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Bridging Bioinformatics and Chem(o)informatics. Gary Wiggins School of Informatics Indiana University [email protected] Yan He (SLIS MLS Student) Meredith Saba (SLIS MLS Student). Provocative Thought.

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bridging bioinformatics and chem o informatics

Bridging Bioinformatics and Chem(o)informatics

Gary Wiggins

School of Informatics

Indiana University

[email protected]

Yan He (SLIS MLS Student)

Meredith Saba (SLIS MLS Student)

provocative thought
Provocative Thought

“While much bioscience is published with the knowledge that machines will be expected to understand at least part of it, almost all chemistry is published purely for humans to read.”

  • Murray-Rust et al. Org. Biomol. Chem. 2004, 2, 3201.
overview of the talk
Overview of the Talk
  • Review of ACS CINF 2004 Papers
  • Review of Relevant Articles
  • Public Chemistry Databases and Data Repositories with Bioinformatics Info/Links
  • Overview of Web Services
  • NIH-funded Projects Underway or Planned at Indiana University
the bigger picture linking bioinformatics to cheminformatics
“The Bigger Picture — Linking Bioinformatics to Cheminformatics”
  • American Chemical Society Division of Chemical Information (CINF) Symposium, Anaheim, Spring 2004
    • All-day session with 16 papers
problems from acs cinf 2004
Problems from ACS CINF 2004
  • Both technical and people factors hinder knowledge exchange between biology and chemistry. (Lipinski)
  • People Problems per Chris Lipinski
    • Meta data capture is complicated by people issues, particularly those between chemists and biologists.
    • Discipline-based disconnects occur distressingly often and are frequently overlooked as a cause of lost productivity.
interdisciplinary collaborations biology and chemistry
Interdisciplinary Collaborations: Biology and Chemistry
  • [What’s] “... important for these collaborations is, not only do you have to accept the other guy’s paradigm or at least live with it; you have to be willing to accept the other guy’s foibles or your perception of the other guy’s foibles (and recognize the opposite of this). We each have our own approaches to how we do science, and it’s just different cultures.”

--Thom Kauffman interview in ACS LiveWire, March 2005, 7.3.

