The International Union of Pharmacology
(IUPHAR)/British Pharmacology Society (BPS) database
(GtoPdb www.guidetopharmacology.org) curates
interactions between receptor ligands, research
compounds, approved drugs and their protein targets
(PMID 29149325). From 2012-15 we were funded by the
Wellcome Trust to expand the predecessor IUPHAR-DB of
receptors and channels to cover all data-supported
targets in the human proteome. Expansion continued
post-2015 with another
Wellcome Grant to extend into
Examples of GPCR database tables
extant and new immunopharmacology targets.
ESSENTIAL FEATURES REQUIRED FOR ESTABLISHING FOUR TYPES OF BIOSAFETY LABORATO...
Expansion of the druggable genome in the IUPHAR/BPS Guide to PHARMACOLOGY and other drug target resources: a key substrate for future medicines
1. Christopher Southan, Joanna L. Sharman, Adam J. Pawson1, Simon D. Harding, Elena
Faccenda and Jamie A. Davies.
IUPHAR/BPS Guide to PHARMACOLOGY, Centre for Discovery Brain Sciences, University of Edinburgh, EH8 9XD, UK.
Expansion of the druggable genome in the IUPHAR/BPS
Guide to PHARMACOLOGY and other drug target
resources: a key substrate for future medicines
Analysis of results
From a current Swiss-Prot human proteome count of 20,341
we can calculate proportional coverage. The sum of all four
sources represents 21% of the proteome, 2-way 4.7%, 3-
way 5.4% and 4-way 3.4%. For the latter set of 740
proteins further analyses were performed. The Gene
Ontology (GO) functional splits (Fig.3) show an even
distribution between transport, binding, catalysis and
receptors. The UniProt x-refs can give a detailed “slice and
dice” for many characteristics. The selection presented (Fig.
4) includes the protease, kinase and GPCR splits, diseases in
OMIM, EC number, transmembrane, PDB structures, Human
Protein Atlas and pathway memberships (n.b. any such
analysis can be performed on any subset from Fig.2).
Examples of GPCR database tables
Conclusions
• For reasons than cannot be expanded here (see PMID
24533037) the four sources have individual selectivity for
literature extraction, different release schedules and small
proportions of likely false-positives.
• Notwithstanding, they colectively provide complementary
and acessible coverage of the DG.
• Expansion is steady (e.g. the 4-way consensus has
increased from 568 in 2016 to 740 in 2018) and may
acellerate via new assay methodologies, probe compound
development and a focus on less characterised targets.
• Despite caviats, (e.g. medicinal chemistry tractability,
druggability and target validation) these sources provide
increasing numbers of potential therapeutic intervention
points for the development of new medicines.
Introduction
The International Union of Pharmacology
(IUPHAR)/British Pharmacology Society (BPS) database
(GtoPdb www.guidetopharmacology.org) curates
interactions between receptor ligands, research
compounds, approved drugs and their protein targets
(PMID 29149325). From 2012-15 we were funded by the
Wellcome Trust to expand the predecessor IUPHAR-DB of
receptors and channels to cover all data-supported
targets in the human proteome. Expansion continued
post-2015 with another Wellcome Grant to extend into
extant and new immunopharmacology targets.
cdsouthan@hotmail.com
UK Node Resource for:
UniProt cross-references (x-refs)
Each of the databases (Fig.1) have curated chemistry-
to-protein mappings for activity modulation and
therefore probable tractability as potential targets.
While data models and curatorial stringencies are
different, the four selectable protein sets are compared
in this work. Human Swiss-Prot lists were downloaded
for each of the sources and a Venn diagram comparison
made (Fig.2). Importantly, each source shows a
proportion of unique content, indicating they have
captured protein-to-chemistry interactions the other
three have not. Note for GtoPdb the 1505 have
quantitative ligand interactions (< from 1410 in 2016).
Figure 1. The four sources of chemistry x-refs for
human reviewed (Swiss-Prot) entries.
Figure 2. Venn diagram comparing the four sources
with protein totals shown after each database name.
Figure 4. Attribute distributions in the 740 protein set. These
were all derived from additional UniProt x-ref selections.
Figure 3. Top-level GO classifications for the 740 protein set.