This document provides an overview of the Guide to PHARMACOLOGY (GtoPdb) database, which contains expert-curated information on protein targets by function, properties, and ligands. It discusses the database's features, growth in content statistics, and capabilities for slicing and dicing the data using various parameters. Examples are given of target and ligand information in GtoPdb and how the data can be analyzed and compared to other databases through advanced queries and cross-references. Ongoing work and future directions are also outlined.

1. www.guidetopharmacology.org
Slicing and dicing expert-curated protein targets
by function, properties and ligands
in the Guide to PHARMACOLOGY
Christopher Southan
Presented to AstraZeneca Mölndal, June 17th 2015
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2. Outline
• Database context and features
• Targets and ligands
• Content statistics
• Slicing and dicing
• Function distributions for targets
• Comparing databases
• High vs low affinity Genome Ontology (GO) splits
• Advanced queries
• Ligands in PubChem
• Acknowledgments and questions
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3. NAR Database Issue 2014
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4. GtoPdb hallmarks
• Built on the NC-IUPHAR tradition of expertise and quality
• Target family Committee model
• ~ 90 subcommittees, ~ 650 expert members, de facto super-curator network
• Wellcome Trust grant objective to cover all human drug targets
• Focus on quantitative ligand > protein relationship capture
• Stringent and selective curatorial judgments from the team
• Extensive use of comments and cross-pointers
• Document binding as compound synonyms
• All recent ligands have short description
• Flexible and responsive coverage
• Steady expansion, content stats and regular releases
• Unique content including peptides and small molecules
• Collaborative (e.g. PubChem, IMGT, Orphanet, HGNC, UniProt)
• Downloads and lists
• Tables of links in British Journal of Pharmacology reviews
• Coupled to bi-annual BJP compendium “The Concise Guide to
PHARMACOLOGY ”
• Unique utility positioning w.r.t. other databases
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5. 5
Front
page

6. Blog coupling
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http://cdsouthan.blogspot.se/

7. Target example: BACE1
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8. Ligands for BACE1
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9. Ligand
example
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10. Kinase
matrix
screen
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11. Content stats (June 2015, Development version)
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12. Steady growth

13. GtoPdb + UniProt + Venny + Panther
• Extensive drug target “slice and dice” options
• Powerful query building
• Our cross-references in UniProt regularly updated
• Hypothesis-test pharmacologically relevant questions
• Either contact us for advanced slicing from GtoPdb that
you can not get directly (e.g. the affinity cuts) or download
our lists or even the whole database for DIY
• You can also slice and dice on the ligand chemistry side
for example via the PubChem Identifier Exchange Service
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14. Function splits between 20,198 human proteins
and 978 GtoPdb tractable targets (small-
molecule quantitative interactions)
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15. Comparative
target content via
UniProt/neXtProt
indexed bioactivity
dbs
Human Swiss-Prot
intersects
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16. Comparing drugged vs tractable
• Human targets, with any ligand = 1371
• Human targets, ligand is approved drug = 464
• Human targets, ligand has pAct 6-8 (lower affinity) = 532
• Human targets, ligand has pAct 9-10 (higher affinity) = 451
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17. Difference between drug targets with high vs low affinity
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• Analysis shows clear
bias towards
transporters for low
affinity drugs and
receptors for high
affinity drugs

18. Slicing and dicing in UniProt via target protein IDs
• Database queries that allow intersects (Boolean AND), unions (Boolean
OR) and differentials (Boolean NOT)
• E.g. Which UniProtKB/Swiss-Prot entries cite “Southan” (= 34)
• AND where organism is “Homo sapiens” (= 9)
• AND have a cross-reference in Guide to PHARMACOLGY (=3)
• Can query with any extrinsic sets (e.g. from GtoPdb)
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19. Properties of tractable targets (via UniProt)
• 978 GtoPdb proteins with small-molecule binding
• 85% pathway membership
• 53% transmembrane domains
• 30% kinases
• 18% have a signal peptide
• 7% are enzymes for epigenetic chromatin modification
• 7% proteases
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20. • Which targets have endogenous peptide interactions?
• AND exogenous synthetic peptide interactions?
• AND synthetic small molecule interactions?
• AND are the targets of approved drugs?
• Answer = 23 (all intersects and diffs can be inspected)
• Which of these have 3D structures ? = 9
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UniProt names & structures
Which pathways are they in ? (Panther )
Advanced pharmacology queries

21. Ligands in
PubChem (I)
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22. Ligands in PubChem (II)
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1. Clin = PubMed clinical
trial phase 1, last 5
years, in PubChem
2. MeSH ‘pharmacology’
in GtoPdb
3. PDB structure in
GtoPdb

23. UniChem & ChEMBL indexing
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UniChem 14-JUN-2015 = 90,269,494

24. Specialised
data sets
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25. Chemistry Connect: Ligands > GtoPdb
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26. • Contact established with OP team
• Will be integrated in future releases
• First stage will be a GtoP <> ChEMBL mapping list
• Next stage should be RDF’isation of GtoPdb
• We are developing web services
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27. Going forward
• Expansion < 1500 protein-ligand quant mappings by 3Q2015
• We welcome AZ steer on content, features and
handling/integrating our downloads
• Wellcome Trust immunopharmacology grant submitted
• Open to non-published (but provenanced)data capture
• Challenges
• Enhancing ontology incorporation, data retrieval and mining
• Resolving structural multiplexing of approved drug structures
• Complex targets
• Optimising the out-link balance for users
• Triaging pharmacological relevance from the genomics data deluge
• Hybrid molecule handling including biologics
• Mapping historical peptide content into PubChem
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28. Acknowledgements and questions
• GtoPdb database team:
• Jamie Davies (Principal Investigator)
• Adam Pawson, Helen Benson, Elena Faccenda (Curators)
• Joanna Sharman (Database Developer)
• Veronika Divincova (Project Administrator)
• Past and present members of NC-IUPHAR and its subcommittees, especially
Michael Spedding, Steve Alexander, Anthony Davenport and John Peters
• The late Professor Tony Harmar, founder and original PI of GtoPdb
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