. Images provided by this technique can be used for quantitative
measures of anatomical status and physiological function over time. 3-He MRI can be used to examine
disease progression as well as response to treatment in real time. Exquisite quantitative measures are
possible to both classify patients disease severity as well as to measure effects of treatments.
Scientists are generally familiar with the use of genetic information for diagnosis of a “disease.”
When the disease is actually a syndrome of multiple phenotypes, it is still possible to apply genetic methods– as long as the different sub-types are clearly defined. When patient groups are carefully segmented it ispossible to examine predictors of diagnosis, prognosis, and drug response using genome-based profiles.
These profiles might include polymorphisms of complex susceptibility genes or empirical profiles of DNA
markers that have not previously been known to be associated with differentiated phenotypes. In the context
of efficacy pharmacogenetics the most critical element is still the definition of the phenotype to describe theeffect of the drug before and after treatment. Therefore, for maximum specificity it is useful to provide themost complete phenotypic measurements that are possible to document. The added advantage of genetic
profiling is that a standardized set of population variants can be assessed in each patient rapidly, and theassociation of specific markers with phenotypes, or changes in phenotypes, be measured quantitatively. It
therefore becomes possible to define the segment of the COPD population most likely to respond to atreatment modality. In addition, of course, there is the added advantage of being able to characterize patients
who experience adverse events using the same genome-wide representative genetic panels.
Recent examples of pipeline pharmacogenetic analyses in Phase 1, IIA, IIB and III will beillustrated. In these cases, the phenotype being investigated is either efficacy or adverse events. The use of
genome-wide SNP screening panels and its applicability to multiple ethnic populations will also bedemonstrated and discussed.

S11High throughput biology:
from genome diversity to transcriptional regulation
Hiroyuki Aburatani
Genome Science Division, Laboratory for Systems Biology and Medicine,Research Center for Advanced Science and Technology, University of Tokyo

As genome sequences of various organisms have become available one after another,
the high-density oligonucleotide array has become a very powerful analytical platform
for functional genomics, besides the regular gene expression and genotyping analysis. A
single array with 5 µm feature size, which generates signal intensity data from morethan 6 million probes, would provide us with various novel research applications. In thisseminar I present several examples for array-based high throughput analyses of humangenome functions.
The genotyping array has been already utilized to analyze allelic gene dosage andcopy number polymorphisms (CNPs). We have recently developed a novel algorithm
called Genome Imbalance map (GIM) to determine the allelic gene dosage using
genotyping array, and applied it for molecular karyotyping of cancer cells. Even a tiny
homozygous chromosomal deletion has been detected with high accuracy, as well as
genomic amplification. Integrated with genotyping information, which are obtained
simultaneously in a single assay, a status of uniparental disomy is precisely detected,
which has not been accomplished by the conventional BAC array analysis. Moreover,frequency and variation of CNPs will be elucidated using copy number analysis bygenotyping array. The genomic tiling array has been applied not only for noveltranscript discovery, but also for identification of transcriptional factor binding sites andepigenomic analysis, like DNA methylation detection and characterization of chromatin
modification, through ChIP (chromatin immunoprecipitaion) on Chip analysis. The data
analysis and integration will be a big challenge for computational biology.
References
Ishikawa S, Komura D, Tsuji S, et al. Allelic dosage analysis with genotyping
microarrays. Biochem Biophys Res Commun. 333(4):1309-1314, 2005
Ge X, Yamamoto S, Tsutsumi S, et al. Interpreting expression profiles of cancers bygenome-wide survey of breadth-of-expression in normal tissues. Genomics in press

S12
System-level identification of Mammalian Circadian Clocks.
Hiroki R. Ueda
Team Leader, Laboratory for Systems Biology and Subunit Leader, Functional
Genomics Subunit, Center for Developmental Biology, RIKEN, Kobe Japan.
Visiting Professor, Tohoku University.
Visiting Professor, Tokushima University.
Increasing number of genome sequences have been determined in various modelorganisms from E. coli to H. sapiens. These genome projects are driving a paradigm
shift in life science, from the molecular level to the system level, and accelerating
innovations based on genome-scale resources and information. Compared to theincreasing needs for system-level understanding of biological phenomena using
genome-scale research infrastructure, there are few studies fully utilizing genome-scale
research infrastructure toward systematic identification of genetic networks underlying
complex and dynamic biological phenomena, especially in mammals. Main difficulty infull exploitation of genome-scale research infrastructure is seamless integration of bothcomputational and experimental technologies.
To address these issues, we attempt to combine both computational andexperimental technologies to develop system identification strategy toward thesystem-level understanding of biological phenomena and apply them for identificationof the genetic network underlying complex and dynamic biological phenomena. In this
symposium, we will introduce the outline of system identification strategy including (i)genome-wide gene expression analysis, (ii) genome-wide promoter analysis using
mammalian promoter database, (iii) in vitro real-time monitoring of gene expression(iii) high-throughput screening of regulators, (iv) in vitro phenotype analysis.
The power of this strategy can be demonstrated by its concrete applications forthe specific biological systems. We applied this strategy to the system identification oftranscriptional networks composed of 16 transcription factors underlying mammalian
circadian rhythms, and revealed the complex transcriptional circuits governed by simple
design principles, and topologically identified Achilles’ heel of mammalian circadianclocks, which was functionally verified using a newly developed in vitro system. In this
symposium, we will also report current progresses on system-level identification of
mammalian circadian clocks.

References:
Hiroki R. Ueda et al. "A transcription factor response element for gene expression
during circadian night" Nature, 418, 534-539, 2002.
Hiroki R. Ueda et al. "Molecular-Timetable Methods for Detection of Body Time andRhythm Disorders from Single-time-point Genome-wide Expression Profiles" PNAS,
101, 11227-11232, 2004.
Hiroki R. Ueda et al. "System-level Identification of Transcriptional Circuits
Underlying Mammalian Circadian Clocks" Nature Genetics, 37, 187-192, 2005.

S13
MC's Comment: The Translational Research Informatics:
A bridge toward the Genome Medicine
Jun Nakaya
Associate Professor, Clinical Genome Informatics Center, School of Medicine,
Kobe University
In the post genomic era, the bridging between the genome science and the clinical medicine is
the fundamental demand. The Translational Research Informatics (TRI) is the informatics thattries to support the translational research, to establish bridging through information, and to
facilitate the translational research. The translational research is the transitional bridging
research of the basic research and the clinical research. It covers not only the drug development
but also the medical device development and the novel medical methodology. From theinformatics side of view, the TRI can be located as the interdisciplinary informatics of thebioinformatics and the medical informatics. Essentially it encompasses bridging these two
informatics and their integration. Supporting the translational research from theinformatics-side needs the following informational terms according to the results compiled
from NIH and our analysis:
1. The prediction tools for the patient's future clinical situation
2. Accumulation of the cause-and-effect cases of the translational research
3. Bioinformatics Tools for the discovery, the simulation, and the prediction
4. The clinical informatics tools for the data transition
5. The clinical informatics tools to defend the patient's security without losing informational
accuracy
6. Bilateral knowledge translation methodology between the basic researchers and theclinicians
7. Integrated database of the clinical data and the omics data
8. Integrated and standardized data exchanging format
9. The Standardized data sharing technology with keeping privacy
In the translational research field, the technologies derived from these terms must be evaluatedby the three criteria. The most important criterion is the safety of the patient, the secondary
important criterion is the clinical efficiency, and the thirdly important criterion is theindustrialization. From a view of these three criteria, the translational research path is the mostcritical path in the entire developmental path between the basic research and the clinicalpractice. This means that the translational research path is the key pathway to optimize the drug
development and the other medical developmental processes. According to the results of the
NIH and our analysis, the most effective issue to achieve this optimization is to establish theaccurate prediction of the future clinical state of the patients during the translational researchphase as indicated in term 1. Here the term 2 is fundamental and essential issue to establish theterm 1. The role of the translational research informatics is to make the medical research more
safe, comfortable, understandable, efficient, and cost-effective through supporting thetranslational research with the informational technology. The TRI can be a bridge to establish
the Genome Medicine and the other future Medicines.

S14
Omics-based systems approach to diseases-A new paradigm for clinical medicine in the post-genomic eraHiroshi Tanaka
Department of Bioinformatics, Tokyo Medical and Dental University
In spite of the rapid increase of the bio-molecular (“omics”) data such as genome,transcriptome, proteome and so forth, the substantial application of omics data toclinical medicine has not been realized yet. This might be ascribed to the fact that there
has neither been explored nor established the paradigm or systemic way where theomics data are incorporated into and efficiently utilized in clinical medicine.
We have just started the national projects for comprehensive omics-based disease
case database. In constructing the database, we adopted a new paradigm for viewing thedisease as a unified system, in which the clinical-pathophysiological-omic hierarchy is
involved. The concrete realization of this paradigm is explored to be established as acomprehensive data scheme of clinico-omic database. Our leading concept fordeveloping database is that, “diseases self-organize into a unified system”, we call thisparadigm “omics-based systems pathology”. In our approach, first, we collect themolecular, pathological and clinical data to store into two hierarchical data models,
which we call the preliminary disease database; one of these two data hierarchies is
traditional one which is comprised of clinical symptomatology, pathophysiology,
etiology and the other is omics data hierarchy composed of genome, transcriptome,
proteome and their related pathway (signaling, gene-regulatory, and metabolic ones).
The relations between these two categories of data are explored by applying thedata-mining method and correlation analysis. After establishing the inter-hierarchicalrelation between the clinical-pathophysiological-omic data, we integrate these data into
the unified hierarchical disease model, which we developed for each of generic class of
diseases, such as infectious disease, cancer, neurological disease, common metabolic
disease, autoimmune disease. With this structured disease case database as a central part,
we are developing the user-directed functions such as deductive retrieval which replies
various inquiries based on the structured clinico-omic relation, for example, given theclinical syndrome, infer the possible molecular process of disease, or given the omics
data, predict the prognosis of disease, etc. In the first term of this project the disease isconfined to cancers (hepatic, colon and oral). In constructing this omics-based system
pathology database, we aim to establish new clinical medicine integrating clinical andomics data and specialized for individual patient.

S15
Future Vision: A possibility of Genome Information toward
the Personalized Medicine and Personalized Healthcare.
Hiroshi Mizushima
Head of Bioinformatics Section. Center for Medical Genomics. National Cancer
Center Research Institute.
Human Genome Sequencing has been completed and personalized medicine andmedical-care is the next scope of research and development. Many researches have been
done collecting medical information and biological or genomic information, andanalyzing for association study finding Genome/Proteome - Phenome relationships.
Recently, Nutritigenomics is another field, to find out the relations between nutrition
and its effect over genomic background. State of the art research is now active on
discovering relationship between these phenotypic information and genomic or
biological information.
In this talk, I want to list up some of the examples of these relationships, and possible
future prospects using genomic information not only for medical care but also for healthcare to keep healthy life.
I will also discuss about the security issues and ethical guidelines for handling thepersonal privacy information such as medical record and genome information, alongwith necessity of standardizing related information for data exchange.

S16
Role of drug transporter studies in the drug discovery and development;
Molecular multiplicity, substrate specificity and drug-drug interaction
Yuichi Sugiyama
Department of Molecular Pharmacokinetics, Graduate School of
Pharmaceutical Sciences, The University of Tokyo
Drug transporters are expressed in many tissues, such as the intestine, liver, kidney, and the brain,
and play key roles in drug absorption, distribution and excretion. The information on thefunctional characteristics of drug transporters provides important information to allow
improvements in drug delivery or drug design by targeting specific transporter proteins. In thispresentation, I will summarize the significant role played by drug transporters in drug disposition,
focusing particularly on their potential use during the drug discovery and development process.
The use of transporter function offers the possibility of delivering a drug to the target organ,
avoiding distribution to other organs (thereby reducing the chance of toxic side-effects),
controlling the elimination process, and/or improving oral bioavailability. It is useful to select alead compound that may or may not interact with transporters, depending on whether such aninteraction is desirable. The expression system of transporters is an efficient tool for screening theactivity of individual transport processes. The changes in pharmacokinetics due to genetic
polymorphisms and drug-drug interactions involving transporters can often have a direct andadverse effect on the therapeutic safety and efficacy of many important drugs. In future, the insilico prediction of the affinity of drug candidates for the transporters may become possible. We
have also constructed a comprehensive database for membrane transport proteins called“TP-Search” (URL: http://www.TP-Search.jp/). Each record has been extracted from approx.
3500 articles published from 1968 to the present. TP-Search contains information about morethan 90 transporters in humans and rodents, including substrate/inhibitor/inducers, tissue
distribution, pathophysiology, knockout animals, gender differences and drug-drug interactions.
The database also gives a brief description of the experimental methods and all information
available in this database is linked to the original references in PubMed.
(References) 1) Mizuno N., Niwa T., Yotsumoto Y. and Sugiyama Y.: Pharmacol Rev. 55:
425-461 (2003), 2) Shitara Y., Sato H. and Sugiyama Y.: Evaluation of drug-drug interaction in thehepatobiliary and renal transport of drugs. Annu Rev Pharmacol Toxicol 45: 689-723 (2005), 3)
Nishizato Y., Ieiri I., Suzuki H., Kimura M., Kawabata K., Hirota T., Takane H., Irie S., Kusuhara
H., Urasaki Y., Urae A., Higuchi S., Otsubo K. and Sugiyama Y. :Polymorphisms of OATP-C(SLC21A6) and OAT3 (SLC22A8) genes: consequences for pravastatin pharmacokinetics. Clin
Pharmacol Ther 73: 554-65 (2003), 4) Sasaki M., Suzuki H., Ito K., Abe T. and Sugiyama Y.:
Transcellular transport of organic anions across a double-transfected Madin-Darby canine kidney
II cell monolayer expressing both human organic anion-transporting polypeptide(OATP2/SLC21A6) and Multidrug resistance-associated protein 2 (MRP2/ABCC2). J Biol Chem
277, 6497-503 (2002)

P6

Structural Analysis and Discrimination of Outer Membrane
Proteins
M. Michael Gromiha and Makiko Suwa
Computational Biology Research Center (CBRC), AIST
2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan
Fishing outer membrane proteins (OMPs) from genomic sequences is one of thechallenging problems in Bioinformatics. We have systematically analyzed thecharacteristic features of the 20 amino acid residues in globular and OMPs and we foundthat the residues, Glu, His, Ile, Cys, Gln, Asn and Ser show a significant difference
between the amino acid compositions of globular and outer membrane proteins. The
higher occurrence of Ser, Asn and Gln in OMPs than globular proteins might be due totheir importance in the formation of E-barrel structures in the membrane, stability ofbinding pockets and the function of OMPs.
We have devised a statistical method for discriminating OMPs from otherglobular and membrane proteins. Our approach correctly picked up the OMPs with anaccuracy of 89% for the training set of 337 proteins. On the other hand, our method hascorrectly excluded the globular proteins at an accuracy of 79% in a non-redundant datasetof 674 proteins. Furthermore, the present method is able to correctly exclude D-helicalmembrane proteins up to an accuracy of 80%. The method based on dipeptide
composition also showed similar results.
The membrane spanning segments in OMPs have been predicted using neuralnetwork based methods. We have developed a web interface for discriminating OMPs
and predicting the membrane spanning segments in OMPs. It is freely available at
http://psfs.cbrc.jp/tmbeta-net/.
References:(1) M.M. Gromiha and M. Suwa (2005) Bioinformatics 21, 961-968.
(2) M.M. Gromiha et al. (2005) Nucleic Acids Res. (in press).

P7
Molecular Dynamics Simulations of HIV-1 Proteases – Insight into theMulti-drug Resistance conferred by Non-active Site Mutation L90M Hirotaka Ode1, Sabrou Neya1, Masayuki Hata1, Wataru Sugiura2, Tyuji Hoshino1,3
Graduate School of Pharmaceutical Sciences, Chiba University1, National Institute of
Infectious Diseases2 , JST3
Abstract
The infection by human immunodeficiency virus type 1 (HIV-1) essentially requires the
enzymatic activity of virally encoded protease, so called HIV-1protease (HIV-1 PR).
This protease is an attractive target for AIDS treatment. Hence, various proteaseinhibiters (PIs) have been designed, and now, seven PIs are clinically available inJAPAN. However, the combination of a short retroviral life cycle and the highmutation rate leads to the selection of drug-resistant HIV-1 variants. Among thepatients who failed in treatment with each SQV or NFV, which is one of the PIs, L90M
mutation is often seen in HIV-1 PR. L90M is a primary mutation responsible for the
resistance to both SQV and NFV. In addition, it appeared to be associated with the otherPIs. But, a crystal structure of PR reveals that the amino acid at codon 90 is located at
the non-active site, which has no direct interactions with substrates or inhibitors. It isdifficult to understand how the non-active site mutation L90M affects the inhibitor
binding. In this study, we executed molecular dynamics simulations and investigated
the resistant mechanism by L90M mutation. We simulated the HIV-1 PRs complexed
not only with the inhibitors (SQV, NFV, LPV) but also with two substrates, using
AMBER7 program package. The simulations revealed that non-active site mutation
L90M affected the binding with SQV and NFV. L90M caused conformational changes
at the active site of PR-SQV or PR-NFV complex, especially on the environment of theresidues at 80 loop. In addition, L90M decreased the binding energies between PR andthese inhibitors.
Whereas, L90M hardly affected the binding with another potent
inhibitor LPV and with the substrates of HIV-1 PR. Thus, those conformationalchanges at the active site might be an index for the fold resistance. These simulations
also suggest that non-active site mutations could affect the active site conformations ofligand-binding proteins.
References
1) Johnson V. A., et al. : Top. HIV Med. vol. 13, pp. 51-57. (2005)
2) Ode H., et al. : J. Phys. Chem. B. vol. 109, pp. 565-574. (2005)

P8
GLYMM: a program for modeling a 3D structure of a carbohydrate chain
and a lipid membrane: the application to molecular dynamics simulationsof glycolipid (GM1) membranes
Kenichi Mori1, Tsubasa Takaoka1, Koji Iwamoto1, Masayuki Hata1, Saburo Neya1,
Tyuji Hoshino1, 2Graduate School of Pharmaceutical Science, Chiba University 1, PRESTO, JST2
We have developed a computer program called GLYMM (GLYcan and Membrane
Modeling) that can construct a 3D structure of a carbohydrate chain and a lipid
membrane for modeling a lipid microdomain such as a lipid raft. A module formodeling a carbohydrate requires only a sequence that can be obtained from thedatabase GLYCAN in the GenomeNet server at Kyoto University, which enables us tomake the 3D structure easily and quickly. A module for modeling a lipid membrane is a
GUI-based program, in which only to click and drag your mouse button are needed formaking a membrane model, however complex the membrane is.
To apply GLYMM to pharmaceutical research, we selected the aggregation
mechanism of Alzheimer β-peptides (Aβ) on glycolipid bilayers. Aβs are known toaggregate on GM1 (glycolipid) including membranes, and the aggregation is enhanced
as the concentration of cholesterol in the membrane increases. We made two types ofmembranes including GM1, POPC, sphingomyelin (SM), and cholesterol (CHL) by
GLYMM, and performed molecular dynamics simulations of these membranes.
Interestingly, we observed microdomains consisting of the GM1 molecules on themembrane where the ratio of GM1:SM:CHL was 20:40:40, while the GM1 molecules
were scattered on the other membrane where the ratio of GM1:POPC was 20:80.
Furthermore, we found that the GM1 molecules in the cluster form were located atintervals of 0.7 or 1.4 nm, which was compatible with the intervals of the appearance ofthe hydrogen bond donors or acceptors of Aβ. Because a GM1 has many hydrogen
donors or acceptors, our results suggest that GM1 clusters would become a template forhydrogen bonds for interacting with Aβ, which would be a seed for Aβ aggregation.
REFERENCES
1. Kakio, A, Nishimoto, SI, Yanagisawa, K, Kozutsumi, Y, Matsuzaki, K.:
Cholesterol-dependent formation of GM1 ganglioside-bound amyloid beta-protein, an
endogenous seed for Alzheimer amyloid, J. Biol. Chem. vol. 276, no. 27, pp. 24985-90(2002)

P9
Computational Chemistry Platform for Drug Metabolomics
Michihisa KOYAMA1, Satoshi KAWAGOE1, Kota KASAHARA1, Hideyuki TSUBOI1,
Akira ENDOU2, Momoji KUBO1, 3, Carlos A. DEL CARPIO1, Ewa BROCLAWIK1,
Kazumi NISHIJIMA4, 5, Tetsuya TERASAKI4, 6, Akira MIYAMOTO4, 1
Department of Applied Chemistry, Tohoku University1, Institute of Fluid Science,
Tohoku University2, PRESTO, Japan Science and Technology Agency3, New Industrial
Creation Hatchery Center4, Development Division, Mochida Pharmaceutical Co. Ltd.5,
Graduate School of Pharmaceutical Sciences, Tohoku University6
To understand metabolic properties of drug in human bodies at the early stage of thedesign process is important for predicting clinical characteristics of drug or avoiding
later stage attrition. Expectations toward in silico methods are greatly increasing in thecurrent post-genome era. In order to predict the uninvestigated metabolic properties of
newly designed drug molecules and to deal with the chemical reaction involved in themetabolic process, computational chemistry method based on quantum chemistry isnecessary. In this study we newly propose a computational chemistry platform for drug
metabolomics, which can be defined as a metabolomics study specific to drug
metabolism. Computational chemistry platform here consists of various originalcomputational chemistry programs developed in our group. Fig. 1 schematicallyillustrates the computational chemistry platform for drug metabolomics. Metabolic
enzyme that explains main pathway to metabolite is predicted by combining Monte
Carlo method and chemical engineering modeling. By adopting interacting structure
predicted by Monte Carlo method as an initial structure, hybrid tight-binding quantum
Prediction of Metabolic Pathway
chemical molecular dynamics
Enzyme C Predict Main Metabolic Enzyme
Drug molecule
Monte Carlo method
calculations are carried out toEnzyme A
Enzyme D +chemical engineeringmodel
predict metabolites of the target
drug molecules. Our platform
DrugmoleculeMainrealizesthepredictionofmetabolic
Water
enzymeEnzyme B
metabolic properties of drug
HEME
Predict Metabolite
molecules in the early stage ofMonte Carlo
Monte Carlo method
program+Quantum Dynamics
drug design process and will
Metabolicreactionchange the paradigm in the current
Hybrid Tight-Binding dynamics
Quantum Chemical Molecular Dynamics
NH Oxidation
drug design process. Details of theplatform will be explained Fig. 1 Schematic of computational chemistry
together with our future visions.
platform for drug metabolomics
C

C

r =k
A

A

A

A

aA

C

-k

A

drug

dA

C θ
A

C

C

C

C

r C = k aC C drug (1 - θ ) - k dC C C θ C(1 - θ )r D = k a C drugA

(1 - θ ) - k

dD

C DθD

D

D

D

A

r =k
B

aB

C

drug

r E = k a C drug (1 - θ ) - k dE C E θ E(1 - θ )- k dB C B θ B

E

B B
B B
B B
B B B B
B B
B
B
B
B

0 fs

E

E

E

50 fs

60 fs

70 fs

80 fs

90 fs

100 fs

E

E

E
E

E

E

E

E

E

E

B

B

E

Enzyme E

E

B

E

E

E

E

E
E
E
E

E

2

P10
Theoretical Study on the Mechanism of Theophylline Metabolism by
Compound I of Cytochrome P450
Mohamed Ismael1, Abdul Rajjak Shaikh1, Hideyuki Tsuboi1, Michihisa Koyama1, Akira
Endou2, Momoji Kubo1,3, Carlos A. Del Carpio1, Ewa Broclawik4, Kazumi Nishijima4,5,
Tetsuya Terasaki4,6, Akira Miyamoto1,4
Department of Applied Chemistry, Tohoku University1, Institute of Fluid Science,
Tohoku University 2, JST-PRESTO3, New Industry Creation Hatchery Center, Tohoku
University 4, Development Division, Mochida Pharmaceutical Co., Ltd.5, Graduate
School of Pharmaceutical Sciences, Tohoku University6
Theophylline is a xanthine derivative methylated at N-1, N-3 positions. Theophylline has
a prominent place in the treatment of asthma, also it was found that theophylline inhibits
the progression of neuroendocrine lung carcinogenesis in hamsters with hyperoxic lunginjury1. Upon oxidation by hepatic microsomal oxidases, the theophylline is Ndemethylated at N-1, and N-3 tertiary amine nitrogen atoms and is also hydroxylated at
C-8 position to produce 3-methylxanthine, 1-methylxanthine, and 1,3-dimethyluric acid,respectively. Among these theophylline metabolites, two main enzymes are responsible.
The demethylation is known to be catalyzed specifically by the CYP1A2 enzyme and thehydroxylation is catalyzed by the CYP2E1 enzyme. In this paper, we describe the C-8hydroxylation of theophylline by a compound I of CYP450 from DFT calculations. We
present new aspects of this enzymatic reaction, in particular how a C-H bond of thesubstrate is activated and dissociated from the energetic point of view. The calculated
spin density distribution shown in Table 1 demonstrates that the Fe=O moiety of thecompound I has two unpaired parallel spin electrons, which are weakly coupled with one
unit of spin mainly located on the thiolate ligand and partly located on the porphyrin ring.
In the initial stages of the reaction theophylline comes into contact with compound I toform a kind of encounter complex where the face of the C8 atom of theophylline isoriented toward the iron atom and close to the oxygen atom of compound I. This suggeststhat the hydrogen abstraction mechanism produces 1,3-dimethyluric acid.
Table 1. Calculated Mulliken charges and spin densities for the Fe and O atoms and theCH3S and porphyrin moieties
Fe
O
CH3S
Porphyrin
Charge Spin Charge Spin Charge Spin Charge Spin
Compound I
0.72
0.9
-0.34
0.8
0.00
-0.5 -0.38 -0.2
Theophylline complex 0.76
0.9
-0.43
0.7
0.00
-0.4 -0.33 -0.2
TS
Hydroxylated complex

0.68

0.8

-0.44

0.4

0.03

-0.2

-0.28

-0.1

0.59

0.7

-0.44

0.0

0.04

0.4

-0.66

0.0

REFERENCE
1. Hildegard M. Schuller, B. Porter, A. Riechert, K. Walker, R. Schmoyer, Lung Cancer,
45 (2004), 11-18.