some questions from the acs cinf 2004 symposium
Some Questions from the ACS CINF 2004 Symposium
  • "Find all proteins related to protein A (i.e. within a given path length of A) in a protein interaction graph, and retrieve related assay results and compound structures.”
  • “Find all pathways where compound X inhibits or slows a reaction, and retrieve Gene Ontology classifications for all proteins involved in the reaction.”
problems from acs cinf 20041
Problems from ACS CINF 2004
  • Commercial vs. public data
  • Batch mode data processing possible in biology, but primitive in chemistry
  • Primary HTS data has a very high noise factor
  • Data format standardization problem
    • Chemoinformatics and bioinformatics use completely different data formats and analysis tools
  • Chemical and protein sequence information has been largely analyzed separately
solutions from acs cinf 2004
Solutions from ACS CINF 2004
  • Linking biological and chemical information in computational approaches to predict biological activity, ADME profiles, and adverse drug reactions (ADR)
  • Energetics of binding for more accurate and sensitive chemical representation of DNA-protein interactions
  • A discovery informatics platform that facilitates archival, sharing, integration, and exploration of synthetic methods and biological activity data
solutions from acs cinf 20041
Solutions from ACS CINF 2004
  • Data pipelining approach makes it possible to apply bioinformatics and chemoinformatics data and analyses together.
  • Visualizations are the best way for people to understand data.
solutions from acs cinf 20042
Solutions from ACS CINF 2004
  • Cabinet (Chemical And Biological Information NETwork, formerly Fedora) servers include
    • Metabolic pathway network chart (Empath)
    • Protein-Ligand Association Network (Planet)
    • Enzyme Commission Codebook (EC Book)
    • Traditional Chinese Medicines (TCM)
    • World Drug Index (WDI), and others.
  • Built on the Daylight HTTP toolkit
overview of the talk1
Overview of the Talk
  • Review of ACS CINF 2004 Papers
  • Review of Relevant Articles
  • Public Chemistry Databases and Data Repositories with Bioinformatics Info/Links
  • Overview of Web Services
  • NIH-funded Projects Underway or Planned at Indiana University
what is chemoinformatics brown
What is Chemoinformatics? (Brown)
  • “…the essence of chemoinformatics is integration and focus rather than its components, which are independent disciplines.”
  • Supporting disciplines:
    • Chemical information
    • Computational chemistry
    • Chemometrics
toolkits as integrators brown
Toolkits as Integrators (Brown)
  • Companies such as Daylight, Advanced Visual Systems, OpenEye, and SciTegic provide integration systems for:
    • Statistical methods
    • Text mining
    • Computational chemistry
    • Visualization
genego s metadrug product
Genego’s MetaDrug Product
  • Toxicogenomics platform for the prediction of human drug metabolism and toxicity of novel compounds
  • Enables the visualization of pre-clinical and clinical high-throughput data in the context of the complete biological system
  • Integrates chemical, biological, and protein function data
  • Examination of vast amounts of available information using its Sofia KnowledgeScan methodology
  • SRS data integration platform
lessons from hip hop salamone
Lessons from Hip Hop (Salamone)
  • Mashup technique
    • Bring together disparate informatics, biological, chemical, and imaging information when conducting research
  • Example of an integration tool:
    • A search for a species returns a page with NCBI genomics information, Yahoo images of the species, and articles culled from Google Scholar
ispecies org search Search
  • For mus musculus
chemogenomics and chemoproteomics gagna
Chemogenomics and Chemoproteomics (Gagna)
  • Chemogenomics (def.)—The description of all potential drugs that can be used against all possible target sites, OR the actions of target-specific chemical ligands and how they are used to globally examine genes
  • Chemoproteomics (def.)—Uses chemistry to characterize protein structure and functions
  • They are “. . . a form of chemical biology brought up to date in the area of genome and proteome analysis.”
new interdisciplinary journals
New Interdisciplinary Journals
  • ACS Chemical Biology (ACS)
  • ChemBioChem; A European Journal of Chemical Biology (Wiley/VCH)
  • Chemical Biology and Drug Design (Blackwell)
  • JBIC; Journal of Biological and Inorganic Chemistry (Springer)
  • Journal of Biochemical and Molecular Toxicology (Wiley)
  • Molecular Biosystems (RSC)
  • Nature Chemical Biology (Nature Publishing)
  • Organic & Biomolecular Chemistry (RSC)
open source software geldenhuys
Open Source Software (Geldenhuys)
  • Log P calculator from Interactive Analysis
  • University of Utah’s Computational Science and Engineering Online
    • Can submit jobs for molecular mechanics, quantum chemical calculations, and biomolecular interfaces for viewing PDB files
  • Virtual Computational Chemistry Laboratory
the blue obelisk guha
The Blue Obelisk (Guha)
  • Several open chemistry and chemoinformatics projects that have pooled forces to enhance interoperability
  • Maintain:
    • Chemoinformatics Algorithms Dictionary
    • Data Repository for standardized data for chemical properties and other facts (e.g., mass)
blueobelisk org
  • Working collaboratively on projects such as:
    • Chemistry Development Kit (CDK)
    • JChemPaint
    • Jmol
    • JUMBO
    • NMRShiftDB
    • Octet
    • Open Babel
    • QSAR
    • World Wide Molecular Matrix (WWMM)
barriers to the use of open source software
Barriers to the Use of Open Source Software
  • Unix command line
  • Problem: Lack of known standards and datasets of compounds for validation, e.g., in docking programs
lessons from the human genome project austin
Lessons from the Human Genome Project (Austin)
  • Keys to success in the HGP were:
