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 (https://research.monash.edu/en/persons/shane-hellyer) and Karen J Gregory (https://research.monash.edu/en/persons/karen-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.

IUPHAR/MMV Guide to MALARIA PHARMACOLOGY

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: https://www.guidetomalariapharmacology.org/malaria/gtmpAbout.jsp.

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 (https://www.ed.ac.uk/profile/dr-david-cavanagh)
Mark J. Coster, Australia
Michael P. Pollastri, USA
Laurent Rénia, Singapore
J. Alexandra Rowe, UK (http://alexrowe.bio.ed.ac.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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Posted in Guide to Malaria Pharmacology

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

Guide to MALARIA PHARMACOLOGY: introducing a new resource

gtommv_bannerWe are pleased to make public the first beta-release (v1.0) of the Guide to MALARIA PHARMACOLOGY (GtoMPdb), a new extension to the existing Guide to PHARMACOLOGY (GtoPdb). The 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.

We have implemented a number of changes to the existing database structure and web interface that were necessary for the capture and presentation of antimalarial data. Many antimalarial compounds have a poorly understood mechanism of action and an unknown molecular target and we have extended the interactions table and updated the web interface to accommodate this. A new “whole organism” assay type has been introduced to capture data from the whole cell assays used routinely in antimalarial drug discovery. Both changes are illustrated below.

Figure 1: The interactions table on an antimalarial ligand summary page

Screen Shot 2019-02-25 at 15.02.56.png

In addition, we have put in place the ability to tag both targets and ligands of relevance to malaria and provide curatorial comments. These comments surface on the website (see Figure 2 below) and are incorporated into the site search.

Figure 2: Malaria comments tab on an antimalarial ligand summary page

Screen Shot 2019-02-25 at 11.42.59.png

A new GtoMPdb portal (www.guidetomalariapharmacology.org) is being developed to provide tailored routes into browsing the antimalarial data in addition to the existing ligand and target browse/search functionality available on the parent GtoPdb site. For beta-release v1.0 we have implemented customised views of the data that include parasite lifecycle and target species activity, with access from either the menu-bar or panels on the homepage (see figure 3 below).

Figure 3: The GtoMPdb portal homepage

Screen Shot 2019-02-25 at 11.11.59.png

The GtoMPdb uses a set of top-level Plasmodium lifecycle stages (collective categories for one or more developmental forms of the parasite) against which interactions in the database can be annotated and which form the basis of organising, navigating and searching for parasitic lifecycle activity. We have developed a new Parasite Lifecycle homepage that provides a short introduction and links to additional pages for each of the top-level lifecycle stages. These in turn contain a more detailed description and a table of interactions for that lifecycle stage (illustrated in Figure 4).

Figure 4: Plasmodium liver stage page, an example of the new Parasite Lifecycle Stage pages

Screen Shot 2019-02-25 at 11.17.10.png

The Target Species homepage provides a short description for Plasmodium species that are of clinical or research importance. It also includes a resource section and links to individual pages for species that have annotated interactions in the database. The figure below illustrates an example of an individual species page. The interactions table displays affinity data for the species but also provides additional details, when available, for the strain used.

Figure 5: Plasmodium falciparum page, an example of the new Target Species pages

Screen Shot 2019-02-25 at 11.27.29.png

Development of the beta-release will continue 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.

If you have any feedback or queries about the resource please contact enquiries@guidetopharmacology.org

This project is supported by a grant awarded to Professor Jamie Davies at the University of Edinburgh by Medicines for Malaria Venture (MMV).

Posted in Guide to Malaria Pharmacology, Technical

Hot Topics: Exciting Times for Ion Channel Pharmacology

Whilst life is always exciting as an ion channel pharmacologist, the last few months have been particularly so, with a large number of publications showing structures of ion channels with regulatory molecules bound to them. In just the last month, the journal, Science, has published several such papers. Three of these concern voltage-gated sodium channels (NaV1.2, NaV1.7) and the binding of potent and selective toxins from animals [1-3]. Another reveals the structure of the primary human cooling and menthol sensor channel TRPM8 bound to synthetic cooling and menthol-like compounds [4].