P11
An in Silico discriminant model constructed for
HERG potassium channel inhibitors
Motoi Tobita1, 2 and Tetsuo Nishikawa1
1. Reverse proteomics research institute, 2-6-7 Kazusa-kamatari, Kisarazu-si, Chiba
292-0818, Japan,
2. Advanced Research Laboratory,
Kokubunji-shi, Tokyo, 185-8601, Japan

Hitachi,

Ltd.

1-280

Higashi-Koigakubo,

Abstract
HERG attracts attention as a risk factor for arrhythmia, which might trigger

torsade de pointes. A highly accurate classifier of chemical compounds for inhibition of
the HERG potassium channel is constructed using support vector machine. For two test
sets, our discriminant models achieved 90% and 95% accuracy, respectively. The
classifier is even applied for the prediction of cardio vascular adverse effects to achieve
about 70% accuracy. While modest inhibitors are partly characterized by properties
linked to global structure of a molecule including hydrophobicity and diameter, strong
inhibitors are exclusively characterized by properties linked to sub-structures of amolecule. This work was supported by a grant from NEDO project of the Ministry of Economy,
Trade and Industry of Japan.

P14
Evaluation of flexible molecular docking programs for virtual screening
Kenji Onodera 1, Kazuhito Satou 1, Hiroshi Hirota 1
Riken Genomic Sciences Center 1
Abstract
Typically two methods, docking performance and scoring performance, areused as measurements to evaluate molecular docking programs. However, it seems thatabove two measurements are not suffice to evaluate programs’ screening performances.
Several researches have reported ranking performances for virtual screening with only
some ligand-protein complexes. Since it is known that each docking program performs
differently due to the nature of active sites, an evaluation based on a limited number ofligand-protein complexes may lead wrong conclusions. In addition, effects from input
ligand conformations and from random tables in the docking programs are overlooked
and those have not been tested. Thus, we evaluated three docking programs (DOCK,
AutoDock, and GOLD) for virtual screening.
To obtain the reliability of the docking programs, more than 100 PDB
registered ligand-protein complexes were tested with three input ligand conformations
each. Since docking score and docked position in the same ligand-protein complex are
different each time due to random number table used in these programs, calculations
were repeated 1000 times each for the complex to obtain the ranges of docking scoresand positions. Then, the docking scores were compared to docking scores from NCI
diversity set screening results to obtain ranking ranges for the ligand-protein complexesin screening results with NCI diversity set. The results showed evaluations from thedocked positions and the docking scores did not estimate the screening performancewell, and ranges of scores and RMSDs differ among the docking programs whendifferent ligand conformations were tested or the same calculations were repeated.
Currently many docking programs are available and many parameters can be modified
in docking programs. Thus, this evaluation method can be helpful to evaluate andimprove them by finding the best parameter settings according to the test results.
References
1) Kontoyianni M, Sokol GS, McClellan LM: Evaluation of library ranking efficacy invirtual screening. J Comput Chem. 2005, 26(1):11-22.
2) Kellenberger E, Rodrigo J, Muller P, Rognan D: Comparative evaluation of eightdocking tools for docking and virtual screening accuracy. Proteins. 2004, 57(2):225242.

P15
Implementation of a Haplotype Reconstruction Algorithm and a tagSNP
Selection Algorithm for Large-scale SNP Information Processing
Sang Jun Kim 1, Sang-Soo Yeo2, Sung Kwon Kim3
Chung-Ang University 1, 2, 3
The human diversity comes from mutations(esp. SNPs) that appear in human genomes.
Association studies between genes and human diversities will take a lot of cost and time,
if they are done by a long DNA region. To reduce cost, many researchers have studied for
haplotyping using the computational methods, and tagSNP selection of a DNA region ofhuman samples. A lot of researches solve these problems to reduce the computation timeand memory consumption, but they have not biological meaning.
In this paper, we propose MarSelHR, which is a Haplotype Reconstruction system
and MarSelTS that is a tagSNP selection system. The MarSelHR has three steps for
haplotype reconstruction. In the first and second step, MarSelHR infer respectively
haplotype pair from unambiguous genotypes and reference set registered haplotype pair.
In the last step, we divide the reference set into several blocks using LD-base block
partitioning approach, and apply the reconstruction algorithm to all of the ambiguous
genotypes with the partitioned reference set.
MarSelTS's algorithm compute LD coefficients between each pair of SNPs, and findpotential block boundary candidates, and then optimal blocks determined each potential
block by a dynamic programming algorithm based on object function. Finally, it selecttagSNP for optimal block by entropy based calculation.
We tested and analyzed MarSelHR's and MarSelTS's performance with Daly data , Patil
data, and artificial haplotype dataset that is generated by haplotype data generating
programs.
References
1) A. Clark. "Inference of haplotypes from PCR-amplified samples of diploid populations".
Molecular Biology and Evolution, 7(2): 111–22, 1990.
2) K. Zhang, "Dynamic Programming Algorithms for Haplotype Block Partitioning :Applications to Human Chromosome 21 Haplotype Data", ACM RECOMB, pp. 332-340,
2003.
Acknowledgement
This work is supported by Korea Science and Engineering Foundation (R01-2003-000-11573-0)

P16Computational study of conformational preference in the tetrahedral intermediates of
the acylation step of ester hydrolysis catalyzed by lipase
Yu TAKANO1,2 and K. N. HOUK2
1
2

Institute for Protein Research, Osaka University

Department of Chemistry and Biochemistry, University of California Los Angeles

Enzymes work as catalysts of an enormous variety of biochemical reactions forming living
systems. The geometrical and physical complementarities of enzymes to transition states andtetrahedral intermediates are necessary for efficient catalysis. The interior of the active site of
enzymes imposes restrictions on the conformational freedom of the transition states and thetetrahedral intermediates.
Lipases are versatile biocatalysts to catalyze hydrolysis of esters and esterification of the
carboxylic acids involved in a whole range of bioconversion reactions. Moreover, because of
accepting the broad range of substrate while remaining high selectivity, synthetic organic chemists
have used these enzymes for regio- and/or stereoselective hydrolyses and esterifications [1].
Three-dimensional X-ray crystallographic structures of lipases from a number of different species
show striking similarities to each other. In particular, the bound phosphonate inhibitors exist in only
a few of the possible conformations. Conformational analysis has been performed for the tetrahedral
intermediates bound to lipases using molecular dynamics (MD) simulations in order to understandhow these conformational factors are related to catalysis by enzymes [2]. The conformational
preference has been compared to those of the transition states and tetrahedral intermediates in theacylation step of ester hydrolysis computed with density functional theory (DFT) [3].
The distributions of conformations in the tetrahedral intermediates bound to lipases indicate thatthese enzymes limit the conformation of the tetrahedral intermediates. The conformational
preference of the enzyme-bound tetrahedral intermediates in the acylation step is different fromthose of the phosphonate inhibitors bound to lipases. Comparison to the computed reaction
pathways of the acylation of methyl acetate implies that the binding cavities of lipases stabilizes themethoxide dissociation step of transacylation more than the association step and probably harnesses
the reaction mechanism in the gas phase for their catalysis rather than in water [2].
REFERENCES
1. R. D. Schmid, R. Verger, Angew. Chem. Int. Ed. vol. 37, pp. 1608–1633 (1998).
2. Y. Takano, K. N. Houk, in preparation.
3. Y. Takano, K. N. Houk, J. Phys. Chem. A vol. 108 pp. 11740-11751 (2004).

P17
Computational Analysis of CYP3A4 Mediated Metabolism of KCNQ2
Potassium Channel Opener
1

Shaikh Abdul Rajjak1, Mohamed Ismael1, Hideyuki Tsuboi1, Michihisa Koyama , Akira
1,3
1
4,5
Endou2, Momoji Kubo , Carlos A. Del Carpio , Ewa Broclawik4, Kazumi Nishijima ,4,6
1,4
Tetsuya Terasaki , Akira Miyamoto
Department of Applied Chemistry, Tohoku University1, Institute of Fluid Science,
Tohoku University2, JST-PRESTO3, NICHe, Tohoku University4, Mochida Pharma Co.,
Japan5, Graduate school of Pharmaceutical Sciences, Tohoku University, Japan6
The Cytochrome P450 enzymes play a central role in drug metabolism by catalyzing thebiotransformation of a wide variety of xenobiotics. CYP3A4 is a major CYP450 isoform
with broad substrate specificity and is estimated to be involved in the metabolism of
approximately 50% drugs used in humans. A detailed understanding of its mechanism isvital for predicting biotransformation of pharmaceuticals and other xenobiotics.
DFT calculations were carried out using DMol3. Docking studies were carried out using
LigandFit module in Cerius2. Molecular dynamics studies were performed using NewRyudo, software developed in our laboratory.
Complexes of the oxyferryl heme model with cysteine and a ligand were optimized using
accurate DFT methods. Our studies indicate that ligand forms weakly bound complex(Fig.1) and next step is the formation of the bond between aromatic ring carbon andoxyferryl oxygen. This step requires activation and involves charge transfer from theligand to the active site, which indicates thathydroxylation may proceed predominantly
through an electrophilic pathway via a cationlike σ complex. This enzymatic mechanism
involves proton shuttles mediated by theporphyrine ring through the N-protonatedintermediate, which directs the proton either tothe oxygen atom to form alcohol or to the ringcarbon atom to produce ketone product. In
addition, formation of epoxide product was also
investigated. Subsequently DFT calculation for
isolated porphyrine and ligand structure, docking
and MD studies were also carried out in protein
environment. This work provides insight into
the detailed mechanism and helps to explain Fig. 1: Spin density distribution on
and predict the electronic contribution of weakly bound complex
ligand substituents to reactivity in thisenzymatic process.
REFERENCE
1) Wu YJ, Davis CD, Dworetzky S, Fitzpatrick WC, Harden D, He H, Knox RJ, Newton
AE, Philip T, Polson C, Sivarao DV, Sun L, Tertyshnikova S, Weaver D, Yeola S,
Zoeckler M, Sinz MW, J Med Chem vol.46, no.18, pp.3778-3781 (2003).

P18
Computational Study on the Enantioselectivity of Lipase Enzyme
toward Non-Natural Organic Compounds
Yoshihiro MORI, Yoichiro YAGI, Yoshinobu NAOSHIMA
Faculty of Informatics, Okayama University of Science
Lipase enzymes coupled with their ease of use are now widely used as anenvironmentally friendly and useful biocatalysts for obtaining enantiomerically pureand biologically important compounds. As part of our continuous works towardutilizing biocatalysts in organic synthesis,1) we required a computational modeling of
the interaction of two popular lipases, Pseudomonas cepacia lipase (PCL) and Candida
antarctica lipase typeB (CALB) with chiral ligands, in order to explain theenantioselectivity for non-natural organic compounds shown by these two enzymes.
This presentation will describe our progress in the molecular dynamics (MD)simulation of lipase enzyme-ligand interactions.2)
The PDB X-ray crystallographic structures of PCL (1hqd) and CALB (1lbs) were
optimized by an initial preparation with Glide, followed by energy minimization using
OPLS-AA force field implemented in MacroModel. Twenty chiral ligands, including
aromatic and aliphatic esters, were initially minimized with OPLS-AA. Each structure
thus obtained was prepared by employing a combination of molecular mechanics andMD simulated annealing technique and the subsequent Hartree-Fock 6-31G**
calculations. Conformation search toward the lipase-chiral ester complexes obtained byflexible docking simulations with the Glide was carried out by Mixed Monte Carlo
Multiple Minimum/Large Scale LowMode procedure. Further, we performed a
300-step and 100 ps MD simulation with the timestep of 1.5 fs for conformations
possessing lower energy and shorter C-O distance (the distance in each complex
between the carbonyl carbon of chiral ester and the oxygen of the active site Ser side
chain OH), among the conformations found in Monte Carlo calculations. The MD
simulations indicate that the C-O interatomic distance is shorter for the fast reacting(R)-enantiomer than for the slow reacting (S)-enantiomer. It is therefore envisaged thatthe difference of the C-O distances between two enantiomers can be related to an
expression of the enantioselectivity of lipase enzymes such as PCL and CALB.
REFERENCES
1) Y. Naoshima, T. Kimura, Y. Mori, and M. Kamezawa, Recent Res. Devel. Org. Bioorg. Chem.,
6, 1-9 (2004). 2) Y. Naoshima, Y. Mori, and Y. Yagi, The 15 International Conference on
Genome Informatics 2004, Poster and Software Demonstrations, P129-1-P129-2.

P19
Extraction of Information on Chemicals-CYP3A4 Interactions from
Literature
Chunlai Feng 1, Fumiyoshi Yamashita 1, Mitsuru Hashida 1
Graduate School of Pharmaceutical Sciences
Kyoto University1
Abstract
CYP3A4 is the most abundant cytochrome P450 isozyme in the human liver that is
responsible for the metabolism of almost 50% of known drugs. To understand functionalclassification of CYP3A4 interactants and their structure-function relationship is
important, in order to predict the metabolism of new chemical entities in drug discovery
and drug-drug interactions in clinical applications. In this study, we performed
information extraction of interaction between chemicals and CYP3A4 from MEDLINE
abstracts. An available human language processing systems “GATE” is used as aplatform, and two name dictionaries that involve >100,000 compounds and >30,000
enzymes respectively were constructed from MeSH datasets. In addition, 21 verbs
appearing frequently in the abstracts were selected from a corpus of two thousand
MEDLINE abstracts obtained by the query “CYP3A4”. Based on dictionaries, key
verbs and simple syntactic analysis (based on sentence type, verb voice, coordinating
conjunction, noun phrase and verb position patterns), a set of rules were constructed to
extract information on chemical-CYP3A4 interactions. Validation of the rules was
carried out using another corpus of randomly selected two hundred MEDLINE abstracts.
More than 85% recall and 97% precision on name identification were achieved. We are
now analyzing common structural features of the CYP3A4 interactants extracted.
References
1) Guengerich FP: Role of cytochrome P450 enzymes in drug-drug interactions. Adv.
Pharmacol. 43:7-35, 1997.
2) Hamish C, Diana Ma, Kalina B, Valentin T, Cristian U, Marin D, Mike D, Niraj A,
Ian R: Developing Language Processing Components with GATE Version 3 (a User
Guide) For GATE version 3.0 (January 2005), The University of Sheffield 2001-2005.
3) Blaschke C, Andrade MA, Ouzounis C, Valencia A: Automatic extraction of
biological information from scientific text:protein-protein interactions. Proc. Int. Conf.
Intell. Syst. Mol. Biol. 60-7, 1999.
4) R. Gaizauskas, K. Humphreys: Using a semantic network for information extraction.
Nat. Lang. Eng., 3(2/3):147-169, 1997.

P20
Spectral Analysis of the Daily Prescription Variations at a Pharmacy
for Infectious and Non-Infectious Diseases
Katsunori SEGAWA1, Tatsuya NAKANO1, Kotoko NAKATA1, Kazushige Ijuin2,
Noriko Hatanaka3 and Yuzuru HAYASHI1
National Institute of Health Sciences1, Tanashi Yakuhin Co. Ltd.2, Kakunoki Pharmacy3
The time variation in the number of viruses in a human body as well as in a society ofhuman being can be described by a mathematical model for the changes in the size of
populations whose members can be born and die [1-3]. We analyzed the daily variationsin the number of formulations prescribed at a pharmacy (Tanashi Yakuhin) from theviewpoints of spectral analysis. The power spectral density of the variations for an
influenza anti-viral agent (Tamiful Dry Syrup) is observed to be right downward,
whereas that for vasodilator (Norvasc) and antidiabetic agent (Daonil) is parallel. It is
concluded that for infectious diseases, the prescription amount at a day is correlatedwith the prescription amount at another consecutive day, but for non-infectious diseases,
they are independent of each other. The stochastic properties of prescriptions will be
useful for the study on the prediction of the future prescription amount for the different
types of diseases [4].
We also tried to examine computer viruses, which were attached to e-mails for National
Institue of Health Sciences, and got an interesting result [5].

References[1] Feller, W., An Introduction to Probability Theory and Its Applications, John Wiley‘ Sons, New York, 1968.
[2] Hino, M. Spectral Analysis (Supekutoru Kaiseki), Asakura Shoten, Tokyo, 1982.
[3] Sumi, A., Olsen, L.F., Ohtomo, N., Tanaka, Y., Sawamura, S., Jpn. J. Appl. Phys., 42,
721-733 (2003).
[4] Ijuin, K., Hatanaka, N., Segawa, K., Nakano, T., Nakata, K. and Hayashi, Y.:
Spectral Analysis of Prescriptions at a Pharmacy for Infectious and Non-Infectious
Diseased. (in preparation)[5] Segawa, K., Nakano, T., Nakata, K. and Hayashi, Y.: Computer viruses act likeviruses. (in preparation)

P21
Computational study on ATP hydrolysis by ABC transporter subunit HisP
1

1,3

Qiang Pei1, Hideyuki Tsuboi1, Michihisa Koyama , Akira Endou2, Momoji Kubo ,
1
4,5
4,6
Carlos A. Del Carpio , Ewa Broclawik4, Kazumi Nishijima , Tetsuya Terasaki , Akira
1,4
Miyamoto1
Department of Applied Chemistry, Graduate School of Engineering, Tohoku University,
2
Institute of Fluid Science, Tohoku University, 3JST-PRESTO, 4NICHe, Tohoku
University, 5Developmen Division, Mochida Pharmaceutical Co. Ltd., 6Graduate Schoolof Pharmaceutical science, Tohoku University
There is great clinical interest in ATP binding cassette (ABC) transporters not only
because of their involvement in multidrug resistance but also because many diseasecausing mutations have been identified in proteins of this family in humans. HisP protein
is the nucleotide binding domain of ABC transporter complex HisQMP2, the periplasmic
histidine permease of S. typhimutium and E. coli. Binding and hydrolysis of ATP or theother nucleotides is considered to power the transport process by transforming
conformational changes from the NBDs to the TMDs, which lead to the movement of
substrate across the membranes.
DFT calculations were carried out using DMol3. Molecular dynamic simulation werecarried out by OPLS-AA force field using MacroModel (Schrödinger, Inc.). QM/MM
molecular dynamics studies were performed using Hybrid-Colors, which was developedin our laboratory.
Two mutant models were made based on the initial x-ray crystal structure of HisP protein
which was taken from protein data bank (PDB ID: 1b0u [1]). One is a phosphorylated
structure, in which Ser41 was bound with a phosphate group in silico; the other one iscatalytic activated model, water407 was substituted by a Mg2+, which has been
mentioned by Huang and colleagues in 1998.[1] After the energy of three structures wereminimized by OPLS-AA force field, we performed a molecular dynamic simulation on
three models. ATP binding site was selected from initial structure and optimized using
accurate DFT methods. Our
Gln100studies indicate that ATP moleculeATP
and Mg2+ and residues forms
weakly bound complex (Fig. 1)
ASP178and next step is the broken of the
2.029
bond between γ phosphor atom
1.966
1.846 Mg
and the oxygen 8. In addition,
isolated ATP, ADP and AMP
WaterPγ
molecules were also investigated,
O8
from our calculation, the energy
Fig. 1: Optimized structure of ATP binding site, theof 12 kJ/mol was released during
distances between Mg and ATP, two residues are also
ATP hydrolysis. QM/MM studies
shown in the figure.
were also carried out using
Hybrid-colors.
REFERENCE
1) L. Hung, I. Wang, K Nikaido, P.Liu, G. Ames and S.Kim, Nature, 396 (1998) 703-707.