    • Comprehensiveness
    • Commitment to open access to the sequence as a research tool without encumbrance
  • Proposed tools for a “genome functionation toolbox”:
    • Whole-genome transcriptome and proteome characterization
    • Development of small inhibitory RNAs (siRNAs) and knockout mice for every gene
    • Small molecules and the druggable genome
chebi chemical entities of biological interest
ChEBI, Chemical Entities of Biological Interest
  • Dictionary of molecular entities focused on small chemical compounds
  • Features an ontological classification, showing the relationships between molecular entities or classes of entities and their parents and/or children
the iupac international chemical identifier inchi
The IUPAC International Chemical Identifier (InChI)
  • Open source, non-proprietary, public-domain identifier for chemicals
  • String of characters that uniquely represent a molecular substance
  • Independent of the way the chemical structure is drawn
  • Enables reliable structure recognition and easy linking of diverse data compilations
  • Accepts as input MOLfiles (or SDfiles) and CML files
  • Download the program to your computer at:
the elsevier mdl nih link via pubchem and discoverygate
The Elsevier MDL/NIH Link via PubChem and DiscoveryGate
  • Cross-indexes PubChem to the Compound Index hosted on Elsevier MDL’s DiscoveryGate platform
  • MDL added 5 million structures from PubChem to their index, resulting in over 14 million unique chemical structures
  • Links go both ways
    • Can move from biological data in PubChem to bioactivity, chemical sourcing, synthetic methodology, and EHS data in DiscoveryGate sources
elsevier mdl s xpharm
Elsevier MDL’s xPharm
  • Comprehensive set of records linking:
    • Agents (compounds) (2300)
    • Targets (600)
    • Disorders (450)
    • Principles that govern their interactions (180)
  • Answers questions such as:
      • What targets are associated with control of blood pressure?
      • What adverse effects are associated with monoamine oxidase inhibitors?
text datamining banville
Text Datamining (Banville)
  • “In the pharmaceutical field, it is ideally the marriage of biological and chemical information that needs to be the ultimate focus of text data mining applications.”
  • Problems:
    • Lack of universal publication standards for identifying each unique chemical entity
    • Selective indexing policies of A&I services
    • Need to understand how chemical structures link to biological processes
chemical datamining software
Chemical Datamining Software
  • SureChem
  • CLiDE
    • Recognizes structures, reactions, and text
    • “OSCAR1” to check experimental data
  • CSR (Chemical Structure Reconstruction)
  • MDL DocSearch—combines MDL’s Isentris platform and EMC’s Documentum
overview of the talk2
Overview of the Talk
  • Review of ACS CINF 2004 Papers
  • Review of Relevant Articles
  • Public Chemistry Databases and Data Repositories with Bioinformatics Info/Links
  • Overview of Web Services
  • NIH-funded Projects Underway or Planned at Indiana University
themes from swissprot s 20 th anniversary conference in silico analysis of proteins
Themes from SwissProt’s 20th Anniversary Conference, “In silico Analysis of Proteins”
  • Knowledgebases, databases and other information resources for proteins
  • Sequence searches and alignments
  • Protein sequence analysis
  • Protein structure prediction, analysis and visualization
  • Proteomics data analysis
chemoinformatics databases j nsd ttir
Chemoinformatics Databases (Jónsdóttir)
  • Lists databases relevant to drug discovery and development, including:
    • General databases
    • DBs for screening compounds
    • DBs for medicinal agents
    • DBs with ADMET properties
    • DBs with physico-chemical properties
  • Curiously does not mention Chemical Abstracts
databases with protein and ligand information j nsd ttir
Databases with Protein and Ligand Information (Jónsdóttir)
  • Protein Data Bank
    • Target Registration Database
    • Relibase—uses structural info to analyze protein-ligand interactions; Relibase+ for protein-protein interaction searching
  • Cambridge Structural Database
  • KEGG LIGAND DB for enzyme reactions
other databases with protein and ligand information
Other Databases with Protein and Ligand Information
  • SitesBase--a database of known ligand binding sites within the PDB
  • Binding MOAD
  • sc-PDB (Kellenberger)
sc pdb http bioinfo pharma u strasbg fr 8080 scpdb index jsp
other databases with protein protein interaction data j nsd ttir
Other Databases with Protein-Protein Interaction Data (Jónsdóttir)
  • YPD, Yeast Proteome Database (for proteins from S. cerevisiae)
  • Human Protein Reference Database
  • BIND, Biomolecular Interaction Network Database (ceased as of 11/16/2005?)
international molecular exchange imex consortium http imex sourceforge net
International Molecular Exchange (IMEx) Consortium
  • BIND ( The Blueprint Initiative AsiaPte. Ltd, Singapore and The Blueprint Initiative North America,Toronto Canada
  • DIP ( UCLA-DOE Institute for Genomics & Proteomics
  • IntAct (, EMBL–European Bioinformatics Institute, Hinxton, UK;
  • MINT ( University of Rome “Tor Vergata”, Rome Italy
  • MPact (, MIPS / Institute for Bioinformatics, Munich, Germany.
protein sites from iu i533 students and others
Protein Sites from IU I533 Students and others
  • LigandDepot—integrated source for small molecules
  • PSIPRED Protein Structure Prediction Server
  • DSSP--a database of secondary structure assignments (and much more) for all protein entries in the PDB
  • Dr. Predrag Radivojac’s I690 class on Structural Bioinformatics
protein secondary structure prediction
Protein Secondary Structure Prediction
  • Methods
    • Neural Network
    • Rule Based
    • Other Machine Learning
    • Homology Based
protein secondary structure prediction software
Protein Secondary Structure Prediction Software
  • PredictProtein


