Guide to IMMUNOPHARMACOLOGY – beta release v1.0, May 2017

We are pleased to announce the first, public, beta-release of the Guide to IMMUNOPHARMACOLOGY (GtoImmuPdb). The GtoImmuPdb is a Wellcome Trust-funded extension to the existing Guide to PHARMACOLOGY (GtoPdb) and the beta-release (v1.0) marks an important milestone in its production and development. GtoImmuPdb aims to provide improved data exchange between immunology and pharmacology expert communities, so to better support research and development of drugs targeted at modulating immune, inflammatory or infectious components of disease. The underlying GtoPdb schema has been extended to incorporate new immune system specific data types (such as processes, cell types and disease) and the GtoPdb website has been developed to surface this new data and incorporate it into the existing search and browse mechanisms. A new Guide to IMMUNOPHARMACOLOGY portal (Figure 1)(www.guidetoimmunopharmacology.org) has been developed, which serves as a unique immunological access-point to the Guide to PHARMACOLOGY.

portal

The GtoImmuPdb enriches the existing GtoPdb by flagging targets and ligands of immunological relevance and linking these targets to immunological process, cell types and relevant diseases. In terms of processes and cell types, GtoImmuPdb has developed top-level categories (Figure 2), that aim to be meaningful and intuitive to immunologists, against which targets and ligands in the database can be annotated. These categories are underpinned by the use of both the Gene Ontology and the Cell Ontology. Using recognised ontologies provides a controlled vocabulary for higher resolution annotation (Figure 3). It also facilitates interoperability between new data types in GtoImmuPdb and external resources that also use these ontologies.

processs_celltype_browse

process_assoc_bcell

Data linking targets and ligands to disease is also incorporated into GtoImmuPdb, with the curation of disease associations using resources such as OrphaNet, Disease Ontology and OMIM (Figure 4).

ligand_v_disease

As well as the development of the GtoImmuPdb Portal, the web-interface has been further developed with immunological data and users in mind. It has been designed to provide a unique ‘GtoImmuPdb view’ of the data, highlighting content of immunological relevance and prioritising immunological data in search results and display. It includes features that highlight targets, target families and ligands of immunological relevance (Figure 5); toggle buttons to enable the GtoImmuPdb view to be switched on and off (Figure 6); and new pages and sections to display immunological data (Figure 7).

Figure2_Harding

 

toggle_target_highlight

 

detailed_view_H1_receptor

Development of the beta-release is ongoing with regular updates planned over the next few months as the quantity of data captured increases and improvements in the site layout and function are made. One of our priorities over the next 6 months is to undertake rigorous site testing with interested user groups to capture more insight and feedback. We welcome those interested and potential future users to get in touch with us.

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

Database release 2017.4

Our 4th database release of 2017 was published on 23rd May 2017. It now includes 15133 interactions between 2813 targets and 8900 ligands. For full release statistics see the Guide to PHARMACOLOGY About page.

Target Updates

Updates have been made to the following target families:

GPCRs:

LGICs:

The Guide to IMMUNOPHARMACOLOGY

The most major update in this release is the inclusion of the first beta-release of the Guide to IMMUNOPHARMACOLOGY (GtoImmuPdb).
GtoImmuPdb is a Wellcome Trust-funded extension to the existing Guide to PHARMACOLOGY (GtoPdb) and this release marks an important milestone in its production and development. GtoImmuPdb aims to provide improved data exchange between immunology and pharmacology expert communities, so to better support research and development of drugs targeted at modulating immune, inflammatory or infectious components of disease. The underlying GtoPdb schema has been extended to incorporate new immune system specific data types (such as processes, cell types and disease) and the GtoPdb website has been developed to surface this new data and incorporate it into the existing search and browse mechanisms. A new Guide to IMMUNOPHARMACOLOGY portal (www.guidetoimmunopharmacology.org) has been developed, which serves as a unique immunological access-point to the Guide to PHARMACOLOGY.

For more information, please see our separate blog post on the Guide to IMMUNOPHARMACOLOGY beta-release.

Ligand activity graphs

In our 2017.2 release we announced the development of ligand activity graphs which can be used to compare activity ranges across species using data extracted from GtoPdb and ChEMBL.activity_graphs

These have recently been updated to use the latest ChEMBL release (ChEMBL_23), which was made available on 19th May 2017. We have also applied a few minor bug fixes to the graphs, most notably with the box plot display. We are very interested in getting feedback on this new feature and would encourage anyone who has used it or looked at it to get in touch with comments.

Website updates

The latest GtoPdb release has also incorporated changes to our news and hot topics pages. The revised news page has been designed to give greater prominence to our blog feed, which is now our main source of news.

Our hot topics page has also been modified to better capture and present interesting topics in pharmacology. It now presents a more refined list of the latest hot topics, with link from selected ones to our blog, where contributors have provided more detailed and enriched comments.

Posted in Database updates, Guide to Immunopharmacology, Technical

Hot topics: New crystal structure of the full-length Smoothened reveals ligand-dependent interaction between extracellular CRD and the 7TM core of the receptor

Molecular details of receptor activation remain scarce when it comes to Class Frizzled receptors and even less is known about the dynamics between the N-terminal cysteine rich domain (CRD) and the transmembrane domain (TMD) in the absence and presence of ligand. The Class Frizzled comprises 10 isoforms of FZDs (FZD1-10) and Smoothened (SMO), which mediate WNT and Hedgehog signalling respectively. The recently published structure of a full-length SMO bound to the stabilizing compound TC114 builds on emerging concepts from earlier crystal structures of SMO and provides novel insight into how structural rearrangements of the CRD relative to the receptor core coordinate receptor activation while relating this to WNT receptors (1). The ligand-dependent communication between the CRD and the TMD is of special interest because of the known requirement of Class Frizzled receptors to bind endogenous agonists with their CRD. The observed movement of the extracellular extension of SMO-TM6 and extracellular loop 3 suggests a bimodal binding of the agonist to the CRD and the TMD as has been seen for Class B receptors. While this study focuses on the ligand-dependent structural rearrangements on the extracellular part of SMO, it remains unresolved how the conformational changes on the extracellular side of Class Frizzled receptors relate to activating movements of the transmembrane helices on the intracellular side – leaving ample room for future discoveries. The present study opens a new chapter in drug discovery through the use of structure- and mechanism-based drug design to fuel ideas and hopes of successfully targeting FZDs by small molecule drugs. On a more general scale, this work published in Nature Communications by the groups of Fei Xu, Wenfu Tan, Houchao Tao and Raymond Stevens contributes to a better understanding of the role of large extracellular domains in GPCRs with regard to ligand recognition and the engagement of the transmembrane core of the receptor for signal initiation.

[1] Zhang X et al. (2017). Crystal structure of a multi-domain human smoothened receptor in complex with a super stabilizing ligand. Nat Commun., 8:15383. [PMID:28513578].

Comments by Shane C. Wright & Gunnar Schulte (Karolinska Institutet)

Posted in Hot Topics

Hot Topics: Protein-phospholipid interplay revealed with crystals of a calcium pump

Ca2+-ATPases are members of the P-type transporters and are usually regarded as post-stimulus recovery mechanisms, allowing calcium ions to be removed from the cytosol either via re-accumulation into sarcoplasmic/endoplasmic reticulum (the SERCA pumps) or extrusion outside the cell (the PMCA pumps).  Here, Norimatsu and colleagues (1) have used X-ray solvent contrast modulation to assess density maps of four activation states of the SERCA1, focussing on the interaction between the ten transmembrane helices and their surrounding phospholipid environment.  Their observations suggest an unexpected movement of the transporter core, which allows an exaggerated ‘waving’ of the cytoplasmic-extended calcium-binding domain during the cycle of calcium transport.

[1] Norimatsu et al. (2017). Protein-phospholipid interplay revealed with crystals of a calcium pump. Nature 545(7653):193-198. [PMID:28467821]

Comments by Steve Alexander (@mqzspa)

Posted in Hot Topics

Hot Topics: New developments in cryo-electron microscopy reveal the first Class B GPCR active structure in complex with a G protein

The last 10 years has seen a huge increase in the numbers of X-ray structures solved for GPCRs. However solving fully active structures in complex with G proteins remains challenging. Recent advances in cryo-electron microscopy (cryo-EM), including improved direct electron detectors and image processing methods, mean that near atomic resolution is becoming possible for protein complexes of smaller molecular size (sub 100kDa). Cryo-EM has the advantage over X-ray crystallography in that structures are determined from single molecules, avoiding the need to grow crystals (although sample preparation is still not trivial).  This is particularly useful for large macromolecular complexes or proteins with labile domains which are difficult to crystallize. A dramatic step up in quality of data collected has been achieved using the volta phase plate which greatly increases the contrast, particularly at the lower end of the molecular size range, meaning that structures of GPCR complexes are now within reach.  The first example of this has been published in Nature by the groups of Patrick Sexton at Monash and Wolfgang Baumeister at the Max Planck in Martinsried. Using the volta phase plate the cryo-EM structure of the full length calcitonin receptor bound to a peptide agonist in complex with the heterotrimeric G protein was solved to 3.8 Ä. The structure reveals the conformational changes which occur upon activation of a Class B GPCR. Cryo-EM is set to revolutionise structural biology and with the rapid progress is now encompassing GPCR complexes. We expect others to follow in the near future.

[1] Liang et al. (2017). Phase-plate cryo-EM structure of a class B GPCR–G-protein complex. Nature, doi: 10.1038/nature22327. [PMID: 28437792]

Comments by Dr. Fiona H. Marshall (Director & CSO, Heptares Therapeutics) (@aston_fm)

Posted in Hot Topics

Hot Topics: Fish peptide treats cardiovascular disease

The novel GPCR peptide ligand Elabela/Toddler (APELA, Ela), first identified in the fish Danio and critical for the development of the heart, has now been identified in the human cardiovascular system. Yang et al. [1] have recently showed that the peptide binds to the apelin receptor in human heart. Using in silico molecular modeling and docking, they proposed a binding mode of ELA to the apelin receptor (APLNR) that is consistent with competition binding experiments. The paper also shows peptide binding is blocked by apelin receptor antagonists and comparable to apelin, increased cardiac output and elicited vasodilatation in rats in vivo. Ela expression was reduced in cardiopulmonary tissues from patients with pulmonary arterial hypertension (PAH) and animal models. However, daily injections of Ela to replace the missing peptide, attenuated the development of PAH in an animal model of this disease, suggesting this could be a potential target for translational research.

[1] Yang et al. (2017). Elabela/Toddler Is an Endogenous Agonist of the Apelin APJ Receptor in the Adult Cardiovascular System, and Exogenous Administration of the Peptide Compensates for the Downregulation of Its Expression in Pulmonary Arterial Hypertension. Circulation, 135(12):1160-1173. [PMID: 28137936]

Comments by Chris Southan (@cdsouthan)

Posted in Hot Topics

Hot Topics: Neurokinin 3 receptor antagonism for menopausal hot flushes

Neurokinin B signalling is increased in menopausal women and has been implicated as an important mediator of hot flushes. A phase 2 trial has assessed the effectiveness of an oral neurokinin 3 receptor antagonist (MLE4901). Results showed it safely and effectively relieved hot flush. The finding that pharmacological blockade of NKB signalling with an oral NK3R antagonist can significantly improve symptoms independently of any hormonal effect fits with the pre-existing data, and indicates promise for such agents. However, larger scale studies of longer duration are needed. Since this condition affects 70% of menopausal women ( i.e. ~ 10 million in the UK) this publication was covered widely in the press, for example in the UK Daily Telegraph with “Could this drug be the key to stopping hot flushes for menopause sufferers?”. Partly as a consequence of press coverage, the paper garnered an impressive Altmetric score of 296 (that will notch up by at least one from this posting).

