Hot topics: Analysis of protein-coding genetic variation in humans

Leck et al. [1] in Nature, describes a tour de force large scale reference data set of high-quality protein-coding variation generated via the Exome Aggregation Consortium (ExAC) [2]. This covers 7,404,909  variants of different types that can be interrogated from an open browser set up by the team  [2]. Many interesting and important aspects of protein variation in both medical and evolutionary contexts are subject to statistical analysis and the results discussed. This includes loss-of function (LoF) with both clinical manifestations and consequent possible opportunities for pharmacological intervention. As just one example they investigate genetic intolerance to 179,774 high-confidence protein truncation variants (PTVd) that mapped to 3,230 highly LoF-intolerant genes. It turns out that 72% have no human disease phenotype in the OMIM or ClinVar databases. The Exac resource provides opportunities for detailed analysis of  functional variation as well as a filter for analysis of candidate pathogenic variants in Mendelian diseases. The paper also indicates that most of the proposed burden of Mendelian disease alleles per-person highlighted in previous reports, is due to misclassification in the literature and/or in databases [3]. In curating target records for GtoPdb the team have been finding it increasingly challenging to select between the many sources of protein variation and different levels of supporting evidence for the phenotypic consequences thereof. On the basis of these papers and our initial assessment of their database, we would now recommend Exac as a first-stop-shop for browsing the genomic variation landscape of GtoPdb targets, with Swiss-Var, ClinVar and Orphanet as orthogonal backup.

[1] Leck et al. (2016). Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (18 August 2016) doi:10.1038/nature19057

[2] Karczewski et al. (2016) The ExAC Browser: Displaying reference data information from over 60,000 exomes. bioRxiv (19 August 2016), 070581, doi:10.1101/070581 

[3] Walsh et al. (2016). Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples.
Genetics in Medicine (17 August 2016), doi:10.1038/gim.2016.90

Comments by Chris Southan

Posted in Hot Topics

Hot Topics: Discovery of opioid analgesics with reduced side effects

Manglik et al. [1] , writing in Nature, believe they may have found a new form of painkiller that works just as well as morphine but lacks its potentially lethal side effect. The authors have found it is not addictive by discovering a biased agonist that selectively targets the G-protein pathway over β-arrestin. Binding of agonists, such as morphine, to the μ-opioid-receptor cause very powerful reductions in the sensation of pain or analgesia via the G-protein signalling pathway but has the major side-effect of respiratory depression (the major cause of death in heroin addicts) and constipation. A further unwanted side effect limiting the use of morphine is addiction by activating the dopaminergic reward circuits. The authors show the new  μ-opioid agonist PZM21 selectively activates the G-protein signalling pathway to give the desired analgesia in animal models but does not activate β-arrestin pathway, so causes little respiratory depression or constipation nor alters the dopamine pathway so would be predicted not to be addictive.

The research is important as the authors report PZM21 in mice was comparable to morphine but longer lasting. Interestingly PZM21 reduced pain in the CNS but not spinal cord in mouse models.

A biased opioid agonist TRV130 is now in Phase III trials by the company Trevena Inc that is structurally unrelated to PZM21 but has a similar pharmacological profile. Taken together, the two compounds suggest that agonists biased to the Gi/o-pathway (rather than possible differences in other pharmacological properties such as pharmacokinetics)  represent a new strategy for pain control.

[1] Manglik A. et al. (2016). Structure-based discovery of opioid analgesics with reduced side effects. Nature. doi:10.1038/nature19112 advance online publication: 1-6.

Comments by Anthony Davenport

n.b. the  two relevant ligands curated into GtoPdb are show below. As a new entry 9286 PZM21 will go live in release 2106.3 (September) but TRV130 was already captured as ligand 7334



Posted in Hot Topics

GtoImmuPdb: technical update August 2016


Since our last update in May 2016 the major development extension to the Guide to Immunopharmacology (GtoImmuPdb) has been to incorporate cell type associations and develop the web-application code to display both process and cell type data.

As a reminder, a early synopsis of the project can be found in this blog post and earlier technical updates from February and May.

