Hot Topics: Resting-State Structure and Gating Mechanism of a Voltage-Gated Sodium Channel

In this report the Catterall laboratory succeeded in solving the high resolution structure of a voltage-gated Na+-channel (Nav) in its resting state (1). Why is this difficult and why is this important? It is difficult because Navs exist in the resting state only at very negative voltages but not at a zero membrane potential required for structural analysis by X-ray crystallography or cryo-EM. Accordingly, all high resolution structures of Navs, whether pro- or eukaryotic, have so far reported channels with the voltage-sensing domains in the depolarized state, i.e. the positively charges S4 helices of the voltage sensors moved “up” towards the extracellular side. Therefore it is not known how the activation gate of the ion pore (formed by the four S6 helices) is kept closed by the voltage sensor in its resting position, i.e. with the S4-helices “down”. Some predictions about how this might work was inferred from structural work on related voltage-gated ion channels (e.g in a TPC1 channel, 2) or from a study in which a chimeric Nav construct was trapped in a closed (“deactivated”) state by a toxin (3). The elegant work presented here by Wideschaisri and colleagues (1) directly addressed this important question by generating suitable mutants of the bacterial Nav, NavAb (4). One mutant (KAV mutation) was engineered to shift its activation threshold to much higher voltages thus holding the channel in the resting state also at 0 mV for structural studies. Moreover, they introduced disulfide crosslinks locking the voltage-sensor in the desired resting (S4 “down”) or activated (S4 “up”) state. The stabilization of these states in these mutants were verified in functional studies to ensure that the structural data have clear functional correlates. Analysis of the X-ray structures (and cryo-EM structure for the KAV mutant) provided important novel insight into the structural rearrangements associated with the transition from the activated/open to the resting/closed state. This includes changes of the helical structure of S4 associated with its striking inward movement of about 11.5 A compatible with a “sliding helix” model. Rearrangements of the four S4-S5 linkers were found to tighten the “collar” around the S5 and S6 segments thus keeping the pore closed.
All this was possible because they used the bacterial NavAb, for their experiments. This comes with the advantage that this channel exists as a tetramer of identical subunits and therefore the mutations are present in all four voltage-sensors. This also facilitated disulfide cross-linking, because these channels lack endogenous cysteines. The disadvantage of NavAb is that it does not reflect the more complex structure of eukaryotic Nav and voltage-gated Ca2+ channels (Cavs) in which all four voltage-sensing- and pore forming elements are different and are tethered together in a single molecule. Nevertheless, there is no doubt that this “sliding helix” model of electromechanical coupling will also apply to eukarytic Navs and Cavs. Understanding all the conformational rearrangements occurring between resting and activated channel states will provide new opportunities for the discovery of state-dependent and subtype-selective Nav- and Cav- channel blocking drugs.

Comments by Jörg Striessnig, University of Innsbruck

(1) Wisedchaisri, G., Tonggu, L., McCord, E., Gamal El-Din, T.M., Wang, L., Zheng, N., Catterall, W.A., 2019. Resting-State Structure and Gating Mechanism of a Voltage-Gated Sodium Channel. Cell 178, 993-1003. doi: 10.1016/j.cell.2019.06.031. [PMID: 31353218]
(2) Kintzer, A.F., Green, E.M., Dominik, P.K., Bridges, M., Armache, J.-P., Deneka, D., Kim, S.S., Hubbell, W., Kossiakoff, A.A., Cheng, Y., Stroud, R.M., 2018. Structural basis for activation of voltage sensor domains in an ion channel TPC1. Proc. Natl. Acad. Sci. U.S.A. 115, E9095–E9104. doi: 10.1073/pnas.1805651115. [PMID: 30190435]
(3) Xu, H., Li, T., Rohou, A., Arthur, C.P., Tzakoniati, F., Wong, E., Estevez, A., Kugel, C., Franke, Y., Chen, J., Ciferri, C., Hackos, D.H., Koth, C.M., Payandeh, J., 2019. Structural Basis of Nav1.7 Inhibition by a Gating-Modifier Spider Toxin. Cell 176, 702-715.e14. doi: 10.1016/j.cell.2018.12.018. [PMID: 30661758]
(4) Payandeh, J., Scheuer, T., Zheng, N., Catterall, W.A., 2011. The crystal structure of a voltage-gated sodium channel. Nature 475, 353–358. doi: 10.1038/nature10238. [PMID: 21743477]

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Hot Topics: The atlas of aminergic GPCR mutagenesis

G protein-coupled receptors (GPCRs) are an important family of signal-transducing membrane proteins capable of binding various types of ligands from the extracellular space and activating various signalling pathways inside the cell, rendering them one of the largest protein target families in pharmaceutical research [1]. Receptors of the aminergic GPCRs family are particularly rewarding drug targets as they are implicated in various disease areas, and structure-based drug design has enabled the understanding of ligand binding and function, and the development of more than 500 approved drugs targeting these receptors. Advances in structural biology allowed the determination of more than 300 crystal structures of more than 60 GPCR subtypes to date [2], however, these still represent only a small fraction of known receptor-ligand associations [3].