Our GtoPdb ligand entry shows a number of aspects related to repurposing and data linking problems associated with multiple synonyms for the same structure in multiple clinical contexts. With its original designation as AZD2624 it was on the AZ Open Innovation Clinical Compound Bank repurposing proposal list (but has now been withdrawn) since it failed in its originally tested indication for schizophrenia (PMID 24525659). Unusually, there is no primary publication on the medicinal chemistry but we were able to get the NK3R in vitro binding data from the NCATs AZD2624 data sheet. It was renamed to AZD4901 for the new indication of Polycystic ovary syndrome (PCOS) but was again not progressed (PMID 27459523). In the meantime WO2015033163 was filed by Imperial Innovations for the use of AZD2624 for the treatment of hot flushes. By 2016 rights had been acquired by Millendo where the structure was renamed MLE4901 for the indications of PCOS and vasomotor symptoms (VMS).

Prague et al. (2017). Neurokinin 3 receptor antagonism as a novel treatment for menopausal hot flushes: a phase 2, randomised, double-blind, placebo-controlled trial. Lancet, S0140-6736(17)30823-1. [PMID: 28385352]

The ligand entry was updated in our 2017.3 release. When it gets submitted to PubChem it may be the only source that connects the one structure to its three synonyms and cross-references the publications and clinical trials for the different therapeutic investigations

Comments by Chris Southan (@cdsouthan)

Posted in Hot Topics

Hot Topics: Crystal structures of human AT2 reveal molecular mechanism for lack of desensitization and internalization

The intracellular signal transduction processes activated by the angiotensin AT2 receptor, are atypical for a GPCR and different from the AT1 receptor. Although the classic motifs a GPCR are present in AT2 receptor; it fails to demonstrate classic features of G-protein signalling such as desensitization by phosphorylation, and receptor regulation by internalization. Zhang et al., (2017) [1] report the crystal structures of human AT2 bound to an AT2-selective ligand and to an AT1 /AT2 dual ligand, capturing the receptor in an active-like conformation.

They provide a potential explanations for the poor coupling of AT2 to G proteins and β-arrestins. Helix VIII a very different conformation to other GPCRs; the authors suggest it plays a dual role in the modulation of AT2 function, stabilizing an active like receptor state, while repressing canonical AT2 activity in a self-inhibitory manner by sterically blocking the G protein and β –arrestin binding sites. However, on switching to a membrane-bound conformation, helix VIII can support the recruitment of G proteins and β-arrestins for AT2 signalling. The authors propose helix VIII works as a gatekeeper for either suppression or activation of the receptor depending on its post-translational modifications and interactions with various receptor partners and its environment.

[1] Zhang et al. (2017). Structural basis for selectivity and diversity in angiotensin II receptors. Nature, 544(7650):327-332. [PMID: 28379944]

Comments by Anthony Davenport

Posted in Hot Topics

Hot Topics: Structural insights into adiponectin receptors suggest ceramidase activity

Adiponectin receptors, divided into Adipo1 and Adipo2, were initially classed as GPCR on the basis of hydropathy analysis suggesting seven transmembrane domains.  However, they appear to be present in the cell membrane in a topology inverted compared to the 7TM GPCR.  The study from Vasiliauskaite-Brooks [1] and colleagues suggests that Adipo1 and Adipo2 exhibit ceramidase activity.  They report that adiponectin binding to Adipo2 enhances this ceramidase activity, providing in silico evidence for this mechanism. It is possible, therefore, that adiponectin receptors may belong to a unique class of catalytic receptor rather than GPCR.

[1] Vasiliauskaité-Brooks et al. (2017). Structural insights into adiponectin receptors suggest ceramidase activity. Nature, 544(7648):120-123. [PMID: 28329765]

Comments by Steve Alexander (@mqzspa)

Posted in Hot Topics

GtoImmuPdb: technical update March 2017

The 4th alpha-release (v4.0) of the Guide to IMMUNOPHARMACOLOGY was released on 23rd March 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 v4.0 Portal

The disease panel on the portal is now active (Fig. 1). This contains two links, which link to different views of the Immuno Disease List page.

portal_v4

Fig 1. GtoImmuPdb v4.0 portal.
New disease panel (lower left-hand side) is now functional. New disease menu item is also included

The Immuno Disease List pages provide an overview of the disease-target and disease-ligand associations, curated in the database specifically for GtoImmuPdb

Alpha-Release v4.0 Navigation

In conjunction with adding the Immuno Disease List pages we have extended the menu-bar navigation to contain a Disease menu. This holds two sub-menu items, one points to the disease list associations to targets and one to the disease list associations to ligands.

Disease List Page

The disease list page is designed to display all disease associations curated as part of the GtoImmuPdb. One single page, it is divided into two views, one showing disease associations to targets in the database and the other showing disease associations to ligands (Fig. 2).

disease_list

Fig. 2. Immuno Disease List page. Showing target to disease associations.

Users can switch between the two views (Targets or Ligands) using a tab at the top of the page.

The format of the list of disease associations is similar for both targets and ligands. Both show one section or row per disease. Along with the disease name any external references to other disease resources (OMIM, Disease Ontology and Orphanet) are shown.

Next to each disease name are the total number of either targets or ligands associated in GtoImmuPdb to that disease. By default, the full details of the target or ligand associations are hidden. These can be displayed by clicking the ‘display all ….’ link.

At the top of the page are two toggle buttons that can be used to show or hide all the associations for all disease, if users so wish.

For targets, when the associations are displayed they show the name of the target and curated comments about the association. It also lists any ligands for which that target is a primary target and highlights if the ligand is an approved drug.

For ligands, it shows the name of the ligand, comments and any literature references for the association.

Immuno-Relevance Searching

The ranking of search results has been developed to apply a weighting to targets and ligands that are returned from a search that are consider of greater immunological relevance. The weighting is only applied when searching from a GtoImmuPdb page, not from the standard GtoPdb pages.

The criteria used to determine the immune-relevancy (and it’s weighting) of a given target or ligand is based on the amount of immunological data curated against it. For example, targets that have process, cell type and disease data annotated against them will rank higher than targets with only process data. The weighting is applied in addition to existing search weightings – so exact matches (to target or ligand name for example) will still score highest. We will be refining this relevancy scoring during testing, both in alpha and beta releases.

Cell Type Associations – Definitions

We have extended the submission tool and database to better capture and store definitions of cell type categories. This enable reference and ligands to be tagged in the definition text. We have also further developed the cell type list page to display these definitions at the top of the table (Fig. 3) – with the ability to toggle their display.

celltype_list_page

Fig. 3. Cell type list view – show toggle-able definitions

http://ow.ly/PakB30amkZQ

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

Tagged with: , ,
Posted in Guide to Immunopharmacology, Technical

Database release 2017.2

Our 2nd database release of 2017 was published on 23rd March 2017. It now includes 15019 interactions between 2809 targets and 8832 ligands. For full release statistics see the About the Guide to PHARMACOLOGY page.

Target updates

The major part of the work to update the target family summary pages has been completed in advance of producing the Concise Guide to PHARMACOLOGY 2017/18 from the database, which is due out later this year. For the next version, we have been working towards trying to make the information more concise, and limiting both ligands and further reading to the 5 most useful in many cases. Obviously there are some targets where it makes sense to have more or less than 5 displayed on the summary page, but in any case, all the ligands can still be viewed on the detailed target page, and the website contains more further reading references than are included in the published Concise Guide. We are very grateful to all the contributors and the editors who have provided information.

Since most of our curation effort has gone into these updates, the only GPCR detailed page updates this time are the Gonadotrophin-releasing hormone receptors.

Ligand updates

We have refreshed our PDB ligand links and now have 1283 links from ligands to individual RCSB PDB ligand pages and the crystal structures they are found in, e.g. LSD recently crystalised with 5-HT2B.

Meanwhile, our development team has prepared the following new website features and updates:

Web services updates

The REST web services have been updated and now include interactions web services  providing lists of target-ligand pairs which can be filtered by target/ligand type and properties, binding affinity etc., and references web services which can retrieve references by id or the full interaction reference set.

Graphs comparing ligand activity across species

We have developed new ligand activity graphs comparing activity ranges across species using data extracted from GtoPdb and ChEMBL. These are available via the ‘biological activity’ tab (screenshot 1) on ligand pages but currently only for ligands that are also in ChEMBL. For example, DPCPX (screenshot 2) shows similar activity at A1 receptors across a range of species tested.

DCPCX_ligand_page

Screenshot 1. New link to view charts of activity data on the DPCPX ligand page biological activity tab

DPCPX

Screenshot 2. Chart showing DPCPX ligand activity data from ChEMBL and GtoPdb across 4 species. Mouse-over a plot to see the median, lower and upper quartiles, and minimum and maximum data points for each activity type.

Mouse-over a plot to see the median, interquartile range, low and high data points. A value of zero indicates that no data are available. A separate chart is created for each target, and where possible the algorithm tries to merge ChEMBL and GtoPdb targets by matching them on name and UniProt accession, for each available species. However, please note that inconsistency in naming of targets may lead to data for the same target being reported across multiple charts.

At the end of the page, below the charts, is a table listing all the data points that were used to build the charts, the source databases, assay details, and links to the original references and PubMed.

The graphs can be useful for comparing data across species when choosing model organisms to use for experiments. For example, the ligand palosuran is known to have 100-fold lower binding inhibitory potency on rat versus human UT receptor (screenshot 3).

Palosuran

Screenshot 3. Palosuran activity at human and rat UT receptors.

Extracting ChEMBL activities

Since ChEMBL contains an enormous amount of data (>14.3 million activities in ChEMBL 22) we have filtered and extracted the most useful data and tried to standardise them to the terms used in GtoPdb. Data are selected according to the following criteria:

  1. The target must have a type of ‘SINGLE PROTEIN’, ‘PROTEIN COMPLEX’, or ‘PROTEIN COMPLEX GROUP’
  2. Affinity types are combined and normalised as follows:
    Kd = Dissociation constant, Kd, K app, K Bind, K calc, Kd’, KD app, KD’, Kd(app), KD50, Kdiss, Relative Kd, Binding constant, K aff, K diss, KD/Ki
    pKd = -Log Kdiss, -Log KD50, pKd, pKD, logKd, -Log Kd, Log Kd, -Log KD, Log KD
    Ki = Adjusted Ki, Ki, ki, Ki app (inact), Ki app, Ki(app), Ki_app, Ki’, Ki”, KI’, K’i, Kiact, Ki high, Ki low, KiH, KiL, Kii, KII, Kic, Ki.c, Ki comp, Ki’ uncomp
    pKi = pKi(app), pKi, -Log K0.5, Log Ki, logKi, -Log Ki, pKiH, pKiL
    IC50 = IC50 app, IC50, IC50 max, I50, Mean IC50, IC50H, IC50L
    pIC50 = pIC50, pIC50(app), -Log I50, logIC50, log IC50, Log IC50, -Log IC50, pI50, pIC50(calc)
    EC50 = EC50
    pEC50 = pEC50 diss, pEC50, -Log EC50, Log EC50, logEC50
    A2 = A2
    pA2 = pA2, pA2(app), pA2 app, pA2/pKB
  3. Raw data (e.g. Kis are converted into their negative log to base 10 values (e.g. pKis)
  4. Activities deemed by ChEMBL curators to be “outside typical range” are ignored (to prevent skew)
  5. Only binding (‘B’) and functional (‘F’) assays are included (no large-scale screening data)

We have tried to be as inclusive as possible with the ChEMBL data, but please note that due to the sheer volume, there will be data that have not yet been manually checked by the ChEMBL curators and we always ask users to refer back to the original references when using the data.