  • NN Predict
structure based docking methods
Structure-Based Docking Methods
  • Method
    • Scans many small molecules and “docks” them to a site of interest on a protein structure
    • Predicts free energy of binding
    • Filters thousands of compounds relatively quickly
    • Top hits can be used for more rigorous computational/experimental characterization and optimization
structure based docking methods1
Structure-Based Docking Methods
  • DOCK
    • Accelrys’s Insight (built on DOCK)
  • FlexX
  • Glide
  • GOLD
useful structure databases
Useful Structure Databases
  • ModBase
  • Dali Database (Fold classification; based on PDB)
  • Protein Structure Analysis, Comparison, &/or Classification [Guide]
scop structural classification of proteins
SCOP, Structural Classification of Proteins
  • Curated database of structural and evolutionary relationships
    • All known protein folds (v. 1.69, July 2005)
      • 70,859 domains organized into 2,845 families, 1,539 superfamilies, and 945 folds
    • Detailed information about close relatives
  • Links to coordinates, images of structures, interactive viewers, and literature references
scop search options
SCOP Search Options
  • Homology search yields a list of structures with significant levels of sequence similarity
  • Keyword search matches words in SCOP and PDB
cath protein structure classification
CATH Protein Structure Classification
  • Like SCOP, structured hierarchically by:
    • Class (determined by secondary structure)
    • Architecture (overall shape, e.g., barrel, sandwich, roll, etc.) – no equivalent in SCOP
    • Topology (grouped into fold families based on overall shape and connectivity of secondary structures)
    • Homologous Superfamily (domains thought to share a common ancestor)
  • As of January 2005, had 43,229 domains classified into 1,467 superfamilies and 5,107 sequence families; A protein family database (CATH-PFDB) contained a total of 616,470 domain sequences classified into 23,876 sequence families
cath search options
CATH Search Options
  • Can browse or search the classification by CATH code
  • CATH codes can be used to search other databases, e.g., DHS, Gene3D, and Impala
gasteiger s biochemical pathways database
Gasteiger’s Biochemical Pathways Database
  • Database of biochemical pathways that represents chemical structures and reactions on the atomic level
  • Gives access to each atom and bond of the substrates of enzyme reactions
  • Allows the study of transition state hypotheses of enzyme reactions
  • Analysis of the physicochemical effects operating at the reaction site allows a classification of enzyme reactions that goes beyond the traditional EC code for enzymes.
  • 1533 biochemical molecules and 2175 reactions
a gene expression database for nci60 scherf
A Gene Expression Database for NCI60 (Scherf)
  • Published in Nature Genetics, 2000
  • First study to integrate gene expression with molecular pharmacology databases
  • Gene expression profiles for NCI60 assessed using microarray technology
  • Gene-drug relationships investigated by how the gene transcription levels vary with respect to drug activities
other relevant databases servers
Other Relevant Databases/Servers
  • Each year Nucleic Acids Research publishes a Database Issue in January and a Web Server Issue in July (See refs in Bibliography section). Examples from the most recent issues:
overview of the talk3
Overview of the Talk
  • Review of ACS CINF 2004 Papers
  • Review of Relevant Articles
  • Public Chemistry Databases and Data Repositories with Bioinformatics Info/Links
  • Overview of Web Services
  • NIH-funded Projects Underway or Planned at Indiana University
web services overview
Web Services Overview
  • What are “Web Services”?
    • A distributed invocation system built on Grid computing
      • Independent of platform and programming language
      • Built on existing Web standards
    • A service oriented architecture with
      • Interfaces based on Internet protocols
      • Messages in XML (except for binary data attachments)
service oriented architecture
Service-Oriented Architecture
  • From Curcin et al. DDT, 2005, 10(12),867
web services for chemistry problems
Web Services for Chemistry: Problems
  • Performance and scalability
  • Proprietary data
  • Competition from high-performance desktop applications

