The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands

Abstract

The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb, http://www.guidetopharmacology.org) provides expert-curated molecular interactions between successful and potential drugs and their targets in the human genome. Developed by the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS), this resource, and its earlier incarnation as IUPHAR-DB, is described in our 2014 publication. This update incorporates changes over the intervening seven database releases. The unique model of content capture is based on established and new target class subcommittees collaborating with in-house curators. Most information comes from journal articles, but we now also index kinase cross-screening panels. Targets are specified by UniProtKB IDs. Small molecules are defined by PubChem Compound Identifiers (CIDs); ligand capture also includes peptides and clinical antibodies. We have extended the capture of ligands and targets linked via published quantitative binding data (e.g. Ki, IC50 or Kd). The resulting pharmacological relationship network now defines a data-supported druggable genome encompassing 7% of human proteins. The database also provides an expanded substrate for the biennially published compendium, the Concise Guide to PHARMACOLOGY. This article covers content increase, entity analysis, revised curation strategies, new website features and expanded download options.

Figure 1.

Hierarchical listing for the ion channel families and subfamilies.

Figure 2.

High level Gene Ontology (GO) functional categories for three sets of human proteins. Set A was generated from the total proteome of 20,204. Set B represents the 1228 targets with quantitative ligand binding data in GtoPdb. Set C represents the 554 targets where at least one approved drug is included in the ligand binding data. Panel D provides the colour key to the top-level GO categories. The charts were generated by loading Swiss-Prot IDs from the protein sets into the PANTHER Gene List Analysis Tool (55).

Figure 3.

PubChem intersects. Figures were obtained via the PubChem interface using mostly pre-existing indexing. The exceptions are custom selects (described below) for patents, INN or United States Adopted Names (USAN) and Lipinski Rule-of-Five (ROF) + 150–800 Mw. With the exception of the SIDs (Row 1) intersects are CID counts. These queries were executed at the beginning of September 2015 when the PubChem CID total was 60.8 million and our own SIDs from release 2015.2 had been processed.

Figure 4.

Relationship growth since 2012. The first (left-most) chart shows the number of targets with curated ligand interactions while the second chart includes only those targets that are supported by quantitative data. The third and fourth charts show the number of approved drugs with data-supported targets and those that may be considered primary targets, respectively.

Figure 5.

Inhibitors table from the detailed view of the BACE2 target entry, with the inclusion of five lead compounds from patents.

Figure 6.

Clinically-Relevant Mutations and Pathophysiology for Kv7.1.

Figure 7.

Clinical data summary tab for the approved drug telmisartan.

Figure 8.

Intersects and differentials for human Swiss-Prot ID cross-referenced source databases that curate chemistry-to-protein mappings. Data were generated via the UniProtKB interface and the diagram prepared using the Venny tool (http://bioinfogp.cnb.csic.es/tools/venny/). The union of all four sets is 3603, based on the Swiss-Prot ID cross-references from UniProtKB release 2015_07.