Drug-to-protein mappings in the Guide to PHARMACOLOGY: Utility as a target validation toolbox
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CONCLUSION Ref: [1] The IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands. Pawson et al, Nucleic Acids Res [2014, 42(database issue):d1098-106] Elena Faccenda 1 , Christopher Southan 1 , Helen E. Benson 1 , Joanna L. Sharman 1 , Adam J. Pawson 1, , Jamie A Davies 1 , and NC-IUPHAR* 1 The University of Edinburgh/Centre for Integrative Physiology, Edinburgh, EH8 9XD, UK. * The International Union of Basic and Clinical Pharmacology Committee on Receptor Nomenclature and Drug Classification. A hallmark of the Guide to PHARMACOLOGY database (GtoPdb) is expert curation of ligand-protein binding from the primary literature [1]. While the IUPHAR-db precursor has covered receptors and channel ligands since 2009 our current Wellcome-funded mission for GtoPdb since 2012 is to cover all human targets of prescription medicines, compounds in clinical development and research leads. Our content thus covers all stages from targets of successful drugs out to functional probes for potential ones. It is also designed to be data mined by users engaging in systems pharmacology, target validation, drug discovery and chemical biology. Which targets are included? • GPCRs, catalytic and nuclear hormone receptors • Enzymes including kinases, proteases and chromatin modifying enzymes • Transporters • Ion channels • Other protein targets www.guidetopharmacology.org Drug-to-protein mappings in the Guide to PHARMACOLOGY: Utility as a target validation toolbox [email protected] Supported by: We especially thank all contributors, collaborators and NC-IUPHAR members RESULTS Content statistics (see www.guidetopharmacology.org/ ) In May 2015 these encompassed interactions between 1460 proteins and 7733 ligands. Of these, 1103 proteins are connected to ligands by 12949 binding constants curated from 5105 references (the kinase screens add more mappings). We have extensive coverage across the traditional target classes of GPCRs, NHRs, ion channels, kinases and proteases and also include emergent classes such as chromatin modifying enzymes and RGS proteins. Note that all targets have a UniProt cross-reference and small molecules are submitted to PubChem. Because GtoPdb substantially covers the drug mechanistic relationship matrix it provides a useful target validation toolbox from several perspectives. Firstly, as a first-stop resource, users can find curated information in a concise form that can be easily queried. Secondly, 80% of our small molecule ligands have an exact vendor match in PubChem for the acquisition of research leads or tool compounds for validation experiments. In addition to small molecules we also curate interactions between >120 approved and investigational monoclonal antibodies and their protein targets. We not only welcome feedback on the utility of our resource but also contact regarding the capture of entities from recent papers as well as unpublished (but openly provenanced) data sets. Future plans • Regularly update the GtoPdb to include novel drug targets and innovative therapeutics, including expansion of approved indications and curators’ notes highlighting repurposing efforts. • Include toxicity data, potentially expanding some of our current cytochrome p450 enzyme pages. • Increase the coverage of introductory materials for important clinical targets. • Pilot sub-portals providing easily accessible pharmacological data targeted to specific research communities (for example a stem-cell section, an environmental pharmacology section or an immunopharmacology section). We have a well-established literature triage for populating the database. This includes inputs from ~60 expert target class subcommittees. The curation team identifies the key relationships from the documents and extracts them into the database, including quantitative activity values where possible, using input tools we have developed. Data sets from selected kinase screening sets have also been added. We use Swiss-Prot IDs for targets, PubChem compound identifiers (CIDs) for small molecules and sequence strings for peptides. We are continually exploring methods for detailed comparisons of content, both intra-GtoPdb and with external data sets. Figure 1. Comparing the Genome Ontology function splits between all human proteins (left, 20,198) and the GtoPdb targets with small-molecule quantitative interactions (right, 978) Figure 1. Comparing the Genome Ontology function splits between all human proteins (left, 20,198) and the GtoPdb targets with small-molecule quantitative interactions (right, 978) The 978 proteins we have curated small-molecule quantitative interactions with are a useful representation of the (data- supported) druggable genome. In Fig. 1 we have used the PANTHER web tool to compare the protein function distribution of this set, compared with the complete human proteome. Not unexpectedly the druggable set shows enrichment of receptors, enzymes and transporters. More detailed breakdown, including location and pathways, is available via PANTHER. Figure 2. Target categorisation for the 978 proteins with small- molecule relationships (expressed as a %). As a complement to the PANTHER analysis we exploited the functionality of the UniProt interface. Of the many options Fig. 2 presents a selection to demonstrate utility of our druggable protein listing. For example, pathway membership can be identified for 85%, 53% include transmembrane domains, 18% have a signal peptide and 7% are enzymes involved in epigenetic chromatin modification. Figure 3. PubChem categorisation of the small- molecules associated with our 978 druggable proteins (expressed as a %). RESULTS-cont’d INTRODUCTION METHODS Fig. 3 shows the % of our ligand set mapped to selectable fields in PubChem. This indicates, for example, how many have lead-like properties, should be obtainable from suppliers, have results in the BioAssay section, or structures in PDB.