-- Geoff Hutchison, it’s a puzzle blog, 2005-01-05

  • ALSO:
    • Lack of a substantial body of trustworthy Open Access databases
    • Non-standard chemical data formats (over 40 in regular use and requiring normalization to one another)
overview of the talk4
Overview of the Talk
  • Review of ACS CINF 2004 Papers
  • Review of Relevant Articles
  • Public Chemistry Databases and Data Repositories with Bioinformatics Info/Links
  • Overview of Web Services
  • NIH-funded Projects Underway or Planned at Indiana University
indiana university planned projects http www chembiogrid org
Indiana University Planned Projects:
  • Design of a Grid-based distributed data architecture
  • Development of tools for HTS data analysis and virtual screening
  • Database for quantum mechanical simulation data
  • Chemical prototype projects
    • Novel routes to enzymatic reaction mechanisms
    • Mechanism-based drug design
    • Data-inquiry-based development of new methods in natural product synthesis
nci developmental therapeutics program dtp
NCI Developmental Therapeutics Program (DTP)
  • Downloadable data:
    • In vitro 60 cell line results
    • in vitro anti-HIV results
    • Yeast assay
    • 200,000+ chemical structures
    • molecular targets
    • microarray data
  • Or search the database at:
iu database of nih dtp data
IU Database of NIH DTP Data
  • Contains over 200,000 chemical structures tested in 60 cellular assays from different human tumor cell lines
  • Also includes microarray assay profiles for the untreated cell lines (~14,000 datapoints)
  • A local PostgreSQL database containing the data that is exposed as a web service
  • Using workflows and complex SQL queries, we can do advanced data mining that exploits the chemical, biological and genomic information for particular audiences (chemists, biologists, etc)
mining the nih dtp database
Mining the NIH DTP database

~14,000 gene expression values

60 cell lines

Cell lines can be clustered based on gene expression similarity

~200,000 compounds

Compounds can be clustered based on similarity of profile

across cell lines, or by chemical structure fingerprint similarity

use of taverna at iu
Use of Taverna at IU
  • A protein implicated in tumor growth is supplied to the docking program (in this case HSP90 taken from the PDB 1Y4 complex)
  • The workflow employs our local NIH DTP database service to search 200,000 compounds tested in human tumor cellular assays for similar structures to the ligand.
  • Client portlets are used to browse these structures
  • Once docking is complete, the user visualizes the high-scoring docked structures in a portlet using the JMOL applet.
  • Similar structures are filtered for drugability, and are automatically passed to the OpenEye FRED docking program for docking into the target protein.
  • A 2D structure is supplied for input into the similarity search (in this case, the extracted bound ligand from the PDB IY4 complex)
  • Correlation of docking results and “biological fingerprints” across the human tumor cell lines can help identify potential mechanisms of action of DTP compounds
taverna workflow
Taverna Workflow

Workflow definition

Available web services


Visual depiction of workflow

pre closing quote
Pre-Closing Quote
  • “There is not going to be a ‘voila’ moment at the computer terminal. Instead, there is systematic use of wide-ranging computational tools to facilitate and enhance the drug discovery process.”
    • Jorgensen. Science, March 19, 2004, 303, 1814.
closing quote
Closing quote

“The future of chemistry depends on the automated analysis of chemical knowledge, combining disparate data sources in a single resource, such as the World-Wide Molecular Matrix, which can be analysed using computational techniques to assess and build on these data.”

  • Townsend et al. Org. Biomol. Chem. 2004, 2, 3299.
post closing quote zzzzzcas
Post-closing quote: zzzzzCAS
  • “In an industry first, Chemical Abstracts Service (CAS) has unveiled a revolutionary new literature searching tool which will permit scientists to search and retrieve the world’s chemical literature—including patents and obscure technical reports—in their sleep.”