We hope this new feature will be useful to our users, and we welcome any feedback you may have.

Posted in Concise Guide to Pharmacology, Database updates, Technical

Recent IUPHAR reviews on Ang(1-7) coupling with GPCRs, treating systemic autoimmune diseases, and small molecule modulators of adenylyl cyclases

The latest ‘state of the field’ IUPHAR reviews are out in the British Journal of Pharmacology:

Karnik SS, Khuraijam D, Tirupula K, Unal H. (2017) Significance of Ang(1-7) coupling with MAS1 and other GPCRs to the Renin-Angiotensin System: IUPHAR Review 22. Br J Pharmacol. doi: 10.1111/bph.13742. [Epub ahead of print] [PMID:28194766]

Ishii M. (2017) Immunology provides a great success for treating systemic autoimmune diseases – a perspective on immunopharmacology – IUPHAR Review 23. Br J Pharmacol. doi: 10.1111/bph.13784. [Epub ahead of print] [PMID:28299772]

In Pharmacological Reviews, the latest IUPHAR review article is:

Dessauer CW, Watts VJ, Ostrom RS, Conti M, Dove S, Seifert R. (2017) International Union of Basic and Clinical Pharmacology. CI. Structures and Small Molecule Modulators of Mammalian Adenylyl Cyclases. Pharmacol Rev. 69: 93-139. [PMID:28255005]

These come hot on the heels of the 100th article in Pharm Revs, and the 21st in BJP, discussed in this blog post.

 

 

Tagged with: , , ,
Posted in Publications

Hot Topics: Structural Basis of Substrate Recognition by the Multidrug Resistance Protein MRP1

Despite a flurry of mammalian ATP binding cassette (ABC) transporter structures in the last 2 years the Holy Grail has still been to determine how these diverse proteins interact with their transport substrates. Jue Chen and colleagues at the Rockefeller have now accomplished this for  the multidrug resistance protein-1 (MRP1/ABCC1) using advances in high resolution cryo-electron microscopy to show the structures of substrate-free and leukotriene C4 bound protein [1]. The paper also lays the foundation for revealing the structural basis for multidrug transport by MRP1 (which is a confounding factor for some chemotherapies) as the flexible substrate binding cavity in the membrane has both polar and a hydrophobic sub-pockets enabling it to interact with chemically diverse drugs. Whether this structural data enables the design of clinically-relevant MRP1 inhibitors will now be the focus of much research.

[1] Johnson Z.L., Chen J. (2017). Structural Basis of Substrate Recognition by the Multidrug Resistance Protein MRP1. Cell. pii: S0092-8674(17)30131-9. [PMID: 28238471]

Comments by Prof. Ian Kerr, University of Nottingham (@iankerr_science)

http://www.nottingham.ac.uk/life-sciences/people/ian.kerr

https://www.linkedin.com/in/ian-kerr-2b94742b/

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.

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

Citation profiles for our NAR and Concise Guide papers

Read more ›

Posted in Publications

Database release 2017.1

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

Hot topics: The orphan GPR139 receptor is activated by peptides

GPR139 is an orphan class A G protein-coupled receptor found mainly in the central nervous system. It has its highest expression in the striatum and hypothalamus, regions regulating locomotion and metabolism, respectively, and it has therefore been suggested as a potential target for Parkinson’s disease and metabolic syndrome. Surrogate ligands have been published by Lundbeck A/S [1], Jansen R&D [2], Takeda Pharmaceuticals [3], as well as the University of Copenhagen (Gloriam group). In a new publication, the latter group describe the first combined structure-activity relationships of all surrogate agonist, and a common pharmacophore model for future ligand identification and optimization [4].

The physiological agonist of GPR139 is still elusive. GPR139 has previously been shown to be activated by the amino acids l-tryptophan and l-phenylalanine (EC50 values of 220 μM and 320 μM, respectively) [5,6], as well as di-peptides [5]. A new publication shows that the endogenous melanocortin 4 receptor agonists; adrenocorticotropic hormone and α- and β-melanocyte stimulating hormone in the low micromolar range. In addition, a potentially novel subpeptide (from consensus cleavage site) represents the most potent putative endogenous activator, so far (EC50 value of 600 nM) [7]. Together, these results indicate that GPR139 is a likely to be a peptide receptor that could act as a secondary target for melanocortin peptides or a yet undiscovered physiological ligand.

[1] Shi, F. (2011). Discovery and SAR of a series of agonists at orphan G protein-coupled receptor 139. ACS Med. Chem. Lett. 2, 303–306. doi:10.1021/ml100293q. PMID: 24900311

[2] Dvorak, C. (2015). Identification and SAR of glycine benzamides as potent agonists for the GPR139 Receptor. ACS Med. Chem. Lett. 6, 1015–1018. 10.1021/acsmedchemlett.5b00247. PMID: 26396690

[3] Hitchchock, S. (2016). 4-oxo-3,4-dihyroI-1,2,3-benzotriazine modulators of GPR139. US Patent US2016/0145218 A1. Takeda Pharmaceutical Company Limited

[4] Shehata, M.A. (2016). Novel agonist bioisosteres and common structure-activity relationships for the orphan G protein-coupled receptor GPR139. Sci. Rep. 6, 36681. doi:10.1038/srep36681. PMID: 27830715

[5] Isberg, V. et al. (2014). Computer-aided discovery of aromatic L-α-amino acids as agonists of the orphan G protein-coupled receptor GPR139. J. Chem. Inf. Model. 54, 1553–1557. doi: 10.1021/ci500197a. PMID: 24826842

[6] Liu, C. (2015). GPR139, an Orphan Receptor Highly Enriched in the Habenula and Septum, Is Activated by the Essential Amino Acids L-Tryptophan and L-Phenylalanine. Mol. Pharmacol. 88, 911–925. doi: 10.1124/mol.115.100412. PMID: 26349500

[7] Nøhr, A.C. et al. (2016). The orphan G protein-coupled receptor GPR139 is activated by the peptides: Adrenocorticotropic hormone (ACTH), α-, and β-melanocyte stimulating hormone (α-MSH, and β-MSH), and the conserved core motif HFRW. Neurochem. Int. 102, 105–113. doi: 10.1016/j.neuint.2016.11.012. PMID: 27916541

Comments by David E. Gloriam and Anne Cathrine Nøhr Jensen (Department of Drug Design and Pharmacology, University of Copenhagen)

Posted in Hot Topics

Hot topics: X-ray crystallographic study defines binding domains for Ca2+ antagonist drugs and their molecular mechanism of action

This year witnessed a tremendous progress in our understanding of the structure-function relationship of voltage-gated Ca2+ channels. This is based on the cryo-electron microscopy structure of the rabbit Cav1.1 Ca2+ channel complex at a nominal resolution of 3.6 Å ([1] see Hot Topics Sep 20, 2016) which is now nicely complemented by a study defining the binding domains for Ca2+ antagonist drugs and their molecular mechanism of action at atomic resolution [2]. The authors took advantage of their elegant previous work solving the structure of bacterial Na+ channels (NavAb) by X-ray crystallography both in a pre-open and inactivated state [3,4]. They also engineered Ca2+ selectivity into its selectivity filter (“CavAb”, [5]) and found high affinity inhibition by the different chemical classes of Ca2+ antagonist drugs similar to L-type Ca2+ channels. Since CavAb assembles as a tetramer, this channel also replicates the four domain structure of the pore-forming subunit of voltage-gated Ca2+ and Na+-channels: an excellent model to investigate the drug-channel interaction at atomic resolution was now at hand.
As predicted for L-type Ca2+ channel α1-subunits by photoaffinity labeling studies 25 years ago [6], dihydropyridines (DHPs, e.g. amlodipine, nimodipine) bind to an extracellularly exposed intersubunit crevice formed by neighbouring S6 helices and the P-helix of the selectivity filter. Drug binding displaces an endogenous lipid molecule in this site. Interestingly, DHP binding induces a conformational changes which breaks the fourfold symmetry of the channel. As a consequence only one molecule can occupy the channel with high affinity and the Ca2+ interaction with the selectivity filter also changes. This results in a higher affinity for Ca2+ ions revealing an intriguing mechanism of action for these drugs: rather than directly blocking the pore, they enhance Ca2+ affinity for the pore such that Ca2+ itself gets stuck in the ion conducting pathway. Therefore DHPs do the opposite of what would be intuitively expected for a “Ca2+ antagonist”, namely preventing Ca2+ interaction with the channel. Instead, they allosterically enhance Ca2+ binding.
In contrast, and also in agreement with photoaffinity labeling and mutational studies phenylalkylamines (PAAs, e.g. Br-verapamil) bind in the central cavity on the intracellular side of the selectivity filter also disrupting fourfold symmetry. Since it is known that PAAs access this site preferentially when the channel opens its intracellular mouth upon activation this nicely explains their known frequency dependent inhibition. Unlike DHPs these drugs bind within the pore and thus must act as pore blockers thus satisfying the term “Ca2+ antagonist”.
This work from the Catterall lab must be regarded as a milestone in Ca2+ channel research. It not only revealed the mechanism of action of one of the most prescribed classes of cardiovascular drugs but also brings us much closer to predicting structural features of new generations of Ca2+ antagonists with high selectivity for different isoforms of voltage-gated Ca2+ channels. Within the L-type Ca2+ channel family this could be relevant for discovering Cav1.3–selective drugs as potential therapeutics for neuroprotection in Parkinson’s disease and neuropsychiatric disorders, such as autism (7).

[1] Wu et al (2016). Structure of the voltage-gated 2+ channel Cav1.1 at 3.6 Å resolution.
Nature 537:191-196. [PMID 27580036]
[2] Tang et al. (2016). Structural basis for inhibition of a voltage-gated Ca2+ channel by Ca2+ antagonist drugs. Nature 537, 117–121 [PMID 27556947]
[3] Payandeh et al. (2011). The crystal structure of a voltage-gated sodium channel. Nature 475, 353–358 [PMID 21743477]
[4] Payandeh et al. (2012). Crystal structure of a voltage-gated sodium channel in two potentially inactivated states. Nature 486, 135–139 [PMID 22678296]
[5] Tang et al. (2014). Structural basis for Ca2+ selectivity of a voltage-gated calcium channel. Nature 505, 56–61 [PMID 24270805]
[6] Catterall and Striessnig (1992). Receptor sites for Ca2+ channel antagonists. Trends Pharmacol Sci 13, 256–262 [PMID 1321525]
[7] Ortner and Striessnig (2016). L-type calcium channels as drug targets in CNS disorders. Channels 10: 7–13 [PMID 26039257]

Comments by Jörg Striessnig (Department of Pharmacology and Toxicology – Institute of Pharmacy, Universität Innsbruck)

Posted in Hot Topics

GtoImmuPdb: technical update December 2016

Our final technical update for 2016 covers our v2.0 alpha-release, presentation at Pharmacology 2016 and future plans.

An early synopsis of the project can be found in this blog post. Previous technical blogs are available for February, MayAugustSeptember & November 2016.