In the most recent paper [5], Schewe and colleagues extend their outstanding work on selectivity-filter gating of K2P potassium (K) channels (Schewe et al. (2016). Cell. PMID: 26919430), to identify a binding site for negatively charged activators of these channels (styled the “NCA binding site”). Activators which bind to this site open a number of different K2P channels (e.g. K2P2.1 (TREK-1) and K2P10.1 (TREK-2)) and several other potassium channels such as hERG channels (KV11.1) and BKCa channels (KCa1.1), all of which are gated at their selectivity filter. This is exciting, because it is notoriously difficult to design, or even identify, activator compounds for ion channels. This work together with the identification of a separate “cryptic binding site” for K2P channel activators (PMID: 28693035) opens possibilities for rationale design of activator compounds targeting these binding sites, which would provide potential novel therapeutic approaches for the treatment of several conditions including chronic pain, arrhythmias, epilepsy and migraine (PMID: 30573346).

One potential problem, identified by Schewe et al, is the promiscuity of the NCA binding site across several K channel families. However, there are enough structural differences in the region around the NCA-binding site between the channel types to overcome this. Provocatively, Schewe et al even suggest that the simultaneous activation of several different K channel types at once may even be advantageous in certain acute conditions such as ischemic stroke and status epilepticus.

Comments by Alistair Mathie (@AlistairMathie) and Emma L. Veale (@Ve11Emma), The Medway School of Pharmacy

(1) Clairfeuille T et al. (2019). Structural basis of α-scorpion toxin action on Nav channels. Science, pii: eaav8573. doi: 10.1126/science.aav8573. [PMIDs:30733386].

(2) Shen H et al. (2019). Structures of human Nav1.7 channel in complex with auxiliary subunits and animal toxins. Science, pii: eaaw2493. doi: 10.1126/science.aaw2493. [Epub ahead of print]. [PMIDs:30765606].

(3) Pan X et al. (2019). Molecular basis for pore blockade of human NaNa+ channel Nav1.2 by the μ-conotoxin KIIIA. Science, pii: eaaw2999. doi: 10.1126/science.aaw2999. [Epub ahead of print]. [PMIDs:30765605].

(4) Yin Y et al. (2019). Structural basis of cooling agent and lipid sensing by the cold-activated TRPM8 channel. Science, pii: eaav9334. doi: 10.1126/science.aav9334. [Epub ahead of print]. [PMIDs:30733385].

(5) Schewe M et al. (2019). A pharmacological master key mechanism that unlocks the selectivity filter gate in K+ channels. Science, 363(6429):875-880. doi: 10.1126/science.aav0569.. [PMIDs:30792303].

 

Posted in Hot Topics

Hot Topics: Ligand biological activity predicted by cleaning positive and negative chemical correlations

New machine learning algorithm for drug discovery that is twice as efficient as the industry standard and identified potential ligands for the M1 receptor, a potential target for the treatment of Alzheimer’s disease.

A paper from Lee et al. [1] (University of Cambridge) in collaboration with Pfizer, describes the development of an algorithm to use machine learning to separate pharmacologically relevant chemical patterns from irrelevant ones. The algorithm compared active versus inactive molecules at the muscarinic acetylcholine receptor, M1 and uses machine learning to identify components of the compounds are important for binding and which arose by chance. Lee et al. built a model using historic data using ~5,000 compounds that were screened for agonist activity, of which 222 were active. Six million molecules from the e−Molecules database were computationally screened. From these ~100 molecules were purchased and screened in CHO cells expressing the M1 receptor with four compounds identified as agonists (EC50 range 80-300nM).

Comments by Anthony Davenport, IUPHAR/BPS Guide to PHARMACOLOGY, University of Cambridge

(1) Lee AA et al. (2019). Ligand biological activity predicted by cleaning positive and negative chemical correlations. PNAS, https://doi.org/10.1073/pnas.1810847116. [Epub ahead of print]. [PNAS: Article]

Posted in Hot Topics