P22
In silico screening for the human P2Y12 receptor
Yosuke Nonaka, Takeshi Hiramoto, Kiyoshi Baba and Norihisa Fujita
Laboratory of Pharmcoinformatics, Department of Bioinformatics,
Ritsumeikan University, Kusatsu, Shiga 525-8577
We have searched endogenous ligands acting on the human P2Y12 receptors, one of the
G-protein coupled receptors (GPCRs) by in silico screening against biologicalcompound-database that contained more than 500 animal-metabolites. The 3D model ofthe P2Y12 receptor necessary for the screening was obtained by the homology modelingusing the previously constructed 3D model of P2Y1 receptor as a template 1),2). The insilico screening using “AutoDock” resulted in selection of cyteinylleukotrienes and5-phosphorybosyl 3-pyrophosphate (PRPP) with high free energy changes, in addition
to the known P2Y12 ligands such as 2MeSADP and ADP. These candidates were
subjected to the intracellular Ca2+ assay using the recombinant CHO cells expressing
P2Y12 receptors. We found that one of cysteinylleukotrienes and PRPP acted on theP2Y12 receptor as agonists with the EC50 values of 1.3nM and 7.8nM, respectively.
Furthermore, we have analyzed the phylogenetic relationship of the P2Y, P2Y-like andcysteinylleukotriene receptors based on the sequence alignment followed by anevolutionary analysis. The analyses showed that the cysteinylleukotriene receptors
belonged to the P2Y-related family. Moreover, an orphan GPCR was detected in a P2Y
subfamily.
1) Hiramoto. T, Nonaka Y, Inoue K, Yamamoto T, Omatsu-Kanbe M, Matsuura H,
Gohda K, and Fujita N. (2004) Identification of Endogenous Surrogate Ligands for
Human P2Y Receptors Through an in silico Search. J.Pharmacol.Sci. 95: 81-93
2) Hiramoto T, Nemoto W, Imai T, Kikuchi T and Fujita N. (2002) Construction of
hypothetical three-dimensional structure of P2Y1 receptor based on Fourier transform
analysis. J Protein Chem. 8: 537-545

P23
Systematic comparisons of consensus scores for computational
ligand-docking
Akifumi Oda1, Keiichi Tsuchida1, Tadakazu Takakura1, Noriyuki Yamaotsu2, Shuichi Hirono2
Toyama Chemical Co. Ltd.1, Kitasato University2
A prediction of a binding mode between a target protein and its ligand molecule plays animportant role in structure-based drug design (SBDD) trials. FlexX is one of the major
ligand-docking programs developed for predicting the three dimensional structure of theprotein-ligand complex, and in this program predicted structures are evaluated by FlexX score.
Because the ability of single scoring function to discriminate between adequate and inadequate
complex structures is sometimes insufficient, the concept of “consensus score”, in which
multiple scoring functions are used simultaneously, reported recently1). Although many
consensus score strategies are proposed and they are effective for evaluations of complex
structures, there are few studies that compare these strategies2). Furthermore, for the selectionsof complex model candidates, systematic discussions about combinations of scoring functions
have not been carried out.
In this study, we compared all possible combinations of nine consensus scoring
strategies(number-by-number,number-by-rank,

rank-by-number,rank-by-rank,

percent-by-number, percent-by-rank, vote-by-number, vote-by-rank, and vote-by-percent) withnine scoring functions (FlexX score, GOLD score, DOCK score, PMF score, ChemScore,
DrugScore, PLP, ScreenScore, and X-Score). Thus, 4599 types of consensus scores werecompared. The systematic naming of consensus scoring strategies was also proposed. The
results indicate that the “vote-by-number” strategy, in which the ligand-docking models are
selected by majority vote of scoring functions, is effective for determining the candidates ofplausible protein-ligand complex. However, for “vote-by-” strategies the adjustments ofparameters were difficult because two different thresholds were needed in these strategies. In
consensus scoring strategies required only one threshold, “number-by-number” gave the best
results. By incorporating these consensus scores in FlexX program, reasonable complex models
seem to be obtained more efficiently than by independently using FlexX score. These strategies
may also be useful for other docking programs to improve the scoring scheme, and the SBDD is
expected to be more effective by these improvements.
References
1) P. S. Charifson et al., J. Med. Chem., 1999, 42, 5100-5109.
2) R. Wang et al., J. Chem. Inf. Comput. Sci., 2001, 41, 1422-1426.

P24
A Multilocus Simulation Study to Multiple Testing for Association Test
Woosung Yang 1, Jun Nakaya1, Norihiro Sakamoto1
Kobe University of Clinical Genome Informatics Center1
Advances in maker technology have made a dense marker map a reality. The alleles attightly linked marker loci may themselves be associated and, therefore, the tests
conducted at each locus may not be independent. When alleles at different marker lociare associated, the Bonferroni correction may lead to a conservative test, and hence a
power loss. Ideally we would like the experimentwise significance level of the test
without being overly conservative, in order to increase the power of our test. To archive
this goal we propose a modified Monte-Carlo approach to determine significance. We
examine the case of tightly linked markers with varying amount of association between
them. Using the computer simulations, we compare the power when either the
Bonferroni or modified Monte-Carlo procedure is used to determine significance. This
proposed procedure guarantees the correct experimentwise level, regardless ofdependencies between the individual test and can be applied whenever it is suspectedthat markers examined have high amounts of association, or as a general approach toensure appropriate significance levels and optimal power.
References
1. Casella G, Berger R.: Statistical inference. Pacific Grove, CA: Wadsworth andBrooks. (1990)
2. Kaplan NL, Martin ER, Weir BS: Power studies for the transmission/disequilibrium
tests with multiple alleles. Am J Hum Genet vol.60, pp.691-702 (1997)
3. Knapp M, Seuchter SA, Bauer MP: The haplotype-relative-risk method for analysisof association in nuclear families. Am J Hum Genet vol.52, pp.1085-1093 (1993)
4. Martin ER. Kaplan NL. Weir BS: Test for linkage and association in nuclear
families. Am J Hum Genet vol.61, 439-448 (1997)
5. Risch N, Merikangas K: The future of genetic studies of complex human diseases.
Science vol.273, pp1516-1517.
6. Speilman RS, Ewens W: The TDT and other family based tests for linkage
disequilibrium and association. Am J Hum Genet vol.59, pp.983-989 (1996)

P25
Kinetic assessment of lenampicillin absorption using Caco-2 cells: in vitro
study aiming for rational design of prodrug
Takashi MIZUMA 1, Sayaka SAKAGUCHI 1, Sachie TANAAMI 1 and Masahiro
HAYASHI 1
Department of Drug Absorption and Pharmacokinetics, School of Pharmacy,
Tokyo University of Pharmacy and Life Science (TUPLS) 1
Although lenampicillin has been used as an ampicillin prodrug to improve intestinal
absorption, the absorption process of lenampicillin has not been characterized in detail.
Therefore, we studied the transport and metabolism of lenampicillin using Caco-2 cells.
Lenampicillin and ampicillin were determined by HPLC. Concentration of lenampicillin,which was added to the apical side, decreased with time. On the basal side,
lenampicillin appeared and the amount increased over time. Ampicillin also appeared on
the basal side, indicating that lenampicillin is in part metabolized to ampicillin during
the absorption process. Transport clearance of lenampicillin from the apical to basalsides across the Caco-2 cell monolayer was approximately 150 times higher than that ofampicillin. Similar results were observed in the uptake clearance of lenampicillin fromthe apical side into Caco-2 cells. However, the transport clearance of lenampicillin fromthe basal to apical sides was higher than that from the apical to basal sides. In addition,
lenampicillin decreased transport of rhodamine123, a P-glycoprotein substrate, from thebasal to apical sides. Verapamil and cyclosporine enhanced transport of lenampicillinfrom the apical to basal sides. These results suggest that the influx (uptake) oflenampicillin by passive diffusion is higher than the efflux by P-glycoprotein, resulting
in enhanced intestinal absorption. (Supported in part by the Japan Society for the
Promotion of Science (T. M.)).

P26
Database and analysis of binding site residues and ligands inmembrane protein-ligand complexes

M. Xavier Suresh, M. Michael Gromiha and M. Suwa
Computational Biology Research Center (CBRC), AIST,
2-42, Aomi, Koto-ku, Tokyo 135-0064, Japan.

Abstract:

Membrane proteins perform various functions such as biological transduction andtransport processes by mediating the flows of ions, energy and information acrossmembranes. The structural analysis on the interactions between the membrane proteins
and ligands provides deep insights into the mechanism of protein-ligand interactions andtheir function. An increasing number of experimentally determined structures of proteinswould help to derive the structural principles in protein-ligand complexes. In this work
we developed a database of membrane protein-ligand interactions from the information
available in PDB and other structural databases. The interactions between amino acidresidue in membrane protein and the functional group of the ligand have been analyzed
through atom contact, hydrophobic behavior, location of the residues, etc. The
preliminary results obtained on the analysis will be presented.

P27
GeneT2D: A database for the genetic variation in type II diabetes
Yu LIN1, Jun NAKAYA1, Norihiro SAKAMOTO1
Kobe University Graduate School of Medicine, 7-5-2 Kusunokicho, Chuo-ku, Kobe,
Hyogo 650-0017, JAPAN1OBJECTIVE: Although, as for a common multi-genetic disease, Type II Diabetes (T2D)
is more likely triggered by the individual lifestyle along with the plural genetic variation
than the genetic disorder such as the type I Diabetes (T1D); comprehensive
understanding of the multi-caused genetic background of T2D is essential to establish
the pharmacogenomics and the gene based lifestyle modification. Here we present a
genetic variation database of T2D (GeneT2D) that includes the comprehensive T2D
related gene information. GeneT2D will be used for finding the relationship between the
genotype and the phenotype of T2D.
METHOD: The data in GeneT2D are collected from the public databases, such as the
Genetic Association Database, the Human Genome Variation Database, and othergene-related databases; meanwhile, we developed a scan tool to update thesusceptibility genes from PubMed. The gene’s name, the position, the variation
information (partly including SNPs), and the gene annotations were collected andconfirmed manually. An open source RDBMS named MySQL is used to manage thedata. A web based interface was built with the PHP scripting language skill and theApache HTTP Server technology for the data representation.
RESULT: There are 124 T2D related genes in the database at the present, and a dailyscan tool is used to inform the new published susceptibility genes. Each entry includes
the gene symbol, the DNA position, the functional effects, the reference, the genevariation, the statistics, the confirmed SNPs, and the redirection information to the otherpublic databases. GeneT2D is organized by MySQL with a web interface.
References
1) M. Alan Permutt, Jonathon Wasson, Nancy Cox: Genetic epidemiology of diabetes.
J Clin Invest. Vol.115, No.6, pp.1431-1439 (2005).
2) Joel N. Hirschhorn: Genetic Approaches to Studying Common Diseases andComplex Traits. Pediatr Res. Vol.57, No.5 Pt 2, pp.74R-77R (2005).
3) Laura Dean, Johanna McEntyre: The Genetic Landscape of Diabetes. Published
online in NCBI (2004).

P28
Amino acid sequence analysis of Saccharomyces cerevisiae proteome
in terms of electric charge distribution
Runcong KE 1, Shigeki MITAKU1
Department of Applied Physics, Graduate School of Engineering, Nagoya University1
Experimental and theoretical studies have indicated the importance of electrostatic
effects in individual protein structure and its stability. However, common features ofsuch effects in all proteins from organism proteome have hardly been investigated. In
our previous work, we analyzed net charge distribution of all amino acid sequences forgenome Drosophila melanogaster and showed that the charge distribution was wellfitted to Gaussian distribution, suggesting random amino acid sequences in genome
level. In this work, we generated random sequences which have the same amino acidcomposition, number of proteins and the size distribution as Saccharomyces cerevisiae
proteome. The histogram of charge distribution from random sequences was sharper
than one from proteome sequences, indicating the positive correlation of charged
residues in proteome sequences. Therefore, we calculated correlation function of
charged residues in all amino acid sequences between S.cerevisiae proteome andrandom sequences. The correlation function in S.cerevisiae proteome could be wellfitted to exponential curve with positive correlation, suggesting that electrostatics
interactions within proteins of eukaryote proteomes are repulsive on average. In contract,the distribution of correlation function in random sequences appeared almost constant.
The role of charge correlation in the protein folding will be discussed.

REFERENCES
1. C. Nick Pace, Roy W. Alston, and Kevin L. Shaw: Charge-charge interactions
influence the denatured state ensemble and contribute to protein stability, Protein
Sci. vol.9, no.7, pp.1395-1398 (2000).
2. Huan-Xiang Zhou: A Gaussian-chain model for treating residual charge-chargeinteractions in the unfolded state of proteins, Proc. Natl. Acad. Sci. U.S.A. vol.99,
no.6, pp.3569-3574 (2002).
3. Runcong Ke and Shigeki Mitaku: Gaussian analysis of net electric charge of proteinsin the Drosophila melanogaster genome, Chem-Bio Informatics J. vol.4, pp.101-109(2004).
4. Runcong Ke and Shigeki Mitaku: Local repulsion in protein structures as revealed bycharge distribution analysis of all amino acid sequences from the Saccharomyces
Cerevisiae genome, Journal of Physics: Condensed Matter (in press) (2005).

P29
Folding dynamics of 10 residues protein, chignolin
Atsushi SUENAGA, Tetsu NARUMI, Noriyuki FUTATSUGI, Ryoko YANAI, Yousuke
OHNO, Noriaki OKIMOTO, Makoto TAIJI
Computational and Experimental System Biology Group, RIKEN Genomic Sciences
CenterCharacterization of the folding dynamics and free energy landscape of proteins areimportant steps in understanding the protein folding mechanism. To understand those of
the protein element, such as α-helix and β-hairpin, provides us information in early
event of protein folding process. Computer simulation is very effective method to
elucidate the folding dynamics and conformational landscape of the protein element at
the atomic resolution. Here we performed folding, unfolding and refolding molecular
dynamics simulations of 10 residues protein, chignolin, which folds into β-hairpin
structure (1). We analyzed the wide range of conformational space obtained from thesesimulations. The chignolin folded β-hairpin structure with going down the funnel-likeenergy hill in which the protein reduces conformational entropy in our simulations. In
the folding process, a specific pathway did not exist, rather multiple pathways
converged to dense conformations. These results indicate that the “folding funnel
theory” (2) is appropriate for interpretation of the folding mechanism of the chignolin.
REFERENCES
1) Shinya Honda, Kazuhiko Yamasaki, Yoshito Sawada, Hisayuki Morii: 10 residue
folded peptide designed by segment statistics, Structure, vol. 12, pp. 1507-1518(2004).
2) Peter E. Leopold, Mauricio Montal, José N. Onuchic: Protein folding funnels: akinetic approach to the sequence-structure relationship, Proc. Natl. Acad. Sci. USA,
vol. 89, pp. 8721-8725 (1992).

P30
Massively parallel calculation of binding free energy for biomolecules
Masakatsu Ito1, Yoshiaki Tanida1, Hideaki Fujitani1,
Michael R. Shirts2, Guha Jayachandran2, Christopher D. Snow2, Vijay S. Pande2
Fujitsu Laboratories Ltd.1, Chemistry Department, Stanford University2

Calculation (kcal/mol)

Although drug-discovery process would be more efficient with accurate estimation ofbinding free energies, such estimation with less than 1.0 kcal/mol error still remains achallenge to computer simulations. We improve this accuracy by applying massively
parallel processing system, BioServer to use Bennett acceptance ratio (BAR) method[1].
The BAR method gives statistically optimal estimation of free energies based on a set ofmolecular dynamics (MD) trajectories. BioServer parallelizes the generation of this set
by running GROMACS MD program on multiple processors with varying initial
velocities and coupling parameters. Typically the simulation for FKBP-ligand complex
is accelerated by 400( ~ 33 × 12 ) processors.
We examined the accuracy of our estimation using eight FKBP ligands complexedwith their receptor. Generalized Amber force field parameters were assigned to theligands using AM1-BCC[2], and AMBER99 parameters to FKBP receptor. Figure 1
shows the comparison of our obtained values with experimental ones. The calculated
free energy differences are wellcorrelated to experimental inhibition-3
L2-4
constants. The root mean square
L3-5
deviation is 0.36 kcal/mol.[3]
L5
-6
L12
This result implies that our system-7L8
can improve the efficiency of
-8
L6
L9
-9
screening process of inhibitor
-10
FK506
candidates using the estimation of
-11
0.1
1
10
100
1000
10000
absolute free energies.
Experimental Inhibition constant (nM)

REFERENCES[1] M. Shirts,

Fig. 1: Comparison of calculated binding

and

V.S.Pande,energy with experimental values

J.Chem.Phys.,122,144107(2005); M.
Shirts, E.Bair, G.Hooker, and V.S.Pande, Phys.Rev.Lett.,91,140601(2003).
[2] J.Wang et al. , J.Comput.Chem.,25,1157 (2004).
[3] H.Fujitani, Y.Tanida, M.Ito, M.R.Shirts, G.Jayachandran, C.D. Snow, V.S.Pande ,ACS National Meeting March 13 (2005).

P31
Construction of the Web-Accessible comprehensive transporter databasefor drug discovery and development, “TP-Search” (Part II)
Takafumi Ochiai 1, Naoki Ozawa12,Yoshimasa Hama 1,Katsuhiko Taki3, Toshiyuki
Ueda4, Noriko Sugawara4, Yukiko Abe4, Hitoshi Matsui4, Kiyomi Ito5, Kazuya Maeda6,
Hiroyuki Kusuhara6 and Yuichi Sugiyama6
Advanced Research Institute for Science and Engineering, Waseda University1,
Wyeth K.K. 2, Nihon Visual Science.Inc3, CAC Corporation4, Hoshi University5,
Graduate School of Pharmaceutical Sciences, the University of Tokyo56
Recently, researchers involved in drug discovery and development have increasingly
paid more attention to the transporter as one of the determinant factors influencing drug
disposition and pharmacokinetics. Over the last several years, so much information
about transporters has been published that there is now a need for a publicly accessible
database with comprehensive well-organized information about transporters for drug
discovery and development purposes. To assist this, we have constructed a transporter
database “TP-Search” on the WEB. (URL:http://www.TP-Search.jp/)
In our previous work, this database covered a huge amount of information about 80
kinds of transporters in humans, rats and mice, and had been prepared from data
extracted from about 2600 research articles, and provided several properties oftransporters, such as substrate/inhibitors, tissue distribution, regulation of expression
level, knockout animals, gender differences and drug-drug interactions. Now, thedatabase covers about 90 kinds of transporters, and has been prepared from data
extracted from about 3300 research articles, and provides additional property ofpathophysiology. The database is updated every three or four months. Users can search
for information not only on transporter name but also on compound name and organ
name. And all information is directly linked to the original references in PubMed,which ensures that the users can confirm its validity and obtain more details. TP-search
will be updated periodically and add properties of transporters that researchers involvedin drug development have great interest, to keep pace with the current advances intransporter research.
References
1) Naoki, O, et al. “Transporter Database, TP-Search: A Web-Accessible Comprehensive
Database for Research in Pharmacokinetics of Drugs”, Pharmaceutical Research, Vol. 21,
No. 11, November 2004, 2133-2134

P32
Particle simulation approach for subcellular spatio-temporal events
Ryuzo Azuma 1, Tetsuji Kitagawa2, Tomoyuki Yamamoto3,
Takeshi Arikuma2, Hiroshi Kobayashi4,Akihiko Konagaya1,2
Genomic Sciences Center, RIKEN 1, Mathematics and Computing Sciences, Tokyo
Institute of Technology2, School of knowledge Sci. JAIST 3,
Graduate Sch. of pharmaceutical Sciences, Chiba Univ.3
Abstract
Sub-cellular dynamics of molecules is now an important topic for biologists thanks tothe advances in molecular imaging and microscopy. The single particle tracking (SPT)and single fluorescent molecule video imaging (SFVI) technologies have contributed togaining understanding of how these molecules move in space as individuals.
Unfortunately, however, it seems that only a limited knowledge is obtained about how
we systematically infer the relation between the molecular and cellular level
phenomena. We have been focusing on this problem and tried to bridge the gap between
the molecular-level and cellular-level properties by making use of a particle simulation
technology we developed recently. This technology has following characteristics suitable
for a bottom-up level expression of molecularinteractions in space. (1) It expresses a single
molecule as a particle, (2) it explicitly assigns
spatial coordinates to particles that move in a 3D
space, (3) these particles interact with each otherby stochastic processes, and (4) any geometry ofmembrane structures can be generated so thatthey constrain movements of some molecules. A
typical snapshot obtained by a simulation basedon this technology is shown in Fig.1. The line plot
is a trajectory from a single particle movement,
which resides initially in the cytoplasm and istranslocated to the nucleus afterward. Thus our
simulation method enables not only highly

Fig. 1:

A trajectory of single
protein molecule translocatedfrom the cytoplasm to the nucleus
through a nuclear pore.

flexible and sophisticated model constructions but also discuss how molecular-level
dynamics affect cellular-level properties. This feature will be demonstrated further byshowing visualized data from simulations with a highly detailed spatial expression of a
cell.

P33
Spontaneous Docking MD Simulations of Aspartate Racemase- Roles of Amino Acid Residues in Docking Process Okimasa Okada1, Tomohiro Seko1, and Masafumi Yohda 2
Fuji Xerox Co., Ltd. 1, Tokyo University of Agriculture and Technology 2

The molecular mechanism of the aspartate racemase from the hyperthermophilicarchaeum, Pyrococcus horikoshii OT3, has been investigated based on its crystal
structure1). Although two sulfur atoms in the active site are thought to act as acid andbase at the racemization process, the distance (0.96 nm) between their sulfur atoms islonger than the estimated preferable distance of 0.8 nm for functional cooperation.
Molecular dynamics (MD) simulations in a wide temperature range have shown that
hinge-like molecular motion of the whole racemase with conformational changes of theside-chains of the active cysteine residues was assigned to the narrowing of the distance
between the two sulfur atoms. In this study, we will present the results of MD
simulation on the roles of other amino acid residues and also docking of substrate.
We observed stochastic conformational changes of the side-chain of the Tyr160 locating
at the entrance of the active site near the enzymatically active temperature. This
conformational change induces large displacement of the phenol group to cover or
uncover the active site as if it is functioning as a gate. We succeeded in the docking of
the L-aspartic acid into the racemase by MD simulations without adding any artificial
interactions. The L-aspartic acid, which was initially placed about 2 nm away from thesulfur atoms in the active site, spontaneously entered into the racemase with theassistance of amino acid residues locating around the cleft. The observed docking
process enabled us to propose the molecular mechanism and roles of the amino acidresidues by comparing with the results of the mutation analyses. The roles of watermolecules will also be discussed at the presentation.

REFERENCE
1) L. Liu, K. Iwata, A. Kita, Y. Kawarabayashi, M. Yohda and K. Miki, J. Mol. Biol. 319(2002) 479-489.