Development Progress

Alpha-Release v2.0

Menu-bars

The menu-bars have been further development to include Processes and Cell Types. This basically extends the menu bar to have direct links to the new data types in GtoImmuPdb. The About and Resources menu items have been modified to make them specific to GtoImmuPdb. The ultimate aim of these developments is to make navigation through GtoImmuPdb user-friendly and logical. This will continue to be developed as we gather feedback. 

Documentation and Tutorial

The documentation and user-guide tutorial were both updated upon v2.0 release.

Ligand List pages

We have developed the ligand list pages (which are linked to from the portal ‘ligand’ panel) to include an immuno tab that when selected lists all ligands tagged in the database as being included in GtoImmuPdb. The page now has a toggle button to switch between the GtoImmuPdb and GtoPdb views. We have also put in place a new ‘immuno ligand’ icon, to be displayed in the table with the other icons when the ligand has been tagged in GtoImmuPdb.

Ligand pages

We have extended the ligand pages to contain a new ‘Immunopharmacology’ section (with in the Summary tab). This contains any specific immunopharmacology comments specific to the ligand.

Pharmacology 2016

During December it was an privlege to be able to attend the BPS Pharmacology 2016. We not only presented a poster describing the Guide to IMMUNOPHARMACOLOGY, but were also given the opportunity to present this as a 2-miunute, one slide, flash poster presentation.  The session was well attended and both the poster and presentation well received.

View poster on slideshare 

View presentation on slideshare

Other Development and Next Steps

The submission tool has been extended to incorporate ligand to disease associations. This is one of the first steps to fully incorporating disease association into GtoImmuPdb. These developments accompany additions to the database schema which now contains new tables to store these associations. Our expectation is to extend the schema and submission tool to also capture target-disease associations.

There are some disease terms in the database already, mostly linked to OMIM, the Disease Ontology or Orphanet. While these data resource may be adequate for annotating and describing immunological diseases and related diseases, we are exploring whether to include ICD disease classifications. Our aim is to have some GtoImmuPdb disease association in place prior to the beta-release in Spring 2017, but we are keeping this under-review.

In the next couple of months we will also be improving the current display of comments and references linked to new data types (processes and cell-types).  We will also be incorporating references to the ligands tagged in GtoImmuPdb, and surfacing their display.

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

Hot topics: Will the real splice variants please stand up?

The number of alternative mRNA splice forms that map to human protein coding loci has increased to the point that nearly all proteins have such associated database records. This gives rise to the paradox that the gene build pipeline from the latest Ensembl GRCh38 reference genome assembly indicates 19,919 protein coding loci (which shrinks to 19,022 with HGNC annotation stringency) but 198,002 transcripts (i.e. nearly 10 transcripts per protein). Their is no question that a small number of these alternative splice forms, AS, (plus alternative initiations) have not only been verified to exist as proteins, have some kind of alternative biochemical functions and are also of pharmacological importance [1].  Notwithstanding, compared to the massive transcript profiling that RNAseq now provides routinely, experimentally verifying AS existence at the protein level at large scale is extremely difficult. This is because it can only be done by splice form specific antibodies, western blots detecting different size forms, top down proteomics (i.e. intact mass measurement) or the detection of alternative exon-specific trypic peptides. A recent  review [2] proposes that expanding data sets from the latter approach are consistently detecting only single quantitatively dominant protein isoforms from each locus. The provocative inference is that the vast majority of the 200K odd predicted and/or verified alternative mRNA transcripts are not actually translated into proteins.  This can be seen as an interesting methodological detection “gulph” between RNAseq and MS-proteomics.  However, their has been previous support for the “single isoform” idea on the basis of transcript data alone [3]. An ancillary conclusion from this paper, generally overlooked in terms of its significance, was that when CDS length was taken into account approximately 50% of major transcripts did not corresponding to the ‘canonical’, max-exon, transcript as annotated in Swiss-Prot. This crucial topic is further discussed in [4].

[1] Bonner, T.I. (2014). Should pharmacologists care about alternative splicing? IUPHAR Review 4. Br J Pharmacol. Mar;171(5):1231-40. doi: 10.1111/bph.12526. PMID: 24670145.

[2] Tress et al. (2016). Alternative Splicing May Not Be the Key to Proteome Complexity. Trends Biochem Sci. Sep 16. doi: 10.1016/j.tibs.2016.08.008. PMID: 27712956.

[3] Gonzàlez-Porta et al. (2013). Transcriptome analysis of human tissues and cell lines reveals one dominant transcript per gene. Genome Biol.  Jul 1;14(7):R70. doi: 10.1186/gb-2013-14-7-r70. PMID: 23815980.

[4] Will the real cannoical protein please stand up.
https://cdsouthan.blogspot.se/2016/11/will-real-canonical-proteins-please.html

Comments by Chris Southan

Posted in Hot Topics

GtoImmuPdb: technical update November 2016

During October we have made the first alpha-release (v1.0) of the Guide to IMMUNOPHARMACOLOGY. This blog post summarises some of the main features of the release and work on the documentation.

This first release marks an important step towards the public deployment of the first beta-release of GtoImmuPdb, scheduled for Spring 2017. We expect to make further alpha-releases over the next few months, as additional features are added.

An early synopsis of the project can be found in this blog post. Previous technical blogs are available for February, May, August & September 2016.

Development Progress

Alpha-Release v1.0

The portal has its own unique branding (header bar, logo and colour scheme) to distinguish it, but retains many of the layout features from the main GtoPdb site. This consistency should help users already familiar with GtoPdb to orientate themselves with the new GtoImmuPdb.

alpha_release_portal

Screenshot of the GtoImmuPdb Portal, alpha-release v1.0

The portal provides a starting-point for accessing data in GtoImmuPdb, tailored to the requirements of users with a specific interest in immunopharmacology. Browsing by target, process and cell-type have been implemented in the alpha_v1.0 release. Ligands can be browsed, but there isn’t yet a immuno specific view for the results.

The portal and other pages with the GtoImmuPdb view toggled on will display a specific Guide to IMMUNOPHARMACOLOGY header and menu-bar. A consistent feature on the GtoImmuPdb pages is a ‘toggle’ button that enables the user to switch out to the standard GtoPdb view (and back).

family2

Family page on GtoImmuPdb, showing new header and toggle button (a key feature of GtoImmuPdb)

Alpha-Release v1.0 Documentation

The main area of development over October 2016 has been to prepare the documentation for the alpha-release. These provide an explanation of the features included, how data was obtained and curated and how to use the site. Detailed release notes have been prepared, which will be incrementally added to or appended to on subsequent releases. They cover the following main sections:

  • GtoImmuPdb portal
  • Receptor Family pages
  • Family Pages
  • Detailed Target pages
  • Immuno Process Association List pages
  • Immuno Cell Type Association List pages
  • Search
  • Database Development

Documentation has also been prepared that gives details on how the data for both the process and cell type associations has been obtained. This includes a detailed spreadsheet on the full GO annotations, obtained via UniProt that form the basis of the immuno process associations.

We have also prepared a tutorial document that is a guide to navigating from the new portal, to access GtoImmuPdb data and understand the new GtoImmuPdb pages.

Alpha-Release v1.0 Data

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

GtoImmuPdb has defined its own set of top-level immunological process categories against which targets in the database can be annotated and which form the basis of organising, navigating and searching for immunological processes and associations.

These categories are:

  • Immune system development and differentiation
  • Proliferation and cell death
  • Production of signals and mediators
  • Regulation and responses to signals
  • Migration and chemotaxis
  • Cell-mediated immunity
  • Inflammation

We have associated sets of Gene Ontology (GO) terms with each of these categories. This enables us to auto-curate targets annotated to any of those terms (or their children) by GO into our top-level immunological categories. GO data is obtained via an OBO file (http://purl.obolibrary.org/obo/go.obo) for the ontology, which is edited to restrict it to immuno-specific terms. We auto-curate targets to the top-level process terms by using GO annotation information from UniProt. Through UniProt, targets were selected that were annotated to the subset of GO terms and also cross-referenced in GtoPdb. This gave a total of 1,855 annotation to 401 targets.

The table below summaries the unique targets (UniProt) annotated under each category

GtoImmPdb ‘High-Level’ Process Distinct UniProt
Immune System Development and Differentiation 124
Proliferation and Cell Death 33
Production of Signals and Mediators 74
Regulation and Responses to Signals 355
Migration and Chemotaxis 81
Cell-Mediated Immunity 99
Inflammation 261

Provision has been made in the database schema to capture curator comments against process information and annotations and the design is fully-adaptable to future changes.

Cell Type Data

The Cell Ontology provides the formalised vocabulary against which we annotated target to cell type associations. GtoImmuPdb has defined its own set of top-level immunological cell type categories against which targets in the database can be annotated and which form the basis of organising, navigating and searching for immunological cell types and associations.

These categories are:

  • pro-B-lymphocytes, B lymphocytes & Plasma cells
  • T lymphocytes (alpha-beta type) and their immediate progenitors
  • T lymphocytes (gamma-delta type) and their immediate progenitors
  • Natural Killer (NK) cells
  • Polymorphonuclear leukocytes (neutrophils, eosinophils, basophils)
  • Mononuclear leukocytes (syn: monocytes) (macrophages, dendritic cells, Kupffer cells)
  • Mast Cells
  • Innate Lymphoid Cell (added November 2016)

We have assigned one or more Cell Ontology terms to each of these categories. The assigned CO terms represents the highest level parent term(s) within the ontology for that category. For the purposes of annotation, it is these CO terms and their children that can be used when annotating a target to a given category. The exception is innate lymphoid cells which at present are not defined and included in the Cell Ontology.

Other Developments & Next Steps

Fixes have been made to out submission tool to include the ability to add/remove cell type categories and to add definitions/description of them.

Our focus in the next month is to develop the ligand browse landing pages (accessed via Ligand panel on the portal home), and add in icons to highlight immuno-flagged ligands throughout the main GtoPdb site.

We also want to develop the menu-bar navigation for GtoImmuPdb, as this will be important for the beta-release.