Development Progress

Cell Type Associations

Previously, we had written a parser to capture and populate cell type data from the Cell Ontology into the database. Since then we have determined a set of 7 high-level, immuno-relevant cell type classes (or categories), against which targets in GtoImmuPdb will be annotated. The 7 classes are as follows:

1: pro-B-lymphocytes, B lymphocytes & Plasma cells [B lymphcytes]
lymphocyte of B lineage CL:0000945
2: T lymphocytes (alpha-beta type) and their immediate progenitors [T lymphocytes (alpha-beta)]
alpha-beta T cell CL:0000789
3: T lymphocytes (gamma-delta type) and their immediate progenitors [T lymphocytes (gamma-delta)]
gamma-delta T cell CL:0000798
4: Natural Killer (NK) cells [NK cells]
natural killer cell CL:0000623
5: Polymorphonuclear leukocytes (neutrophils, eosinophils, basophils) [Polymorphonuclear leukocytes] [Granulocytes]
granulocyte CL:0000094
6: Mononuclear leukocytes (syn: monocytes) (macrophages, dendritic cells, Kupffer cells) [Mononuclear leukocytes]
monocyte CL:0000576
macrophage CL:0000235
dendritic cell CL:0000451
7: Mast cells
mast cell CL:0000097

We have assigned one or more Cell Ontology parent terms to each class. Curators will be able to annotate targets with any child terms of those parents when adding/editing cell type associations. There is also provision for free text comments about the association and the ability to include any references.

Submission Tool

The submission tool has been extended to enable the capture of cell type-target associations and related data. It has also been modified to better capture data relating to process associations (namely to include references).

GtoImmuPdb Portal


Cell type associations form in submission tool

We have continued work on the alpha-version of the GtoImmuPdb portal, and extensions to the main GtoPdb web-application to incorporate and surface GtoImmuPdb data. Previously we had implemented a toggle on target family pages to highlight targets of relevance to GtoImmuPdb. The idea behind this is so that whichever route a user takes to get to a list of targets or target families – the immuno-view can be easily switch on or off.

We have also extended the detailed target pages to display immunopharmacology comments (specific to the target), cell type associations and process associations.


Cell type and process association data being surface on the detailed target pages.

The layout of the cell type associations contains one section per each high-level cell type class. Within each section all Cell Ontology terms that have been annotated against the target are displayed, alongside comments and references.


Close-up of Immuno Cell Type Associations section of detailed target page

Similarly, the layout of the process associations has one section per high-level process class, which includes comments and references. it also includes a list of Gene Ontology (GO) Processes that are annotated to the target. These GO annotations are not input by our curators, but picked up from GO and UniProt (auto-curated). We are including the GO evidence code for these annotations.

Please note, all web-app development is only available on our restricted access test site.

Our next steps will be to improve the layout of these sections – potentially collapsing the list of GO and Cell Ontology terms (in some cases the number of terms annotated to a target can be quite high).
We will also be working on extending the code that handles our site search to include all aspects of cell type associations.
We anticipate the full alpha-release to be made in late September/early October 2016.

This project is supported by a 3-year grant awarded to Professor Jamie Davies at the University of Edinburgh by the Wellcome Trust (WT).

Posted in Guide to Immunopharmacology, Technical, Uncategorized

GtoPdb database release 2016.3

We are pleased to announce our third database release of 2016. Version 2016.3 was published on 21st July 2016. The database is available through the Guide to Pharmacology website, download pages and web-services. In this release, our curators have added comments to all approved drugs in the database (1,290). These comments are included in the ligand details we submit to PubChem.

Target updates:

Website updates

A short, introductory video of the Concise Guide to Pharmacology has been added to the homepage. We will be bringing you more news about the concise guide through this blog in the future – so look out for those. The first, and introduction to the Concise Guide can be found here.

We have also made some minor modifications to our news, updates and announcements, consolidating these in this blog so that there is a single new feed.

Database Statistics

In total the database now contains 14,577 curated interactions across 2,789 human targets and 8,611 ligands.


Number of human targets in GtoPdb 2016.3. Measured by number of distinct UniProt entries includes for a given target class


Breakdown of ligand classes in GtoPdb 2016.3

View all the latest database content stats here.

Posted in Database updates

FREE – Concise Guide to Pharmacology 2015/2016


The Concise Guide to PHARMACOLOGY 2015/2016 provides concise overviews of the key properties of over 1750 human drug targets with their pharmacology, plus links to the open access knowledgebase of drug targets and their ligands (, which provides more detailed views of target and ligand properties.

This compilation of the major pharmacological targets is divided into eight areas of focus:

  • G protein-coupled receptors
  • Ligand-gated ion channels
  • Voltage-gated ion channelss
  • Other ion channels
  • Nuclear hormone receptors
  • Catalytic receptors
  • Enzymes and transporters.

These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside key references and suggestions for further reading. The Concise Guide is published in landscape format in order to facilitate comparison of related targets.


Discover the FREE Concise Guide

Watch the introductory 4 minute video on YouTube.


