Hot topics: Intracellular allosteric antagonism of the CCR9 receptor

Chemokines and their GPCR receptors are important drug targets in a broad range of diseases due to their role in immune defence (by controlling migration, activation and survival of immune cells), viral entry, tumour growth and metastasis. This article presents the crystal structure of the CCR9 receptor in complex with vercirnon (GtoPdb ligand ID: 9046) at 2.8Å resolution. Remarkably, vercirnon binds to the intracellular side of the receptor, exerting allosteric antagonism and preventing G-protein coupling. This binding site explains the need for relatively lipophilic ligands and describes another example of an allosteric site on G-protein-coupled receptors that can be targeted for drug design, not only at CCR9, but potentially extending to other chemokine receptors.

[1] Oswald et al. (2016). Nature 540(7633):462-465. Intracellular allosteric antagonism of the CCR9 receptor. [PMID: 27926729]

Comments by Curation Team

Posted in Hot Topics

Hot topics: Visualizing GPCR ‘Megaplexes’ Which Enable Sustained Intracellular Signaling

Spotlight article [1] discussing the range of cutting-edge techniques that have been employed to visualize ‘megaplexes’ consisting of a G protein-coupled receptor (GPCR) bound to β-arrestin in intracellular endosomes following agonist-induced internalization. Surprisingly, the complex includes simultaneous binding of the heterotrimeric G protein, which retains full functional activity and supports sustained signaling from within the cell.

[1] Marshall F.H. (2016). Trends Biochem Sci. 41(12):985-986. Visualizing GPCR ‘Megaplexes’ Which Enable Sustained Intracellular Signaling. [PMID: 27825513]

Comments by Curation Team

Posted in Hot Topics

Hot topics: Structural basis for the gating mechanism of the type 2 ryanodine receptor RyR2

A report on the structures of type 2 ryanodine receptor from porcine heart at near-atomic resolution [1]. Structural and mutational characterisations provide important insights into the gating and disease mechanisms of RyR2.

Guide to Pharmacology: Ryanodine receptor family.

[1] Peng et al. (2016). Structural basis for the gating mechanism of the type 2 ryanodine receptor RyR2. Science. 354 (6310). [PMID: 27708056]

Comments by Curation Team

Posted in Hot Topics

GtoImmuPdb: technical update February 2017

The 3rd alpha-release (v3.0) of the Guide to IMMUNOPHARMACOLOGY was released on 30th January 2017. This blog post summarises some of the main features of the release and other developments as we moved toward our first public, beta-release in Spring 2017

An early synopsis of the project can be found in this blog post. You can also review our previous technical blogs on GtoImmPdb.

Development Progress

Alpha-Release v3.0

No major changes have been made to the portal in this release. There are some minor edits to the help and tutorial to reflect other changes. We expect to be bale to implement links from the disease portal in the next release.

portal_v3

Figure 1: Alpha-release v3.0 portal

As a reminder, the portal provides a starting-point for accessing data in GtoImmuPdb, tailored to the requirements of users with a specific interest in immunopharmacology. It accesses the same database as GtoPdb, but provides specific immuno-focussed views of the data, which can be toggle on and off.

Ligand Summary Pages

The ligand summary pages have been modified to create a specific immunopharmacology tab which contains all immunopharmacology related data for that ligand.

ligand_summary_immu_tab

Figure 2: Ligand summary page for ABT-737, showing immunopharmacology tab

The immunopharmacology tab displays ligand specific comments related to immunopharmacology as well as the newly included disease association data.

The inclusion of both these type of data has required extensions to the database schema. Firstly, extending of ligand tables to house the immunopharmacology comments. Secondly, adding in a series of new tables to house disease to ligand and disease to target associations, plus any references related to these associations.

Disease Associations

For the disease associations, as well as extending the database schema, we have extended the submission tool to aid capturing this data and providing a way for curators to edit and update these associations in the database.

As for the data itself, we are utilising a mixture of disease resources – OMIM, Orphanet and the Disease Ontology, to provide a controlled vocabulary against which we can annotate, and as a way by which we can cross-reference our disease associations to other resources. Basically this is so we can be as sure as possible that what we are calling and describing a disease as, is conforming to other understood and accepted descriptions of that disease or condition.

The GtoPdb database contains over 2,000 disease terms (including synonyms), 1,400 of which are currently curated as being associated with a target protein. Of these, there are about 270 association to roughly 80 distinct immuno targets. Our curators will be checking these association and ensuring that the ones of highest relevance to immunopharmacology will be recorded in GtoImmuPdb.

Displaying diease

Figure 2 shows the display of a ligand to disease association and figure 3 below shows how the data on target to disease association is being surfaced on the detailed target pages. This is a example on our test database and is not real data, but illustrates the intended style of display.