P34
Transplantation of the PEACH5.8 from UNIX to Windows System,
and Practice in the Simulation of TNA
Yoshiro Nakata , Wataru Ootani, Noriaki Saitoh
Department of Biophysics, Faculty of Engineering, Gunma University

PEACH Ver.5.81), a program for molecular dynamics simulation of biochemicalmolecules, was transplanted from UNIX to Windows computer2,3). Visual Fortran was
utilized as the compiler.
TNA, (L)- α -threofuranosyl oligonucleotides, would be considered a possible
ancestor of RNA. The TNA strands can also pair up with complementary strands of
RNA and DNA. MD simulations of TNA model structure were performed at several
different temperatures. The structures of 1N1O and 1JGR in Protein Data Bank4) wereused as the initial structure of the simulations. The 1N1O molecule is B-form DNA
duplex containing (L)-α-threofuranosyl nucleotides, and 1JGR is just B-form DNA
which have the same base sequences. MD simulations were carried out under theperiodic boundary condition by using the parallel computing with MPICH Ver1.2.55).
The number of solvent water molecules for the simulation was 10831 molecules. The
simulations were performed at the atmospheric pressure for 500ps with a 0.4ps coupling
parameter.
The results of MD simulations indicated that the 1N1O structure is a little bit
unstable, compared with 1JGR structure with the data of the root mean squaredeviations (RMSD) of the structure.
References
1) http://staff.aist.go.jp/y-komeiji/peach/peach.html
2) J.Comput.Chem.Jpn, Vol.2, No.4, pp149-154(2003)
3) http://www3.nibh.go.jp/~nakata/peach/peachw.html
4) http://www.rcsb.org/pdb/
5) http://www-unix.mcs.anl.gov/~ashton/mpich.nt/References

P35
Semi-automatic interpretation of experimental results based on conceptual
network
Asako Koike 1,2 and Toshihisa Takagi 1
Dept. of Computational Biology, Graduate School of Frontier Science, The Universityof Tokyo, Kiban-3A1(CB01) 5-1-5, Kashiwanoha Kashiwa, Chiba, 277-8561, Japan 1,
Central Research Laboratory, Hitachi Ltd. 1-280 Higashi-Koigakubo Kokubunji, Tokyo,
185-8601, Japan 2

With the exploitation of high-throughput technologies such as DNA/Protein array,
RNAi, and differential display, the demand of automatic/semi-automatic interpretationof experimental results based on the amount of published text is increased. We havedeveloped a system called BioTermNet to cluster genes/concepts and calculate theexplicit and implicit relationships between multiple concepts based on information
extraction and information retrieval techniques to efficiently interpret high-throughput
experimental results. The relationships between multiple concepts are mainly presented
using three methods: association matrix, its hierarchical tree, and conceptual network.
An overview of the relationships between multiple genes/concepts can be obtained byusing the association matrix and its hierarchical tree, while the detailed and/or causal
relationships can be presented by the conceptual network. The association matrices arecalculated using the vectors whose components are the co-occurred concepts using Lnu
term weighting and protein interactions extracted by syntactic analysis, individually.
The conceptual network is calculated by the hybrid method of Lnu term weighting andthe protein-interactions and functions using syntactic analysis (PRIME data developed
our laboratory). The adequacy of the definition of gene relatedness is verified using
physically interacting gene pairs and genes with the same gene ontology annotation.
When this system is used to interpret microarray data of cancer cells, it creates aplausible distance matrix, its hierarchical tree, and an understandable conceptual
network connecting similarly expressed genes and other concepts. Further, this systempresents reasonable commonly related concepts such as diseases, phenotypes, drugs,
and functions of similarly expressed genes with/without mediating protein/geneinteractions. This system is also useful for knowledge discoveries such as to findpotentially new target diseases for drugs. The BioTermNet system is available at
http://btn.ontology.ims.u-tokyo.ac.jp:8086/.

P36
Development of Effective Computer-Aided Drug Design Strategy
Noriaki Okimoto, Atsushi Suenaga, Noriyuki Futatsugi, Ryoko Yanai, Tetsu Narumi,
Yosuke Ohno, Makoto Taiji（Times New Roman/Times12pt）
Computational and Experimental Systems Biology Group, RIKEN Genomic Sciences
CenterLead discovery is one of the most important components in rational drug
design. The structure-based virtual screening is very effective for lead discovery1,2. To
predict ligand–receptor complex structures and estimate their binding affinity, many
docking programs, such as DOCK, AutoDock, and FlexX, have been widely used.
However, the ability on estimation of binding free energy for the docking programs is
not accurate because most of them do not consider the flexibility of proteins and thesolvation effects. Therefore we used a docking approach of flexible ligand – flexible
receptor in an aqueous environment based on molecular dynamics simulation, and try todevelop more cost-effective and more accurate computer-aided drug design strategy. In
this study we evaluated the ligand binding affinities on several different proteins andresearch more efficient method to estimate the binding free energy between ligand andreceptor.

References
1.

Kuhn, B., Gerber, P., Schulz-Gasch, T. & Stahl, M. Validation and Useof the MM-PBSA Approach for Drug Discovery. J Med Chem 48, 4040-8(2005).
2.
Wang, J., Kang, X., Kuntz, I.D. & Kollman, P.A. Hierarchical database
screenings for HIV-1 reverse transcriptase using a pharmacophore model, rigid
docking, solvation docking, and MM-PB/SA. J Med Chem 48, 2432-44 (2005).

P37
Novel Type of Secondary Structure Breaker in Soluble Proteins
Kenichiro Imai, Shigeki Mitaku
Nagoya University, Graduate School of Engineering, Department of Applied Physics
Breaking signal of secondary structure put strong limitation on the tertiary structure ofprotein. Pro and Gly are well-known secondary structure breakers and enhance theoccurrence of disordered structure. In addition to these residues, amino acids withamphiphilic side chains having both a polar group and a flexible hydrocarbon (Arg, Lys,
His, Glu, Gln) are more likely to be present in disorder region.1 Previously, we foundthat these amphiphilic residues commonly exist at the terminal region of transmembrane
helices.2-4 The amino acid composition of disordered region in soluble proteins together
with those at terminal region in membrane proteins strongly suggest that clusters ofamphiphilic residues generally correlate with breaking points for secondary structures
and amphiphilic clusters are thought to be a new type of secondary structure breaker.
Herein, we examined the distribution of amphiphilic clusters together with Pro and Gly
in amino acid sequence of soluble protein to determine the environment that drives asegment to form a loop. In addition to Pro and Gly clusters, clusters of amphiphilic
residues were found to be secondary structure breakers depending on specific conditions,
including the average hydrophobicity, the helical periodicity, the density of Ser and Thrand presence of Trp and Tyr clusters. We carried out principal component analysis ofenvironmental factors to identify candidate breakers in secondary structure breaking
regions. Predicted breakers were located in the loop segments with accuracy of 90%,
covering 63% of all loop segments.
References
1. Dunker, A.K., Lawson, J.D., Brown, C.J., Williams, R.M., Romero, P., Oh, J.S.,
Oldfield, C.J., Campen, A.M., Ratliff, C.M., Hipps, K.W., Ausio, J., Nissen, M.S.,
Reeves, R., Kang, C., Kissinger, C.R., Bailey, R.W., Griswold, M.D., Chiu, W., Garner,
E.C. and Obradovic, Z. Intrinsically disordered protein. J Mol Graph Model. 19, 26-59(2001).
2. Mitaku, S., Hirokawa, T., and Tsuji T. Amphiphilicity index of polar amino acids as
an aid in the characterization of amino acid preference at membrane-water interfaces.
Bioinformatics. 18, 608-616 (2002).
3. Hirokawa, T., Boon-Chieng, S. and Mitaku S. SOSUI: Classification and secondary
structure prediction system for membrane proteins. Bioinformatics. 14, 378-379 (1998).
4. Mitaku, S. and Tsuji T. Features of transmembrane helices useful for membraneprotein prediction. CBIJ. 4, 110-120 (2004).

P38
A solvent site-dipole field mediating DNA-protein binding
Nobuyuki Hamasaki1, Daisuke Mitomo1, Junich Higo1, Akihiko Yamagishi1, Hiroh
Miyagawa 2
School of Life Science, Tokyo University of Pharmacy and Life Science1
Taisyo Pharmaceutical co., Ltd2
It has been believed that binding of bio-molecules, realized in enzyme-substrate or
DNA-protein complexes, is controlled by a random diffusion of the bio-molecules.
This implies that the binding occurs by chance only when the binding sites of the biomolecules encounter to each other with appropriate relative orientations. On the other
hand, a computer-simulation work proposed another binding mechanism that the biomolecules can approach to each other through a field, i.e., solvent site-dipole field (1-3).
In this mechanism, water-molecule orientations coherently order in space between the
bio-molecules even when the two bio-molecules are apart by about 20 Å to each other.
Physico-chemically saying, this ordering makes the electrostatic interactions longer
range than expected from a continuum theory. However, no experimental support on
the existence of the solvent site-dipole field has been provided yet, because highly timedependent measurement at an atomic resolution is required to detect the field. If twobio-molecules are known to bind experimentally and also computationally, analysis on
the simulation data may provide a key to understand the binding mechanism. For thispurpose, we chose a DNA-peptide complex (DNA-protein HMG-I(Y) fragmentcomplex (4)) for the simulation. We observed that the two bio-molecules, which were
initially put apart by about 20 Å to each other in explicit solvent, bound during thesimulation. The analysis showed that the solvent site-dipole field mediate the binding.
We will report details of the analysis.

References
1. Higo J., Nakasako M.: J. Comp. Chem., 23, 1333 (2002).
2. Higo J., Sasai M., Shirai H., Nakamura H., Kugimiya T.: Proc. Natl. Acad. Sci. USA,98, 5961 (2001).
3. Yokomizo T., Yagihara S., Higo J.: Chem. Phys. Lett. 374, 453 (2003).
4. Huth R.H., Bewly, C.A.: Nature Struct. Boil., 4 , 657 (1997).

P39
Development of a Three-Dimensional-Structure Database of Natural
Metabolites (3DMET) and Problems about Conversion from 2D to 3DStructures.
Miki Maeda and Hisataka Numa
Genome Research Department, National Institute of Agrobiological Sciences
We have been developing a three-dimensional-structure database of natural metabolites(3DMET1) and the first release of the database is accessible since July 1st, 2005. The
database provides 3D-structures of metabolites and several molecular properties basedon the 3D-structures. The structures are searchable by their properties. For example, themolecular weights of compounds can be specified between 100 and 200. The databaseincludes 1123 compounds with 3D-structures in this release.
The 3D-structures were deduced from the 2D-strctures of the COMPOUND database (apart of the LIGAND database2) of KEGG3. The 2D mol files of COMPOUND wereconverted to 3D SYBYL mol2 files by using SYBYL4/CONCORD5 and MOE6. Everystructure was minimized by the MM calculation module of each program. Respective
results by SYBYL and MOE may not reflect their initial chiralities. Therefore, thechiralities of the two structures were compared by aRSchirality of MOE. The compounddata was added to the 3DMET database if the following two conditions were satisfied:(1) The chiralities of the two structures obtained from the same compound wereidentical and (2) the data of the COMPOUND database included the PATHWAY,
REACTION, or ENZYME line. In this version, only the structures whose chiralities
were strictly matched each other were only collected. However, some identicalstructures were excluded due to different descriptions in the data derived from the orderof hydrogen added by the programs. These structures will be recovered in the future
release.
References
1) 3DMET: A Three-Dimensional-Structure Database of Natural Metabolites.
http://www.3dmet.dna.affrc.go.jp/
2) Goto, S., Nishioka, T., and Kanehisa, M., LIGAND: Chemical Database for Enzyme
Reactions. Bioinformatics, vol. 14, pp591-599 (1998)
3) KEGG: Kyoto Encyclopedia of Genes and Genomes. http://www.genome.jp/
4) SYBYL, Tripos Inc. St. Louis, MO, USA.
5) CONCORDTM, Optive Research, Inc., Austin, TX, USA (Now, Tripos Inc.).
6) MOE (Molecular Operating Environment), Chemical Computing Group, Montreal,
Canada.

P40

How effective for fold recognition is a potential of mean force
that includes relative orientations between contacting residues in proteins?

Sanzo Miyazawa 1 , Robert L. Jernigan2
miyazawa@smlab.sci.gunma-u.ac.jp, Faculty of Technology, Gunma University, Japan1
L. H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, USA2
”Statistical potentials”, which are estimated from statistical preferences observed in protein
crystal structures, are widely used to recognize and fold protein structures as well as to assessinteractions between proteins. A pairwise isotropic contact potential between the 20 types ofamino acids is a typical statistical potential. In order to extend the capability of the contact
potential, we estimate the statistical distribution of relative orientations between contacting
residues from a database of protein structures and evaluate the potential of mean force forrelative orientations between contacting residue. A total contact energy for contacting residue
pair is evaluated as a sum of the isotropic contact energy and the present orientational potential. Polar angles and Euler angles are used to specify two degrees of directional freedom
and three degrees of rotational freedom for the orientation of one residue relative to anotherin contacting residues, respectively. A local coordinate system affixed to each residue based
only on main chain atoms is defined for fold recognition. The 4435 protein domains definedin SCOP-1.61 were used with sampling weights determined on the basis of a sequence identity
matrix between them; the effective number of contacting residue pairs used is equal to 1467302.
The number of contacting residue pairs in the database will severely limit the resolution of thestatistical distribution of relative orientations, if it is estimated by dividing space into cells
and counting samples observed in each cell. To overcome such problems and to evaluate thefully-anisotropic distributions of relative orientations as a function of polar and Euler angles,
we choose a method1 in which the observed distribution is represented as a sum of δ functions
each of which represents the observed orientation of a contacting residue, and is evaluated asa series expansion of spherical harmonics functions. The sample size limits the frequencies
of modes whose expansion coefficients can be reliably estimated. High frequency modes are
statistically less reliable than low frequency modes. Each expansion coefficient is separately
corrected for the sample size according to suggestions from a Bayesian statistical analysis. As
a result, many expansion terms can be utilized to evaluate orientational distributions. Also,
unlike other orientational potentials, the uniform distribution is used for a reference distribution in evaluating a potential of mean force for each type of contacting residue pair from its
orientational distribution, so that residue-residue orientations can be fully utilized to recognize
protein structures. The zero energy level of the orientational potential, which is formulated
as a logarithm of the probability density, is defined such that the expected value of orientational energy for the native folds is equal to zero for each type of contacting residue pair. It isshown by using decoy sets (”Decoys’R’Us”) that the discrimination power of the orientational
potential in fold recognition increases by taking account of both the polar and the Euler angle
dependences, and becomes comparable to that of a simple contact potential. In the result,
the total energy potential taken as a simple sum of contact, orientation, and backbone (φ, ψ)potentials identifies native structures better than any other method including a CHARMM
potential. In addition, the results strongly indicate that all these energy terms complement
each other and are needed to recognize native structures in a wide range of decoys from nearnative to denatured structures.
1. Onizuka, K., Noguchi, T., Akiyama, Y., Matsuda, H.: Intelligent Systems, 17, 48-54, (2002).

P41
MOLWORKS+G: Integrated Workbench for the Chimo-Bio Molecular
Design by Grid Computing
Fumikazu KONISHI 1, Toru YAGI2, Akihiko KONAGAYA１
RIKEN GSC, Advanced Genome Information Technology Research Group 1,
BestSystems Inc.2
In this paper, we present MolWorks+G an integrated workbench for a molecular design
based on Grid computing. In silico drug design takes a computational challenge ofmolecular dynamics (MD) simulations for protein science brought about by theinteractions among chemical compounds, genes, and proteins. The target simulations
required mass computations will appear to demonstrate significant result in the
post-genomic era. In this direction, a peta FLOPS computer will be available fortime-consuming and expensive applications such as MD simulation, and a virtual
screening for target compound validation will play a key role in drag design for novel
drug discovery. In order to perform a virtual screening, we need to prepare a parameterfor target chemical compound before. Those compounds had been collected in database.
A chemical compound database such as the Cambridge Structural Database1 (CDC)
registers 325,000 entries of the crystal structure information of the organic compounds
and the organo-metallic compounds, and it will use for a docking simulation by using
MD approaches. However there is a realistic issue about the preparation for the force
field parameters which are calculated from those each compounds by quantum chemical
calculation programs such as Gaussian2 or Q-Chem3. To calculate a force field for eachtarget compound for drug discovery needs a validation process of several candidates’
structures by Chemist and/or Pharmacologist.
REFERENCES
1.
The Cambridge Structural Database: a quarter of a million crystal structures andrising F. H. Allen, Acta Crystallogr., B58, 380-388, 2002
2.
Gaussian - http://www.gaussian.com/
3.
Q-Chem - http://www.q-chem.com/
4.
Tajima, S., Nagashima, U., Hosoya, H., J. Comput. Chem. Jpn., 2002, 1, 103
5.
MolWorks – http://www.molworks.com
6.
GAMESS - http://www.msg.ameslab.gov/GAMESS
7.
Stewart, J.J.P., J. Comput. Chem., 1989, 10, 209, 221.
8.
Pople, J. A., Beveridge, D. L., “Approximate Molecular Orbital Theory”,
McGraw-Hill, New York, 1970.

P42
A mutation in a cuticle collagen causes hypersensitivity to bisphenol A in
Caenorhabditis elegans
Masahito WATANABE 1, Nanako MITANI1, Naoaki ISHII2, Keizaburo MIKI3
Japanese Institute of Pearl Science.1
Department of Molecular Life Science, Tokai University School of Medicine2
BIOS Inc.3

To assess toxicity of various environmental chemical pollutants, we have used thenematode, Caenorhabditis elegans. In this study, we isolated a novel mutant, bis-1(nx3),
that is hypersensitive to bisphenol A (BPA), one of endocrine disrupting chemicals(EDC). The mutation (glycine substitution) was located on a cuticle collagen gene,col-121. Other collagen mutants so far studied, dpy-2(e8), dpy-7(e88) and dpy-10(e128)
also showed sensitivity to BPA, indicating that collagen mutations cause
hypersensitivity to BPA. These collagen mutants may provide a starting point for a
useful tool of EDC assay.

REFERENCE
1) Masahito Watanabe, Nanako Mitani, Naoaki Ishii, Keizaburo Miki: A mutation in
cuticle collagen causes hypersensitivity to the endocrine disrupting chemical, bisphenol
A, in Caenorhabditis elegans, Mutat Res. Vol.570, 71-80 (2005).

P43
KiBank, major new developments and status in 2005
Junwei Zhang1, Masahiro Aizawa1, Shinji Amari1, Yoshio Iwasawa2,
Tatsuya Nakano3, Kotoko Nakata3
Collaborative Research Center of Frontier Simulation Software for Industrial Science,
Institute of Industrial Science, University of Tokyo 1, AdvanceSoft Corporation 2,
Division of Safety Information on Drug, Food and Chemicals, National Institute of
Health Sciences 3
KiBank (http://kibank.iis.u-tokyo.ac.jp), launched on Oct 1st, 2003, is a databasedeveloped for computer-aided drug design. It contains protein and chemical structures
as well as experimental binding affinities (Ki values) [1, 2]. KiBank has kept growing
since last year. The entries of proteins and chemicals were expanded and some new
application functions were added. Presently, over 250 target proteins (including 50
with structures) covering nuclear and membrane receptors, enzymes, transporters, ion
channels and ion pumps, nearly 8000 chemicals (including 4000 with structures) and
11600 Ki values are available. With the data update, download buttons for the affinity
data and the chemical and protein structural data were added. For each chemical with Ki
values, we also provide a page displaying binding affinity data between the chemical
and the corresponding proteins. The data in KiBank have been being widely used instructure based drug design (SBDD), quantitative structure-activity relationship (QSAR)
research and some other computational approaches analyzing the protein-ligand
interactions [3].
Reference
1. Masahiro Aizawa, Kenji Onodera, Junwei Zhang, Shinji Amari, Yoshio Iwasawa,
Tatsuya Nakano and Kotoko Nakata: KiBank: A Database for Computer-Aided Drug
Design Based on Protein-Chemical Interaction Analysis, Yakugaku Zasshi. vol.124(9),pp.613-619 (2004).
2. Junwei Zhang, Masahiro Aizawa, Shinji Amari, Yoshio Iwasawa, Tatsuya Nakano
and Kotoko Nakata: Development of KiBank, a database supporting structure-based
drug design, Comput Biol Chem. vol.28 (5-6), pp.401-407 (2004).
3. Kaori Fukuzawa, Kazuo Kitaura, Masami Uebayasi, Kotoko Nakata, Tsuguchika
Kaminuma and Tatsuya Nakano: Ab initio Quantum Mechanical Study of the Binding
Energies of Human Estrogen Receptor α with Its Ligands: An Application of Fragment
Molecular Orbital Method, J Comput Chem. vol.26, pp.1-10 (2005).

P45
Software system for the prediction of mitochondria localization on thebasis of physicochemical profiles in amino terminal segments
Toshiyuki TSUJI1,2, Shigeki. MITAKU1
Department of Applied Physics Graduate School of Engineering Nagoya University1,21st Century COE program “Frontiers of Computational Science” Nagoya University 2
Most proteins in mitochondria are synthesized in cytoplasm by genomic informationand translocated to mitochondria from the cytoplasmic side. It is considered thattranslocation machineries recognize some signals in amino acid sequences at the amino
terminus. We first prepared two dataset of amino acid sequence for the mitochondrialand cytoplasmic proteins. Then, several physicochemical properties of amino terminal
segments were compared between the two types of proteins.
The results indicated that the amino terminal segments of mitochondrial proteinsstatistically have many positive charges, moderate hydrophobicity, and preferredaromatic residues. About 85% of positive data could be predicted by these parameters.
Similar analysis for the carboxyl terminal segments will be the future work for thecomplete prediction of mitochondria localization of proteins.

REFFERNCES
1.

Roise, D., and Schatz, G., (1988) Mitochondrial presequences J Biol Chem. 263,
4509-4511
2. Kovermann, P., Truscott, K.N., Guiard, B., Rehling, P., Sepuri, N.B., Muller, H.,
Jensen, R.E., Wagner, R., and Pfanner, N. (2002) Tim22, the essential core of themitochondrial protein insertion complex, forms a voltage-activated and signal-gated3.

channel Mol Cell. 9, 363-373
Abe, Y., Shodai, T., Muto, T., Mihara, K., Torii, H., Nishikawa, S., Endo, T., andKohda D (2000) Structural basis of presequence recognition by the mitochondrial
protein import receptor Tom20 Cell 100, 551-560

P46
Comparative Analyses for Selecting Effective siRNA Target Sequences
Shigeru TAKASAKI 1, Akihiko KONAGAYA1
Advanced Genome Information Technology Group, RIKEN Genomic Sciences Center 1
Abstract
Because the short interfering RNA (siRNA) widely used for studying gene functions
in mammalian cells varies markedly in its gene-silencing efficacy, several siRNA design
rules/guidelines have been reported recently. Analyzing the reported siRNA design
guidelines from qualitative and quantitative points of views, we found that they were
not always effective selection rules for many other mammalian genes. Though somerules from the guidelines are suitable for extracting effective sequences for specificgenes, they might sometimes be unsuitable for selecting sequences for other genes.
Since the gene-silencing efficacy depends very much on the target sequence positions
selected from the target gene, we examined 361 effective siRNA sequences from 227
different mammalian cDNAs in the literature. As a result, we got eleven preferred andeight unpreferred nucleotides different from the ones used in the previous guidelines.
These sequence-dependent nucleotides could be used as a more general guideline forselecting new siRNA sequences in target genes. We proposed a measure (score) for
selecting effective siRNA candidates based on the positional features of specificsignificant nucleotides and demonstrated the effectiveness of the proposed scores for the
recently reported genes. We also discussed the A/T content efficacy and optimal GC
content in siRNA sequences.
References
1) Takasaki, S, Konagaya, A: COMPARATIVE ANALYSES FOR SELECTIING
EFFECTIVE SIRNA TARGET SEQUENCES, Proceedings of KDDMBS pp.12-28,
2005.
2) Reynolds, A, Leake, D, Boese, Q, Scaringe, S, Marshall, W.S et al.: Rational siRNAdesign for RNA interference. Nat. Biotech. 22, 326-330, 2004.
3) Ui-Tei, K, Naito, Y, Takahashi, F, Haraguchi, T, Ohki-Hamazaki H et al.: Guidelines
for the selection of highly effective siRNA sequences for mammalian and chick
RNA interference. Nucleic Acids Res. 32, 936-948, 2004.
4) Amarzguioui, M. & Prydz, H.: An algorithm for selection of functional siRNAsequences. Biochem. Biophys. Res. Commun. 316, 1050-1058, 2004.
5) Takasaki, S, Kotani, S, Konagaya, A: An Effective Method for Selecting siRNA
Target Sequences in Mammalian Cells. Cell Cycle 3, 790-795, 2004.

