Analysing curated protein targets: Partitioning the drugged and the druggable
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CONCLUSIONS AND OUTLOOK 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] Christopher Southan, Elena Faccenda, Helen E. Benson, Joanna L. Sharman, Adam J. Pawson, and Jamie A Davies http://www.slideshare.net/cdsouthan/southan-basel-poster201529may IUPHAR/BPS Guide to PHARMACOLOGY, Centre for Integrative Physiology, The University of Edinburgh, EH8 9XD, UK. A hallmark of the Guide to PHARMACOLOGY database (GtoPdb) is expert curation of ligand-protein data from the primary literature that includes inputs from ~90 target class subcommittees (1). While the IUPHARdb precursor covered receptors and channel ligands since 2009, our Wellcome-funded mission since 2012 is to cover all human targets of drugs, compounds in clinical development and research leads. Our March database release captured interactions between 1460 proteins and 7733 ligands. Of these, 1103 proteins are mapped to ligands by 12949 binding constants (typically IC50, Ki or Kd) curated from 5105 references. This includes 978 proteins mapped to 3456 small-molecules. Our content thus facilitates comparative analysis of successful (drugged) targets with compounds tested in vivo and extends to potential targets with moderately potent lead-like chemistry in vitro (druggable). www.guidetopharmacology.org Analysing curated protein targets: Partitioning the drugged and the druggable Supported by: We especially thank all contributors, collaborators and NC-IUPHAR members RESULTS (1) As you can see from this small set of results, GtoPdb offers extensive drug target “slice and dice” options, along with powerful specificity for query building. While a variety of tools and resources can be used for this, we find the combination of UniProt, PANTHER and Venny particularly flexible. This “slice and dice” approach can be used for pharmacologically-relevant questions (e.g. highlighting multiple pathway perturbation points and/or particular druggable target classes) that can enable drug discovery work. The database not only substantially covers the data- supported human drug mechanistic relationship matrix but is also continually expanding with approximately quarterly releases. It thus becomes a valuable resource from several perspectives. As a first-stop web portal, users can find curated information in a concise form that can be easily queried. In addition, ~80% of our small molecule ligands have a vendor match in PubChem that facilitates the acquisition of leads or tool compounds. This enables researchers to quickly move from in silico analysis to experimental studies of target validation, pathway intervention points and functional genomic perturbations. You can also download our data in various formats (www.guidetopharmacology.org/download.jsp) and are welcome to contact us for additional options that we may be able to assist with (e.g. the affinity cuts). We also welcome contact for recent paper recomendations (e.g. your own) as well as unpublished (but openly provenanced) pharmacologically relevant data sets. For curation we use Swiss-Prot IDs as primary identifiers for targets, PubChem IDs for small molecules, sequence strings for peptides and IMGT identifiers for clinical antibodies. The protein IDs enable detailed analysis of the target landscape via the UniProt interface (www.uniprot.org/) and the PANTHER resource for Genome Ontology, GO (www.pantherdb.org/). The Venny web tool (http://bioinfogp.cnb.csic.es/tools/venny/) was used to display intersects and differential lists that could be re-input to PANTHER. 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 (GO) function splits between all human proteins (left, 20,198) and the GtoPdb targets with small molecule quantitative interactions (right, 978), using the PANTHER web tool. The analysis of the druggable set shows enrichment of receptors, enzymes and transporters together with a reduced proportion of structural proteins and transcription factors. A more detailed breakdown, including cellular locations and enzyme classifications, is available via PANTHER. Figure 2. Ten categories determined for GtoPdb proteins with small-molecule relationships by UniProt queries (expressed as a % of 978 targets). These results indicate 85% of the druggable genome has pathway membership (indicated by a Reactome database cross-reference), 53% include transmembrane domains, 47% interact with an approved drug, 37% are kinases, 18% have a signal peptide and 7% are enzymes involved in epigenetic chromatin modification. RESULTS (2) INTRODUCTION ANALYSIS METHODS Figure. 3 (left) Venn diagram of human targets, with any ligand (1,371), where the ligand is an approved drug (464), ligand has pAct 6-8 (low-affinity (532)), ligand has pAct 9-10, (high-affinity (451)). Right is the PANTHER GO split for the 242 approved drug targets with high affinity ligands Compared with the entire druggable set (figure 1) the result shows a clear bias towards receptors for high- affinity approved drugs. The analogous result for low- affinity drug targets (the 175) shows a bias towards transporters RESULTS (3)
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Transcript of Analysing curated protein targets: Partitioning the drugged and the druggable
CONCLUSIONS AND OUTLOOK
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]
Christopher Southan, Elena Faccenda, Helen E. Benson, Joanna L. Sharman, Adam J. Pawson, and Jamie A Davies http://www.slideshare.net/cdsouthan/southan-basel-poster201529may
IUPHAR/BPS Guide to PHARMACOLOGY, Centre for Integrative Physiology, The University of Edinburgh, EH8 9XD, UK.