As well as listing the disease names and synonyms, we also provide the external references (X-Refs) to other disease resources as a useful cross-pointer. In addition curator comments and references are displayed.

target_to_disease

Figure 3: Example of proposed display of disease to target association data on detailed view page in GtoImmuPdb. 

GtoImmPdb uses the same underlying database as GtoPdb. This is has been extended to include and integrate GtoImmPdb data. The primary data-types of interest to GtoPdb, that have been addresses so far, are processes and cell-types. The database schema has been extended to accommodate these data-types and to associate them with targets in the database.

Immuno Process Data

We have made some minor adjustments to the capturing to process association data from the Gene Ontology (GO). We have been obtaining the GO annotations from UniProt – so that we can restrict the data to targets cross-referenced in GtoPdb (human with quantitative interactions). Previously we’d also been restricting by those protein targets annotated to either immune system or inflammatory processes. This step has been removed, as we can check this against our own GO process tables (captured from the original OBO file). This usefully avoids any latency that could exist between GO updates and UniProt updates.

Searching Disease Data

Extensions have also been made to the search mechanisms to incorporate any immuno disease to ligand or target associations and their synonyms, descriptions and comments.

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

Reference Citations in BJP and NAR metrics

Introduction
Like other database resources, we collate measures of impact for various uses. These include our own internal bi-annual IUPHAR/GtoPdb review meetings, documenting outputs of past grants and applications for new ones. While there is a widening choice of these metrics  (including website accesses, data downloads, hits on Open Access papers, papers linked to grant numbers and Altmetrics scores) citation counts remain an important component. Recently, we were asked to include these in our application to become an ELIXIR Europe Core resource (following on from our successful incorporation into the ELIXIR UK node). In addition, related to a recent funding pre-application, we have gratefully received recommendation letters, one of which happened to highlight our citation achievements. What we now need to explain here, underpinned by data, is why some of our recent citation counts appear anomalously high (notwithstanding the fact that different sources generate different numbers anyway). We will also broaden the scope of this post by mentioning other metrics and promotional strategies.

As a technical preamble,  the good news is that PubMed (including PubMed Central, PMC) and European PubMed Central (EPMC) now have divergent but powerfully complementary bibleometric feature sets for the same 22 million abstracts and 4 million full texts (e.g. Entrez and MeSH x-refs on one side vs EBI x-refs on the other) . The bad news is, as usual, that  anyone wanting to explore bibliometrics in detail is now faced with a two-stop-shop (actually a de facto transatlantic split). This is manifest below where I have to hop between the two, depending on what filters I need. The fact that EPMC citation are significantly lower than for PubMed, Thomson WOS or Google Scholar (in ascending order) may be related to the PMC embargo periods for BJP.

Our Nucleic Acids Research Database papers
Including our previous incarnation as IUPHAR-DB, the Edinburgh team and their collaborators are a tad proud to have accumulated five papers in Nucleic Acids Research Annual Database issues. These are listed below, along with the citation counts from EPMC (which was what the  ELIXIR form specified).

EPMC link Title Year Cites
PMID 18948278 Harmar et al., IUPHAR-DB: the IUPHAR database of G protein-coupled receptors and ion channels 2009 80
PMID 21087994 Sharman et al., IUPHAR-DB: new receptors and tools for easy searching and visualization of pharmacological data 2011 51
PMID 23087376 Sharman et al.., IUPHAR-DB: updated database content and new features 2013 33
PMID 24234439 Pawson et al., The IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands 2014 477
PMID 26464438 Southan et al., The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands 2016 76

Up until 2013 we consider our citation counts for the three papers (depending on comparative benchmarks of course) as not only realistic but also quite respectable. However, even though it was associated with our IUPHAR-DB to GtoPdb re-branding campaign in 2012, the 10-fold citation jump between our 2013 and 2014 efforts is clearly anomalous. As it happens, the causality is easy to spot from PubMed (because the Boolean result set combinations cannot be done directly via EPMC). Performing the appropriate intersects in PubMed shows that, from 621 citations, no less than 588 (i.e. 95%) come from the British Journal of Pharmacology (BJP). This reason for this is because our paper was selected as one of the reference citations derived from the Tables of Links (ToLs). These valuable connections between entities specified by authors and GtoPdb entries, were initiated in November 2014. An example legend from within a 2015 paper is shown below.

screen_for_reference_blog_post_2

The selected references in the table legend include our 2014 NAR, along with whichever reviews from the Concise Guide series matched the theme of the specific article. The key point is that the BJP Wiley Editors added these to the author citation lists as obligatory “reference citations”. Thus, for this article from 2015, these would typically include PMID 24234439 , PMID 24517644 and PMID 24517644. Since the ToLs are included in most BJP papers these rapidly accumulated citations  with EMPC counts of 477, 421 and 388, respectively. As of 2016, the updated reference publications have notched up by two years and the table legends thus point to the newer titles of PMID 26464438, PMID 26650445 and PMID 26650439. Consequently these have also accumulated citations quickly with counts of 76, 189 and 147, respectively. These may even eventually outpace the previous set since the ToLs (although for a lower proportion of articles) have also recently been introduced into the British Journal of Clinical Pharmacology (BJCP). Thus, from 128 PubMed cites for PMID 26464438, BJCP has contributed 25 and BJP 85.