--Author unknown

  • Randy Arnold
  • Xiao Dong
  • Sean Mooney
  • Peter Murray-Rust
  • David J. Wild
  • I533 Chemical Informatics Seminar Students
  • Elsevier Science
bibliography articles books and conference papers
Bibliography: Articles, Books, and Conference Papers
  • “The Bigger Picture: Linking Bioinformatics to Cheminformatics” [CINF Symposium] Abstracts [1-16], 227th ACS National MeetingAnaheim, CA, March 28-April 1, 2004
  • Austin, C.P. “The completed human genome: implications for chemical biology.” Current Opinion in Chemical Biology 2003, 7, 511-515.
  • Bajorath, Jürgen, ed. Chemoinformatics: concepts, methods, and tools for drug discovery. Totowa, N.J. : Humana Press, c2004. (Methods in molecular biology ; v. 275)
  • Banville, Debra L. “Mining chemical structural informationo from the drug literature.” Drug Discovery Today January 2006, 11(1/2), 35-42.
  • Brown F. “Editorial opinion: chemoinformatics - a ten year update.”Current Opinion in Drug Discovery and Development 2005 May; 8(3): 298-302.
bibliography articles cont d
Bibliography: Articles (cont’d)
  • Coles, Simon J.; Day, Nick E.; Murray-Rust, Peter; Rzepa, Henry S.; Zhang, Yong. “Enhancement of the chemical semantic web through InChIfication.” Organic & Biomolecular Chemistry2005, 3, 1832-1834.
  • Curcin, Vera; Ghanem, Moustafa; Guo, Yike. "Web services in the life sciences." Drug Discovery Today2005, 10(12), 865-871.
  • Gagna CE, Winokur D, Clark Lambert W. “Cell biology, chemogenomics and chemoproteomics.” Cell Biol Int. 2004; 28(11): 755-64.
  • Geldenhuys, W.J.; Gaasch, K.E.; Watson, M.; Allen, D.D.;Van Der Schyf, C.J. “Optimizing the use of open-source software applications in drug discovery.” Drug Discovery Today February 2006, 11(3/4), 127-132.
  • Guha, R.; Howard, M.T.; Hutchison, G.R.; Murray-Rust, P.; Rzepa, H.; Steinbeck, C; Wegner, J.; Willighagen, E.L. “The Blue Obelisk—Interoperability in chemical informatics.” Journal of Chemical Information and Modeling 2006 Web Release Date: 22-Feb-2006; DOI: 10.1021/ci050400b
bibliography articles cont d1
Bibliography: Articles (cont’d)
  • Jónsdóttir, S.O.; Jorgensen, F.S.; Brunak, S. “Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates.” Bioinformatics 2005 May 15; 21(10): 2145-60.
  • Jorgensen, William L. “The many roles of computation in drug discovery.” Science March 19, 2004, 303, 1813-1818.
  • Kauffman, Thom. “Profile.” [interview] LiveWire, March 2005, 7.3;
  • Murray-Rust, Peter S.; Mitchell, John B.O.; Rzepa, Henry S. “Communication and re-use of chemical information in bioscience.” BMC Bioinformatics2005, 6, 180.
  • Murray-Rust, Peter; Mitchell, John B.O.; Rzepa, Henry S. “Chemistry in bioinformatics.” BMC Bioinformatics2005, 6, 141-144.
  • Povolna, Vera; Dixon, Scott; Weininger, David. “Cabinet—Chemical and Biological Informatics NETwork.” in: Oprea, Tudor I., ed. Chemoinformatics in Drug Discovery. Weinheim: Wiley-VCH, 2004, 241-269.
bibliography articles cont d2
Bibliography: Articles (cont’d)
  • Salamone, Salvatore. “Hip Hop offers lessons on life sciences data integration.” Bio-IT World February 2006, 36.
  • Scherf Uwe, Ross Douglas T., Waltham Mark, Smith Lawrence H., Lee Jae K., Tanabe Lorraine, Kohn Kurt W., Reinhold William C., Myers Timothy G., Andrews Darren T., Scudiero Dominic A., Eisen Michael B., Sausville Edward A., Pommier Yves, Botstein David, Brown Patrick O., Weinstein John N. “A gene expression database for the molecular pharmacology of cancer.” Nature Genetics 2000, 24, 236-244.
  • Souchelnytskyi, S. "Bridging proteomics and systems biology: What are the roads to be traveled?" Proteomics 2005 (November), 5(16), 4123-4137.
  • Tetko, Igor V. “Computing chemistry on the web.” Drug Discovery Today November 2005, 10(22), 1497-1500.
bibliography articles cont d3
Bibliography: Articles (cont’d)
  • Zimmermann, Marc; Thi, Le Thuy Bui; Hofmann, Martin. “Combating illiteracy in chemistry: Towards computer-based chemical structure reconstruction.” ERCIM News January 2005, 60, 40-41.