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 Technical

GtoPdb Ligands in PubChem

GtoPdb and  its precursor IUPHAR-DB have been capturing the structures of pharmacologically relevant ligands since 2005.  The fig.1. snapshot below  shows the approved drug section of our eight-category ligand classification

As an active collaboration with the  PubChem team, we have submitted our ligand records for every GtoPdb release since  2012.  For the current release of 2016.4 the query  (“IUPHAR/BPS Guide to PHARMACOLOGY”[SourceName])   retrieves 8674 Substance Identifiers (SIDs)  and  6565 Compound Identifiers (CIDs). The excess of 2109 SIDs is accounted for by antibodies, small proteins and large peptides that cannot form CIDs.  At just over 92 million CIDs from 473 sources, a range of property filters and full Boolean operations for combining query sets,  PubChem provides an opportunity to “slice and dice” our ligand set in detailed, comparative  and informative ways.  A set of results is shown below.

gtop_02

The utilities of these intersects are outlined below (in order of counts):

  1. CNER refers to “Chemical Named Entity Recognition” for the automated extraction of chemistry from patents by sources submitting to PubChem (of which SureChEMBL is the largest at 16.3 million). This means that users can track-back most of our ligands to early  patent filings that can often include more SAR than eventually appeared in the papers.
  2. Our low overlap with DrugBank indicates both sources are complementary in bioactive compound selection (i.e. the OR union is 12605)
  3. The possibility of sourcing purchasable compounds is important for experimental pharmacologists. From the 64 million vendor structures in PubChem we have nearly an 80% overlap and similarity searches may pick up analogues where there is no exact match.
  4. The “BioAssay active” tag overlaps extensively with ChEMBL entries but users can check for a range of activities for a ligand that maybe additional to the values we have extracted from selected papers.
  5. The MeSH term “pharmacological action” is useful but our impression is that NLM is falling behind in the PubChem indexing of this term.
  6. PDB ligand structures are valued database cross-references for many reasons.
  7. We have introduced a new feature that allows users to retrieve just our 1291 approved drug SID entries (Query “approved[Comment] AND “IUPHAR/BPS Guide to PHARMACOLOGY”[SourceName]”). The “PubChem Same Compound” select  then generates 1174 small-molecule CIDs. This facilitates different types of comparative analysis between drug lists.
  8. As expected, our overlap with ChEMBL structures is high but we have captured 1147 structures not in this source, mainly due to different journal capture and shorter release cycles.
  9. The selection “unique to GtoPdb” indicates those CIDs where we are the only source in the whole of PubChem. These are predominantly novel structures we have extracted from papers but in some cases we have selected a different structure from other sources.
  10. There may be interest in which pharmacologically active peptides we have CIDs for. A simple Mw-cut isolates 178 entries

In regard to 7) a snapshot from our list of approved drugs is shown below

gtop_03

 

 

Posted in Uncategorized

Hot topics: X-ray structure of the endothelin ETB receptor

Endothelin is a peptide that acts via two G-protein coupled receptors. ETA mainly causes vasoconstriction. In contrast ETB  predominantly acts as a beneficial clearing receptor and by the release of endothelium derived relaxing factors, vasodilatation [1,2]. This paper  describes for the first time the crystal structure of  the endothelin ETB receptor [3]. To date less than 20 structures of Family A, GPCRs (targets of nearly half of all drugs) have been solved experimentally. The number solved for small peptides ligands are limited to the opioid receptor and  the 13 amino acid neurotensin. This manuscript extends information to a much larger  21 amino acid peptide and interestingly demonstrates interaction over a substantial portion of the molecule. The authors propose a model whereby the N-terminal tail and the ECL2 β-sheet of ETB together form a lid-like architecture that covers the orthosteric pocket, predicted to form a very stable complex. This provides one structural explanation for the unusual property of ET-1  in causing long lasting responses. Mutations in ETB in receptors can result in Hirschsprung disease in humans, characterized by an absence of enteric ganglia in the distal colon and a failure of innervation in the gastrointestinal tract [2]. ETB receptor mutations are also associated with lethal white foal syndrome in horses as a result of limiting migration of melanocytes, pigment-producing cells found in hair follicles and skin.

[1] Guide to PHARMACOLOGY: ETB receptor

[2] Davenport et al. (2016). Endothelin. Pharmacol Rev. 68:357-418. PMID: 26956245

[3] Shihoya et al. (2016). Activation mechanism of endothelin ETB receptor by endothelin-1. Nature, 537, 363-368. PMID: 27595334

Comments by Anthony Davenport

Posted in Hot Topics

Hot topics: Synthesis and SAR for depsipeptide natural products as selective G protein inhibitors

A team including the Gloriam Group at the University of Copenhagen (also the home of GPCRDB) have paper out in Nature Chemistry reporting the first total synthesis of YM-254890 and FR900359 [1] . These are related cyclic depsipeptide natural products that specifically and potently inhibit the Gq subfamily of G proteins, a relatively rare but useful and pharmacological property [3]. By a combination of solution and solid-phase approaches the team generated sufficient YM-254890 and FR900359 material for confirmation of the structures , pharmacological characterisation and the synthesis of ten new analogues of YM-254890 for SAR analysis. The paper also includes docking studies based on the X-ray crystal structure of YM-254890 in PDB 3AH8 [3]

[1] Xiong et al. (2016). Total synthesis and structure–activity relationship studies of a series of selective G protein inhibitors. Nat Chem, advance online publication, doi:10.1038/nchem.2577

[2] Schrage R, et.al. (2015) The experimental power of FR900359 to study Gq-regulated biological processes. Nat Commun. 14;6:10156. doi: 10.1038/ncomms10156, PMID 26658454

[3] Nishimura A. et. al.(2010) Structural basis for the specific inhibition of heterotrimeric Gq protein by a small molecule. Proc Natl Acad Sci; 107(31): 13666–13671. doi: 10.1073/pnas.1003553107, PMID 20639466

The two key potent ligands from the paper are included in the new GtoPdb release 2016.4. Details of this particular curation exercise are given in this blog post.
http://guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=9335

lig9335

http://guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=9336

lig9336

Comments by Chris Southan

Posted in Hot Topics

Hot topics: X-ray structure of P2X3 receptor

Extracellular ATP is able to activate two families of cell-surface receptors, one of which is the ligand-gated ion channel family of P2X receptors. This family of cation channels is distinct from the remainder of the ligand-gated ion channels, as they are constructed of three (usually homomeric) subunits each with two transmembrane domains. Amongst the P2X receptors, the P2X3 is associated particularly with synaptic transmission in the sensory system and has, therefore, attracted a lot of attention as a potential target for novel analgesics and/or bladder dysfunction therapies.

In this report [1], multiple crystal structures of the P2X3 receptor are described, which allow a novel insight into the gating of a ligand-gated ion channel during the rest-agonist activated-refractory cycle, as well as with antagonist bound.

[1] Mansoor et al. (2016). X-ray structures define human P2X3 receptor gating cycle and antagonist action. Nature 538:66-71. doi: 10.1038/nature19367. [PMID 27626375].

Comments by Steve Alexander

 

Posted in Hot Topics

GtoPdb database release 2016.4

We are pleased to announce our fourth database release of 2016. Version 2016.4 was published on 13th October 2016. The database is available through the Guide to Pharmacology website, download pages and web-services.

Target updates:

Website updates

A new dendrogram visualisation of VGICs is included on the ion channel page (http://www.guidetopharmacology.org/GRAC/ReceptorFamiliesForward?type=IC). It shows a representation of the amino acid sequence relations of the minimal pore regions of the voltage-gated ion channel superfamily. the visualisation was taken from:

The VGL-Chanome: A Protein Superfamily Specialized for Electrical Signaling and Ionic Homeostasis. Frank H. Yu and William A. Catterall. Sci STKE. 2004 Oct 5;2004(253):re15. PMID: 15467096. DOI: 10.1126/stke.2532004re15

Synpharm

We have created a new sister database to the main Guide to PHARMACOLOGY – SynPharm, a database of drug-responsive protein sequences. The sequences in SynPharm are derived from interactions from the Guide to PHARMACOLOGY and using data from the Protein Data Bank. It is expected that the SynPharm database will grow as the principle Guide to PHARMACOLOGY database is updated – or indeed as further structural data is added to the PDB database pertaining to interactions already documented.

Please read the introductory SynPharm blog post (4th October 2016).

A summary of the current data can be found at synpharm.guidetopharmacology.org/about/data.

Database Statistics

In total the database now contains 14,701 curated interactions across 2,794 human targets and 8,674 ligands. More specifically, the database contain 1,429 human targets that have quantitative interactions to a ligand.

human_targets_pie

Number of human targets in GtoPdb 2016.4. Measured by number of distinct UniProt entries includes for a given target class

ligand_bars

Breakdown of ligand classes in GtoPdb 2016.4

PubChem Links

We refresh our PubChem Substance (SID) submissions at every release and this takes a week or so to surface in their system.  For 2016.4 our  SIDs increased  from 8612 to 8675  (if you want to execute the same query use “IUPHAR/BPS Guide to PHARMACOLOGY”[SourceName]).  The same query at the Compound Identifier (CID) level increases from 6519 to 6565.  As previously  mentioned the 2,110 SIDs that do not merge into CIDs are antibodies, small proteins and large peptides.  Note we have a slight shortfall in the CID numbers you  will find listed in our ligand download lists.  This is because for novel compounds where we were the first submitters to PubChem we now have to catch up with adding the new CIDs into our records.

Posted in Database updates

Why Data Citation Is a Computational Problem

By Peter Buneman

The database development team encouraged me to write this off-topic blog on data citation, as it may be of interest to people involved with the IUPHAR/BPS Guide to Pharmacology (GtoPdb).

It must be almost ten years ago that Tony Harmar mentioned that he was thinking of buying digital object identifiers for the then IUPHAR database. It turned out that he was hoping that this would confer some scholarly recognition to the database, but what he really wanted to do was to get people to cite it, just as they would cite any other publication. Among other things, he wanted to ensure that the relevant contributors and curators received proper credit.

I thought about the problem for a while, wrote a rather naive paper about it, and more or less forgot about it for a few years. Then data citation became a hot topic, and with some colleagues started to think about it again. Here’s a problem: GtoPdb does a passable job of specifying the citation for each page that you see in the Web presentation, but what citation would you provide for some arbitrary SQL query on the underlying data? It turns out that this is a ubiquitous problem in data citation, and one that is tricky to solve in general.

My colleagues Susan Davidson, James Frew and I produced a general approach to this and sent it to Communications of the ACM — a publication that is widely read by computer scientists. They liked it to the extent that they made it a cover story and produced a film about it.

So thanks to Tony for the idea and thanks to the curators of GtoPdb for letting us use their database as a guinea pig.

Follow this link to the full CACM article, Why Data Curation Is A Computational Problem.

Follow this link to the video, https://vimeo.com/177314966

Posted in Uncategorized

SynPharm: A New Annexe to the Guide to PHARMACOLOGY

We have created a new sister database to the main Guide to PHARMACOLOGY (GtoPdb) – SynPharm, a database of drug-responsive protein sequences.

Each sequence in SynPharm is derived from a GtoPdb interaction. In each case we have identified the continuous protein sequence within the receptor chain that facilitates that interaction, and provided structural, visual, spatial and affinity data.

SynPharm ligand receptor complex

A peptide ligand (R-spondin-1) bound to its receptor (LGR4), with the bind sequence highlighted in green. See its page for more details.

Bind Sequences

Each sequence in the database represents a potentially ligand responsive protein sequence. In addition to providing a pharmacological reference as to the portion of protein chains which actually mediate their interactions with drugs, it is also hoped that SynPharm could act as a library of transferable protein modules to synthetic biologists, enabling the drug responsiveness to be conferred to a protein of choice.

In order to allow researches to assess the likelihood that a bind sequence (as the drug responsive elements are termed) will function in isolation, certain metrics are provided. We provide a ‘contact ratio’ – the ratio of internal contacts (all non-hydrogen atom pairs within the sequence within 5 Angstroms of each other, excluding atoms within two covalent bonds of each other) and external contacts (all non-hydrogen atom pairs between the sequence and the rest of the chain, less than 5 Angstroms) – and a distance matrix to show the ‘globularity’ of the sequences. Each sequence also contains a manipulable 3D  visualisation of the sequence in question.

residue_matrix_synpharm

A example of a residue distance matrix. The bind sequence is represented by a dotted black line within the context of the protein chain it derives from.

In addition, we provide pages for each of the ligands that interact with a sequence, along with a small selection of the data on the ligand from the main Guide to PHARMACOLOGY database.

Creating the Data

Each interaction in the Guide to PHARMACOLOGY was mapped to one or more PDB files where possible. Some already had PDB information, and where this was not the case, the RCSB web services were queried by SMILES, InChI, name and peptide sequence (in the case of ligands) and accession number (in the case of targets) to identify more. In total, 704 interactions mapped to at least one PDB code, and after manually removing some false maps, this came down to 672. Though a relatively small proportion of the 15,000 or so interactions that GtoPdb contains, it is merely an indicator that most interactions observed have do not yet have high quality structural data.