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Transcript of Drug-to-protein mappings in the Guide to PHARMACOLOGY: Utility as a target validation toolbox
CONCLUSION
Ref: [1] The IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands. Pawson et al, Nucleic Acids Res [2014, 42(database issue):d1098-106]
Elena Faccenda1, Christopher Southan1, Helen E. Benson1, Joanna L. Sharman1, Adam J. Pawson1,, Jamie A Davies1, and NC-IUPHAR*1The University of Edinburgh/Centre for Integrative Physiology, Edinburgh, EH8 9XD, UK. * The International Union of Basic and Clinical Pharmacology Committee on Receptor Nomenclature and Drug Classification.
A hallmark of the Guide to PHARMACOLOGY database (GtoPdb) is expert curation of ligand-protein binding from the primary literature [1]. While the IUPHAR-db precursor has covered receptors and channel ligands since 2009 our current Wellcome-funded mission for GtoPdb since 2012 is to cover all human targets of prescription medicines, compounds in clinical development and research leads. Our content thus covers all stages from targets of successful drugs out to functional probes for potential ones. It is also designed to be data mined by users engaging in systems pharmacology, target validation, drug discovery and chemical biology.
Which targets are included? • GPCRs, catalytic and nuclear hormone receptors• Enzymes including kinases, proteases and chromatin modifying enzymes• Transporters• Ion channels• Other protein targets
www.guidetopharmacology.org
Drug-to-protein mappings in the Guide to PHARMACOLOGY: Utility as a target validation toolbox
Supported by:
We especially thank all contributors, collaborators and NC-IUPHAR members
RESULTS
Content statistics (see www.guidetopharmacology.org/)In May 2015 these encompassed interactions between 1460 proteins and 7733 ligands. Of these, 1103 proteins are connected to ligands by 12949 binding constants curated from 5105 references (the kinase screens add more mappings). We have extensive coverage across the traditional target classes of GPCRs, NHRs, ion channels, kinases and proteases and also include emergent classes such as chromatin modifying enzymes and RGS proteins. Note that all targets have a UniProt cross-reference and small molecules are submitted to PubChem.
Because GtoPdb substantially covers the drug mechanistic relationship matrix it provides a useful target validation toolbox from several perspectives. Firstly, as a first-stop resource, users can find curated information in a concise form that can be easily queried. Secondly, 80% of our small molecule ligands have an exact vendor match in PubChem for the acquisition of research leads or tool compounds for validation experiments. In addition to small molecules we also curate interactions between >120 approved and investigational monoclonal antibodies and their protein targets.We not only welcome feedback on the utility of our resource but also contact regarding the capture of entities from recent papers as well as unpublished (but openly provenanced) data sets.
Future plans• Regularly update the GtoPdb to include novel drug targets and innovative therapeutics, including expansion of approved indications and curators’ notes
highlighting repurposing efforts.• Include toxicity data, potentially expanding some of our current cytochrome p450 enzyme pages.• Increase the coverage of introductory materials for important clinical targets.• Pilot sub-portals providing easily accessible pharmacological data targeted to specific research communities (for example a stem-cell section, an
environmental pharmacology section or an immunopharmacology section).
We have a well-established literature triage for populating the database. This includes inputs from ~60 expert target class subcommittees. The curation team identifies the key relationships from the documents and extracts them into the database, including quantitative activity values where possible, using input tools we have developed. Data sets from selected kinase screening sets have also been added. We use Swiss-Prot IDs for targets, PubChem compound identifiers (CIDs) for small molecules and sequence strings for peptides. We are continually exploring methods for detailed comparisons of content, both intra-GtoPdb and with external data sets.
Figure 1. Comparing the Genome Ontology function splits between all human proteins (left, 20,198) and the GtoPdb targets with small-molecule quantitative interactions (right, 978)
Figure 1. Comparing the Genome Ontology function splits between all human proteins (left, 20,198) and the GtoPdb targets with small-molecule quantitative interactions (right, 978)
The 978 proteins we have curated small-molecule quantitative interactions with are a useful representation of the (data-supported) druggable genome. In Fig. 1 we have used the PANTHER web tool to compare the protein function distribution of this set, compared with the complete human proteome. Not unexpectedly the druggable set shows enrichment of receptors, enzymes and transporters. More detailed breakdown, including location and pathways, is available via PANTHER.
Figure 2. Target categorisation for the 978 proteins with small-molecule relationships (expressed as a %).
As a complement to the PANTHER analysis we exploited the functionality of the UniProt interface. Of the many options Fig. 2 presents a selection to demonstrate utility of our druggable protein listing. For example, pathway membership can be identified for 85%, 53% include transmembrane domains, 18% have a signal peptide and 7% are enzymes involved in epigenetic chromatin modification.
Figure 3. PubChem categorisation of the small-molecules associated with our 978 druggable proteins (expressed as a %).
RESULTS-cont’dINTRODUCTION
METHODS
Fig. 3 shows the % of our ligand set mapped to selectable fields in PubChem. This indicates, for example, how many have lead-like properties, should be obtainable from suppliers, have results in the BioAssay section, or structures in PDB.