Site-directed mutagenesis (SDM) is a versatile and frequently employed tool in pharmacological investigations used to infer structural features of protein-ligand interactions [4]. Mutation studies complement structural information provided by crystal structures by defining the roles and relative importance of residues involved in binding, functional activity, and selectivity for ligand chemotypes which have not yet been co-crystallized with their receptors. Community-wide GPCR structure modelling challenges have shown that the best models could be constructed by careful incorporation of mutation and SAR data relating to ligand binding [5]. However, an integrated analysis of receptor and ligand structures and SAR, mutation data, and binding mode prediction has been so far lacking.

The study of Vass et al. can be regarded as a meta-analysis of the site-directed mutagenesis literature for aminergic G protein-coupled receptors [6]. Through an exhaustive database and literature search, the researchers from VU University Amsterdam, Polish Academy of Sciences, University of Copenhagen and Sosei Heptares have collected 6692 mutational data points for 34 aminergic GPCR subtypes of 8 species from 302 publications, covering the chemical space of 540 unique ligands from mutagenesis experiments. This large body of mutation data was also annotated with the structure-based GPCR residue numbering enabling a comparison of mutation effects across different GPCR subtypes and sub-families, and mapped onto the residue positions in the available aminergic crystal structures. Mutation effects were binned into four categories: increased effect, no effect, decreased, and abolished effect, and the data is presented in large overview tables for the five aminergic sub-families. For ligands which had not yet been co-crystallized with their respective receptors, the authors provide predicted binding modes using a combined docking and interaction fingerprint approach to rationalize the mutation effects in light of the ligand SAR.

For each receptor sub-family, a discussion of the known structural receptor-ligand interactions, the ligand chemical space, the structural determinants of receptor-ligand interactions from mutation studies in the amine, major, minor pockets, and the extracellular vestibule, and the possibility of mutation effect extrapolation is provided. The authors also discuss mutation effects of the same ligands across different receptors providing insights into the receptor specific determinants of ligand binding. Finally, an overview is provided of some applications, and the possibilities and limitations of using mutation data to guide the design of novel aminergic receptor ligands.

The authors have deposited the data on Zenodo and in the GPCRdb, and a KNIME workflow was also provided using the 3D-e-Chem KNIME nodes to ease further analysis of the data by the readers [7].

Comments by Chris De Graaf (@Chris_de_Graaf), Director Computation Chemistry, Sosei Heptares.

(1) Santos et al. (2017). A comprehensive map of molecular drug targets. Nat Rev Drug Discov. doi: 10.1038/nrd.2016.230. [PMIDs: 27910877]

(2) Munk et al. (2019). An online resource for GPCR structure determination and analysis. Nat Methods. doi: 10.1038/s41592-018-0302-x. [PMIDs: 30664776]

(3) Vass et al. (2018). Chemical Diversity in the G Protein-Coupled Receptor Superfamily. Trends Pharmacol Sci. doi: 10.1016/ [PMIDs: 29576399]

(4) a) Munk et al. (2016). Integrating structural and mutagenesis data to elucidate GPCR ligand binding. Curr Opin Pharmacol. doi: 10.1016/j.coph.2016.07.003. [PMIDs: 27475047] b) Arimont et al. (2017) Structural Analysis of Chemokine Receptor–Ligand Interactions. J Med Chem doi: 10.1021/acs.jmedchem.6b0130. [PMIDs: 28165741]. c) Jespers et al. (2018). Structural Mapping of Adenosine Receptor Mutations: Ligand Binding and Signaling Mechanisms. Trends Pharmacol Sci. doi: 10.1016/ [PMIDs: 29203139]

(5) a) Kufareva et al. (2011) Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment. Structure. doi: 10.1016/j.str.2011.05.012. [PMIDs: 21827947]; b) Kufareva et al. (2014). Advances in GPCR modeling evaluated by the GPCR Dock 2013 assessment: meeting new challenges. Structure. doi: 10.1016/j.str.2014.06.012. [PMIDs: 25066135]

(6) Vass et al. (2019). Aminergic GPCR-Ligand Interactions: A Chemical and Structural Map of Receptor Mutation Data. J Med Chem. doi: 10.1021/acs.jmedchem.8b00836. [PMIDs: 30351004]

(7) (a); (b)

Posted in Hot Topics

Database Release 2019.3

We have now made the third IUPHAR/BPS Guide to Pharmacology database release of 2019 (2019.3). It includes updates focussed on preparation for the next edition of The Concise Guide to PHARMACOLOGY (2019/20), due out later this year.

