Bridging Bioinformatics and Chemoinformatics

1
Bridging Bioinformatics and Chem(o)informatics

• Gary Wiggins
• School of Informatics
• Indiana University
• wiggins_at_indiana.edu
• Yan He (SLIS MLS Student)
• Meredith Saba (SLIS MLS Student)

2
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.

3
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

4
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
• http//www.acscinf.org/new/docs/meetings/227nm/227
  cinfabstracts.htm

5
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.

6
Interdisciplinary Collaborations Biology and
Chemistry

• Whats ... important for these collaborations
  is, not only do you have to accept the other
  guys paradigm or at least live with it you have
  to be willing to accept the other guys foibles
  or your perception of the other guys foibles
  (and recognize the opposite of this). We each
  have our own approaches to how we do science, and
  its just different cultures.
• --Thom Kauffman interview in ACS LiveWire, March
  2005, 7.3. http//pubs.acs.org/4librarians/livewir
  e/2006/7.3/profile.html

7
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.

8
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

9
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

10
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.

11
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
• http//www.metaphorics.com/products/cabinet.html

12
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

13
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

14
Chemoinformatics and Disease
15
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

16
Genegos 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
• http//www.genego.com/

17
BioWisdom

• Examination of vast amounts of available
  information using its Sofia KnowledgeScan
  methodology
• SRS data integration platform
• http//www.biowisdom.com/

18
Lessons from Hip Hop (Salamone)

• Mashup technique
• Bring together disparate informatics, biological,
  chemical, and imaging information when conducting
  research
• Example of an integration tool iSpecies.org
• A search for a species returns a page with NCBI
  genomics information, Yahoo images of the
  species, and articles culled from Google Scholar

19
iSpecies.org Search

• For mus musculus

20
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.

21
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)

22
Open Source Software (Geldenhuys)

• Log P calculator from Interactive Analysis
• http//www.logp.com
• University of Utahs Computational Science and
  Engineering Online
• Can submit jobs for molecular mechanics, quantum
  chemical calculations, and biomolecular
  interfaces for viewing PDB files
• http//www.cse-online.net
• Virtual Computational Chemistry Laboratory
• http//www.vcclab.org

23
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)
• http//www.blueobelisk.org/

24
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)

25
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

26
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

27
ChemDB http//cdb.ics.uci.edu/CHEM/Web/
28
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

29
Vioxx Entry in ChEBI
30
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
• http//www.iupac.org/inchi/license.html

31
Generation of InChI for Vioxx with wInChI
32
Vioxx Entry in PubChem Compounds Found with InChI
33
Vioxx Bioassay Data in PubChem
34
Vioxx PubChem Link to External Sources of
Information
35
The Elsevier MDL/NIH Link via PubChem and
DiscoveryGate

• Cross-indexes PubChem to the Compound Index
  hosted on Elsevier MDLs 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

36
Elsevier MDLs 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?

37
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 AI services
• Need to understand how chemical structures link
  to biological processes

38
Chemical Datamining Software

• SureChem
• http//surechem.reeltwo.com/
• CLiDE
• Recognizes structures, reactions, and text
• http//www.simbiosys.ca/clide/
• OSCAR
• OSCAR1 to check experimental data
• http//www.ch.cam.ac.uk/magnus/checker.html
• http//www.rsc.org/Publishing/ReSourCe/AuthorGuide
  lines/AuthoringTools/ExperimentalDataChecker/
• CSR (Chemical Structure Reconstruction)
• http//www.scai.fraunhofer.de/uploads/media/MZ-ERC
  IM05_04.pdf
• MDL DocSearchcombines MDLs Isentris platform
  and EMCs Documentum

39
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

40
Themes from SwissProts 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

41
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

42
Databases with Protein and Ligand Information
(Jónsdóttir)

• Protein Data Bank
• Target Registration Database
• Relibaseuses structural info to analyze
  protein-ligand interactions Relibase for
  protein-protein interaction searching
• Cambridge Structural Database
• KEGG LIGAND DB for enzyme reactions
• http//www.genome.ad.jp/ligand

43
Other Databases with Protein and Ligand
Information

• SitesBase--a database of known ligand binding
  sites within the PDB
• http//www.bioinformatics.leeds.ac.uk/sb/main.html
  
• Binding MOAD
• http//www.bindingmoad.org/
• sc-PDB (Kellenberger)
• http//bioinfo-pharma.u-strasbg.fr8080/scPDB/inde
  x.jsp

44
sc-PDB http//bioinfo-pharma.u-strasbg.fr8080/sc
PDB/index.jsp
45
Isatin Search on sc-PDB
46
Other Databases with Protein-Protein Interaction
Data (Jónsdóttir)

• YPD, Yeast Proteome Database (for proteins from
  S. cerevisiae)
• http//www.biobase.de/pages/index.php?id139
• Human Protein Reference Database
• http//www.hprd.org/
• BIND, Biomolecular Interaction Network Database
  (ceased as of 11/16/2005?)
• http//www.bind.ca/Action

47
International Molecular Exchange (IMEx)
Consortiumhttp//imex.sourceforge.net/

• BIND (http//www.blueprint.org) The Blueprint
  Initiative AsiaPte. Ltd, Singapore and The
  Blueprint Initiative North America,Toronto Canada
• DIP (http//dip.doe-mbi.ucla.edu) UCLA-DOE
  Institute for Genomics Proteomics
• IntAct (http//www.ebi.ac.uk/intact),
  EMBLEuropean Bioinformatics Institute, Hinxton,
  UK
• MINT (http//mint.bio.uniroma2.it/mint/)
  University of Rome Tor Vergata, Rome Italy
• MPact (http//mips.gsf.de/genre/proj/mpact), MIPS
  / Institute for Bioinformatics, Munich, Germany.

