IUPHAR/BPS Guide to Pharmacology: concise mapping of chemistry, data, and targets

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  1. 1. www.guidetopharmacology.org IUPHAR/BPS guide to pharmacology (GtoPdb): Concise mapping for the triples of chemistry, data, and protein target classifications Christopher Southan, Adam J. Pawson, Joanna L. Sharman, Elena Faccenda, Simon Harding, Jamie Davis, IUPHAR/BPS Guide to PHARMACOLOGY, Centre for Integrative Physiology, University of Edinburgh ACS Wed, Mar 16 CINF 140:Chemistry, Data & the Semantic Web: An Important Triple to Advance Science 1:30 PM - 4:45 PM Room 25B 1:35pm - 2:00pm 1 http://www.slideshare.net/cdsouthan/southan- nciuphar-acssandiego-59444512
  2. 2. Abstract (will be skipped for presentation) 2 The International Union of Basic and Clinical Pharmacology Committee on Receptor Nomenclature and Drug Classification (NC-IUPHAR) provides authoritative reports on G protein-coupled receptors (GPCRs) Nuclear Hormone Receptors and Ion Channels as pharmacology-based classifications. While these recommendations surfaced as Pharmacological Review papers (i.e. unstructured) since the 1990s, they were already underpinning the protein tables in GtoPdb's predecessor, IUPHAR-DB, by 2003. By 2012 this hierarchical data structure had expanded into the GtoPdb schema covering essentially all target classes for pharmacology, drug discovery and chemical biology. As of August 2015 the expert-curated relationship capture from the literature covers 1505 target-to-ligand mappings of which 1228 human protein IDs have quantitative interaction data recorded against 5860 chemical structures. The motivation, evolutionary trajectory, the need for community engagement to fill data gaps and future directions of the resource will be outlined. Descriptions will cover the challenges of cross-referencing alternative gene/protein hierarches, each of which has different navigational utilities and linkages to chemistry in GtoPdb. These now extend beyond receptors to enzymes and include NC-IUPHAR, HGNC, UniProt, Ensembl, InterPro, Gene Ontology and E.C. numbers. The adaption of our classifications to encompass a new immunopharmacology project will also be discussed.
  3. 3. Outline Introduction to NC-IUPHAR Evolution of IUPHAR-DB to GtoPdb Relationship statistics Target hierarchy and navigation Triple challenges with taxol Protein mapping and data gaps Introducing Guide to Immunopharmacology Conclusions and plans 3
  4. 4. International Union of Basic and Clinical Pharmacology Committee on Receptor Nomenclature and Drug Classification (NC-IUPHAR) Section within IUPHAR umbrella organisation since 1987 Issuing guidelines for the nomenclature and classification of human biological targets of current and future medicines Facilitating the interface between the Human Genome Project entities as functional units and potential drug targets Designating pharmacologically important polymorphisms Developing an authoritative and freely available, global online resource the IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb) Establishment of target-specific subcommittees (650 members) Associated with over 90 PubMed entries since 1995 Co-applicant on UK Wellcome Trust grants for the Edinburgh University-based GtoPdb and GtoImmPdb projects 4
  5. 5. NC-IUPHAR 2015 output 5
  6. 6. NC-IUPHAR Human Gene Nomenclature Committee collaboration 6
  7. 7. IUPHAR-DB launched in 2009: unique model of committee-underpinned annotation 7
  8. 8. 2012 to 2016: evolution of GtoPdb with major expansion 8 Human targets Ligands
  9. 9. GtoPdb relationship statistics (Jan 2016) 9 6,149 Ligands 1,786 Swiss-Prot IDs 14,117 affinity values 15,000 PubMed IDs Our basic triple
  10. 10. Top Level NC-IUPHAR target classification NC-IUPHAR underpinned but largely HGNC-concordant Defers to target-class nomenclature outside NC-IUPHAR domains (e.g. MEROPS for proteases, ESTER for a/b hydrolases) Includes pharmacologist-preferred NC-IUPHAR naming (e.g. Calcium- activated potassium channel KCa1.1 = KCNMA1) 65 Quaternary Structure Subunit annotations NC-IUPHAR use of lower-case and symbols can be problematic 10
  11. 11. Navigation: ligands > primary target 11
  12. 12. Navigation: paper > chemistry > target > affinity data 12
  13. 13. Trouble with triples (I) : so which taxol drug structure is it? 13 Probably not the virtual D52 12 CIDs include CAS 33069-62-4
  14. 14. Trouble with triples (II): so which is the molecular target? 14
  15. 15. Trouble with triples (III): so which structure > activity > target ? 15 22 CIDs share 4842 PubChem Bioassay results 89% are aligned against CID 36314 12 record actives None of the mixtures have results
  16. 16. GtoPdb: parsimonious annotation 16 We curate selectively and with high stringency This results in minimal rather than maximal triples coverage
  17. 17. More trouble with triples: which targets are real and which IDs cross-map1:1? UniProt, human =151,569 UniProt, human, Swiss-Prot = 20,198 + neXtProt = 20,040 + HGNC = 19,836 + Ensembl = 18,933 + CCDS = 18,286 + Entrez Gene ID = 18,245 + RefSeq = 18,244 + Evidence at protein level = 14,065 17
  18. 18. Even more trouble with triples: prodrugs and data gaps 18 Data gaps could be experimentally filled with established assays For example, some early ACE inhibitors have no purified human protein results (only rat, rabbit or hamster) Prodrugs may have no recorded activity so cannot be target mapped On a good day we can get Ki and IC50 from the same paper How do we convince/entice folk to fill the gaps?
  19. 19. Utility of different target hierarchies NC-IUPHAR Swiss-Prot HGNC HGNC families and stems InterPro (includes Pfam) Genome Ontology (GO) EC numbers for enzymes Protein Ontology ChEMBL groupings Pathways (systems pharmacology) UniProt key words and cross-references Terminology for oligomeric complexes and splice variants is problematic 19
  20. 20. Introducing the Guide to Immunopharmacology Wellcome Trust funded project initiated 4Q15 Abbreviation will be GtoImmPdb. Homepage portal providing an immunological perspective onto the database. Will use same schema as GtoPdb but extended to integrate GtoImmPdb data. Search via biological processes and target annotations to terms in the Gene Ontology (GO) Mapping to a simplified specific process list Provide search options via the Cell Ontology. 20
  21. 21. Intersects between GO immunology, GO inflammation and GtoPdb targets with quantitative ligand interactions 21
  22. 22. Conclusions and plans Resolving triples across the bioactivity big data landscape is difficult Our approach is concise small data relationship mapping NC-IUPHAR > new nomenclature engagements Consolidate GtoPdb (< 2000 stringent target mappings) Instantiate GtoImmPdb RDF-ise GtoPdb for OpenPhacts PubChem BioAssay submission (target class splits) PubChem SID splits (e.g. approved drugs) Fill in legacy data gaps Expand (flexible) rules and relationship handling e.g. protein interaction inhibitors, hybrid therapeutics, ligands with unknown molecular mechanism Work on chemistry mapping retrieval 22
  23. 23. References, acknowledgments and questions 23 http://www.ncbi.nlm.nih. gov/pubmed/24234439 Please visit us http://www.guidetopharmacology.org/ Curation rules are outlined in our FAQ, the 2014 and 2016 NAR papers and blogposts Funders are acknowledged in the title slide To retrieve NC-IUPHAR's 95 Pharmacological Reviews nomenclature publications in PubMed : (International[Title] AND Union[Title] AND Pharmacology[Title] AND "Pharmacol Rev"[Journal])


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