Content Updates

GtoPdb now contains over 9,600 ligands, with around 7,300 have quantitative interaction data to biological targets. 1,426 of the ligands are approved drugs. The database contains over 1,700 human targets, with just over 1,500 of these having quantitative interaction data. Full stats can be found on our About Page.

Here’s a brief summary of some of main curatorial updates:

  • The cereblon protein has been added as a new target. For simplicity it is included in the Enzymes section of the Guide, as it is an important component of the E3 ubiquitin ligase complex, although it has no intrinsic catalytic activity. Cereblon is included in the Guide as its binding by thalidomide class drugs has been identified as the molecular mechanism that underlies the teratogenicity of this drug class. We have included quantitative data for interactions between cereblon and the three approved thalidomide type drugs (thalidomide, lenalidomide and pomalidomide), as well as an Immunopharmacology comment and information about clinical variants in disease.
  • The neuromedin U receptor family has an updated detailed introduction.
  • Several ‘new to the GtoPdb’ corticotropin releasing factor-1 (CRF-1) receptor antagonists (ligand IDs 10375-10379), their receptor interaction data and histories as clinical candidates have been added, including verucerfont and pexacerfont.
  • Nudix hydrolase 7, an enzyme that is involved in peroxisomal CoA/acyl-CoA homeostasis, and the first reported covalent NUDT7 inhibitor (NUDT7-COV-1) were added.
  • The Guanyly Cyclases were reorganised. A new family, Receptor guanylyl cyclases (RGC) family, was created and the existing RGC family was renamed Transmembrane guanylyl cyclases (and added as a sub-family of the new family). Nitric oxide (NO)-sensitive (soluble) guanylyl cyclase was also moved within this new family. The NPR-C (natriuetic peptide receptor 3) target was moved to the Transmembrane guanylyl cyclases family and the Natriuretic peptide receptor family removed entirely from the Catalytic receptor class.
  • We generated HELM annotation and SMILES for the small cyclic peptide apelin receptor agonist MM07 and these were submitted to PubChem. See reference PMID:25712721

Guide to Malaria Pharmacology (GtoMPdb)

Earlier this year we issue a blog post introducing the Guide to Malaria Pharmacology. This gives a good background to the project and illustrates how we plan to handle curation of this data and how we are developing the new portal that accesses the data.

Thursday 25th of April was World Malaria Day 2019 and to raise awareness we issued a blog post and a news release, in conjunction with Edinburgh Infectious Diseases and the School of Biological Sciences. These highlighted the release of the GtoMPdb and also provided an account of the long association malaria research has had with Edinburgh.

In this database release these are the recent advancements made in the GtoMPdb.


Screenshot showing antimalarial ligands with Target Candidate Profiles (TCPs)

Other Updates

ChEMBL Target Links

Following on from the update to these links in the last release, we’ve finished updating the various place across the GtoPdb site the link out to ChEMBL.

Site search

Our site-wide search now works using ‘*’ as a wildcard indicator at the end of a search string. This helps make our search behaviour more consistent with other web-resources.

Other minor updates

Posted in Database updates, Guide to Malaria Pharmacology, Hot Topics

Hot Topics: Time to FRET about GPCR activation dynamics?

G protein coupled receptors (GPCRs) are crucial for the transduction of extracellular stimuli to the intracellular space. Upon activation, GPCRs undergo large conformational changes to engage transducers and stimulate intracellular responses. However, the kinetics of agonist induced GPCR conformational changes are relatively understudied. An exception to this is the class A rhodopsin receptor, which has a covalent agonist and fast (< 1ms) activation kinetics. In contrast, other GPCRs are thought to activate across the low to mid millisecond range [1]. For Class C GPCRs, which are distinct from class A receptors in that they contain large extracellular agonist binding domains and exist as obligate dimers, the site of agonist binding is >100Å from where the transducer interacts [2]. Class C GPCR activation involves both dimer rearrangement and activation of the 7-transmembrane (7-TM) domain, which are thought to occur over 20-200ms [3-5]. An outstanding question is whether the activation kinetics of rhodopsin are indeed faster than other GPCRs, or if previous experimental approaches lacked sufficient resolution to reveal fast kinetics in other receptor families.