  • Zimmermann, Marc; Fluck, Juliane; Thi, Le Thuy Bui; Kolarik, Corinna; Kumpf, Kai; Hofmann, Martin. “Information extraction in the life sciences: Perspectives for medicinal. chemistry, pharmacology and toxicology.” Current Topics in Medicinal Chemistry 2005, 5(8), 785-796.
bibliography databases
Bibliography: Databases
  • Andreeva, A.; Howorth, D.; Brenner, S.E.; Hubbard, T.J.P.; Chothia, C.; Murzin, A.G. “SCOP database in 2004: refinements integrate structure and sequence family data.” Nucleic Acids Research 2004, 32 Database issue D226-D229 doi: 10.1093/nar/gkh039
  • Chen J, Swamidass SJ, Dou Y, Bruand J, Baldi P. “ChemDB: a public database of small molecules and related chemoinformatics resources.” Bioinformatics. 2005 Nov 15; 21(22): 4133-9.
  • Dunkel, M.; Fullbeck, M.; Neumann, S.; Preissner, R. “SuperNatural: a searchable database of available natural compounds.” Nucleic Acids Research 2006, 34, Database issue D678-D683 doi: 10.1093/nar/gkj132
  • Gold, Nicola D.; Jackson, Richard M. “A searchable database for comparing protein-ligand binding site for the analysis of structure-function relationships.” Journal of Chemical Information and Modeling 2006, 46(2), 736-742.
bibliography databases cont d
Bibliography: Databases (cont’d)
  • Kanehisa, M.; Goto, S.; Hattori, M.; Aoki-Kinoshita, F. Itoh, M.; Kawashima, S.; Katayama, T.; Araki, M; Hirakawa, M. “From genomics to chemical genomics: new developments in KEGG.” Nucleic Acids Research 2006, 34, Database issue D354-D357. doi: 10:1093/nar/gkj102.
  • Kellenberger, Esther; Muller, Pascal; Schalon, Clarire; Bret, Guillaume; Foata, Nicolas; Rognan, Didier. “sc-PDB: An annotated database of druggable binding sites from the Protein Data Bank.” Journal of Chemical Information and Modeling 2006, 46(2), 717-727.
  • Kirwin, J.J.; Shoichet, B.K. “ZINC—A free database of commercially available compounds for virtual screening.” Journal of Chemical Information and Modeling 2005, 45, 177-182.
  • Kouranov, A.; Xie, L. de la Cruz, J.; Chen, L.; Westbrook, J.; Bourne, P.E.; Berman, H.M. “The RCSB PDB information protal for structural genomics.” Nucleic Acids Research 2006, 34, Database issue D302-D305 doe: 10:1093/nar/gkj120
  • Kumar, M.D.S.; Gromiha, M.M. “PINT: Protein-protein interactions thermodynamic database.” Nucleic Acids Research 2006, 34 Database issue D195-D198 doi: 10.1093/nar/gkj017
bibliography databases cont d1
Bibliography: Databases (cont’d)
  • Lo Conte, L.; Brenner, S.E.; Hubbard, T.J.P.; Chothia, C.; Murzin, A.G. “SCOP database in 2002: refinements accommodate structural genomics.” Nucleic Acids Research 2002, 30(1): 264-267.
  • Murzin, A.G.; Brenner, S.E.; Hubbard, T.; Chothia, C. “SCOP: A structural classification of proteins database for the investigation of sequences and structures.” Journal of Molecular Biology 1995, 247, 536-540.
  • Okuno, Y.; Yang, J.; Taneishi, K.; Yabuuchi, H.; Tsujimoto, G. “GLIDA: GPCR-ligand database for chemical genomic drug discovery.” Nucleic Acids Research 2006, 34, Database issue D673-D677 doi: 10.1093/nar/gkj028.
  • Pearl F, Todd A, Sillitoe I, Dibley M, Redfern O, Lewis T, Bennett C, Marsden R, Grant A, Lee D, Akpor A, Maibaum M, Harrison A, Dallman T, Reeves G, Diboun I, Addou S, Lise S, Johnston C, Sillero A, Thornton J, Orengo C. The CATH Domain Structure Database and related resources Gene3D and DHS provide comprehensive domain family information for genome analysis.” Nucleic Acids Research. 2005, 33 Database Issue D247-D251.
bibliography databases cont d2
Bibliography: Databases (cont’d)
  • Wheeler, D.L. et al. “Database resources of the National Center for Biotechnology Information.” Nucleic Acids Research 2006, 34 Database Issue D173-D180 doi: 10.1093/nar/gkj158
  • Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey, Jennifer. “DrugBank: a comprehensive resource for in silico drug discovery and exploration.”Nucleic Acids Res. 2006 Jan 1;34(Database issue): D668-72.
biotech validation suite for protein structures
Biotech Validation Suite for Protein Structures
  • Send the server a PDB file