BJP citation rankings
To get an overview of BJP internal reference citations (i.e. not the external NAR papers) we can view their 24321 papers ranked by citation count (a selectable setting in EPMC but not in PubMed). Unsurprisingly, first and third places are taken by reference citations in the form of two successive editorials on “Animal research: reporting in vivo experiments: the ARRIVE guidelines” as PMID 20649561 , with 898 cites and PMID 20649560 with 830. A subsequent section of these results is shown below, from fifth to 11th place.

screen_for_reference_blog_post

Related to GtoPdb and earlier Edinburgh outputs, we can see the 2011 and 2008 versions of “Guide to Receptors and Channels” (GRAC) were also selected as  reference citations, consequently ranking then at fifth and seventh. As is implicit from above, since the introduction of ToLs, the “Concise Guides” (effectively the successors of GRAC) have now replaced these as reference citations (n.b. depending on the time elapsed from Jan 2017 the live citation numbers in the table above will have increased in the links). Additional aspects on this citation theme are expounded in this blog post.

PubMed Commons
In addition to keeping an eye on citations per se we also folow up on some of the newer ways of increasing the findability and connectivity of our work in the ever more complex bibleometrics/Social Media ecosystem.  These efforts are modest (compared to what can be done) since we have our heads down for the Day Job but some of them have become necessary  house-keeping . These include grant linking,  the addion of  ORCHIDs  for team members (both of these as  EPMC functionality) and making sure papers are entered into our very own Edinburgh Research Explorer (actually highly ranked in Google for title searches).

Two other aspects may be of  interest (they can’t be detailed here but background is in the links). The first of these is the use of PubMed Commons. that has several utilities for us, including being able to “daisy chain” forward citaion pointers (but you wont see them in EPMC yet).  For example, amoung the 73 PubMed  citations for our 2009 NAR paper, 7 are 2015 and 2 from 2016. Thus, some recent authors are still citing our oldest paper (we see this across the series in fact  but, to be fair, some of them could be giving us the courtesy of multiple NAR cites although I have not checked). We therefore came up with the strategy of adding discrete pointers in PubMed Commons. As it happened, the last one (pictured below) was added most recently, even thought it is first in the chain by abstract date.

commons-pointer

So, if  the scholars in question happen to check PubMed (n.b. but not  BJP authors, since the most recent NAR reference would have been added by the Editors anyway) we have now have a set of  comments to point the four older papers forward to the fifth 2016 paper (and should we be fortunate enough to get accepted for a future NAR Database issue, we would then add a new comment to the chain).  Consequent to the posting above, an unexpectedly prominent  ping appeared  below, on the 2nd of Feb, highlighted in yellow.

pubmed_commons_comment_feb_2017

In a nutshell, “Featured comments” just happend to automatically select ours (but it seems like an actual human edited it)  which consequently featured on the PubMed front page, no less, giving us 24 hours of micro-fame!  As icing on the cake, the concomitant dailly auto-tweet of the heuristic chart-toppers, shown below, reached 4394 followers of the PMCom account (and was re-tweeted by us of course)

pubmed_commons_tweet_feb_2017

Altmetrics
Continuing on the metrics theme, in the panel below you can see Altmetrics scores for our same five NAR papers, plus the one Kudos entry.

altmetrics_nars

These outlinks can be found under the right-most “External links” tab on any EPMC entry that has them. The Altmetrics Rosette and sub-scores give a general measure of interest asssociated with a paper (but dont forget this may not necessarily be completely positive)  broken down by category, as you can see for our 2016 NAR below;

altmetric

Interesting aspects of Almetic scores include that they are faily immediate (i.e. accumulating within the first month or so)  and tend to move in the oposite direction to the slower accumulation of cites (i.e.they flatten off).  Here again, we alow ourselves a little warmth of feeling to see that the Altmetrics hueristics (while not incontravertable)  puts us close to the top-10% of comparable publications for both our GtoPdb NARs (i.e. we got the word out). The older papers, published during LBA (Life Before Altmetrics), clearly pick up lower scores. To conclude by putting it on the  record, we are most appreciative of colleagues and compatriots who explicitly draw positive attention to our work in both traditional and altenative ways.