To this end, Grushevskyi and colleagues have used FRET recordings to detect submillisecond activation dynamics of a prototypical class C GPCR, metabotropic glutamate receptor subtype 1 (mGlu1), demonstrating that mGlu1 undergoes two temporally distinct conformational changes upon activation [6]. Inter-subunit movements were detected by labelling the second intracellular loop of one protomer with CFP and the other with YFP. Intra-subunit changes detected by labelling each protomer with YFP in the second intracellular loop and CFP in the C-terminus. Synchronous activation of receptors was achieved via two complementary methods. UV-induced uncaging of glutamate in intact cells resulted in an increase in inter-subunit FRET and a decrease in intra-subunit FRET, which the authors believe represent movement of protomers towards each other and outward movement of TM6, respectively. Dimer rearrangement occurred with an average time constant of ~2ms, with 7TM conformational changes occurring approximately 10 times slower. Rapid solution exchange in outside-out Xenopus oocyte patches resulted in a similar two-step activation profile. Both methods revealed that initial mGlu1 dimer rearrangement occurs faster than previously reported [4,5], and is only loosely coupled to subsequent 7TM domain conformational changes. Receptor deactivation also occurred in two discrete steps, with inter-subunit rearrangements again preceding intra-subunit conformational changes. Occupancy of both binding sites was required for optimal activation and deactivation kinetics, as inter-subunit rearrangements in both directions were significantly slower in receptor mutants that only bind agonist in one protomer.

This study has revealed the existence of metastable intermediate activation states i.e. states in which the dimer rearrangement or the 7TM conformational changes exist in isolation. How these intermediate states influence mGlu1 signalling is unknown, as the fluorescently labelled mGlu1 dimers are unable to couple to G proteins [3]. Additionally, whether the intra-subunit FRET changes do indeed represent specific TM6 movements is somewhat ambiguous, given that the C-terminus to which CFP is attached is predicted to be highly flexible. However, should this activation mechanism be relevant and applicable across all Class C GPCRs, it may contribute to the complexity of Class C pharmacology. Allosteric agonists of Class C GPCRs bind to sites in the 7TM domain, activating receptors in the absence of orthosteric ligand [2], indicating that the 7TM-active state represents a physiologically relevant signalling conformation. These intermediate receptor activation states may also influence transducer coupling. Different orthosteric/allosteric ligand combinations shifting the balance between the various active states, stabilising unique conformations and engaging distinct downstream signalling pathways could play a part in the biased and probe dependent pharmacology apparent for many Class C GPCR ligands. Exploring multiple GPCRs with different orthosteric and allosteric ligand combinations is a crucial next step in understanding how the kinetics of receptor activation relates to ligand pharmacology. Understanding how drug-like compounds impact receptor activation kinetics and stabilise intermediate receptor states will likely play a large role in rational drug design programs going forward.

Comments by Shane D Hellyer ( and Karen J Gregory (, Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Australia.

(1) Lohse M.J. et al. (2012). Fluorescence/bioluminescence resonance energy transfer techniques to study G protein-coupled receptor activation and signalling. Pharmacol Rev, 64: 299-336. [PMIDs: 22407612]
(2) Leach K & Gregory K.J. (2017) Molecular insights into allosteric modulation of Class C G protein-coupled receptors. Pharmacol Res, 116: 105-118. [PMIDs: 27965032]
(3) Hlacvackova V et al. (2012) Sequential inter- and intrasubunit rearrangements during activation of dimeric metabotropic glutamate receptor 1. Sci Signal, 5: ra59. [PMIDs: 22894836]
(4) Marcaggi P et al. (2009) Optical measurement of mGluR1 conformational changes reveals fast activation, slow deactivation, and sensitization. PNAS, 106: 11388-11393. [PMIDs: 19549872]
(5) Vafabakhsh R et al. (2015) Conformational dynamics of a class C G-protein-coupled receptor. Nature, 524: 497-501. [PMIDs: 26258295]
(6) Grushevskyi E.O. et al. (2019) Stepwise activation of a class C GPCR begins with millisecond dimer rearrangement. PNAS. pii: 201900261. doi:10.1073/pnas.1900261116. [Epub ahead of print] [PMIDs: 31023886]

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World Malaria Day 2019: A New Guide to Malaria Pharmacology

Thursday 25th 2019 is World Malaria Day and we’d like to highlight our new resource, currently under development, called The IUPHAR/MMV Guide to Malaria Pharmacology (GtoMPdb). Based in Edinburgh, this new resource is directed by Professor Jamie Davies and his team and funded by the Medicines for Malaria Venture (MMV).