  • Server provides a comprehensive check of the protein, including:
    • Atomic volume analysis
    • Full geometric analysis
    • NMR restraint data
  • An in silico toxicology prediction suite
  • Based on the CDK toolkit
  • Built on CML
  • Released as OpenSource under the GPL
  • Standalone PC software
  • User Manual:
tools for genomic and proteomic scientists vis vis cell biology gagna et al
Tools for Genomic and Proteomic Scientists vis-à-vis Cell Biology (Gagna et al.)
  • Tools to fully exploit the techniques in cellular biology
    • Light microscopy for high resolution images
    • Fractionation of cells into basic components via ultracentrifugation
    • Analysis of individual cells through flow cytometry
    • LCM, normal and diseased TMAs (tissue microarrays), quantitative computer image analysis, cell micromanipulation, and high-throughput microscopy
inchi generation on the web
InChI Generation on the Web
  • The following websites provide the facility to generate InChIs:
    •' freely available structure-drawing program ChemSketch includes the facility to generate InChIs from drawn structures.
    • Server Side Structure Editor v1.8 includes a facility for generating InChIs as you draw the structure.
advances in macromolcular crystallography by ccg
Advances in Macromolcular Crystallography by CCG
  • More protein structures available now
    • Use of 3D info in bioinformatics makes functional inferences more dependable
      • CCG Structural Family Database distributed with MOE
        • Includes fold detection methodology to ID structurally similar proteins
        • Simultaneous sequence and structural alignment of large collections of proteins
        • 3D structural family analysis for insight into conserved geometry, water molecules, salt bridges, hydrogen bonds, hydrophobic contacts, and disulfide bonds
ccg s cheminformatics offerings
CCG’s Cheminformatics Offerings
  • MOE Molecular Database
  • Mo lecular Descriptors calculated and used for classification, clustering, filtering, and predictive model construction
  • QSAR/QSPR Predictive Modeling
  • Diversity and Similarity Searching
  • High Throughput Conformational Search
  • 3D Pharmacophore Search
components of the semantic web for chemistry
Components of the Semantic Web for Chemistry
  • XML – eXtensible Markup Language
  • RDF – Resource Description Framework
  • RSS – Rich Site Summary
  • Dublin Core – allows metadata-based newsfeeds
  • OWL – for ontologies
  • BPEL4WS – for workflow and web services
    • Murray-Rust et al. Org. Biomol. Chem. 2004, 2, 3192-3203.
web services integration projects biosciences
Web Services Integration Projects: Biosciences
  • myGrid
  • BioMOBY
biot 2006
BIOT 2006
  • Major themes, areas and suggested topics include
  • - Bio-molecular and Phylogenetic Databases
  • - Molecular Evolution and Phylogenetic analysis
  • - Drug Delivery Systems
  • - Bio-Ontology and Data Mining
  • - Sequence Search and Alignment
  • - Microarray Analysis
  • - System Biology
  • - Pathway analysis
  • - Identification and Classification of Genes
  • - Protein Structure Prediction and Molecular Simulation
  • - Functional Genomics
  • - Proteomics
  • - Tertiary structure prediction
  • - Drug Docking
  • - Gene Expression Analysis
  • - Biomedical Imaging
proteomics what is it
Proteomics: What is it?
  • Proteomics is the study of protein expression, regulation, modification, and function in living systems for understanding how living systems use proteins. Using a variety of techniques, proteomics can be used to study how proteins interact within a system, or how proteins change due to applied stresses.
  • Requires advanced measurement techniques, especially separations and mass spectrometry
proteomics needs informatics for
Proteomics Needs Informatics for:
  • Locating peaks in 2 or more dimensions
  • MS/MS spectra interpretation
  • Protein/Peptide quantification
  • Peptide detectability
  • Experimental data  Biological information
    • enzyme or pathway regulation
    • disease susceptibility
    • drug efficacy