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

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http://www.slideshare.net/cdsouthan/southan-

nciuphar-acssandiego-59444512

Abstract (will be skipped for presentation)

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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 1990’s, 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.

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

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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

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NC-IUPHAR

2015 output

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NC-IUPHAR – Human Gene Nomenclature Committee collaboration

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IUPHAR-DB launched in 2009:

unique model of committee-underpinned annotation

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2012 to 2016: evolution of GtoPdb with major expansion

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Human targets

Ligands

GtoPdb relationship statistics (Jan 2016)

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6,149

Ligands

1,786

Swiss-Prot IDs

14,117 affinity

values

15,000

PubMed IDs

Our basic “triple”

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

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Navigation: ligands > primary target

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Navigation: paper > chemistry > target > affinity data

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Trouble with triples (I) :

so which taxol drug structure is it?

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Probably not the

virtual D52

12 CIDs include CAS 33069-62-4

Trouble with triples (II): so which is the molecular target?

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Trouble with triples (III):

so which structure >

activity > target ?

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• 22 CIDs share 4842

PubChem Bioassay

results

• 89% are aligned against

CID 36314

• 12 record actives

• None of the mixtures

have results

GtoPdb:

parsimonious

annotation

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• We curate

selectively and

with high

stringency

• This results in

minimal rather

than maximal

triples coverage

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

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Even more trouble with triples: prodrugs and data gaps

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• 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?

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

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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.

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Intersects between GO immunology, GO inflammation and

GtoPdb targets with quantitative ligand interactions

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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

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References, acknowledgments and questions

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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])