Malaria and Edinburgh

Malaria and Edinburgh have a long association. This was marked most notably by the announcement by Patrick Manson, at a meeting of the British Medical Association (BMA) in Edinburgh in July 1898, of the discovery by Ronald Ross of the mosquito cycle of the malaria parasite, in a lecture on ‘The mosquito and the malaria parasite’. The first Nobel prize to be awarded to a British subject was awarded in 1902 to Ross for this discovery is now displayed in the Museum of Scotland in Edinburgh. He received the award for showing how the mosquito was the vector for the transmission of malaria. More about Malaria research in Edinburgh.


The IUPHAR/MMV Guide to MALARIA PHARMACOLOGY (GtoMPdb) database portal is a new extension to the existing Guide to PHARMACOLOGY database (GtoPdb). GtoMPdb is being developed as a joint initiative between Medicines for Malaria Venture (MMV) and the International Union of Basic and Clinical Pharmacology (IUPHAR), with the aim of adding curated antimalarial data to GtoPdb and providing a purpose-built portal that is optimized for the malaria research community.

The parent Guide to PHARMACOLOGY database (GtoPdb) has been extended to incorporate the additional information required to describe the activity and target interactions of antimalarial compounds. It provides a searchable database with quantitative information on Plasmodium molecular targets and the prescription medicines and experimental drugs that act on them. The development of this resource is important because until now there has been no single purpose-built portal into open access, expert curated information on Plasmodium molecular targets and the antimalarial compounds that act on them, including approved drugs, clinical candidates and research leads. This initiative will facilitate access by the malaria research community to lead, target and efficacy data integrated from disparate global R&D efforts.

More information about IUPHAR and MMV, and the project can be found here:

This blog post gives more detailed information about the development of GtoMPdb.

See also the Edinburgh Infectious Disease news page.

Expert Advisory Committee for the IUPHAR/MMV Guide to Malaria Pharmacology project

David R. Cavanagh, UK (
Mark J. Coster, Australia
Michael P. Pollastri, USA
Laurent Rénia, Singapore
J. Alexandra Rowe, UK (
Chris Swain, UK
Matthew H. Todd, UK
Elizabeth A. Winzeler, USA

Scientific Advisors for IUPHAR/MMV Guide to Malaria Pharmacology project

Jeremy N. Burrows, Switzerland
Brice Campo, Switzerland
Stephen P.H. Alexander, UK
Anthony P. Davenport, UK
Jamie A. Davies, UK
F. Javier Gamo, Spain
Michael Spedding, France
Stephen A. Ward, UK

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Hot Topics: Rise up against statistical significance, probably.

A recent commentary in Nature has the provocative title “Retire Statistical Significance” (1, with a list of more than 800 signatories) and has been widely interpreted as a call for the entire concept of statistical significance to be abandoned. Closer reading of the commentary suggests that the main message of the paper is a call to stop the use of P values or confidence intervals in a categorical or binary sense in order to be absolute as to whether a result supports or refutes a scientific hypothesis. This remains a radical proposal but perhaps does not signal the end for statistical tests in biomedical research just yet.

For pharmacologists, particularly those who wish to publish in the British Journal of Pharmacology (BJP), the proposals in Amrhein et al. (1) are a problem. They appear to directly contradict advice given in the guidelines for publication in the BJP, introduced by Curtis et al. (2), namely: “when comparing groups, a level of probability (P) deemed to constitute the threshold for statistical significance should be defined in Methods, and not varied later in Results (by presentation of multiple levels of significance).” In other words, statistical tests must produce a categorical outcome based on a P value of a defined threshold (normally as P = 0.05, or a 95% confidence interval) for all data sets in the paper.

So, which is correct? How should potential future authors in BJP and elsewhere approach this? In the spirit of the Amrhein et al. (1) article, I do not propose to make a binary choice here. After all, in the wider sense, both approaches seek to address the same issues of reliability and reproducibility in scientific research; issues which are particularly problematic in the area of biomedical science and thus pharmacology. The BJP approach is based around objectivity and removal of bias (whether unconscious or not). Here, decisions are largely taken away from the experimenter with a predefined statistical threshold coupled to a number of guidance statements around experimental design. There is much merit in this approach, and the journal does encourage authors to make appropriate caveats (3) but, inevitably, when such absolute, categorical decisions are made, P = 0.04 will take science in a different direction to P = 0.06. As Colquhoun (4) and others have shown, much too often this will be the wrong direction.

