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

By C. Southan, J. Sharman, H. E. Benson, E. Faccenda, Adam J Pawson, Stephen P. H. Alexander, O. Buneman, A. Davenport, John C. McGrath, John A. Peters and 5 more. Nucleic Acids Res., 2015

1,110 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Pharmacology

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Citations in the corpus

Why data citation isn't working, and what to do about it (2020, background): “For example, do we need to create a new set of summaries for each update to the database? In the following background section, we describe the current state of affairs with data citation in databases; we also describe a well-known curated database, the IUPHAR/BPS Guide to Pharmacology database (GtoP”
GPCRdb: the G protein‐coupled receptor database – an introduction (2016, background): “GPCRdb: the GPCR database BJP British Journal of Pharmacology (2016) 173 2195 – 2207 2203 complexes in the PDB.”
DGIdb 3.0: a redesign and expansion of the drug–gene interaction database (2017, neutral): “With these more frequent, incremental updates to the DGIdb, there has been a substantial increase in drug–gene interaction content from IUPHAR’s Guide to Pharmacology and CIViC, a 23 and 200% increase, respectively (5,11).”
PubChem BioAssay: 2017 update (2016, methodology): “The database also contains literature-based data including both publication driven depositions submitted by journal authors and those contributed by PDBbind (10), IUPHAR (11), Bind-ingDB (12), ChEMBL (13) and other literature curation projects.”
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences (2018, methodology): “To build the training dataset, we obtained known DTIs from three databases: DrugBank [34], KEGG [35], and IUPHAR [36].”
G Protein-Coupled Receptors as Targets for Approved Drugs: How Many Targets and How Many Drugs? (2018, methodology): “…ligand-target interactions from three online databases: ChEMBL (Bento et al., 2014), DrugBank (Wishart et al., 2007), and the International Union of Basic and Clinical Pharmacology/BPS (British Pharmacological Society) Guide to PHARMACOLOGY (GtoPdb) (Southan et al., 2016; Alexander et al., 2017).”
GPCRdb: an information system for G protein-coupled receptors (2015, methodology): “GPCRdb is cross-referenced to the IUPHAR/BPS GuideToPharmacology (GToPdb) (5), UniProt (22) and BitterDb (37) databases.”
The ChEMBL database in 2017 (2016, methodology): “Improvements have been made to the existing protein family classification for ion channels and transporters to more closely align the ChEMBL system with other resources (the IUPHAR/BPS Guide To PHARMACOLOGY (28) and TCDB (29)) and new classes have been introduced for epigenetic regulators (following”
The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching (2017, methodology): “Examples include jCompoundMapper [11], ScaffoldHunter [12, 13], OMG [14], PaDEL [15], Chem-Des [16], ReactPRED [17], SMSD [18–20], WhichCyp [21], MetaPrint2D [22], MetFrag [23], and the IUPHAR/BPS Guide to Pharmacology [24], BRENDA [25] and QSAR DataBank [26] databases.”
Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics (2017, background): “In particular, a number of freely accessible databases complementarily provide comprehensive information about the therapeutic targets with additional information about the clinical trial drugs (11–13), biomarkers (11), molecular activity data (11,13–15), drug-binding sites (16,17) and targetaffilia”
Curcumin, the golden nutraceutical: multitargeting for multiple chronic diseases (2017, background): “Treatment with turmeric powder resulted in a decrease in fasting blood sugar from 1400 to 700 mg·L 1 in a patient suffering from diabetes for 16 years (Srinivasan, 1972).”
The IUPHAR/BPS Guide to PHARMACOLOGY in 2018: updates and expansion to encompass the new guide to IMMUNOPHARMACOLOGY (2017, methodology): “From 2012 to 2015, the scope expanded to define the data-supported druggable genome and the resource was re-named GtoPdb (4,5).”
Carotenoids: biochemistry, pharmacology and treatment (2016, methodology): “TARGETS Other protein targetsa Catalytic receptorsc Bcl-2 IFN-α receptor Bcl-xL IGF1R IL-1β TLR4 TNF-α Enzymesd Nuclear hormone receptorsb AMPK RARα CDK2 COX-2 iNOS MMP9 LIGANDS cAMP LDL (LRP4) IL-1α LPS IL-2 MCP-1 (CCL2) IL-4 NADPH IL-6 PGE2 IL-12 TGFβ1 These Tables list key protein targets and lig”