P47
Development of a Workbench for Selective Nuclear Receptor Modulator
Naomi Komiyama 1, Tatsuya Nakano 2, Kaori Fukuzawa 3, Junpei Komiyama 4,
Masumi Yukawa 5, Kotoko Nakata 6, Tsuguchika Kaminuma 7
Chem-Bio Informatics Society 1, The National Institute of Health Sciences 2,
Mizuho Research Institute 3, The University of Tokyo4, Tokyo Medical and Dental
University 5, IIS, University of Tokyo / Advance Soft Co 6, Hiroshima University 7

Nuclear receptors are known as important therapeutic targets for many diseases
including endocrine and metabolic disorders and cancer. Selective Nuclear Receptor
Modulators (SNuRMs) are drug candidate chemicals for these targets. Although there
already exist many SNuRM drugs in the market, many of them can not escape from side
effect problems. Thus the research on SNuRM is one of the hot spots in drug discovery
research. We are interested in this topic from two reasons. One is to use them as sample
systems to test large scale molecular computing methods such as the FMO (fragment
molecular orbital) method. Second it is to use them as materials for tutorial and trainingof computational chemistry particularly for rational drug design. Our eventual goal is toprovide essential methods, materials, and sample numerical data to start modeling andanalyzing ligand-binding domains of nuclear receptors and to carry docking simulationof nuclear receptors with ligands. We called this facility as the Workbench for Selective
Nuclear Receptor Modulator study.
Among 48 superfamily members of nuclear receptors, about half of them are
identified some ligands. There already exists more than 80 X-ray crystallography data
deposited in PDB, some of which are only receptors and some are with ligands. In
addition there are large amount of Ki values. We worked on these data to produce data
sets from which one can easily start 3D modeling and energy calculations. The
Workbench has sample data files for nuclear receptors, their ligands, their DNA
response elements (DNA sequence patters of binding site of the nuclear receptors), 3D
graphical models for nuclear receptors and their complex with ligands, data analyzing
tools, and model viewers. The workbench has been developed in parallel with carrying alarge scale molecular docking study FXR.
References
1) T. Kaminuma, CBI Journal, 3, 130-156 (2003)
2) Fukuzawa, K., et al., J. Comp. Chem., 26, 1-10 (2005)

P48
In silico analysis of biosynthesis rate limiting factor in cell-free system
Naoya FUJITA 1, Motoaki SEKI2, Kazuki SHINOZAKI2, Kengo KINOSHITA1,
Tatsuya SAWASAKI3, Yaeta ENDO3, Kenta NAKAI1,
Inst. of Med. Sci., Univ. of Tokyo1, GSC, RIKEN2, CSTRC, Ehime Univ.3
For the functional analysis of proteins, it is fundamental to get proteins themselves.
Wheat germ cell-free system, which is an in vitro biosynthesis system using wheat germ
extracts, is a helpful method for the purpose. It was technically improved to yield
sufficient amounts, but depending on the proteins it still results in low production.
Using informatics, we searched the reason for these low biosynthesis rates, andespecially focused on potential interactions and entanglements between amino acidsequences on polysome. 425 protein kinases from Arabidopsis thaliana were used asdataset.
As possible interactions, we assumed disorders and coiled coils and predicted themusing DISOPRED2 and COILS. Disordered regions are flexible and dynamic parts of aprotein. Coiled coil structures are constructed between α-helices, and usually dimers
and trimers are made. The result is that those two interactive regions are suggested asrate limiting factors, and two tendencies are observed.
If the N-terminus is a highly disordered region, the yield will be low. However,proteins that have few disordered region in the N-terminus are distributed in wider
range of biosynthesis rate. Therefore, in the low disorder case, we checked the coiled
coil structure. The difference of yield distribution between proteins that contain coiled
coils and other proteins is significant and leads to another tendency. For proteins withlow disorder, if a coiled coil structure exists, the yield will not be high. Some additional
experiments are currently being carried out in order to improve our results.
In summary, we showed that the sequence of the N-terminus region of protein kinasesof A. thaliana plays a significant role in determining their biosynthesis yields. A highdisorder or presence of coiled coils in this region will result in a low yield. Whether
these tendencies also apply to other protein classes and are valid in different organisms
remains to be determined.
References
1) Tatsuya Sawasaki, Tomio Ogasawara, Ryo Morishita, and Yaeta Endo: A cell-freeprotein synthesis system for high-throughput proteomics, Proc. Natl. Acad. Sci. USA 99,
pp.14652-14657 (2002)

P49
Cis-Regulatory Machinery Unit (CiRMU) Model: A portable system forstudying gene transcriptional processes
Masumi Yukawa 1, Yoshitomo Tanaka 1, Hiroshi Tanaka 1, Kotoko Nakata 2,
Naomi Komiyama 3, Tsuguchika Kaminuma 4
Tokyo Medical and Dental University 1, IIS, University of Tokyo / Advance Soft Co 2,
Chem-Bioinformatics Society 3, Hiroshima University 4
Descriptions and analyses of gene expression processes are now emerging as one of
the most highlighted areas of bioinformatics. In addition to the existingtranscription factor databases new bioinformatics tools for searching target genes oftranscription factors, analyzing promoter structures, identifying cis-regulatory
elements, identifying DNA response elements, and elucidating and making
databases of gene regulatory networks have been developed. Some of these systems
are of large scale and open for public, while some are quite small and used only bylimited number of researchers. We are developing a small scale computer program
called CiRMU（Cis-Regulatory Machinery Unit）. The system is designed to assist for
extracting and editing data and knowledge relevant for making logical frame work
models of gene expression processes.
The design consideration of this system is as follows: (1) it has interface to search
public websites for transcription factors, their ligands, their cofactors, and theirtarget genes in addition to its own small database storing these data, (2) it has
graphical user interfaces that enable researchers to trace and analyze pathways
and networks that consist of cell signal transductions that trigger gene expression,
protein synthesis, and metabolic processes by the resultant proteins, (3) it has
storage and retrieval capabilities for original papers, (4) it is easy to be implement
and be update, (4) when several units of this system is combined they can produce
causal multiple loops (networks) of gene expressions.

Gene expression processes are intrinsically complicated and dynamic. They depend on
tissue and cell types that are basically related to the cell lineage, environment of thecells, and also timing. Therefore we are not aiming to design general purpose system but
focusing our initial effort on special type of transcription factors, namely nuclear
receptors, that are deeply related to various metabolic syndromes.

P50
A Knowledge Environment for Adipocyte and Adipokine Research
Tsuguchika Kaminuma 1, Masumi Yukawa 3, Naomi Komiyama 2,
Yoshitomo Tanaka 3, Hiroshi Tanaka 3
Hiroshima University 1, Chem-Bio Informatics Society 2,
Tokyo Medical and Dental University 3

One of the emerging needs in Bioinformatics in the post genome sequence era is how
to handle vast amount of data and knowledge. Although many of the current knowledge
offering systems claim user friendliness, it is not so easy for biomedical researchers totouch on the most recent information and to access the proper knowledge sources
without any help of information specialists. What will be need in the near future is someagent either of human, computer, or their combination that facilitate easy accessenvironment to the rich biomedical knowledge resources.
In order to develop such an agent type system we made a preliminary study choosing
a specific biomedical research area, namely adipocyte and adipokine. Adipocytes arecells in adipose tissues and adipokines are the molecules produced in such cells. In thepast decade these cells and molecules are identified as the key players in metabolic
syndromes particularly at their onset stages. Adiponectine, leptin, resistin, TNF-alpha,IL-6 are some of these molecules. Currently mutually reciprocal roles of adiponectin
and TNF-alpha are attracting special interests of clinical researchers in these fields.
Structurally they belong to the same superfamily yet function quite oppositely.
Adiponectin give good effects to obesity and diabetes while TNF-alphas work badly.
We made detailed survey of large number of public databases from genome databases
to disease-oriented databases to find data and knowledge on Adiponectin and
TNF-alpha relevant for resolving this problem. These surveys include gene structure,promoter structure, cis-regulatory elements, DNA binding factors that trigger gene
expressions of these two molecules, ligands to these biding factors, target genes of thesebinding factors, protein isoforms, proteins that interact to these molecules, pathways
and networks that include these molecules as their nodes.
It was found that the relevant knowledge is tremendous, and the part of these
difficulties.

P51
Protein-protein interaction sites prediction and protein-protein docking by
the methods of MD, grid scoring and the pair-wise interaction potential
in CAPRI Rounds 3-5
Genki Terashi1, Mayuko Takeda-Shitaka1, Daisuke Takaya1, Katsuichiro Komatsu1,
Kazuhiko Kanou1, Mitsuo Iwadate1, Hideaki Umeyama1
School of Pharmaceutical Sciences, Kitasato University1
CAPRI (the Critical Assessment of PRedicted Interaction) is a communitywide
experiment to assess the capacity of protein-docking methods to predict protein-protein
interactions. We developed a protein-protein docking program that was applied to the all
targets of CAPRI. In CAPRI Rounds 1-2, we assumed that, because there are manyionic charges that weaken electrostatic interaction forces in living cells, the hydrophobic
interaction force might be important entropically. As a result of Rounds 1-2, thepredictions for binding sites and geometric centers were acceptable, but those of thebinding axes were poor because only the largest benzene cluster was used for generating
the initial docking structures. These were generated by fitting of benzene clusters
formed on the surface of receptor and ligand. In CAPRI Rounds 3-5, the grid scoringsum on the protein-protein interaction surface and the pairwise potential of the aminoacid residues, which were indicated as coming easily into the protein-protein interaction
regions, were used as the calculation methods along with the smaller benzene clusters
that participated in benzene cluster fitting.
Good predicted models were obtained for Targets 11 and 12. When the modeled
receptor proteins were superimposed on the experimental structures, the smallest
Ligand-rmsd values corresponding to the rmsd between the model and experimental
structures were 6.2 and 7.3 Å, respectively.
1. Terashi G, Takeda-Shitaka M, Takaya D, Komatsu K, Kanou K, Iwadate M,
Umeyama H. Searching for protein-protein interaction sites and protein-protein
docking by the methods of molecular dynamics, grid scoring and the pair-wiseinteraction potential of amino acid residues. Proteins 2005;60(2); in press.
2. Komatsu K, Kurihara Y, Iwadate M, Takeda-Shitaka M, Umeyama H. Evaluation of
the third solvent clusters fitting procedure for the prediction of protein–protein
interactions based on the results at the CAPRI blind docking study. Proteins
2003;52:15-18.
3. Yoneda S, Umeyama H. Free energy perturbation calculations on multiple mutation
bases. J Chem Phys 1992;97:6730-6736.

P52
Protein Structure Prediction in the 6th round of Critical Assessment ofTechniques for Protein Structure Prediction (CASP6)
using CHIMERA and FAMS
Mayuko TAKEDA-SHITAKA1, Genki TERASHI1, Daisuke TAKAYA1,
Kazuhiko KANOU1, Mitsuo IWADATE1, Hideaki UMEYAMA1
School of Pharmaceutical Sciences, Kitasato University1
Homology modeling is the method that constructs 3D model structures of unknown
proteins based on known homologous 3D structures. Homology modeling has become akey component in post-genomic era because of huge amounts of protein sequences andthe growing number of known structures. The Critical Assessment of Techniques forProtein Structure Prediction (CASP) is the blind contest of protein structure prediction.
In CASP6, the CHIMERA-group predicted full-atom models of all targets using
SKE-CHIMERA. SKE-CHIMERA is a web user interface system for protein structure
prediction that allows human intervention at necessary stages, using enormous
information from our own data and from publicly available data. Using
SKE-CHIMERA, we iterated manual step (template selection and alignment by
in-house program CHIMERA) and automatic step (three-dimensional model building by
in-house program FAMS). The official CASP6 assessment showed that
CHIMERA-group was one of the most successful predictors in homology modeling,
especially for FR/H (Fold Recognition/Homologous). Here, we introduce the method of
CHIMERA-group, and discuss its successes and failures in CASP6.
References1.

Takeda-Shitaka M, Terashi G, Takaya D, Kanou K, Iwadate M, Umeyama H. Protein Structure
Prediction in CASP6 using CHIMERA and FAMS. Proteins; submitted.

2.

Takeda-Shitaka M, Takaya D, Chiba C, Tanaka H, Umeyama H. Protein Structure Prediction in
Structure Based Drug Design. Curr Med Chem 2004;11:551-558.

3.

Yoneda T, Komooka H, Umeyama H. A computer modeling study of the interaction between tissue
factor pathway inhibitor and blood coagulation factor Xa. J Protein Chem 1997;16:597-605.

4.

Takeda-Shitaka M, Umeyama H. Elucidation of the cause for reduced activity of abnormal humanplasmin containing an Ala55-Thr mutation: importance of highly conserved Ala55 in serine
proteases. FEBS Lett 1998;425:448-452.

5.

Ogata K, Umeyama H. An automatic homology modeling method consisting of database searches
and simulated annealing. J Mol Graphics Mod 2000;18:258-272.

P53
Performance of multi-stage docking simulation
with a fast Poisson-Boltzmann solver and re-scoring methods
Hiroaki FUKUNISHI 1 , Jiro SHIMADA1 , Daisuke TOKUSHIMA1 , Takumi WASHIO 1 ,Hiroshi KURAMOCHI2
NEC Corporation 1 , NIPPON KAYAKU CO., LTD.2
In order to find novel drug candidates in compound library by in silico screening, we
performed “Multi-stage docking simulation” in which molecular model is changed,
stage by stage, from the simple /empirical one to the more realistic/rigorous.
Specifically, our approach comprises the following four stages:(1) Docking simulation by FlexX to find plausible docked poses to the active site.
(2) Re-scoring of the compounds and poses by our newly developed score.
(3) Refining the poses and estimating binding energy on the basis of molecularmechanical model incorporated with solvation effect through a fast PoissonBoltzmann solver (MM + PBSA).
(4) Estimating more accurate binding energy by full-atom MD and by QM/MM
simulations, though this final stage is beyond the scope of this report.
We applied our approach to DNA Gyrase, which plays an important role inreplication and transcription of DNA. Since DNA Gyrase is an essential enzyme inprokaryotes and there is no highly homologous
enzyme in mammalian, it is considered to be an
ideal target for antibiotics. According to in
Silico screening result published, Gyrase is adifficult target for FlexX, compared with othertargets1 . Our result indicated that MM+PBSA
combined with our newly developed rescoring
method was superior to the FlexX and otherscoring as implemented in SYBYL. Figure 1
Fig.1 Binding structures docked byshows binding structures docked by FlexX and
FlexX (orange) and optimized bysubsequently optimized by MM+PBSA.
MM+PBSA (by atom); fig by InsightII.
References
1) M.Stahl, M.Rarey, J.Med.Chem., vol. 44, 1035 (2001).
*Current address (T.W.): JST and Tokyo University

P54
FAMS Complex: A fully automated homology modeling system for protein
complex structures
Mayuko TAKEDA-SHITAKA1, Genki TERASHI1, Daisuke TAKAYA1, Kazuhiko
KANOU1, Mitsuo IWADATE1, Katsuichiro KOMATSU2, Kinji FUCHIKAMI2 andHideaki UMEYAMA1
School of Pharmaceutical Sciences, Kitasato University 1, In-Silico Sciences, Inc.2
Formation of protein-protein complexes contributes many biological functions.
Therefore, three-dimensional structures of protein complexes are essential for deeper
understanding of protein functions and the mechanisms of diseases at molecular level.
In comparison with individual proteins, however, complex structures are difficult tosolve experimentally because of the technical limitations. Thus a method that can
predict protein complex structures is highly valuable. In this study, we developed new
software FAMS Complex; a fully automated homology modeling system for protein
complex structures consisting of two or more molecules. It requires only sequences andalignments of target protein as input and constructs all components simultaneously andautomatically. FAMS Complex will become the essential tool for structure-based drug
design such as in silico screening to accelerate drug discovery before the experimental
structure is solved. Moreover, in this post-genomic era that the huge amounts of protein
sequence information are available, the major goal is to determine protein-protein
interaction networks on a genomic scale. FAMS Complex will also contribute to thisgoal, because its procedure is fully automated and is suited for large-scale genome widemodeling.

REFERANCES
1)
Takeda-Shitaka, M.; Takaya, D.; Chiba, C.; Tanaka, H.; Umeyama, H. Curr.
Med. Chem., 2004, 11, 551.
2)
Takeda-Shitaka M., Nojima H., Takaya D., Kanou K., Iwadate M., andUmeyama H. Chem. Pharm. Bull., 2004, 52, 643.
3)
Ogata, K.; Umeyama, H. J. Mol. Graphics Mod., 2000, 18, 258.

P55
Fully automated protein structure predictions at CASP6
using homology-modeling server SKE-FAMSD
K. Kanou1, M. Iwadate1, G. Terashi1, D. Takaya1, M. Takeda-Shitaka1 and H. Umeyama1
1- Department of Biomolecular Desig School of Pharmaceutical Sciences, Kitasato University

To construct high-quality predictions of protein structures, fully-automatic homology-modelingserver “SKE-FAMSD” calculate some alignments for amino acid sequences using 7 kinds ofmethods, BLAST [1], PSI-BLAST, PSF-BLAST, RPS-BLAST, IMPALA, FASTA and Pfam.
PSF-BLAST is PSI-BLAST whose profile sequence group of PSSM construction process isrevised, and the selection criterion is E-value<=0.001 from template PDB sequence on
PSI-BLAST search. For selecting the “best alignment” in all alignments calculated by 7 kinds ofmethods, the score-function that was constructed by model length, homology% and degree of
secondary structure agreement between PSI-PRED and STRIDE was defined:
score = f (k i , Hom, Len, SS )Len is residue length of model protein. Hom indicate homology % value, the ratio between thenumber of match residues and Len . SS is so called Q3 value, degree of secondary structureagreement between PSI-PRED and STRIDE. ki are coefficients. The subscript number "i"indicate kind of alignment method, 0 is PSI-BLAST, 1 is BLAST, 2 is RPS-BLAST, 3 isFamily-BLAST, 4 is IMPALA, 5 is FASTA, 7 is Pfam.
The next step is the construction of structures using best scored alignment byhomology-modeling program FAMS [2]. In the gap-regions modeling, the fragment selection
procedure of FAMS was modified to select fragment of same protein family. Therefore selection
criteria were RMSD of fitting, and degree of SCOP [3] ID agreement between template PDB andfragment.
Finally, for refining constructed models by FAMS, both of Energy Minimization and Molecular
Dynamics(MD) are applied. The condition for MD calculation (temperature, time, position
constrain, torsion angle constrain etc..) was optimized using high resolution X-ray structures.
We had participated CASP6 experiment (http://predictioncenter.llnl.gov/casp6/Casp6.html) in
fully-automatic section, and all target protein structures were predicted using “SKE-FAMSD”
server.
References[1]. Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W. and Lipman, D.J. (1997). Gapped
BLAST and PSI-BLAST: a new generation of protein database search programs Nucleic Acids Res/Nucleic
Acids Res 25, 3389-3402.
[2]. Ogata, K. and Umeyama, H. (2000). An automatic homology modeling method consisting of database searches
and simulated annealing J Mol Graph Model/J Mol Graph Model 18, 258-272, 305-256.
[3]. Lo Conte, L., Brenner, S.E., Hubbard, T.J., Chothia, C. and Murzin, A.G. (2002). SCOP database in 2002:
refinements accommodate structural genomics Nucleic Acids Res/Nucleic Acids Res 30, 264-267.

P56
Development and application ofa molecular dynamics simulation system : prestoX
Satoru Kubota 1, Yoshifumi Fukunishi 2, Ikuo Fukuda 1, Masaru Horie 1,
Katumi 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: prestoX. The aim of
this system is general simulation for protein-ligand complexes and membrane proteins.
The latest version, prestoX version 3 will be released on this December.
We developed a new in silico screening method, multiple targets screening (MTS)
method, based on a multi-receptor vs. multi-ligand docking affinity matrix, andexamined the robustness against the change of the scoring system. According to thismethod, compounds in a database were docked to multiple proteins. The compounds,
which likely bind to the target protein among these proteins, are selected as themembers of the candidate-hit compound group. We prepared the two new scores, one isthe ∆G score, which is designed to reproduce the protein-ligand binding free energy,
and another is the hit-optimized score, which is designed to maximize the hit ratio of insilico screening. Using the Sievgene docking score, ∆G score and the hit-optimized
score, the MTS method is more robust than the multiple active-site correction scoring
method. Also we provided methods for classifications of proteins and ligands and amethod to design a focused library.
In addition, the component methods of prestoX were designed for parallel computers
and GRID computer systems.
This work was supported by grants from NEDO and METI.

P57
Transcriptome according to a thermodynamic view
Tomokazu KONISHI 1
Bioresource Sciences, Akita Prefectural University1

While decoding genomic information is routine work for a cell, it remains challenging
for researchers. Indeed, although genome sequences have been determined for manyorganisms and the transcriptome has been measured under various conditions, we arestill unable to predict a transcriptome from genome sequences, and the genome cannot
be interpreted in terms of a transcriptome. Without an appropriate “grammar” for the
genome, and due to the difficulty of dealing with the sheer volume of the genome
information, an alternative approach is necessary. This poster presents a model that
describes the embodiment of quantitative information in a genome from athermodynamic perspective. The model is derived from a physical interpretation ofbiochemical mechanisms, and describes the concentration of each transcript according
to the three forms of energies representing the functions of proteinous factors in a cell.

1. Tomokazu Konishi: Redeveloping Microarray Data Treatment Methods forTranscriptome Study, Pharmaceutical Discovery, Sep 15, 45-50 (2004)
2. Tomokazu Konishi: Three-parameter lognormal distribution ubiquitously found in
cDNA microarray data and its application to parametric data treatment, BMC
Bioinformatics, vol. 5, pp. 5 (2004)
3. Tomokazu Konishi, Masanori Yoshida, Kenya Shibahara: SuperNORM: A Computer
Program for the Parametric Normalization of Microarray Data, Genome Informatics,
vol. 14, pp. 292-293 (2003).
4. Tomokazu Konishi: Parametric treatment of cDNA microarray data, Genome
Informatics, vol. 13, pp. 280-281 (2002).