For this reason, I prefer the Amrhein et al. (1) proposals, but, to my mind, they come with at least two requirements. One of these requirements is data transparency and availability. If authors do not provide a statement about statistical significance, it is incumbent on them to make their data freely available so others, particularly those researchers working closely in the field, can study the data in detail in order to support or refute the messages of the paper, ideally, perhaps, in the form of post-publication peer review. A second requirement is trust. In the absence of a statistical significance rule book or convention (however flawed), authors must provide a subjective narrative around the results and readers must expect that they can trust this narrative to be both informed and unbiased. However transparent and available the underlying data, most readers will rely on the authors to guide their understanding and interpretation of the research. In an environment where “researchers’ careers depend more on publishing results with ‘impact’ than on publishing results that are correct” (5), this is surely the big challenge.

Comments by Alistair Mathie (@AlistairMathie), The Medway School of Pharmacy

(1) Amrhein V, Greenland S & McShane B. (2019). Scientists rise up against statistical significance. Nature, 567(7748):305-307. doi: 10.1038/d41586-019-00857-9. [PMID:30894741]

(2) Curtis MJ et al. (2019). Experimental design and analysis and their reporting: new guidance for publication in BJP. Br J Pharmacol, 172(14):3461-71. doi: 10.1111/bph.12856. [PMID:26114403]

(3) Curtis MJ et al. (2019). Experimental design and analysis and their reporting II: updated and simplified guidance for authors and peer reviewers. Br J Pharmacol, 175(7):987-993. doi: 10.1111/bph.14153. [PMID:29520785]

(4) Colquhoun D. (2019). An investigation of the false discovery rate and the misinterpretation of p-values. R Soc Open Sci, 1(3):140216. doi: 10.1098/rsos.140216. eCollection 2014 Nov. [PMID:26064558]

(5) Casadevall A. (2019). Duke University’s huge misconduct fine is a reminder to reward rigour. Nature, 568(7). [World View: Article]

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Database Release 2019.2

We are pleased to announce a new IUPHAR/BPS Guide to Pharmacology database release! This release, 2019.2, is the second of the year and includes updates focussed on preparation for the next edition of The Concise Guide to PHARMACOLOGY (2019/20), due out later this year.

Content Updates

GtoPdb now contains over 9,600 ligands, with around 7,300 have quantitative interaction data to biological targets. 1,416 of the ligands are approved drugs. The database contains over 1,700 human targets, with just over 1,500 of these having quantitative interaction data. Full stats can be found on our About Page.

Over 200 new ligands have been added in this release, with more than 50% of these having quantitative interaction data.

In preparation for the next Concise Guide to PHARMACOLOGY our expert subcommittees have been providing update across all target classes.

Guide to Malaria Pharmacology (GtoMPdb)

Earlier this month we issue a blog post introducing the Guide to Malaria Pharmacology. This gave a very good background to the project and illustrated how we plan to handle curation of this data and how we are developing the new portal that accesses the data.

In this database release these are the recent advancements made in the GtoMPdb.

  • The Antimalarial targets family and the Antimalarial ligands family have been updated, giving a total of 25 P. falciparum (3D7) targets and 57 ligands tagged as antimalarial in the database.
  • New species P. yoelii
  • Extended GtoMPdb search to cover parasite lifecycle stages and malaria species

Other Updates

ChEMBL Target Links

Our target out-links to ChEMBL have been updated, many thanks to Anna Gaulton for her support in this. Not only have we update our exisitng links, but we have added around ~800 new outlinks.

Contributor Lists

As part of the preparation for the next Concise Guide to PHARMACOLOGY we have update a substantial portion of our contributor records.

Endogenous/natural ligands

Work has been undertaken to review the curation of endogenous/natural ligand lists with an attempt to correlate this with ligands marked as endogenous in the interaction data. We hope to soon provide a downloadable list of all natural/endogenous ligands for targets.

Bug Fixes

  • Google Analytics tracker fixed for GtoMPdb
  • Family overviews have internal links corrected for ligands
  • Out links to HGNC
  • Interaction table style modified improve style and better handle wrapped text


Posted in Database updates