A hallmark of the Guide to PHARMACOLOGY database (GtoPdb) is expert curation of ligand-protein data from the primary literature that includes inputs from ~90 target class subcommittees (1). While the IUPHARdb precursor covered receptors and channel ligands since 2009, our Wellcome-funded mission since 2012 is to cover all human targets of drugs, compounds in clinical development and research leads. Our March database release captured interactions between 1460 proteins and 7733 ligands. Of these, 1103 proteins are mapped to ligands by 12949 binding constants (typically IC50, Ki or Kd) curated from 5105 references. This includes 978 proteins mapped to 3456 small-molecules. Our content thus facilitates comparative analysis of successful (drugged) targets with compounds tested in vivo and extends to potential targets with moderately potent lead-like chemistry in vitro (druggable).
www.guidetopharmacology.org
Analysing curated protein targets: Partitioning the drugged and the druggable
Supported by:
We especially thank all contributors, collaborators and NC-IUPHAR members
RESULTS (1)
As you can see from this small set of results, GtoPdb offers extensive drug target “slice and dice” options, along with powerful specificity for query building. While a variety of tools and resources can be used for this, we find the combination of UniProt, PANTHER and Venny particularly flexible. This “slice and dice” approach can be used for pharmacologically-relevant questions (e.g. highlighting multiple pathway perturbation points and/or particular druggable target classes) that can enable drug discovery work. The database not only substantially covers the data-supported human drug mechanistic relationship matrix but is also continually expanding with approximately quarterly releases. It thus becomes a valuable resource from several perspectives. As a first-stop web portal, users can find curated information in a concise form that can be easily queried. In addition, ~80% of our small molecule ligands have a vendor match in PubChem that facilitates the acquisition of leads or tool compounds. This enables researchers to quickly move from in silico analysis to experimental studies of target validation, pathway intervention points and functional genomic perturbations. You can also download our data in various formats (www.guidetopharmacology.org/download.jsp) and are welcome to contact us for additional options that we may be able to assist with (e.g. the affinity cuts). We also welcome contact for recent paper recomendations (e.g. your own) as well as unpublished (but openly provenanced) pharmacologically relevant data sets.
For curation we use Swiss-Prot IDs as primary identifiers for targets, PubChem IDs for small molecules, sequence strings for peptides and IMGT identifiers for clinical antibodies. The protein IDs enable detailed analysis of the target landscape via the UniProt interface (www.uniprot.org/) and the PANTHER resource for Genome Ontology, GO (www.pantherdb.org/). The Venny web tool (http://bioinfogp.cnb.csic.es/tools/venny/) was used to display intersects and differential lists that could be re-input to PANTHER.
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 (GO) function splits between all human proteins (left, 20,198) and the GtoPdb targets with small molecule quantitative interactions (right, 978), using the PANTHER web tool.
The analysis of the druggable set shows enrichment of receptors, enzymes and transporters together with a reduced proportion of structural proteins and transcription factors. A more detailed breakdown, including cellular locations and enzyme classifications, is available via PANTHER.
Figure 2. Ten categories determined for GtoPdb proteins with small-molecule relationships by UniProt queries (expressed as a % of 978 targets).
These results indicate 85% of the druggable genome has pathway membership (indicated by a Reactome database cross-reference), 53% include transmembrane domains, 47% interact with an approved drug, 37% are kinases, 18% have a signal peptide and 7% are enzymes involved in epigenetic chromatin modification.
RESULTS (2)INTRODUCTION
ANALYSIS METHODS
Figure. 3 (left) Venn diagram of human targets, with any ligand (1,371), where the ligand is an approved drug (464), ligand has pAct 6-8 (low-affinity (532)), ligand has pAct 9-10, (high-affinity (451)). Right is the PANTHER GO split for the 242 approved drug targets with high affinity ligands
Compared with the entire druggable set (figure 1) the result shows a clear bias towards receptors for high-affinity approved drugs. The analogous result for low-affinity drug targets (the 175) shows a bias towards transporters
RESULTS (3)