P58
Automated genotyping of human CYP2C19 SNPs by a novel SNP typing
system and development of a database of it
Yukari KUREBAYASHI 1,2, Naoko MATSUMOTO1,2, Fumiko KAKIHARA1,2
Mitsuharu HIRAI3, Setsuo HASEGAWA1, Masafumi YOHDA2
Sekino Clinical Pharmacology Clinic 1,
Tokyo University of Agriculture and Technology 2 , ARKRAY, Inc. 3
Proton pump inhibitors (PPIs) are widely used for therapy of peptic ulcer disease. They
are metabolized by CYP2C19 in the liver. The frequency of 2C19 poor metabolizers is
relatively high (approximately 20%) in Japanese population, and the genetic variations
result in differences of their kinetics and pharmacological actions, e.g. clinicaldifferences in Helicobacter pylori eradication rate. In this study, we developed a novelautomatic SNPs-typing system and applied it for the genotyping CYP2C19.
The developed system is based on analysis of a melting curve of probe DNA bound to
the target SNP site using a fluorescence quenching probe. The system enables
automated and multiplex SNP genotyping from sample preparation. The results of SNP
typing are consistent with the results obtained by allele specific primer PCR method.
We have also developed software for automated typing from the output data, melting
curves, and built a data base of CYP2C19 genotypes. This full automation analyzing
system can be translated to the clinical setting for order-made medicine using PPIs for
peptic ulcers, which are the most common diseases and need optimized therapy inJapan.
References
1) Frieder Langenbucher: Handling of computational in vitro/in vivo correlation
problems by Microsoft Excel:Ⅰ. Principles and some general algorithms, European
Journal of Pharmaceutics and Biophamaceutics, Jan;53(1):1-7. (2002)
2) Furuta T, Shirai N, Sugimoto M, Ohashi K, Ishizaki T.: Pharmacogenomics ofproton pump inhibitors. Pharmacogenomics. Mar;5(2):181-202.(2004)

P59
Gene expression profiles of the effect of cryoprotectants on the freeze-thaw
damage in yeast
Yuko Momose 1, Masakazu Yamaoka2
The National Institute of Advanced Industrial Science and Technology 1,
Among the methods of culture collection storage, freezing is very convenientand low cost. But some cultures get serious damage and cannot survive after
freeze-thaw so that we need suitable cryoprotectant for such organisms. In order tounderstand the effects of cryoprotectant, we analyzed the gene expression profile ofyeast before and after freeze-thawing with and without that cryoprotectant.
The yeast Saccharomyces cerevisiae was frozen at -20 oC (cooling rate, 1
oC/min) for 1 week with or without cryoprotectant and thawed at 30 oC for 5min. Afterfreeze-thaw, we checked viability using flow cytometry. We analyzed the gene
expression profile of yeast before and after freeze-thawing with and without thosecryoprotectants.
We had already studied the genome-wide responses of yeast cells to various kinds ofstresses, we compare that of freeze-thaw with or without cryoprotectant to the otherstress, which were both physical and chemical, we analyzed with the responsive andstress specific genes we selected, and the hierarchical clustering analysis of gene
expression profiles was carried out.

P60
Analysis of drug-resistant mutation patterns of HIV-1 protease under highly
active antiretroviral therapy using Bayesian network modeling
Kaoru Mogushi 1, Fengrong Ren1, Naoki Hasegawa1, Hiroshi Tanaka1
Department of Bioinformatics, Tokyo Medical and Dental University1
INTRODUCTION: The emergence of antiviral drug resistance has been the largest
obstacle in the treatment of AIDS for the last decade. It has been reported that themultiple mutations are usually required for the development of resistance for theseanti-HIV drugs, because some mutations need to act as accessory mutations to increase
the level of resistance or to compensate for losses in viral fitness1. Characterizing theevolutionary patterns of within-patient HIV is crucial to the challenge of anti-HIV drug
resistance acquirement. In this study, we employed the Bayesian network approach forcharacterizing the patterns of drug resistant mutations and applied it to a large data setof HIV-1 protease gene (273 sequences) serially collected from a single patient under
HAART (highly active antiretroviral therapy) over four years.
METHODS: Bayesian network is a graphical model which represents probabilistic
dependency. We implemented a program to construct such a network using both
selected mutation sites and drug combination sets (DCs) as discrete variables. Prior to
the analysis, 19 mutation sites which had information entropy greater than 0.2 bits werechosen. The selected mutation sites were 10, 11, 12, 14, 15, 20, 22, 30, 43, 46, 55, 62,
73, 74, 77, 84, 88 and 90. Six different DCs were administrated for the patient, and eachprotease sequence was associated with one of the DCs.
RESULTS and DISCUSSION: The constructed network of 273 HIV-1 protease genes
showed that there were different mutation patterns for different DCs. The dependencies
between the mutations were clarified to a certain extent by this network. For instance,
mutations at site 88 were inferred to have strong interaction with the ones at site 30 on
the network, consisting with the finding that these mutations compensate for losses inviral fitness each other. We suggest that Bayesian network approach may be useful for
detecting and characterizing drug-resistant mutation patterns, with the potential ofcontributing to evaluation of anti-HIV therapy.
References
1) Wu TD et al. J Virol. 2003 Apr;77(8):4836-47

P61
Comparison of the validation methods
with linear and non-linear classifier
Chihoko YOSHIMURA1, Taizo HANAI2, Masahiro OKAMOTO2
Drug Discovery Lab., Hanno Research Center, Taiho Pharmaceutical Co., LTD1,
Laboratory for Bioinformatics, Graduate School of Systems Life Sciences, Kyushu
University2
Abstract
In microarray data analysis, we can use thousands of gene’s expression values but the
number of samples is relatively limited. Therefore, to build classifier for the purpose ofoutcome prediction or cancer subtype detection from microarray data tends to result in“overfitting”. So it is important to estimate the classification error for the general data,and it is called “validation method” for classifier to know this error. In this study, wetested some validation methods with linear and non-linear classifier. The dataset used inthis study was divided into training dataset and test dataset. And we compared theestimated error, calculated from training data based on validation method and true error,
derived from test dataset. As the dataset, we used the lymphoma patient’s gene
expression data[1] and built the classifier to predict 5 years survival. This dataset
consisted of 210 samples. 140 samples are used for building classifier and model
estimation, and the rest 70 samples were used for verification of the true error.
To build the classifier, we used linear Support Vector Machine (SVM) and Multiple
Linear Discriminant and Fisher’s Linear Discriminant as linear classifier, and SVM withpolynomial and gauss kernel function as non-linear classifier. With these classifier, as
the validation methods, resubstitution, 10-fold Cross Validation, Leave One Out Cross
Validation, ξ-α estimate, and .632 bootstrap method were tested.
Here we report that the linear SVM with .632 bootstrap had the smallest difference
between estimated error and true error.
Reffernce[1] The dataset is available at http://llmmp.nih.gov/DLBCL/P62
Computational Proteomic Differential Analysis: i-OPAL
Mitsuhiro Kanazawa1,3 , Hisae Anyoji1 , Hsiao-kun Tu1 , Takao Kawakami1,2 ,Toshihide Nishimura1,2 , Atsushi Ogiwara1,2
1

Medical ProteoScope Co., Ltd., 2 Clinical Proteome Center, Tokyo Medical University,
3
Graduate School of Pure and Applied Sciences, University of Tsukuba

In recent years, analytical technology of proteomics has been significantly improved,
and it is focused as one of methods to discover biomarker or target molecule thatcharacterize certain diseases, symptoms, responses of treatments, adverse events bycertain drugs and so on. Several experimental methods for proteome differential display
have been developed to discover biomarkers based on the labeling methods, but theanalytical efficiency of those methods is viewed with suspicion because of samplingbias and artificial errors.
We therefore deve loped computational proteomic differential analysis system i-OPAL(internal standard guided Optimal Profile Alignment) to discover biomarkers based on
LC-MS measurements. This analysis requires no artificial labels to compare the profiles
derived from different conditions. It not only overcomes sampling bias and artificial
errors, but also realizes the extensive and unbiased quantitative analysis.
i-OPAL’s basic approach consists of 2 steps: one is Intra-group alignment (Step 1) andanother is Inter-group alignment (Step2). Step 1 bundles peptide signals into sheaves
using dynamic programming based alignment sample by sample in a group, and creates
consensus signal map of its group. Here, the volume of each bundled signal indicates
peptide expression levels by its group. Step 2 aligns those consensus signal maps ofeach group, and compares bundled signal volumes as quantitative difference of related
location between signal maps. The bundled signal that has significant quantitative
difference among groups is biomarker or target molecule itself.
We believe this approach becomes a huge breakthrough of biomarkers discovery andthe elucidation of disease/drug mechanism.
Group B

Group A

Step 1: Bundle signals sample by sample
X-axis: Retention time (min)Y-axis: Mass-to-charge ratio (m/z)
Z-axis: Ion signal intensity
Signal map of Group B

Signal map of Group A

Biomarker / Target molecule
Step 2: Align and compare the bundled signal quantities

Figure: i-OPAL basic analysis steps
References
1) V. Pravdova, B. Walczak, and D.L.Massart, A comparison of two algorithms for
warping of analytical signals, Anal. Chim. Acta: 77 - 92, 2002

P63
NMR-based Metabolomics—2/3
2) How to analyze NMR-spectra of mixture samples by chemometricmethod; in examples of vegetable metabolome
Masako Fujiwara 1, Itiro Ando2, and Kazunori Arifuku 1
1
JEOL DATUM LTD,2
Envioronmental Research Center LTD
We measured 1H NMR spectra of over 175 kinds of green, oolong, black and othertea infusions using JEOL ECA-500spectrometer. The chemometric analysis combinedwith PCA and SIMCA was used to discern the category of the tea spectra by ‘Aile2 formetabolome’ software. This approach for latent-information extraction can be applied tothe biological samples as a powerful tool for whole-system diagnostics and metabolic
function.
References
1. Gweawlle Le Gall, Lan J.Colquhoun, and Marianne Deferenez: Metabolite Profiling
Using 1H NMR Spectroscopy for Quality Assessment of Green Tea, Camellia
sinesnsis (L.), J. Agri. Food Chem. 52, pp 692-700,(2004)
2. Henrik Antti, Elaine Holms and Jeremy Nicholson: Multivariate Solutions to
Metabonomic Profiling and Functional Genomics. Homepage of Chemometrics,
Editorial October 2002
3. Kazunori Arifuku, Keiko Hirakawa, Kaoru Koike, Kyoko Uekusa, Masako Fujiwara,
and Youkichi Ohno: Integrated NMR-Spectroscopic and Chemometirc Analysis of
Mixtures; Two Hundred Kinds of Tea, 46th Experimental NMR Conference, poster
287, 2005
4. Nicholas V. Reo: NMR-based Metabolomics, Drug and Chemical Toxicology, vol25,
No.4, pp375-382, 2002

P64

Structural analysis by molecular dynamics simulations: Protein-protein
interactions of Ras-Raf and Ras-RalGDS complexes
Noriyuki Futatsugi 1, Mikako Shirouzu 2, Atsushi Suenaga 1, Noriaki Okimoto 1, Tetsu
Narumi 1, Toshikazu Ebisuzaki 3, Shigeyuki Yokoyama 2, Akihiko Konagaya 1, Makoto
Taiji 1
Computational and Experimental Systems Biology Group, RIKEN Genomic Sciences
Center 1, Protein Research Group, RIKEN Genomic Sciences Center 2, EbisuzakiComputational Astrophysics Laboratory, RIKEN 3
In this study, we investigated protein-protein interactions of Ras protein and its targets1(Raf1 RBD, RalGDS RBD) by using molecular dynamics (MD). Comparing
simulations with experimental results, we could know the detailed atomic-level originof the affinity change in mutants, and confirm the prediction ability of the simulations
which is important for large-scale in silico screening. For the Ras-Raf1 andRas-RalGDS complexes, we have performed simulations both for wild type and mutant
Ras (E37G/I36A). The mutations of these residues are known to affect loss of thebinding affinities. In the MD simulations with using very fast special-purpose
computers, the MDGRAPE-2, we found obvious changes in the binding region between
the wild type and the mutant Ras complexes. In the mutant Ras complex, somedistances in the binding region became longer and hydrogen bonds were broken. In
addition, we performed to calculate free energies by the MM-PBSA analysis and freeenergies in the mutant Ras complexes were larger than those of the wild type Ras
complexes. Furthermore, we found that the binding patterns of the Ras-Raf1 and theRas-RalGDS are different in motion of explicit water molecules and these watermolecules are involved in the binding mechanism. In brief, it guesses that the mutationof amino acids decreases binding affinities vastly. These results indicate that the
mutations cause local structural changes in the binding region of Ras and this local
change makes affinity lower.

References
1) M. Shirouzu et al. Biochemistry, 38 5103-5110 (1999)

P65
NMR-based Metabolomics—3/3
3) High-field NMR-spectroscopy for metabolomic analysis;
An application to urine samples from model rats
Tadashi Nemoto 1, Taeko Kataoka 1, Itiro Ando 2, Katsuo Asakura 3, Kazunori Arifuku 4,
Kenji Kanazawa 1, Masako Fujiwara 4
National Institute of Advanced Industrial Science and Technology (AIST) 1,
Environmental Research Center LTD2, JEOL LTD 3, JEOL DATUM LTD 4

NMR spectroscopy is known as powerful tool for structural proteomics. We tried
high-field NMR spectroscopy to metabolomic analysis for discriminating healthy or
unhealthy and other physiological conditions.
Rat urine samples were measured as 1D 1H NMR spectrum by using a JEOL
ECA-800 spectrometer (800.03MHz). Without any spectral assignment, spectrum wereconverted into numeric datasets and then subjected to pattern recognition analysis
by “Alice2 for Metabolome”. It resulted clear classification reflecting thedisease status and/or circadian rhythm of the rats.
REFERENCES

1.

E. Holmes, A. W. Nicholls, J. C. Lindon, S. Ramos,2 M. Spraul, P. Neidig, S. C.
Connor, J. Connelly, S. J. P. Damment, J. Haselden, J. K. Nicholson, Developmentof a model for classifcation of toxin-inducedlesions using 1H NMR spectroscopy ofurine combined with pattern recognition. NMR Biomed., 11:235–244 (1998)
2. J. K. Nicholson, J. Connelly, J. C. Lindon, E. Holmes, Metabonomics: a platform
for studying drug toxicity and gene function, Nat. Rev. Drug Discov.,
1:153-61(2002)
3. T. M. D. Ebbels, E. Holmes, J. C. Lindon, J. K. Nicholson, Evaluation of metabolic
variation in normal rat strains from a statistical analysis of 1H NMR spectra of urine.
J. Pharm. Biomed. Anal., 36: 823-833(2004)
4. Kazunori Arifuku, Keiko Hirakawa, Kaoru Koike, Kyoko Uekusa, Masako
Fujiwara Youkichi Ohno: Development of Analytical software for NMR-based
Metabolomics; “Alice2 for Metabolome” The Annual Meetings of the NMR
Society of Japan, proceeding，2P092, (2004)

P66
NMR-based Metabolomics—1/3
1) Newly developed software; “Alice2 for Metabolome”;
Integrated NMR spectroscopic and chemomerirc analysis for mixture
Kazunori Arifuku1, Itiro Ando2, Masako Fujiwara 1
1
JEOL DATUM LTD,2
Environmental Research Center LTD

For the analysis of mixture samples by NMR spectroscopy, large number of spectral
processing is needed. Each spectrum is bucket-integrated and the datasets are submitted
to chemometric solutions. We have developed integrated software packages of ‘Alice2
for Metabolome’(JEOL). The essential modules of Principal Component Analysis(PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are implemented in
the software and well-suited and fitted to NMR spectral processing and analyzing formetabolome on the single interface.
References
1. Kazunori Arifuku, Keiko Hirakawa, Kaoru Koike, Kyoko Uekusa, Masako Fujiwara
Youkichi Ohno: Development of Analytical software for NMR-based Metabolomics;“ Alice2 for Metabolome” The Annual Meetings of the NMR Society of Japan,
proceeding，2P092, (2004)
2. Kazunori Arifuku, Keiko Hirakawa, Kaoru Koike, Kyoko Uekusa, Masako Fujiwara
Youkichi Ohno: Integrated NMR-Spectroscopic and Chemometirc Analysis of
Mixtures ;Two Hundred Kinds of Tea, 46th Experimental NMR Conference, poster 287
proceeding, (2005)
3. Svante Wold, Michael Sjostrom,; SIMCA: A Method for Analyzing Chemical Datain Terms of Similarity and Analogy, pp 243-282
4. Svante Wold; Pattern Recognition by Means of Disjoint Principal Components
Models, Pattern Recognition, Pergamon Press Vol 8．pp. 127-139 (1976)

P67
Controlling dissociation and association of amyloid protofibrils using highpressure
Yuji O. Kamatari 1,2,3, Shigeyuki Yokoyama2,3,4, Hideki Tachibana5, Kazuyuki
Akasaka2,6
Gifu University1, RIKEN Harima Institute2, Riken Genome Science Center3, The
University of Tokyo4, Kobe University5, Kinki University6
A number of diseases such as Alzheimer’s disease, Parkinson’s disease, prion disease,and autosomal dominant hereditary amyloidosis from lysozyme variants, are associatedwith the formation of amyloid fibrils. Understanding of mechanism of amyloid
formation and controlling it are a central issue in amyloidosis. In this study, thedissociation and reassociation processes of amyloid protofibrils initiated bypressure-jump have been monitored with real-time 1H NMR spectroscopy using
intrinsically denatured disulfide-deficient variant of hen lysozyme.
Uponpressure-jump up to 2 kbar, the matured protofibrils grown in several months become
fully dissociated into monomers within a few days. Upon pressure-jump down to 30
bar, the dissociated monomers immediately start reassociating. The association anddissociation cycle can be repeated reproducibly by alternating pressure, establishing a
notion that the protofibril formation is simply a slow kinetic process toward
thermodynamic equilibrium. The outstanding simplicity and effectiveness of pressure
in controlling the protofibril formation opens a new route for investigating mechanismsof amyloid fibril-forming reactions. The noted variation in the pressure-induced
dissociation rate with the progress of the association reaction suggests multiple
mechanisms for the elongation of the protofibril. This reversible system of amyloid
protofibril formation offers a particularly simple model system for thermodynamic andkinetic studies of protofibril formation as well as for screening drugs for amyloidosis.

Reference
Kamatari YO, Yokoyama S, Tachibana H, Akasaka K. 2005, Pressure-jump NMR
Study of Dissociation and Association of Amyloid Protofibrils. J Mol Biol. 349,
916-921.

P68
The evolutionary mechanism of protein-protein interaction network
Takeshi Hase1,Soichi Ogishima1 , So Nakagawa1,Hiroshi Tanaka1
Department of Bioinformatics, Tokyo Medical and Dental University1
There are a lot of researches about topological properties of protein-protein interaction
network (PIN). Distribution of PIN shows a scale free property [1], as well as a highclustering. It means a network have a lot of triangles. [2].
To uncover the evolutionary mechanism of PIN, concept of duplication is used toconstruct the evolutionary model. This model can reproduces a scale free network, but
is not appropriate in forming high clustering [3]. Vazquez improves duplication modelusing self-interaction as a driving forces of creating triangles and reproduces a highclustering very well. However, there are no evidences that all triangles are made by
duplication of self-interaction proteins [4]. Then, we clarify the rate of contribution ofself-interaction for high clustering and find that their contribution rate is only 30%. It
suggests the existence of another mechanism for high clustering.
We find another mechanism that creates triangles by connecting new nodes and2nd-neighbors. In this study, we show the results of our mechanism and the biologicalfeature supports our hypothesis.
References
1. H. Jeong, S.P. Mason, A.-L. Barabasi, Z.N. Oltvai: Lethality and centrality in protein
networks, Nature, vol.411, pp.41-42 (2000)
2. A. Wagner: The Yeast Protein Interaction Network Evolves Rapidly and Contains
Few Redundant Duplicate Genes, Molecular Biology and Evolution, vol.18,
pp.1283-1292 (2001)
3. R. Pastor-Satorras, E. Smith, R.V. Sole: Evolving protein interaction networks
through gene duplication, Journal of Theoretical Biology, vol.222, pp.199-210 (2003)
4. A. Vazquez, A. Flammini, A. Maritan, A. Vespignani: Modeling of protein
interaction networks, ComPlexUs, vol.1, pp.38-44 (2003)

P69
Is there a code for protein-DNA interaction?
Yes, there exists the analog code for protein-DNA interaction.
Tomoki YOSHIDA 1, Misako AIDA 1, Akinori SARAI 2
Center for Quantum Life Sciences (QuLiS), and Department of Chemistry, Graduate
School of Science, Hiroshima University 1, Department of Bioscience andBioinformatics, Kyushu Institute of Technology (KIT) 2
Based on experimental structural data, it was suggested that there was “no code forrecognition” in protein-DNA interaction.1) It seemed impossible to predict a recognition
sequence for protein from the base-amino acid interaction level; no clear one-to-one
correspondence is observed in the base-amino acid interaction (redundancy) or no
unique conformation is observed for any base-amino acid pair (conformational
flexibility). Here, we propose the “analog recognition code” for protein-DNA
interaction. We take into account both the redundancy and the flexibility in constructing
the code.2) The analog recognition code is a set of the calculated interaction free energy(∆∆G) landscapes between an amino acid side chain and 32 independent kinds of threestacking base pairs. The ∆∆G values are calculated around the coordinate positions in
the major groove; the region having low ∆∆G value represents the preferable
recognition position for each side chain.3, 4) By means of the analog recognition code wecan predict the recognition position for each side chain along any length of DNA in the
major groove. There may be no code having a clear one-to-one correspondence, which
may be called a “deterministic” recognition code, but there exists the “analog”
recognition code for protein-DNA interaction.
REFERENCES
1) Matthews B W, “Protein-DNA interaction. No code for recognition,” Nature, 335, 294-295(1988).
2) Fabio Pichierri, Misako Aida, M. Michael Gromiha and Akinori Sarai, “Free-energy maps ofbase-amino acid interactions for DNA-protein recognition,” Jornal of the American Chemical
Society, 121, 6152-6157 (1999).
3) Tomoki Yoshida, Tomohiro Nishimura, Misako Aida, Fabio Pichierri, Michael M. Gromiha
and Akinori Sarai, “Evaluation of free energy landscape for base-amino acid interactions using
ab initio force field and extensive sampling,” Biopolymers, 61, 84-95 (2002).
4) Misako Aida, Giorgina Corongiu and Enrico Clementi, “Ab initio force field for simulationsof proteins and nucleic acids,” International Journal of Quantum Chemistry, 42, 1353-1381(1992).