Each interaction-PDB map was turned into a sequence by first identifying the HET code and ID of the ligand within that PDB file (generally provided by the PDB REMARK records), then identifying the residues that facilitate binding (again most PDB files already annotate this but in cases where this is not true, atomic distances were used to identify probable residues), and then using these to construct a continuous sequences. Not all maps were suitable to this – some had binding sites split across multiple protein chains, and yet more contained too many missing residues – residues flagged as missing from the crystallographic (or otherwise) experiment from which the PDB was derived. Ultimately 540 interactions had at least one PDB map that could be used to create a sequence.

It is expected that the SynPharm database will grow as the principle Guide to PHARMACOLOGY database is updated – or indeed as further structural data is added to the PDB database pertaining to interactions already documented.

A summary of the current data can be found at synpharm.guidetopharmacology.org/about/data.

Posted in Technical

GtoImmuPdb: technical update September 2016

The focus of development in the last month has been on preparing the GtoImmuPdb portal for alpha-release and building landing-pages for process and cell type association lists.

An early synopsis of the project can be found in this blog post. Previous technical blogs are available for February, May & August 2016.

Development Progress

List pages for process and cell type associations

We have developed landing pages that are reached when clicking on any of the main process or cell type categories in either of the process or cell type panel on the GtoImmuPdb portal (see Figure 1).

sep_portal_link_to_lists

Figure 1: Links to process and cell type association lists pages from GtoImmuPdb portal (www.guidetopharmacology.org/immuno)

These pages list the protein targets in the database that are associated with either immunological processes or immune system cell types. In each case, the pages are split, using tabs, to show targets associated with each of the main process or cell type categories. The targets in each list are then separated by their target-class (e.g. GPCRs, ion channel, enzymes etc.). An example of the immuno process association list page is shown in figure 2.

immuno_process_association_page

Figure 2: GtoImmuPdb process association list page. Showing targets associated with the ‘proliferation and cell death’ category.

The table for the process associations gives the target name and family, any process association comments and lists Gene Ontology (GO) terms annotated to the target (with ID and evidence code). Two further columns show if the target has been specifically tagged as being in the Guide to IMMUNOPHARMACOLOGY by our curators, with any associated comments.

It is worth a reminder here that we auto-populate the GO annotations from UniProt. Therefore, we will see targets appearing under the process associations that have not been directly curated into GtoImmuPdb by our curators. For the time-being we will continue with the distinction between targets associated to processes (via GO only) and those that our curatorial team have identified as being of immunological relevance, and therefore directly curated as being ‘in GtoImmuPdb’.

The table for the cell type associations (see figure 3) gives the same colums as for process associations. With the exception that it lists Cell Ontology terms with their IDs.

celltype_association_page

Figure 3: GtoImmuPdb cell type association list page

Other Developments

Work has continued on implementing the site search to incorporate all new columns for process and cell type associations. This includes top-level category names, GO and Cell Ontology terms & definitions and all association comments.

A few fixes have been made to out submission tool to better handle cell type association input.

Portal development

A new logo has been added to the Guide to IMMUNOPHARMACOLOGY portal. A bespoke design by Dr. Adam Pawson. The menu bar has been adjusted to include a link to the GtoPdb home page. We have also modified some of the links within the menu-bar to keep the GtoImmuPdb focus (for target links).

Alpha-release

Our plan is to release the first alpha-version of the Guide to IMMUNOPHARMACOLOGY at the beginning of October.

This will be an internal release on our development site and will be accompanied by detailed release notes and a user guide to navigating the new pages.

 

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

Hot topics: High resolution structure of the voltage-gated skeletal muscle Ca2+ channel complex

In a recent article in Nature [1], Wu et al. present the cryo-electron microscopy structure of the rabbit Cav1.1 complex at a nominal resolution of 3.6 Å. Enrichment of purified channel particles without carbon film increased resolution and allowed to delineate structural features of the channel beyond those published by the authors in Science [2] six months earlier. The new structure reveals the channel in a (most likely) inactivated state (pore closed, voltage-sensors “up”), provides more complete structural detail of the α2δ-subunit and its interaction with extracellular surface of the pore-forming α1-subunit and unveils formation of a globular domain by direct interaction of the proximal C-terminal tail of α1 with its intracellular III-IV linker.
The new structural information provides new perspectives to address long-standing open questions. It will help to model human disease-related missense mutations within the Cav1.1 α1-subunit structure revealing the molecular mechanisms causing aberrant channel function, such as the formation of omega-pores in hypokalemic periodic paralysis [3]. The drug binding domains for Ca2+ channel blockers, widely used as antihypertensive drugs by blocking highly homologous Cav1.2 L-type channels in arterial resistance vessels, are highly conserved in Cav1.1. Together with recently published high resolution structure of the receptor sites for these drugs within the Ca2+-selective bacterial Na+-channel (NavAb) derivative CavAb [4], the new Cav1.1 structure will now allow to further refine the molecular details of drug interactions with L-type Ca2+ channels. The unexpected finding of a globular domain formed by the proximal C-terminus and the cytoplasmic III-IV linker of the pore subunit could provide the structural missing link for understanding how the C-terminus mediates protein kinase A regulation of the channel and controls voltage- and Ca2+-dependent channel gating in Cav1.1 and other voltage-gated Ca2+ channels.
Finally, it will be interesting to see how well the Cav1.1 α1-subunit structure was predicted by homology modeling using bacterial Na+-channels (like NavAb) or mammalian K+-channels as a template [5].

[1] Wu et al. (2016). Structure of the voltage-gated Ca2+ channel Cav1.1 at 3.6 Å resolution. Nature 537:191-196. [PMID 27580036].
[2] Wu et al. (2015). Structure of the voltage-gated calcium channel Cav1.1 complex. Science 350: aad2395. [PMID 26680202].
[3] Wu et al. (2012). A calcium channel mutant mouse model of hypokalemic periodic paralysis. J. Clin. Invest. 122: 4580–4591. [PMID 23187123].
[4] Tang et al. (2016). Structural basis for inhibition of a voltage-gated Ca(2+) channel by Ca(2+) antagonist drugs. Nature 537: 117–121. [PMID 27556947].
[5] Tuluc et al. (2016). Molecular interactions in the voltage sensor controlling gating properties of Cav calcium channels. Structure 24:261–271. [PMID 26749449].

Comments by Jörg Striessnig (Department of Pharmacology and Toxicology – Institute of Pharmacy, Universität Innsbruck)

Posted in Hot Topics

Hot topics: Allosteric Modulation of Receptor Function and Regulation

Changeux and Christopoulos have recently described in Cell [1] how common mechanisms link the allosteric sites of activation and response within the four major receptor families of ligand- and voltage-gated ion channels, G-protein-coupled receptors, nuclear hormone receptors, and receptor tyrosine kinases. As stated in the classical “Monod-Wyman-Changeux” model [2], the signal transduction mechanism operates through the selective stabilization of the particular state to which any ligand preferentially binds. Recent research shows that these states are affected by multiple factors including oligomerization, distinct conformational ensembles, intrinsically disordered regions, and allosteric modulatory sites. These processes can be perturbed by mutations that shift the equilibrium of receptor functional states and lead to disease [3]. Conversely, marketed medicines now include a large number of allosteric modulators with the advantages of fine-tune physiological responses and offer higher on-target selectivity via more diverse binding sites [4]. Such modulators can also display increased functional selectivity through biased agonism (i.e. the association with a distinct receptor conformation and signal routing). This review summarises the unifying mechanisms for the allosteric modulation of receptor classes and provides a clear demonstration of the associated pharmacological targeting opportunities.

[1] Changeux, J.-P. and A. Christopoulos (2016). Allosteric Modulation as a Unifying Mechanism for Receptor Function and Regulation. Cell. 166(5): p. 1084-1102. [PMID: 27565340]

[2] Monod, J., J. Wyman, and J.P. Changeux (1965). On the nature of allosteric transitions: a plausible model. J. Mol. Biol. 12: p.88-118. [PMID: 14343300]

[3] Changeux, J.-P. (2013). 50 years of allosteric interactions: the twists and turns of the models. Nat. Rev. Mol. Cell Biol. 14(12): p.819-29. [PMID: 24150612]

[4] Gentry, P.R., P.M. Sexton, and A. Christopoulos (2015). Novel Allosteric Modulators of G Protein-coupled Receptors. Journal of Biological Chemistry. 290(32): p.19478-19488. [PMID: 26100627]

Comments by David E. Gloriam (Department of Drug Design and Pharmacology, University of Copenhagen).

 

Posted in Hot Topics

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

Lek 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 http://exac.broadinstitute.org/  [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 GPCRdb, Swiss-Var, ClinVar and Orphanet as orthogonal backup.

[1] Lek et al. (2016). Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291. [PMID: 27535533]

[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. Aug 17. doi: 10.1038/gim.2016.90. [Epub ahead of print]. [PMID: 27532257]

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 2106.4 (September) but TRV130 was already captured as ligand 7334

Capture

 

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

subtool_celltypes

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.

target_detail_2

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_cells

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

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. More specifically, the database contain 1,429 human targets that have quantitative interactions to a ligand.

human_targets

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

ligands_stats

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 (www.guidetopharmacology.org), 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.

CGtP_cover1516

Discover the FREE Concise Guide http://onlinelibrary.wiley.com/doi/10.1111/bph.v172.24/issuetoc

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.

gtip_portal_colour_1_028CCF

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.

immu_targets_toggle_may2016

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

Recent DrugBank Changes

The DrugBank database has just announced (09 May 2016) more restrictive access conditions including user registration. Not unexpectedly, this has prompted discussion on Twitter and elsewhere (e.g. ThinkLab. including some from the DrugBank team).  We enjoy long-standing contacts with the Wishart group but do not feel it is appropriate to comment per se.  Nevertheless, it does seem appropriate for us to re-state our own position, and also to highlight the overlap in content between different resources. These details are not new, but have just not been juxtaposed before.

The British Pharmacological Society (BPS) has committed support for GtoPdb until 2020 and the Wellcome Trust support for GtoImmuPdb until 2018. Needless to say the management team (between, IUPHAR, BPS and the University of Edinburgh) are engaged in sustainability planning beyond those dates. We have also just applied for UK ELIXIR Node consideration.

In accordance to the commitment to openness of both funders, GtoPdb (and GtoImmuPdb when it is available) are licensed under the Open Data Commons Open Database License (ODbL) and contents under the Creative Commons Attribution-ShareAlike 3.0 Unported license.  Thus, beyond appropriate attribution as a source, anyone can do anything with our content (even if we have seen minimal attributions of just a web-link to the deprecated IUPHAR-DB!). Also for the record, we have no intention of using a log-in but we do track usage and downloads since these are an important aspect of our own impact assessment.

As has been described, bioactivity databases are complex and each has unique coverage and lacunae (see http://www.ncbi.nlm.nih.gov/pubmed/24533037). We cannot therefore indicate for whom GtoPdb might at least partially substitute for DrugBank but we can point out some overlaps and differences. Firstly we should declare that, while we have been funded to capture human drug target relationships, we generally do not curate anti-infectives (although for various reasons we do have a few entries and it does not preclude doing this under new funding). Secondly, we do not annotate neutraceuticals that are metabolites. Thirdly, our drug and ligand annotation and relationship mappings have other selectivity differences to DrugBank (see http://www.ncbi.nlm.nih.gov/pubmed/26464438 and our FAQ). The upshot is that  DrugBank has 7422 and we have 6293 structures with a PubChem CID entry. The overlap of 1339 thus extensively covers approved drugs for non-infectious diseases and some clinical candidates. Note also we update new drug approvals in our approximately quarterly releases (anyone needing more details on intersects and differences between the two sources is welcome to contact us).