48
Protein Sites from IU I533 Students and others

• LigandDepotintegrated source for small molecules
• http//ligand-depot.rutgers.edu/index.html
• PSIPRED Protein Structure Prediction Server
• http//bioinf.cs.ucl.ac.uk/psipred/
• DSSP--a database of secondary structure
  assignments (and much more) for all protein
  entries in the PDB
• http//swift.cmbi.ru.nl/gv/dssp/
• Dr. Predrag Radivojacs I690 class on Structural
  Bioinformatics
• http//www.informatics.indiana.edu/predrag/2006spr
  ingi690/2006springi690.htm

49
Protein Secondary Structure Prediction

• Methods
• Neural Network
• Rule Based
• Other Machine Learning
• Homology Based

50
Protein Secondary Structure Prediction Software

• PredictProtein
• http//www.predictprotein.org/
• Chou-Fasman http//fasta.bioch.virginia.edu/fasta_
  www/chofas.htm
• NN Predict
• http//www.cmpharm.ucsf.edu/nomi/nnpredict.html

51
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

52
Structure-Based Docking Methods

• DOCK
• http//dock.compbio.ucsf.edu/
• Accelryss Insight (built on DOCK)
• http//www.accelrys.com/products/insight/
• FlexX
• http//www.biosolveit.de/FlexX/
• Glide
• http//www.schrodinger.com/ProductDescription.php?
  mID6sID6
• GOLD
• http//www.ccdc.cam.ac.uk/products/life_sciences/g
  old/

53
Useful Structure Databases

• ModBase
• http//modbase.compbio.ucsf.edu/modbase-cgi-new/se
  arch_form.cgi
• Dali Database (Fold classification based on PDB)
• http//ekhidna.biocenter.helsinki.fi/dali/start
• Protein Structure Analysis, Comparison, /or
  Classification Guide
• http//www.bio.vu.nl/nvtb/Structures.html

54
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
• http//scop.mrc-lmb.cam.ac.uk/scop/

55
SCOP Search Options

• Homology search yields a list of structures with
  significant levels of sequence similarity
• Keyword search matches words in SCOP and PDB

56
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
• http//cathwww.biochem.ucl.ac.uk/latest/index.html
  

57
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

58
Gasteigers 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
• http//www2.chemie.uni-erlangen.de/services/biopat
  h/index.html

59
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

60
Correlation Matrix Between Drug Activity and Gene
Expression
61
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

62
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

63
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)

64
Service-Oriented Architecture

• From Curcin et al. DDT, 2005, 10(12),867

65
Web Services for Chemistry Problems

• Performance and scalability
• Proprietary data
• Competition from high-performance desktop
  applications
• -- Geoff Hutchison, its 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)

66
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

67
Indiana University Planned Projectshttp//www.ch
embiogrid.org

• 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

68
Web Services for Chemistry at IU
69
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
• http//dtp.nci.nih.gov/docs/dtp_search.html

70
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)

71
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
72
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

73
Taverna Workflow
Workflow definition
Available web services (WSDL)
Visual depiction of workflow
74
Taverna in Action
75
Overall Workflow
76
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.

77
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.

78
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 worlds chemical
  literatureincluding patents and obscure
  technical reportsin their sleep.
• --Author unknown

79
Acknowledgements

• Randy Arnold
• Xiao Dong
• Sean Mooney
• Peter Murray-Rust
• David J. Wild
• I533 Chemical Informatics Seminar Students
• Elsevier Science

80
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 http//www.acscinf.org/new/
  docs/meetings/227nm/227cinfabstracts.htm
• 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.

81
Bibliography Articles (contd)

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82
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83
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84
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• http//www.scai.fraunhofer.de/uploads/media/MZ-ERC
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• Zimmermann, Marc Fluck, Juliane Thi, Le Thuy
  Bui Kolarik, Corinna Kumpf, Kai Hofmann,
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89
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
• http//biotech.ebi.ac.uk8400/

90
Knowledge-Driven Bioinformatics Enhanced with
Chemistry
91
ToxTree

• 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 http//ecb.jrc.it/DOCUMENTS/QSAR/TOXT
  REE/toxTree_user_manual.pdf

92
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

93
InChI Generation on the Web

• The following websites provide the facility to
  generate InChIs
• www.acdlabs.com/download/chemsk.htmlACD/Labs'
  freely available structure-drawing program
  ChemSketch includes the facility to generate
  InChIs from drawn structures.
• pubchem.ncbi.nlm.nih.gov/edit/PubChem Server
  Side Structure Editor v1.8 includes a facility
  for generating InChIs as you draw the structure.

94
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

95
CCGs 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

96
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.

97
Web Services Integration Projects Biosciences

• myGrid
• http//www.mygrid.org.uk/
• BIOPIPE
• http//biopipe.org/
• BioMOBY
• http//biomoby.org/

98
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

99
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

100
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