Chris Southan

Posted in Publications, Uncategorized

Database release 2017.1

stats

We are pleased to announce our first database release of 2017 on 26th Jan 2017. While there are no major updates in this release, it includes several bug fixes and new ligands of relevance to immunopharmacology. Our new portal, the Guide to Immunopharmacology, is now in alpha release version 3, with the first public beta release due in Spring 2017. If you would like to get involved in testing please contact us (http://www.guidetopharmacology.org/).

New contributor faculty pages
A new feature of the 2017.1 release is our contributor faculty pages. Every database contributor now has an individual page providing additional details, such as ORCIDs, home page links and subcommittee membership. We will be building on this in future releases. You can link to contributor pages by clicking on names on the main list of all contributors (http://www.guidetopharmacology.org/GRAC/ContributorListForward) or from target page contributor lists.

New ligands – 2016 drug approvals
While we generally pick up new drug approvals as they are announced, this is the time of the year when we do a cross-check against the complete list of FDA 2016 approvals (http://www.fda.gov/Drugs/DevelopmentApprovalProcess/DrugInnovation/ucm483775.htm).  This established we have 15 of the 22 entries, since we do not add anti-infectives or imaging reagents without specific target binding. As has already been alluded to in the press, this looks a really bad year compared to 2015 (https://cdsouthan.blogspot.se/2016/01/the-2015-fda-approved-small-molecule.html).

The last 2016 approval under the wire was nusinersen, an antisense ologonucleotide .
becoming the second approval of this class after eteplirsen. These breakthrough polynucleotide therapeutic modalities are of course excellent news for the benefit of patients but they do present us with particular curatorial challenges. The first of these is we cannot assign target binding data but we do briefly describe the published molecular mechanism of action in the bioactivity tab, in both these cases suppression of defective exon skipping.  The next two problems are related as what formal molecular descriptors to use and how to render these as images (i.e. to produce an informative molecular picture).  In a nutshell, since eteplirsen is outside the PubChem size range we have chosen Varna as an informative picture, despite the fact that two external sources (indicated in the entry) actually managed a formal rendering but produced different InChIKeys.  Since nusinersen should be just inside the PubChem limit size we have both a Varna image and a SMILES string (from ChemSpider)  producing a Mw of 7126 so we will check (since we will be the first submitters) how PubChem handles this.

We have already captured the first 2017 FDA approval as plecanatide for the treatment of Chronic Idiopathic Constipation (CIC) in adult patients.

It typically takes a week or so for our refreshed submissions in PubChem to go live. When the new statistics are available we will post them here.

Content fixes
Since we welcome user feedback on both navigability and content it was good to see an uptick for this in 2016.  We are particularly grateful when users send us correction suggestions that we can then fix.  Two cases are in this release.  The first of these was a name mismatch in our olmutinib entry. The incorrect synonyms HM-71224 and LY3337641 (which refer to a blinded Hanmi BTK inhibitor) have been removed. The second was a structural error for afuresertib. We explain such fixes in the revised entries.  For this and a host of other obvious reasons  any and all integrators/consumers of our content are encouraged to keep on top of our new releases. We know this is a tough job, so if we can help, get in touch.

Links to SLC tables
We have added links from transporter pages to the Bioparadigms SLC Tables database. This site aggregates lots of information relevant to the Solute Carrier superfamily. We look forward to collaborating with the developers of the SLC tables in future, as their site grows. Representative example: http://www.guidetopharmacology.org/GRAC/FamilyDisplayForward?familyId=165

Database content and statistics
The number of targets stands at 2797 and ligands at 8765 with 14890 curated quantitative interactions. See http://www.guidetopharmacology.org/about.jsp.stats

Human targets with curated ligand interactions: 1648
Human targets with quantitative binding data: 1392
Human target with quantitative binding data to ligands with PubChem CIDs: 1265

PubMed stats

In the next release
We are working on improvements to our web services and a new version will be coming soon. The Concise Guide to Pharmacology editors and contributors are busy working on updating the concise view pages of GtoPdb with a view to releasing the updates in Spring 2017 for a new version of the Concise Guide due out in the British Journal of Pharmacology later this summer.

Posted in Database updates, Technical

IUPHAR review 100 in Pharm Revs and review 21 in BJP

Two new IUPHAR reviews have been published online in January 2017.

The first is the 100th in Pharmacological Reviews, a review on the nomenclature and properties of Calcium-Activated and Sodium-Activated Potassium Channels by Kaczmarek et al. For database entry click here.

The second is the 21st review in the British Journal of Pharmacology, an article on the evolution of RGS (Regulators of G protein signaling) proteins as drug targets by Benita Sjögren. For database entry click here.

Tagged with: , ,
Posted in Publications