P70
Investigation of Structural and Dynamic Properties of Importin-β Based ona Molecular Dynamics Study
Yoshinori Hirano 1, Noriaki Okimoto1,2, Atsushi Suenaga2,
Naoko Imamoto3, Toshikazu Ebisuzaki1
Computational Astrophysics Laboratory, RIKEN1,
Bioinformatics Group, Genomic Sciences Center, RIKEN2,
Cellular Dynamics Laboratory, RIKEN 3
We performed a series of molecular dynamics (MD) simulations of importin-βs toinvestigate their structural and dynamic properties. The starting structures in our
simulations were two importin-βs, one with the importin-β binding (IBB) domain ofimportin-α and one without it. Both structures were constructed based on the X-ray
crystallographic structure of the full-length importin-β complexed with the IBB
domain1). All MD simulations were carried out using the modified Amber 6.02) for
MDM. In the simulation results, the superhelical structures of the importin-βs showed a
far greater degree of loosening in aqueous solution than the crystal structure. It indicatedthat the structure of importin-β with IBB domain was tightly packed undercrystallization. The dynamics of the importin-β was produced by the motions of the
N-terminal domain, C-terminal domain, and connecting region. The free importin-βexhibited a stronger dynamic property than the IBB-bound importin-β, due to theabsence of the crystal packing interaction (the IBB domain binding to importin-β). In
the IBB-bound importin-β, the C-terminal domain and connecting region formed
numerous hydrogen bonds with the IBB domain, and appeared to augment the increase
in its binding affinity. The N-terminal domain, on the other hand, showed nointeractions with the IBB domain. The dynamic properties of the importin-β observed inour simulations provided an excellent interpretation of the structural change of theimportin-β which could be induced upon release of IBB domain.
References

1) G. Cingolani, C. Petosa, K. Weis, and C. W. Müller, Nature, vol. 399,
pp.221-229 (1999).
2) D. A. Case, D. A. Pearlman, J. W. Caldwell, T. E. Cheatham III, W. S. Ross, C. L.
Simmerling, T. A. Darden, K. M. Merz, R. V. Stanton, A. L. Cheng, J. J. Vincent,
M. Crowley, V. Tsui, R. J. Radmer, Y. Duan, J. Pitera, I. Massova, G. L. Seibel, U.
C. Singh, P. K. Weiner, and P. A. Kollman: (1999) AMBER 6. San Francisco,
University of California

P71
Matlab/GNU Octave as a Platform for Analyses of Metabolic Networks
Jun Ohta1
Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical
Sciences1; Okayama 700-8558, Japan; E-mail: jo25@md.okayama-u.ac.jp
Matlab and GNU Octave are softwares suitable for manipulation of matrices. Both canalso manipulate strings and be easily customized using script files and functions,indicating their potential as a platform for a variety of analyses in the area of
bioinformatics. I am interested in structure-function relationship in metabolic networks,
and have been developing a system for analyses of metabolic networks using Matlab
and GNU Octave1,2,3,4. Atom is the smallest node in metabolic networks. Atom-level
connectivity in metabolic networks is classified into inter-metabolite connectivity
through enzymatic reactions and intra-metabolite connectivity through chemical bonds.
For detailed analyses of metabolic networks, all the atom-level connectivities are
expressed in a matrix, connectivity matrix, in my approach. Then, a variety of analyses
are performed on Matlab/GNU Octave using script files and functions including thosewritten by each researcher. This approach has been named ‘connectivity matrix method’.
Connectivity matrix (CM) can describe both inter- and intra-metabolite atom-level
connectivities including information of compartmentation, responsible reactions,stoichiometry, and valency of chemical bonds. Each row of CM is a combination of 3
row vectors, [va 1, va 2, vc], where va 1, va 2 and vc indicate 2 different atoms and one
connectivity, respectively. Calculation of the paths connecting 2 specific atoms,
discovery of hidden atom-level cycles and calculation of net balance of the reaction
sequence constituting such a path with the aid of balance matrix can be performed using
m-files for Matlab/GNU Octave. Information of atom-mapping can be written on mol
files for visualization of those paths obtained. Information of CM method and CMs forindividual reactions are available on the web, www.metabo-info.org. Procedures of CM
method on Matlab/GNU Octave will be presented.
References
1) Programs and Abstracts, First IECA Conference on Systems Biology of E. coli,
112 (2003)
2) Molecular & Cellular Proteomics 2(9), 830 (2003)
3) Proceedings for 5th International Conference on Systems Biology, 141 (2004)
4) Course Book, Program and Abstracts, First FEBS Advanced Course on Systems
Biology: From Molecules & Modeling To Cells, 140 (2005)

P72
A Grid-based Information Integration System for Drug Discovery by using
Freely Accessible Compound Databases
Gen Kawamura1, 2, Masato Kitajima1,3, Takahiro Kosaka1, Susumu Date1,
Shinji Shimojo4, Hideo Matsuda1
Graduate School of Information Science and Technology, Osaka University. 1,
Aztec-System Co., Ltd. 2, Fujitsu Kyushu System Engineering Limited. 3,
Cybermedia Center, Osaka University. 4
E-mail: matsuda@ist.osaka-u.ac.jp
In this study, we present an information integration system for drug discovery by using
available compound databases and an employing grid technology. Drug discovery
involves a number of stages including disease modeling, target protein identification,lead compound identification, and clinical trials. To cope with such a discovery process,we utilize grid technology for integrating various databases1 and develop metadata fordescribing relationships between heterogeneous data that belong to different domains(such as, protein-compound interactions) 2. In particular, to discovery the candidate
subset of ligands, we have prepared a library of 1820174 compounds, each withchemical structure, using catalogs of compounds from freely accessible databases3,4.
The compounds have been annotated with attributes such as MACCS-KEY fingerprint,
molecular weight, calculated LogP and added information.
The effectiveness of our system is demonstrated by evaluating results of several
example queries.

References
1) I. Foster, C. Kesselman, J.M. Nick, and S. Tuecke, “The Physiology of the Grid. An
Open Grid Services Architecture for Distributed Systems Integration.” Open Grid
Service Infrastructure WG, Global Grid Forum, 2002, http://www.ggf.org/
2) G. Schneider, and S.-S. So, “Modeling Structure-Activity Relationships,” G.
Schneider and S.-S. So (ed.), Adaptive Systems in Drug Design, Landes Bioscience,
Georgetown TX, 2002.
3) M. von Grotthuss, J. Pas, and L. Rychlewski, “Ligand-info, searching for similarsmall compounds using index profiles.” Bioinformatics 2003, 19, 1041-1042.
4) PubChem: http://pubchem.ncbi.nlm.nih.gov/

P73
Conformational analysis of an antimicrobial peptide, Pediocin PA-1
Hitoshi Goto1, Tatsunori Matsui1, Toshiyuki Kamakura1, Naofumi Nakayama1,
Tatsuya Tominaga2, Hideo Nakajima2
1

Toyohashi University of Technology, 2 Saitama Industrial Technology Center North Institute

Pediocin PA-1 is a broad-spectrum lactic acid bacteria bacteriocin that shows a particularly strong
activity against Listeria monocytogenes, a foodborne pathogen of special concern among the foodindustries. This antimicrobial peptide is the most extensively studied class IIa bacteriocin (pediocinfamily), and it has been sufficiently well characterized to be used as a food biopreservative. This
work focuses on the elucidation of structures and biological activation mechanism of pediocin PA-1
and the derivatives, which are subject to a single-amino acid substitution of various residues. In thisconference, our original technique of conformational analysis for over 10,000 unique conformers
found by using the exhaustive conformation search method CONFLEX5/MMFF94s will be
proposed. In comparison with the structures obtained from some homology modeling tools, it issuggested that CONFLEX5/MMFF94s can produce many of more stable conformations of pediocinand the related peptides (Fig. 1-2). On the other hand, comparison between the conformational
analysis and the experimental bioactivity shows that some characteristic residues are closely related
to antimicrobial activity.

Fig. 1 Homology Model based on SakP[N24C+44C] NMR structure:
MMFF94s ΔE=161.31 kcal/mol, ΔG=153.23 kcal/mol

Fig. 2 Most Stable Conformer in CONFLEX/MMFF94s
Search: MMFF94s ΔE=0.00 kcal/mol, ΔG=0.76 kcal/mol
This work is performed in the Rational Evolutionary Design of Advanced Biomolecules (REDS) Project, Saitama
Prefecture Collaboration of Regional Entities for the Advancement of Technological Excellence supported by JST.

P74
Normal mode analysis of the complex of HIV-1 protease with inhibitor
vibrational behavior
Toshiyuki KAMAKURA1, Hitoshi GOTO1
Toyohashi University of Technology1
In order to elucidate the predominant structures and biological function ofprotein-ligand complex, it is important to understand both the kinetical andthermodynamical behaviors in biological environment.
Especially, temperaturedependency on their biological activity reveals that the thermal fluctuation plays animportant role on the dynamic interaction between protein and ligand. In this study,
we propose a new conformational analysis approach to understanding protein-ligand
complex functions. Practical mixing of normal modes based on thermodynamic
analysis for the protein-ligand complex gives an important knowledge for finding manyplausible bio-activated conformations. This vibrational simulation will be suggested
for the difference of dynamical behaviors between HIV-1 protease and the complex with
inhibitor in water. We measured displacements of Cα atoms on 28-60 residues,
generally called “flap-region”, each residue from equilibrium structure (Fig 1). In only
protease system (PR), Gly49 fluctuates with neighbor residues. In protease-ligand
system (PLG) and protease-ligand in waters (PLH), Gly49 does not fluctuate. In
these all systems, Ser37’ fluctuates, but smaller than Gly49. Asp25 and Asp25’, which
are active site, are motionless in any systems. In the previous MD simulation1), themutation protease, which Gly49 were mutated to Trp, had favorable binding free energy.
From these comparing, our
PR
PLG
PLH
approach may be favor forsearching the inhibitors.
This
work was supported by grants (No.
17300094) from the Ministry of
Education, Science, Sports andCulture of Japan.
displacement[Ang]

ASP25

GLY49

GLY49

GLY49

PHE99
PRO1'

PHE99
PRO1'

PHE99
PRO1'

ASP25'

ASP25'

ASP25'

SER37'

SER37'

SER37'

PHE99'

PHE99'
0 1 2 3 4 5 6 7 8 9 10

PHE99'
0 1 2 3 4 5 6 7 8 9 10

0 1 2 3 4 5 6 7 8 9 10time[ps]time[ps]

4-5
3-4
2-3
1-2
0-1

residue

PRO1

ASP25

residue

PRO1

ASP25

residue

PRO1time[ps]

Fig 1 Displacements of Cα of HIV protease and -ligand complexes.
Vertical axis is residue number, and horizontal axis is simulation
time. The distances of Cα atoms from equilibrium structure are colored.

References
1) Wei Wang, Peter A. Kollman: Free Energy Calculations on Dimer Stability of the
HIV Protease using Molecular Dynamics and a Continuum Solvent Model, Journal of
Molecular Biology, vol.303, pp.567-582 (2000).

P75
Genome Functional Analysis based on Systematic Map of Protein
Interactions
Carlos A. Del Carpio1, Pei Qiang1, Eichiro Ichiishi2, Michihisa Koyama1, Momoji
Kubo1,3, Akira Miyamoto1,2
Department of Applied Chemistry, Tohoku University1, New Industry Creation
Hatchery Center, Tohoku University 2 , JST-PRESTO3
With the constant accumulation of genomic data around the world, the need for genome-wide functional
analysis is of paramount importance in order to elucidate the intertwined relations among genes, proteinsand metabolites. In the present work we present a new methodology for docking unbound protein
molecules. The process is oriented to serve as a tool for elucidation of the gamut of protein interactions
encoded in the genome. The methodology is characterized by its simplicity and easiness of embedment inany rigid body docking process based on point complementarity. It is oriented to allow limited free but
not unrealistic interpenetration of the side chains of protein surface amino acid residues. The
methodology assists in deletion of atomic-scale details on the surface of
the interacting monomers, leading to the extraction of the mostcharacteristic flattened shape for the molecule as well as the definitionof a soft layer of atoms to allow smooth interpenetration of theinteracting molecules during the docking process. Although theFig.1. MIAX predicted complex

methodology(red) Vs. Crystal structure (blue)

does

not

perform

structuralor

conformational

rearrangements in the interacting monomers, results output by thealgorithm are in fair agreement with the relative position of themonomer in experimentally reported complexes. The algorithm
performs especially well in cases where the complexity of the protein
surfaces is high, that is in hetero dimmer complex prediction. It isoriented to play the role of a fast screening engine for proteins known

Fig.1. MIAX predicted complex(red) Vs. Crystal structure (blue)

to interact but for which no information other than that of the structures at the isolated state is available.
The importance of the methodology will increase in structural-function studies of thousand of proteinsderived from large scale genome sequencing projects being executed all around the globe. Fig.1 shows acomplex inferred automatically from the isolated sub-units. The methodology has been embedded in thegeneral system for protein-protein interaction developed by Del Carpio et al[1,2] in recent years.
References
1.

Del Carpio CA., Peissker T,

Yoshimori A,

Ichiishi E. Genome Informatics14:238-249(2003).

2. Del Carpio CA, Ichiishi E, Yoshimori A, Yoshikawa T; PROTEINS: Structure,
Function and Genetics (2002). 48(4):696-732

P76
GenoSearch: High Performance Inter-Genomic Sequence Retrieval Systemusing Suffix Array Technique
Aki HASEGAWA, Akihiko KONAGAYA
Informatics Infrastructure Team, Genome Core Technology Facilities
RIKEN Genomic Sciences Center
Bio-sequence analysis often requires the location and frequency of the occurrences of agiven sequence. Several tools have been developed including SSAHA [1],PatternHunter [2], BLAT [3] as well as FASTA [4] and BLAST family [5][6]. However,higher search performance is strongly required to deal with multiple genome sequences
and non-redundant protein databases which become tens of giga bytes in total.
GenoSearch solves this issue by providing pre-built indices based on suffix arrays [7].
The GenoSearch can extract the location and frequency information of a given sequence
from hundreds of genome sequences in sub seconds to several seconds according to thecomplexity of a query pattern. The query pattern is divided into simpler sub patterns
prior to the search. Then the sub query is performed for each sub pattern in plannedorder. Final result is built up with results of sub query and cached results of frequently
searched sub pattern. In this way, the GenoSearch speeds up sequence pattern retrieval.
The indices for bio-sequences are updated regularly according to the renewal of thebio-sequence file. The GenoSearch is also available as the Web Service at the GSC site(http://www.gsc.riken.jp/Informatics/).
References[1] Z. Ning, A. J. Cox and J. C. Mullikin, Genome Res., 11, 1725–1729 (2001).
[2] M. Li, B. Ma, D. Kisman and J. Tromp, J. Bioinf. Comput. Biol., 2, 417–439(2004).
[3] W. J. Kent, Genome Res., 12, 656–664 (2002).
[4] W. R. Pearson and D. J. Lipman, Proc. Natl. Acad. Sci., 85, 2444–2448 (1988).
[5] S. F. Altschul, T. L. Madden, A. A. Schaffer, J. Zhang, Z. Zhang, W. Miller and D.
J. Lipman, Nucleic Acids Res., 25, 3389–3402 (1997).
[6] Z. Zhang, S. Schwartz, L. Wagner and W. Miller, J. Comput. Biol., 7, 203–214(2000).
[7] U. Manber and G. Myers, SIAM J. Comput., 22, 935–948 (1993).

P77
Evaluation of polymorphism for an organic molecular crystal
Shigeaki Obata 1, Hitoshi Goto1, Naofumi Nakayama1
Toyohashi University of Technology 1
Crystal structure prediction for organic molecules can provide immense valuable
scientific knowledge concerning on crystal growth, polymorphism, rate of dissolution,
electronic and optical properties, and etc. Although many computational applications
for challenging these problems have been already developed by some research groups,
approach to the practical solution seems to be difficult to overcome so far. As a part ofour long-standing project toward the goal of predicting the crystal packing of molecularsolids, we will introduce our recent work on description of conformational behaviors foran organic molecule, hydroxymalonic acid, packed into the known solid state. As thefirst step in our approach, the nine stable conformers of hydroxymalonic acid weregenerated by our original conformation search program CONFLEX, andvirtually-constructed crystal structures based on those geometries were subjected tominimization by our original crystal packing application KESSHOU. Lattice energy ofeach crystal structure was evaluated as the total energy of all intermolecular interactions
in MMFF94s potential functions. As the result of the computation, it was found that themost stable crystal structure that has the lowest lattice energy and was constructed fromthe 5th stable conformer in vacuum has been the best agreement with X-ray crystal
structure (Figure 1).
(a)

(b)

Figure 1 Comparison of Crystal Structures: (a) X-ray structure in the Cambridge Structural
Database (Refcode: HMALAC10, a=4.494, b=8.819, c=10.882 Å), (b) the most stable crystal
structure optimized by KESSHOU (a=4.557, b=8.969, c=10.842 Å.).

This work was supported by grants (No. 17300094) from the Ministry of Education,
Science, Sports and Culture of Japan.

P78
Workflow for Open Genome Grid Service
Ryo UMETSU1, Shingo OHKI1, Akihiko KONAGAYA1,2
Advanced Genome Information Technology Research Group, GSC, RIKEN1,
Bioinformatics Laboratory, Tokyo Institute of Technology2
We propose a workflow platform for genome informatics incorporated with the
Bioinformatics Web Services constructed on Open Bioinformatics Grid (OBIGrid). Web
Services or Grid Services have a great potential to provide a powerful bioinformatics
environment based on the service oriented architecture (SOA). The grid services enable
for the bioinformaticians to access the public databases and to use the freebioinformatics application tools without considering computation resources nor
operation costs. The grid services also provide full options and the same application
programming interfaces as provided by the corresponding UNIX commands on a local
computer.
However, care must be taken when dealing with genome wide data, especially for
scalability and session management. It often happens that a single father task produces
thousands of daughter tasks in a bioinformatics workflow. In addition, computational
results may become giga bytes order in total. We solved the above issues by providing
asynchronous Web Service facilities, a thread pool incorporated with a task scheduler
and a global file system on J2EE. The feasibility of our system has been demonstrated
by the GLIMMER2-BLASTALL-SELECTIONFILTER workflow for micro bacterial
genome sequences.

P79
P79
Drug Interaction Ontology (DIO)- OWL formalizations of interaction causal logic Sumi Yoshikawa1)2), Kentaro Watanabe2), Kazumi Matsumura1)2), Akihiko Konagaya1)2)
1)
Genomics Science Center (GSC), RIKEN (The Institute of Physical and Chemical Research)
2)
Graduate School of Information Science and Technology, Tokyo Institute of Technology
We previously reported the development of Drug Interaction Ontology (DIO) 1) 2) 3)
designed for inference system of possible drug-drug interactions, based on
drug-biomolecule and biomolecule-biomolecule interactions. Ontology was employed for
encoding literature information associated with drug behaviors in a body system;
metabolism, transport, target reactions, gene expressions etc. Taking account of
spatiotemporal nature of interaction events, DIO represents interaction as triadic
relationship model; comprising of three name spaces assigned for input, object, and output.
For example, in case of drug metabolism reaction, a substrate drug, enzyme and/or
cofactors, and metabolized product. Besides triadic relationship model, other features are:1) it deals with both molecular level direct interactions and molecular pathway levelindirect interactions, 2) it deals with reaction effect in terms of positive/negative (i.e.
facilitation, inhibition) with orientation, and 3) it provides semantics of “effect”
differentiating biochemical interpretations and pharmacological / clinical ones.
DIO was originally implemented as relational database scheme. Here we introduce its
representation using an ontology language OWL, edited by Protégé(http://protege.stanford.edu/). The drug interaction causal logic is under implementation
by Prolog and we plan to discuss its prototype.
References
1) Yoshikawa S, Konagaya A. DIO: Drug Interaction Ontology – Application to Inferences in Possible
Drug-drug Interactions. 2003 In: Proceedings of The 2003 International Conference on Mathematics
and Engineering Techniques in Medicine and Biological Sciences (METMBS '03), June 23 - 26, 2003.
Las Vegas, Nevada, USA. 231-236.
2) Yoshikawa S, Sato S, Konagaya A. Application of Drug Interaction Ontology (DIO) for Possible
Drug-drug Interactions. In: Chem-Bio Informatics Society Year 2003 Proceedings: 320.
3) Yoshikawa S, Satou K, Konagaya A. Drug Interaction Ontology (DIO) for Inferences of Possible
Drug-drug Interactions. In: MEDINFO 2004, M. Fieschi et al. (Eds) :IOS Press;2004: 454-458.

P80
Transition state of a SH3 domain detected with principle component
analysis and a charge-neutralized all-atom protein modelDaisuke Mitomo1 and Junichi Higo1
1

School of Life Science, Tokyo University of Pharmacy and Life Science

To explore nature of transition state of src SH3 domain, we did ten molecular dynamics
simulations with using a charge-neutralized all-atom (CNAA) model, currentlyproposed, where all of the atoms in the protein and water molecules were explicitly
treated, but their atomic partial charges were set to zero.

We could assess an effect ofside-chain atoms using this model more efficiently than other models.

Each simulation

was done for 90 ns at 300 K, starting from the native structure, and the protein unfoldedin each run.

The integrated trajectories (10 × 90 = 900 ns) were analyzed by aprincipal component analysis, and showed a clear free-energy barrier between foldedand unfolded-state conformational clusters.

The transition state (i.e., a conformational

ensemble at the barrier) was characterized by conformational deformations of a segment,
to which high F values had been experimentally assigned.

Contrary, there was no

significant free-energy barrier in a two-dimensional profile with using Rg (radius of
gyration) and Q value (native-contact reproduction ratio) as the coordinate axes.
Analysis suggested that the transition state from the CNAA model has different naturefrom one found in course-grained chain models without side-chains, and that looseningof atom packing in the segment might be an important determinant for the transition
state.
References
1.

Jackson SE, Fersht AR. Folding of chymotrypsin inhibitor 2. 1. Evidence for a
two-state transition. Biochemistry 1991;30:10428-10435.

2.

Plaxco KW, Simons KT, Ruczinski I, Baker D. Topology, stability, sequence, andlength: defining the determinants of two-state protein folding kinetics.
Biochemistry 2000;39:11177-11183.

3.

Maxwell KL et al. Protein folding: Defining a "standard" set of experimental
conditions and a preliminary kinetic data set of two-state proteins. Protein Sci
2005; 14: 602-616.

P81
CBDB: Cell Behavior Database- Toward understanding high order level biological phenomena Michiaki IWAZUME 1 , Hiroyuki KANEKO2 , Marina DAN 3
Brain Science Institute, RIKEN 1 , Keio University2 , Hierarchical Biology Lab. 3

A large amount of molecular level information on biological phenomena, such asembryogenesis, cell proliferation, cell differentiation, signal transduction,transcriptional regulation, cell response, etc. is rapidly increasing because of the
progress of genome science in recent years. In order to understand high-order biologicalphenomena, it is necessary to bridge the gap between the amount of information in themolecular level and the amount of information in the cell functional level. However, thebiological knowledge in the cell functional level has not been systematized well.
We have been seeking a method to systematically describe and understand cellularbehaviors in morphogenesis. Various morphogenetic processes were manually selected
and described according to the following format: “cell(s)=(subject)” “doessomething=(output matter)” “under a certain condition=(input matter)”. CBDB is abiological database on cell behaviors in the format with images and movies whichindicate
To sophisticate and make use of the CBDB, we propose an AI-based approach formore multiple searching of instances and knowledge discovery from the CBDB. As thefirst step, we attempted to construct the ontology for understanding morphogenetic
phenomena. The ontology will also facilitate clearer definition of terms and concepts,
and knowledge sharing among communities of various fields.
In this study, We introduce the CBDB and the prototype of ontology-based
information retrieval system.
REFERENCES

1. M. Ashberner et al., “Gene Ontology: tool for the unification of biology. Nature
Genetics” ，Vol.5, 25-29, Nature America Inc. , (2000)
2. M.IWAZUME et al. "Ontogenesis Ontology −Toward To Systematically Describing
and Understanding Cellular Behaviors in Morphogenesis", The 15th Annual Conferenceof Japanese Society for Artificial Intelligence, 2B2-6, (2001).
3. R. Stevens, C. A. Goble and S. Bechhofer, “Ontology -based Knowledge
Representation for Bioinformatics”, Journal Briefings in bioinformatics, (2000).