Posted in Uncategorized

GtoPdb database release 2016.2

The latest update of the database, version 2016.2, has been released (30th March 2016). The summary of the main updates is below. Database content in various formats can be downloaded from our download pages and accessed via web services.

This database release comes only 2 months after our last release. This has been done to coincide with the release of the IUPHAR/ASPET Pharmacology Education Site. An education portal that will be closely linked to the GtoPdb and provides high-quality training in the principles and techniques of basic and clinical pharmacology.

Target updates:

Database Statistics

In total the database now contains 14,327 curated interactions across 2,775 targets and 8,400 ligands

targets_2016.2

Number of human targets in GtoPdb 2016.2. Measured by number of distinct UniProt entries includes for a given target class

ligands_2016.2

Breakdown of ligand classes in GtoPdb 2016.2

View all the latest database content stats here.

Posted in Database updates

Assessment of GtoPdb in-links

 

A hallmark of GtoPdb is our curation of out-links as opposed to adding these by automated cross-referencing. The latter can give rise to not only false +ves and false -ves but also 1:many relationships that users find difficult to resolve.  Also valuable are in-links from other relevant resources. Not only do these facilitate reciprocal navigation but also linked-data queries via inter-database web services. The Edinburgh team engages extensively with other databases at many levels, including long-standing collaborations and conference catch-ups. Indeed, a component of our value is expert selection of out-links for our GtoPdb entries, especially since pharmacology spans the domain complexity  of bioinformatics, chemistry and genomics. Reciprocity of linking (a.k.a. cross-pointing) between any two databases thus becomes an enabling feature for both.

This document reviews in-links to GtoPdb from public sources that have come to our attention. However, there may be others we are not aware of (e.g. inside pharmaceutical companies). Those resources that we specifically out-link to are listed in Table 5 in our NAR article PMID 26464438. There are other cases where reciprocal in-links are under consideration but not yet instantiated (e.g. NURSA , ESTHER  DrugBank and Open PHACTS).

As an open database we welcome relevant in-links.  Notwithstanding, there are caveats, especially where these have been instigated without contact or our technical input. Two main problems arise. Firstly  automated parsing may not be consolidated by specificity checks on their side. Secondly their update frequency my not be synchronised with our own new releases. To assess the latter we can use source entity counts and loading dates but these are not always provided. For example, we have been contacting resources we know who have not yet replaced IUPHAR-DB content by GtoPdb but its difficult to find all instances.  Those in-links we know of are listed below (but please contact us if you are aware of others).


For PubChem (a global chemistry and bioactivity portal) we have 8201 ligand submissions as SIDs that each include a GtoPdb url. Of these 6192 are merged into Compound identifiers (CIDs) with a defined chemical structure. Most of the SIDs 2009 SIDs without CIDs are large peptides, small proteins and antibodies that cannot form a CID. Note we have some SID duplicates structures where we have separate GtoPdb ligand IDs pointing to radiolabel citation data without specified substitution positions that have a CID. We also have 55 BioAssay entries for 5HT sub-family chemistry mappings.

image1


HGNC is responsible for approving unique symbols and names for human loci, including protein coding genes, ncRNA genes and pseudogenes. We have a long-standing collaboration via NC-IUPHAR. An example outlink is shown below.

image2


UniProtKB/Swiss-Prot: We are included in the Cross-References for protein entries. These can be selected using the menu below

image3

The query currently produces 1829 proteins with GtoPdb links as having quantitative ligand interactions.


neXtProt (PMID 25593349) is a protein-centric knowledgebase developed at the SIB Swiss Institute of Bioinformatics focused solely on human proteins. In a sense this is “forked” from Swiss-Prot but is technically distinct. It inherits our UniProt links.

image4


IMGT/mAb-DB is a high-quality integrated information system focused on clinical antibodies. We have a long-standing collaboration. A link example is shown below.

image5


ChEMBL, a database of bioactive drug-like small molecules with calculated properties and abstracted bioactivities. A target link example is below.

image6

Our ligands get a nested link in the ChEMBL interface via UniChem.

image7

UniChem produces cross-references between chemical structure identifiers from different databases within the EBI. We are listed as a source

image8

This was updated on 17-NOV-15 as 6006 chemical entities

***********************************************************************

MEROPS information resource for peptidases (also termed proteases, proteinases and proteolytic enzymes) and the proteins that inhibit them. An example of our cross-links is shown below.

image9

The addition of these links is specified in the latest MEROPS NAR publication (PMID 26527717)


GPCRdb (in new NAR as PMID 26582914) , the information system for G protein-coupled receptors. Consequent to extensive contact and collaboration we have mentions in their paper and web resource. In addition we have pioneered reciprocal web services. A link example is shown below.

image10


image11


ChemSpider. This is a leading chemistry portal of 34 million compounds and also a reference structure source for OpenPhacts

image12

We can see from this example link for CS19973960 that we have IDs but no direct outlink at the moment.

***********************************************************************

Oprhanet is the portal for rare diseases and drugs with whom we have regular contact. We are one of the eight gene-mapped IDs (see Part IV in the user guide and sample entry below)

image13


CARLSBAD is an integrated resource based on filtered subsets from the bioactivity databases listed below (PMID 23794735).

image14

Thus the 2012 release subsumed the 2011 IUPHAR-DB set of 2297 ligands. 


In the GLASS database of GPCR-ligand associations (PMID 25971743) we are cited as one their five sources. An example x-ref is shown below but the statistics are not specified.

image15


ChemProt -2.0 (PMID 23185041).

This resource with an emphasis on visual navigation in a disease chemical biology specifies eight protein < > ligand sources collated in 2012. X-ref counts were not specifies but this includes IUPHAR-DB from 2011.


The RefSeq LinkOut feature facilitates access to relevant online resources beyond the NCBI Entrez system. These include GtoPdb in the protein section with the example for NP_036236 (BACE1) shown below.

image16


ZINC A free database of commercially-available compounds for virtual screening (PMID 26479676). The x-ref below is for IUPHAR-DB

image17

ZINC has just undergone a major update in PubChem so our entries can be intersected


GeneCards automatically integrates gene-centric data from ~125 web sources. As we can see we became source 104 (but named IUPHAR)

image18

We have had collaborative contact w.r.t. to their MalaCards disease database where we are source 28.

image19


DGIdb 2.0 is a resource for mining clinically relevant drug-gene interactions (PMID 26531824). They quote on their paper “new content was imported from the IUPHAR/BPS Guide To Pharmacology (GTP) (6), accounting for 10 225 interaction claims and 1,969 druggable gene category claims” . An example of a search result specific for us, is shown below

image20

We also get a chemistry page, with the download date

image21

And a gene page (below)

image22

They have initiated contact and cross-mapping issues can now be addressed directly via GitHub.


We are indexed in the Protein Ontology resource that provides an ontological representation of protein-related entities by explicitly defining and showing the relationships between them.

image23

Wikipedians have been pointing to us for some time. Examples of an established target and a more recent ligand entry are shown below.


 

image24

image25

Capture

Tagged with: , ,
Posted in Uncategorized

GtoImmuPdb: technical update Feb 2016

The Guide to Immunopharmacology (GtoImmuPdb) held it’s first grant-holders meeting in London on 7th January. The meeting discussed and set-out plans for the initial phase of the project. This included discussion on new data types to include, form and content of a new homepage, which ontologies to utilise and how new data content would be generated.

An earlier synopsis of the project can be found in our previous blog post.

Some key technical decisions have been made:

  • The shortened name for the database will be GtoImmuPdb.
  • The GtoImmPdb will have its own homepage. This portal will provide an immunological perspective onto the database.
  • GtoImmPdb will use same database as GtoPdb. It will be extended to integrate GtoImmPdb data.
  • Processes/pathways – to provide a way to search data via biological processes we will be utilising target annotations to terms in the Gene Ontology (GO). There will be mapped to a simplified, GtoImmPdb specific, process list.
  • Cell Types – to provide a way to search data via cell-types we will be using cell-type terminology from the Cell Ontology.

Development progress

Methods to include GO biological process terms in the database have been generated. These selected all terms under 2 parent terms, immune system process (GO:0002376) and inflammatory response (GO:0006954), resulting in a total of 1,953 terms. All these terms, plus their parent-child relationships will be included in the database and support curation and querying.

To capture an initial set of annotations of these terms against targets in GtoPdb the advanced search of UniProt was utilised. Through UniProt, targets were selected that were annotated to the subset of GO terms and also cross-referenced in GtoPdb. This gave a total of 1,364 annotation to 317 targets.

Our next steps will be to integrate this data with our front-end search mechanisms and provide access to this search via the a new GtoImmPdb portal.

Submission Tool

Minor addition to the submission tool have been made to allow curators to flag targets and ligands in GtoPdb as being relevant to GtoImmuPdb. General comments fields also add for GtoImmuPdb relevant targets.

Other planned tasks

  • Domain name for portal will be guidetoimmunopharmacology.org (not yet running)
  • Alpha-version of homepage to be in place by April meeting (with limited function)
  • Extend GO annotation to ligands (e.g. cytokines)

Links

CiteULike Group – Immunopharmacology related 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

GtoPdb database release 2016.1

We are pleased to announce the first database release of 2016, version 2016.1 published on 4th February 2016. Below is a summary of the main updates. As always, database content, of various types and formats can be downloaded from our download pages.

Here is a summary of the latest updates:

Target updates:

Database Statistics

In total the database now contain 14,249 curated interactions across 2,769 target and 8,328 ligands.

human_targets_pie_2016_1

Breakdown of GtoPdb content by human target class

ligand_classes_2016_1

Breakdown of GtoPdb content by ligand class

 

View all the latest database content stats here.

Posted in Database updates

GtoPdb database release 2015.3

Hot on the heels of the online advance access publication of our Nucleic Acids Research Database Issue 2016 update article we’re pleased to announce our third database release of 2015, 2015.3 published on 19th October.

Here is a summary of the latest updates:

Target updates:

Website updates:

View the latest database content stats here.

Posted in Database updates

New project to develop “The Guide to Immunopharmacology”

We are very pleased to announce a new initiative (from 1st Nov 2015) to establish “The Guide to Immunopharmacology: Integration of targets, diseases and therapies into an expert-driven database”.

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

Brief synopsis of the project
Immune/inflammatory/ infection responses and disorders have become an increasing focus of pharmacological
R&D. We will enrich GtoPdb with kinome resources linking to diseases to assist selection of new targets, tool compounds and drugs. Suggested priorities are established (JAK, PI3K, IKK) and less validated (RIPKs, IRAKs, MAP3Ks) target kinases in innate immunity.

This will later extend  to adaptive immunity and kinases in selected pathogens. New data will be linked according to the existing GtoPdb expert-curation model but with a strong focus on translational aspects (e.g. clinical benefit, biomarkers and biological endpoints). In addition an immunology-orientated  portal will be developed.

Co-applicants include kinase, immunity/inflammation and parasite biology experts: Dr Michael Spedding, Professor Francesca Levi-Schaffer, Professor Clare Bryant, Professor Christian Doerig, Professor Stephen Anderton, Dr Steve Alexander, Dr Doriano Fabbro and Dr Anthony Davenport. Data selection will be guided by new IUPHAR expert subcommittees set up for this task.