Tagged with:
Posted in Concise Guide to Pharmacology

Hot Topics: Linking chemistry to papers

The key value of our curation is the extraction of chemistry-activity-target data from papers. Giving this relationship a formal structure in our database records not only provides direct value for users but this is also propagated globally by other databases that link to and/or subsume our content. Within the pharmacology/chemogenomic database ecosystem the largest  source of chemistry <> PubMed ID links is PubChem. Many PubChem records include depositor-provided cross-references to scientific articles in PubMed, both related to chemical structures and bioassay data. The recent paper by Kim and the PubChem team [1] includes a detailed statistical analysis of these relationships that add up to 5.6 million connections between 2.2 million PMIDs and 301,000 compound records (CIDs). The paper also describes and compares in detail the different depositors, publisher-supplied and Mesh chemisty <> PMID links.

Since we are one of the PubChem depositors of these relationships,  we were pleased to see not only a positive mention in this paper but also a detailed breakdown of our own contribution of 11,250 CID <> PMID relationships (presented in Table 1). Although these are small numbers compared to the total,  it should be noted that ~95% of these are generated automatically (i.e. not curated) by the IBM patent extraction system that they operated on PubMed in parallel with patent document processing up to 2010. Note this chemistry-to-literature connectivity is slowly being expanded by journals, include the British Journal of Pharmacology [2].

[1] Kim et al. (2016). Literature information in PubChem: associations between PubChem records and scientific articles.  Journal of Cheminformatics,  8:32,  DOI: 10.1186/s13321-016-0142-6 [PMID: 27293485].

Comments by Curation Team

Posted in Hot Topics

GtoImmuPdb: technical update May 2016

Development of the Guide to Immunopharmacology (GtoImmuPdb) continues and this is an update of progress since our last update in February 2016. Since then, the GtoImmuPdb April Meeting was held in Edinburgh, where a detailed update on the status of GtoImmuPdb was delivered and discussions held about key points to focus over the next phase of development.

As a reminder, a early synopsis of the project can be found in this blog post.

Development Progress

Refinements have been added to the way GO biological process, of relevance to immunology, are identified and extracted from OBO files. The OBO-Edit export omitted some terms where ancestral relationship involved combinations of being ‘part-of’ something that in-turn ‘regulates’ a parent term that falls under either immune system process (GO:0002376) or inflammatory response (GO:0006954). As of 19 May 2016 the database holds 1,957 GO process terms. There are 393 targets (with cross-references to UniProt) annotated to these terms, with the total number of annotations being 1,379.

Extensions have been made to the web-application search mechanism to incorporate the high-level GtoImmuPdb process categories. These categories are: Immune system development and differentiation; Proliferation and cell death; Production of signals and mediators; Regulation and responses to signals, Cell-mediated immunity; Inflammation. The search links the GtoImmuPdb process categories to targets and is currently functional on our test site (restricted access). This needs to be extended to include GO process term, GO IDs and GtoImmuPdb process definitions.

Parsers have been developed to capture and populate cell type data from the Cell Ontology. The database now holds the cell types from the ontology, plus relationships and associations to GO processes (which will be helpful in cross-referencing). Our next steps are to determine high-level, immuno-relevant cell type classes for use on the site. A potential source are categories similar to the Immunological Genome Project;  B-cells, γδT-cells, αβT-cells, T-cell activation, NK cells, myeloid cells, stromal cells, dendritic cells & stem cells. The database also needs extended further to capture target to cell-type relationships and develop associated submission tool (for editing/curation) and web-application (to surface the data to users) extensions.

GtoImmuPdb Portal

An alpha-version of the GtoImmuPdb portal has been developed (restricted access). The layout aims to compliment the GtoPdb site, whilst ensuring it is distinct through styles, logos and branding.


Early mock-up of Guide to Immunopharmacology portal.

The targets box has been developed to link to lists of targets and automatically highlight target families where there is relevance to GtoImmuPdb (this is defined by the curators by flagging targets as relevant). A toggle-button enables users to switch on/off the immuno-view. The ability to toggle the view is likely be extend to other pages. The next step will be to extend the detailed target view to display GtoImmuPdb relevant data, in the first instance general comments and process associations.


View of GPCR targets, with GtoImmuPdb toggle (blue) highlighting relevant families.


Submission Tool

Extensions have been made to enable curators to view, edit and manage associations between the high-level GtoImmuPdb Process categories and GO process terms. It also allows annotation of targets to the high-level process terms. This includes adaptations to the main webapp code that will be essential in ‘surfacing’ the process data on target detail pages. Future work will include extending to edit/manage cell type data and to enable interactions to be curated as relevant to GtoImmuPdb.

This project is supported by a 3-year grant awarded to Professor Jamie Davies at the University of Edinburgh by the Wellcome Trust (WT).

Posted in Guide to Immunopharmacology, Technical

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