P82
Drug discovery based on the structural dynamics of prion
Kazuo Kuwata
Division of Prion Research, Center for Emerging Infectious Diseases, Gifu University,
Gifu 501-1194
Several different compounds and antibodies have been reported to inhibit production ofabnormal prion in vitro, however the mechanism of effect for most of those substances
is unclear and there is still no effective drug in vivo. Because prion diseases occur as aresult of conformational changes in the cellular prion protein, PrPC, it should be feasible
to design drugs that interrupt the critical conversion process. We previously observed apartially denatured form of the prion protein using the high-pressure NMR and the
CPMG relaxation dispersion methods, and hypothesized that slow exchange dynamics
on the micro- to milliseconds time scale might be critical for the subsequent pathogenic
conversion. Thus we incorporated the dynamical information in residue level into insilico screening and established a novel strategy termed “Dynamics Based Drug Design(DBDD)”. According to this DBDD strategy, we could find several low-molecularweight anti-prion substances, by a combination of in silico and in vitro drug screening.
These substances may stabilize the normal conformation of PrPC, with the result of
reducing the population of the intermediate conformer, PrP*, thereby inhibiting its
conversion to the pathogenic form.
References
Kazuo Kuwata, Yuji O. Kamatari, Kazuyuki Akasaka, Thomas L. James. Slow
conformational dynamics in the hamster prion protein. Biochemistry, 43, 4439-4446,
2004
Kazuo Kuwata, Tomoharu Matsumoto, Hong Cheng, Kuniaki Nagayama, Thomas
L. James, Heinrich Roder, NMR-detected hydrogen exchange and molecular
dynamics simulations provide structural insight into fibril formation of prion
protein fragment 106-126. Proc. Natl. Acad. Sci. 100，14790-5，2003
Kazuo Kuwata, Hua Li, Hiroaki Yamada, Guiseppe Legname, Stanley B. Prusiner,
Kazuyuki Akasaka and Thomas L. James: Metastable conformers of the hamster
prion protein characterized by high pressure NMR. Biochemistry 41,
12277-12283 (2002)

P83
Parametric Normalization for GeneChip and Tiling Array Data
Tomokazu KONISHI 1,
Midori KATAKAMI2, Kenya SHIBAHARA2,
Masahiro HAYASHI3 and Takushi NAKAJIMA3Bioresource Sciences, Akita Prefectural University1,
INTENSITY Co. Ltd. 2, Skylight Biotech Inc.3
Normalization is the first process of data analysis. Since data obtained by GeneChip
technology have variations that have been provoked by hybridization and scanning, inprior to higher level of data analyses, such variations should be reduced or canceled;such cancellation of the technical variations is the data normalization. Consequently,
appropriate method of calculation as well as proper standard is required for this process.
The validity and accuracy of whole data analyses depend on those of the normalization.
In this poster, we introduce a parametric method for normalizing GeneChip data.
Parametric methods are a branch of linear normalization methods, and are characterized
by their objectivity. Since they use statistical data distribution as the standard, highaccuracy can be expected in the analyses if a proper distribution model would beavailable. We have introduced an appropriate model, the three parameter lognormal
distribution model, for to normalize microarray data obtained by Pat Brown-type chips.
This poster shows applications for the model to GeneChip data, which base on analternative experimental platform. Despite the differences of the platform, the model fit
well to the data. Using the model, reproducibility and stability of measured ratios weresignificantly improved. Although the noise levels are different between experiments, we
could expect ca. 80-90 % of genes to be found within a reliable range of intensity.
1. Tomokazu Konishi: Redeveloping Microarray Data Treatment Methods forTranscriptome Study, Pharmaceutical Discovery, Sep 15, 45-50 (2004)
2. Tomokazu Konishi: Three-parameter lognormal distribution ubiquitously found in
cDNA microarray data and its application to parametric data treatment, BMC
Bioinformatics, vol. 5, pp. 5 (2004)
3. Tomokazu Konishi, Masanori Yoshida, Kenya Shibahara: SuperNORM: A Computer
Program for the Parametric Normalization of Microarray Data, Genome Informatics,
vol. 14, pp. 292-293 (2003).
4. Tomokazu Konishi: Parametric treatment of cDNA microarray data, Genome
Informatics, vol. 13, pp. 280-281 (2002).

P84
Consensus scores for efficient selection of computational protein-ligand
docking models
Tadakazu Takakura1, Akifumi Oda1, Keiichi Tsuchida1, Noriyuki Yamaotsu2, Shuichi
Hirono2
Toyama Chemical Co. Ltd.1, Kitasato University2
Here we compare the performance of nine consensus scoring strategies(number-by-number, number-by-rank, rank-by-number, rank-by-rank, percent-by-number,percent-by-rank, vote-by-number, vote-by-rank, and vote-by-percent), in which multiple

scoring functions are used simultaneously to evaluate candidate structures for a
protein–ligand complex1, in combination with nine scoring functions (FlexX score,
GOLD score, DOCK score, PMF score, ChemScore, DrugScore, PLP, ScreenScore, and
X-Score). In this study, the trade-off between accuracy and efficiency was investigated(the study using conditions under which reasonable models could be obtained for all test
systems without exception is reported by A.Oda et al. in this symposium).
Our results demonstrate that choosing the most appropriate type of consensus
score is essential for model selection in computational docking; the rank-by-number,rank-by-rank, percent-by-number, and percent-by-rank strategies are effective selection
methods, and these results differs from the result we obtained for model selection using
conditions under which reasonable models could be successfully obtained for all test set
without exception (in which the number-by-number strategy was more appropriate).
Thus, using the appropriate consensus scoring strategy according to the demands of thesituation is essential. For rank-by-number strategy, ChemScore, DrugScore, and PLP
were included in many of the consensus scores which can efficiently select the docking
poses. It has been reported previously that these scoring functions work well when they
are used independently.2 Our results suggest that they are appropriate not only forindependent scoring, but also for consensus scoring. By incorporating these consensus
scores into the FlexX program, reasonable complex models can be obtained more
efficiently than those selected by independent FlexX scores. These strategies might also
improve the scoring of other docking programs and more effective structure-based drug
design should result from these improvements.
References
1) P. S. Charifson et al., J. Med. Chem., 1999, 42, 5100-5109.
2) R. Wang et al., J. Med. Chem. 2003, 46, 2287–2303.

P85
Molecular dynamics simulation of prion using a coarse-grained model
Hironori K. Nakamura1, Mitsunori Takano2, Kazuo Kuwata1
Division of Prion Research, Center for Emerging Infectious Diseases, Gifu University1,
Department of Physics, School of Science and Engineering, Waseda University2
Abstract
There are fatal, untreatable neurodenerative diseases known as prion diseases such as
bovine spongiform encephalopathy (BSE) for cow, scrapie for sheep, Creutzfeldt Jakob
disease (CJD) for human etc. It has been considered that prion diseases are caused by
the conformational change of prion protein from a normal form (PrPC) to an abnormal
form (PrPSc). The mechanism of the conformational change has been unclear.
To shed light on the conformational transition, we conducted molecular dynamics
simulations of a prion protein using a simple chain model. In this study, we used theJanus-type Go model we proposed recently, which is an extended model from an
off-lattice Go model where the PDB structure can be set as the native conformation [1].
The Janus-type Go model uses information of two conformations in contrast that only
one native conformation is used in the traditional Go model, which is popular in theprotein folding study. A putative PrPSc conformation of PrP27-30 modeled in an
amyloid form [2] and a NMR structure of cellar form were used in this study.
Using the Janus-type Go model, we could successfully conduct a simulation fromC
PrP to PrPSc. In the trajectory we found a new stable conformation different from both
PrPC and PrPSc. Features of the stable intermediate conformation indicate some possible
scenarios for directed elongation of an amyloid fibril and mechanism of a trimer
formation. For drug discovery, the newly found intermediate conformation may be used
as a target for a docking of ligand, which may control the conformational change from
PrPC to PrPSc.
References
1) Cecilia Clementi, Hugh Nymeyer, Jose N. Onuchic: Topological and energetic
factors: what determines the structural details of the transition state ensemble and"en-route" intermediates for protein folding? An investigation for small globular
proteins. J. Mol. Biol. vol. 298, pp.937-953 , (2000).
2) Cedric Govaerts, Holger Wille, Stanley B. Prusiner, Fred E. Cohen: Evidence forassembly of prions with left-handed β-helices into trimers., Proc. Natl. Acad. Sci. USA.
vol. 101, pp. 8342-8347, (2004)

P86
Molecular Interaction Between Estrogen Receptor and Their Ligands
Kaori FUKUZAWA 1,2, Yuji MOCHIZUKI 2,3, Shigenori TANAKA 2,4, Kazuo
KITAURA2,5 and Tatsuya NAKANO2,6
Mizuho Information & Research Institute, Inc.1, JST-CREST2, Advancesoft3, Kobe
University4, National Institute for Advanced Industrial Science and Technology 5,
National Institute of Health Sciences 6
We have theoretically examined binding energies of typical ligands to the humanestrogen receptor ligand-binding domain (hER LBD) and analyzed their interaction.[1]Our method, ab initio fragment molecular orbital (FMO) method,[2] has been applied
for the calculation of ER-ligand complex, by dividing molecules into small fragments.
The ligand binding energies and inter-fragment interaction energies (IFIE) were
calculated at Hartree-Fock (HF) and MP2 levels,[3] and the orbital interactions such as
the charge-transfer from lone-pair to π* between the crucial fragments were also
analyzed based on a configuration analysis (CAFI) [4,5]. The calculated relative binding
energies were very well correlated with the experimental values. We found that thevalues of charge transfer between ER and ligands are related to that of ER-ligand
binding energies.
On the molecular interaction between ER and ligand, there are strong electrostaticinteractions between ligand and surrounding some charged/polarized residues, and weakvan der Waals interactions between ligand and surrounding many hydrophobic residues.
Both interactions are equally contribute to the total binding energies. The strongest
interaction occurs between Glu353 and ligand, and the main component is chargetransfer from the lone pair orbital of carbonyl oxygen in the Glu353 to the σ*(OH)orbital of phenol group in the ligand. Our approach is the powerful tool to understanddetailed molecular interaction in quantum mechanical level.
REFERENCES
1. K. Fukuzawa et.al., J. Comp. Chem. 26, 1-10 (2005).
2. T. Nakano et.al., Chem. Phys. Lett. 351, 475-480 (2002).
3. Y. Mochizuki et.al., Chem. Phys. Lett., 396, 473-479 (2004).
4. Y. Mochizuki, Chem Phys. Lett., 410, 165–171 (2005).
5. Y. Mochizuki et.al., Chem Phys. Lett., 410, 247–253 (2005).
6. ABINIT-MPand
BioStation
Viewerarefreelyhttp://www.fsis.iis.u-tokyo.ac.jp/result/software/index.html

available

from:

P87
Computer-aided identification of cytokine mimetics derived from plant extracts
Edgardo E. Tulin,1 Zenaida T. Ecleo,1 Igor Kurochkin,2Yasushi Okazaki3 and Christian Schönbach2
1

PhilRootcrops, Leyte State University, Visca, Baybay, Leyte, Philippines;
Immunoinformatics Team, RIKEN Genomic Sciences Center, 1-7-22 Suehiro-cho,
Tsurumi-ku, Yokohama, Kanagawa, Japan and 3Research Center for Genomic Medicine,
Saitama Medical School, 1397-1 Yamane, Hidaka, Saitama, Japan.
2

Plant extracts offer a promising source of molecules that mimic the function of cytokines
in cellular regulation. We aim to identify cytokine-mimetic molecules from five
Philippine indigenous food crop plant extracts to isolate active components which areessential in molecular functional characterization and classification of mimetic molecules
and targets. Biologically active extracts from yam roots and leaves, taro roots, “lagundi”
leaves, sweet potato leaves and “buyo” leaves were purified and active protein
components were isolated and subjected to mass spectrometry. Computational analysesof the mass spectrometry data revealed homologs of a tuber storage protein in yam. The
protein has been reported as an anti-hypertensive agent and an inhibitory molecule forangiotensin converting enzyme activity. The homologs appear to be growth stimulatory
proteins capable of inducing growth of bone marrow cells and spleen cells in vitro. Thisfinding necessitates further characterization on biochemical and computational level tovalidate the cytokine-mimetic effects.

Author Index
A Abe, Yukiko

P31

Hamasaki, Nobuyuki

P38

Aburatani, Hiroyuki
Aida, Misako
Aizawa, Masahiro

S11

Hanai, Taizo
Hase, Takeshi

P61
P76

Akasaka, Kazuyuki
Akiyama, Yutaka

P67

Hasegawa, Aki
Hasegawa, Naoki
Hasegawa, Setsuo

Amari, ShinjiAndo, Itiro
Anyoji, Hisae

P43

Hashida, Mitsuru
Hata, Masayuki
Hatanaka, Noriko

P19

Arifuku, Kazunori
Arikuma, Takeshi

P63, P65, P66

Hayashi, Masahiro
Hayashi, Yuzuru

P25, P83

Higo, Junichi
Hirai, Mitsuharu
Hiramoto, Takeshi

P38, P80

Hirano, Yoshinori
Hirono, Shuichi

P70

Hirota, Hiroshi
Horie, Masaru
Hoshino, Tyuji

P14

Houk, K. N.
Ichiishi, Eichiro

P16

Ijuin, Kazushige
Imai, Kenichiro
Imamoto, Naoko

P20

Ishii, Naoaki
Ismael, Mohamed

P42

Ito, Kiyomi
Ito, Masakatsu

P31

Iwadate, Mitsuo

P51, P52, P54,
P55

Iwamoto, Koji
Iwasawa, Yoshio

P8

P69
P43
S5
P63, P65, P66
P62
P32

Asakura, Katsuo
Azuma, Ryuzo
B Baba, Kiyoshi

P65

Bishop, Betty
Boren, Christine

S7

Broclawik, Ewa

P9, P10, P17,
P21

C Carpio, Carlos A. Del

P9, P10, P17,
P21, P75

D Dan, Marina
Date, Susumu

P81

E Ebisuzaki, Toshikazu
Ecleo, Zenaida T.

P64, P70

P32
P22
S7

P87

Endo, Yaeta

P48

Endou, Akira

P9, P10, P17,
P21

F Feng, Chunlai
Friesner, Richard
Fuchikami, Kinji

P19

Fujita, Naoya
Fujita, Norihisa

P48

Fujitani, Hideaki
Fujiwara, Masako
Fukuda, Ikuo

P30

Fukunishi, Hiroaki
Fukunishi, Yoshifumi

P53

Fukuzawa, Kaori
Futatsugi, Noriyuki
G Goto, Hitoshi
Gromiha, M. Michael
Groot, Annie De
H Hama, Yoshimasa

I

P72

S3
P54
P22

P68
P60
P58
P7, P8
P20
P20
P58
P22
P23, P84
P56
P7, P8P75
P37
P70
P10, P17
P30

P43

J

Iwazume, Michiaki
Jayachandran, Guha

P81

P63, P65, P66
P56

Jernigan, Robert L.
K Kakihara, Fumiko
Kamakura, Toshiyuki

P40

Kamatari, Yuji O.

P67

Kaminuma, Tsuguchika

S1, P47, P49,
P50

Kanazawa, Kenji
Kanazawa, Mitsuhiro
Kaneko, Hiroyuki

P65

P56
P47, P86
P29, P36, P64
P73, P74, P77
P6, P26
S7
P31

P30
P58
P73, P74

P62
P81

Kanou, KazuhikoP51, P52, P54,
P55

Marcon, Luisa

S7

Martin, Bill
Matsuda, Hideo

S7

Matsui, Hitoshi
Matsui, Tatsunori
Matsumoto, Naoko

P31

Matsumura, Kazumi
Mcmurry, Julie

P79

Miki, Keizaburo
Mitaku, Shigeki
Mitani, Nanako

P42

Kasahara, Kota

P9

Katakami, Midori
Kataoka, Taeko
Kawagoe, Satoshi

P83

Kawakami, Takao
Kawamura, Gen

P62

Ke, Runcong
Kim, Sang Jun
Kim, Sung Kwon

P28

Kimura, S. Roy
Kinoshita, Kengo

S4

Mitomo, Daisuke
Miyagawa, Hiroh

P38, P80

P48

Kitagawa, Tetsuji
Kitajima, Masato
Kitaura, Kazuo

P32

Miyamoto, Akira

P9, P10, P17,
P21, P75
P40

Klutzer, Michelle
Knopf, Paul

S7

Miyazawa, Sanzo
Mizuma, Takashi

P32

Mizushima, Hiroshi
Mochizuki, Yuji

S15

Kobayashi, Hiroshi
Koike, Asako
Komatsu, Katsuichiro

P60

Komiyama, Junpei
Komiyama, Naomi

P47

Mogushi, Kaoru
Momose, Yuko
Mori, Kenichi

P18

Konagaya, Akihiko

P32, P41, P46,
P64, P76, P78,
P79

Mori, Yoshihiro
N Nakagawa, So

P65
P9
P72
P15
P15

P72
P86
S7
P35
P51, P54
P47, P49, P50

P72
P73
P58
S7
P28, P37, P45
P42
P38

P25
P86
P59
P8
P68

Nakai, Kenta
Nakajima, Hideo
Nakajima, Takushi

P48
P73

Konishi, Fumikazu
Konishi, Tomokazu

P41

Kosaka, Takahiro

Nakamura, Haruki
Nakamura, Hironori K.

P56

P72

Koyama, Michihisa

P9, P10, P17,
P21, P75

Nakano, Tatsuya

P20, P43, P47,
P86

Kubo, Momoji

P9, P10, P17,
P21, P75

Nakata, Kotoko

P20, P43, P47,
P49

Kubota, Satoru
Kuramochi, Hiroshi
Kurebayashi, Yukari

P56

Nakata, Yoshiro
Nakaya, Jun

P34

Kurochkin, Igor
Kusuhara, Hiroyuki

Nakayama, Naofumi
Naoshima, Yoshinobu

P73, P77

P87

Narumi, Tetsu
Nemoto, Tadashi
Neya, Saburou

P29, P36, P64

Nishijima, Kazumi

P9, P10, P17,
P21

Nishikawa, Tetsuo

P11

Kuwata, Kazuo
L Lin, Yu
M Müller, Werner
Maeda, Kazuya
Maeda, Miki

P57, P83

P53
P58
P31
P82, P85
P27
S8
P31
P39

P83
P85

S13, P24, P27
P18P65
P7, P8

Nishimura, Toshihide

P62

Shibahara, Kenya

P83

Nonaka, Yosuke
Numa, Hisataka

P22

Shimada, Jiro
Shimojo, Shinji

P53

O Obata, Shigeaki
Ochiai, Takafumi
Oda, Akifumi

P77

Shinozaki, Kazuki
Shirai, Hiroki
Shirouzu, Mikako

P48

Ode, Hirotaka
Ogishima, Soichi

P7

P30

P68

Shirts, Michael R.
Snow, Christopher D.

Ogiwara, Atsushi
Ohki, Shingo
Ohno, Yosuke

P62

Suenaga, Atsushi

P29, P36, P64,
P70

P36

Sugawara, Noriko

P31

Ohno, Yousuke
Ohta, Jun

P29

P7

Okada, Okimasa
Okamoto, Masahiro
Okazaki, Yasushi

P33

Sugiura, Wataru
Sugiyama, Yuichi
Suresh, M. Xavier

Okimoto, Noriaki

P29, P36, P64,
P70

Omagari, Katumi

P56

Onodera, Kenji
Ootani, Wataru
Ozawa, Naoki

P14

P Pande, Vijay S.
Pei, Qiang
Petrovsky, Nikolai
Q Qiang, Pei
R Rajjak, Shaikh Abdul

S6Ren, Fengrong
Rivera, Dan

P39
P31
P23, P84

P78

P71
P61
P87

Suwa, Makiko
T Tachibana, Hideki
Taiji, Makoto

P72
S2
P64
P30

S16, P31
P26
P6, P26
P67
P29, P36, P64

Takagi, Toshihisa
Takakura, Tadakazu

P35
P85

P31

Takano, Mitsunori
Takano, Yu
Takaoka, Tsubasa

P30

Takasaki, Shigeru

P46

P21

Takaya, Daisuke

P51, P52, P54,
P55

Takeda-shitaka, Mayuko

P51, P52, P54,
P55

Taki, Katsuhiko
Tanaami, Sachie

P31

Tanaka, Hiroshi

S14, P49, P50,
P60, P68

Tanaka, Shigenori
Tanaka, Yoshitomo

P86

Tanida, Yoshiaki

P30

Terasaki, Tetsuya

P9, P10, P17,
P21

Terashi, Genki

P51, P52, P54,
P55
P11

P34

P75
P17
P60
S7

P23, P84
P16
P8

P25

Rodriguez-annon, Claire
Roses, Allen D.
S Saitoh, Noriaki

S7

Sakaguchi, Sayaka
Sakamoto, Norihiro

P25

Sarai, Akinori
Satou, Kazuhito
Sawasaki, Tatsuya

P69

Schönbach, ChristianSchönbach, Christian

P87

Segawa, Katsunori
Seki, Motoaki
Seko, Tomohiro

P20

Tobita, Motoi
Tokushima, Daisuke
Tominaga, Tatsuya

P48

Tsuboi, Hideyuki

P9, P10, P17,
P21

Shaikh, Abdul Rajjak

P10Tsuchida, Keiichi

P23, P84

S10
P34
P24, P27
P14
P48
S9

P33

P49, P50

P53
P73

Tsuji, Toshiyuki

P45Tu, Hsiao-kun
Tulin, Edgardo E.

P62

U Ueda, Hiroki R.
Ueda, Toshiyuki
Umetsu, Ryo
Umeyama, Hideaki
W Washio, Takumi
Watanabe, Kentaro
Watanabe, Masahito
Weiner, DavidY Yagi, Toru
Yagi, Yoichiro

P87
S12
P31
P78
P51, P52, P54,
P55
P53
P79
P42
S7
P41
P18

Yamagishi, Akihiko
Yamamoto, Tomoyuki
Yamaoka, Masakazu

P38

Yamaotsu, Noriyuki
Yamashita, Fumiyoshi

P23, P84

Yanai, Ryoko
Yang, Woosung
Yeo, Sang-soo

P29, P36

Yohda, Masafumi
Yokoyama, Shigeyuki

P33, P58

Yoshida, Tomoki
Yoshikawa, Sumi
Yoshimura, Chihoko

P69

Yukawa, Masumi
Z Zhang, Junwei

P32
P59
P19
P24
P15
P64, P67
P79
P61
P47, P49, P50
P43