We owe thanks to many folk for the success of this proposal, including for their inputs to the preparation phase and letters of support (to whom we have already communicated our appreciation).

Further details will be surfaced in due course but we are also pleased that the British Pharmacological Society will continue to support the core Guide to PHARMACOLOGY resource during and after this project.

While technical decisions remain on exactly what interfaces and data structures are instantiated, we envisage both resources will be dovetailed into an expanded central database with different front-ends for users.

Any parties with Immunopharmacology interests we have not yet engaged with are welcome to make informal contact as we go forward.

Tagged with:
Posted in Guide to Immunopharmacology

Database Release August 2015

We’re pleased to announce our second public release of 2015 (version 2015.2) where we have introduced the new features and content updates listed below (and detailed on our download pages) with the objective of becoming more “consumable”.  This is not only an invitation for subsumation into local systems via API calls but also via data downloads.  Please note there are a number of good reasons to contact us early on in the course of any integration efforts, including technical assistance or optimisation where we can, as well as basic professional courtesy for us to know who is using our stuff.  We realise some local instanciations will involve proprietory internal systems (e.g. pharmaceutical companies or competitive intelligence brokers) but we would simply like feedback on how the process (e.g. ETL procedures) went, without needing any disclosure of your internal architecture or content.  We envisage 2015.3 to be ready by about November.

Target updates

Content stats

Interaction type Count
Targets with ligand interactions 1505
Targets with quantitative ligand interactions 1228
Targets with approved drug interactions 554
Primary targets with approved drug interactions 312
Ligands with target interactions 6796
Ligands with quantitative interactions (approved drugs) 5860 (738)
Ligands with clinical use summaries (approved drugs) 1724 (1231)
Number of binding constants 44691
Number of binding constants curated from the literature 13484

Table 1. Interaction counts. Primary target indicates the dominant MMOA.

The table shows a few of the main relationship statistics for the 2015.2 release. Further tables with target and ligand category breakdowns are available on the GtoPdb About page.

Relationship growth since 2012

Figure 1. 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.

Website updates
We have released the first version of our REST web services for beta testing. These provide access to the data in JSON format for integration with other databases and websites. JSON is ideal for reading in with JavaScript and is easier to parse than XML. For further information and the list of web services available please see this page: http://www.guidetopharmacology.org/webServices.jsp. We welcome all feedback, bug reports, and suggestions for further development. Please try them out and let us know how you get on by emailing us. The web services offer access to lists of targets and ligands as well as specific information about targets, ligands and interactions. The web services base URL is http://www.guidetopharmacology.org/services and all target web services can be accessed with the prefix {base_url}/targets while for ligands it is {base_url}/ligands. For example, to download a list of agonists at the target GPER (GtoPdb target Id 221) with pKi >= 7 you would use this URL: http://www.guidetopharmacology.org/services/targets/221/interactions?type=Agonist&affinityType=pKi&affinity=7 For a longer list of examples see the web services documentation page.

Tagged with:
Posted in Database updates, Technical

Curating PDB links between proteins and 3D chemical structures

We know users find our curated PDB links of high value, particularly where the ligand co-crystalised within the target protein is an activity-mapped approved drug, clinical candidate or key metabolite. The comparative SAR insights these can provide are substantial, especially for those located in recent GPCR and ion channel structures. Notwithstanding, our experience indicates that verification of an explicit link (i.e. “this” chemical structure is in “this” protein sequence) is distinctly non-trivial for a range of reasons (some of which are outlined in this blog post). This means we keep our curation rules under review and carry out statistical audits of our PDB ligand content. This post is somewhat technical but what you see below is a Venn diagram that compares our ligands with two independent sources of small-molecule structure assignments (note these are derived from 3D coordinates but the analysis is actually comparing 2D structures).

PDB6

The keys for the segments are as follows:

  • “GtoP>PDB 293” indicates the ligands where we have curated a direct PDB link between the protein and chemical structure, counted as PubChem CIDs.
  • “UC>GtoP>PDBe 938”  refers to any GtoPdb ligands where (via UniChem mappings) a PDBEurope (PDBe Chem) link is recorded and have CIDs.
  • “GtoP>MMDB 988”  refers to any GtoPdb  ligands where the PDB link is recorded via the NCBI Molecular Modelling Database (MMDB) not PDBe.

The high level interpretations are: a) We freely admit to a backlog of ~ 500 PDB ligand structures for which we could retrospectively add links. The main reason is simply a legacy effect, where older ligands have more recently appeared as PDB structures. However, some of these will be hetero-atom structures rather than specifically bound ligands or in the “wrong” targets (e.g. ACE inhibitor in Drospholia ACE protein). b) The 170 and 35 intersects are both interesting and problematic. The represent discordant PDB small molecule structures assigned either by PDBe (35) or MMDB (170) but not both (i.e. the 560).

We have three consequences going forward to enhance the database. Firstly, we have established contacts with both the UniChem and PDBe teams at the EBI (we already have one of the principal scientists from the MMDB on our chemical Curation Commitee) so we can engage with them on technical aspects. Secondly, priorities permitting, we will certainly do some back-filing of the ~ 500 missing links by triage (e.g. select newest clinical candidates first). Thirdly, we are now in a better position to cross-check any new PDB ligands for the types of discontinuities outlined above. We can then add curatorial comments and cross-pointers as appropriate.

As ever, comments on this topic are welcome.

Posted in Uncategorized

Database Update March 2015

The latest GtoPdb update was released on March 13th and this first release of 2015 includes major updates in several areas.

GPCR updates:

Kinase updates

  • Added affinity data (where available) for all of the kinase inhibitors in the database (including those we previously only had large-scale matrix screening data for), tagging primary targets where appropriate
  • Also added information for kinase inhibitors in clinical trials

Epigenetics target updates

Protease updates

  • This target class now includes a first (for the database) with a predrug > prodrug > drug triplet. These were curated from a paper describing how MMP12 produces its own inhibitor in a two-step activation procedure. We now have ligand entries for the peptide substrate of the protease, the prodrug and the drug.

Ligand updates

  • More than 420 new ligands added in this release.
  • Many new kinase inhibitors
  • New monoclonal antibodies and small molecules included in pharma pipelines (including any novel drug targets)
  • Sourced available binding affinity data for all monoclonal antibodies in the database using a combination of BLAST sequence analysis, patent and literature searches, tagging primary targets as appropriate
  • More than 20 compounds annotated with new/expanded drug approval information

Disease information updated

  • Target pathophysiology sections have been updated with standardised disease nomenclature
  • Disease name synonyms added
  • More links added to the OMIM and Orphanet disease databases
  • Added Disease Ontology nomenclature and links to the Ontobee disease ontology browser

Website updates

  • New “Specialist databases” section created to highlight database links that are of particular interest on specific target and ligand pages, for example the IMGT/mAb-DB resource for antibodies

Ligand search tools

  • Our chemical structure search tool now uses the Marvin JS structure editor from ChemAxon, which replaces the older Java version. The new JavaScript version has the advantage of cross-platform compatibility
  • This version should also be compatible with tablets and mobile devices
  • Useful features include the ability to import molecules through various file formats, structural identifiers or by compound name
  • Compounds can also be exported in many different formats or as an image file

As always, updated data files are available to download with all the new data.

Posted in Database updates

21 years of NC-IUPHAR reviews and general publication updates

Update, 12 March.  Inspired by the propitious event described below we have taken the opportunity to update all our publication links. This covers not only IUPHAR reviews but also papers arising from the database and additional recent pubublications co-authored by Edinburgh team members.

******************************************************************************

Last week (first one in March) the Secretary General of IUPHAR, and outgoing chair of NC-IUPHAR, Michael Spedding, informed us that 21 years have elapsed since the first NC-IUPHAR publication:

Vanhoutte PM, Barnard EA, Cosmides GJ, Humphrey PP, Spedding M, Godfraind T. (1994) International Union of Pharmacology Committee on Receptor Nomenclature and Drug Classification. Pharmacol Rev. 46(2): 111-6. [PMID: 7938161]

The direct Google Scholar retrieval for IUPHAR shows impressive metrics (including at least six articles with between 1000 and 3000 citations):

http://scholar.google.se/scholar?start=60&q=International+union+of+pharmacology&hl=en&as_sdt=1,5

Notwithstanding, in honour of the occasion, we decided to explore some additional bibliometrics. In PubMed. After some exploratory query tuning we achieved reasonable specificity  with “International[Title] AND Union[Title] AND Pharmacology[Title]” which returned 96 titles:

http://www.ncbi.nlm.nih.gov/pubmed/?term=International[Title]%20AND%20Union[Title]%20AND%20Pharmacology[Title]&cmd=DetailsSearch

Inspection indicated a few false positives  as preceding the invited review model. So, by adding the restrict for “Pharmacological Rev.” we get a cleaner list of 90 true positives:

http://www.ncbi.nlm.nih.gov/pubmed/?term=International[Title]%20AND%20Union[Title]%20AND%20Pharmacology[Title]%20AND%20%22Pharmacol%20Rev%22[Journal]&cmd=DetailsSearch

Note that no less than four of these (as of 2nd March) are already 2015 articles (PMIDs 5713288, 5713287, 25535277, 25287517).

To retrieve the equivalent list from the British Journal of Pharmacology, we used the query  “IUPHAR[Title] AND review[Title]” to retrieve 13. However (for reasons we hope to fix), PubMed left the IUPHAR tag off review number six. Consequently the query to bring all 14 invited reviews back needs to be:

http://www.ncbi.nlm.nih.gov/pubmed/?term=IUPHAR[Title]+AND+review[Title]+OR+24428732[uid]

By making a union of the two sets (International[Title] AND Union[Title] AND Pharmacology[Title] AND “Pharmacol Rev”[Journal]) OR (IUPHAR[Title] AND review[Title] OR 24428732[uid]) we can return what looks a like a clean list of all IUPHAR reviews indexed in PubMed (you are welcome to contact us  if you know of any false-negatives).

Capture

You can access these 104 via the following MyNCBI public collection link: http://www.ncbi.nlm.nih.gov/sites/myncbi/christopher.southan.1/collections/47601391/public/ (note this should update automatically for new publications).  If you click on the “PubMed Commons Related Comments ” link from the facets on the LH side, you will find six articles have comments from curation team members pointing to marked-up entity links, effectively from the article to the GtoPdb. There are also some “pivots” you can do from this list to other entities indexed in the Entrez system. One of these answers the question, “how many PubMed Central articles cite (any one of) these 104 PMIDs?”.  The answer is a very impressive 5978 (note these are generally only ~ 30% of the Google Scholar citations for a number of reasons). A full publication list (along with database team output) is also maintained on the GtoPdb website at http://www.guidetopharmacology.org/nciupharPublications.jsp.

The GtoPdb team would thus like to echo the congratulations to all past and present IUPHAR-invited authors.

For those less familiar with our modus operandi we can point out the “virtuous circularity” between these review articles and the content of our database. As expected, authors and subcommittee members for target families overlap extensively.  Thus these authors both draw on the database for extant material to prepare  a review, while also feeding updates to the curation team.  As mentioned above, and in a previous post we are now instigating direct links back to the database for target and ligand entities mentioned in these reviews.  Note also that committee members (or potential new ones wishing to contribute)  are welcome to suggest ideas for new reviews to Eliot Ohlstein for Pharm Rev or Steve Alexander for the Br J Pharmacol.

Tagged with: ,
Posted in Publications