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]

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Hot Topics: An online resource for GPCR structure determination and analysis

G protein-coupled receptors (GPCRs) transduce physiological and sensory stimuli into appropriate cellular responses and mediate the actions of one-third of drugs. Structures of GPCRs are therefore extremely valuable for understanding basic receptor function and rational drug design. Today, 310 structures of 59 distinct receptors (https://gpcrdb.org/structure/statistics) have revealed the general bases of receptor activation, signalling, drug action and allosteric modulation. However, there are still no structures for the vast majority – 85% – of the 398 non-olfactory GPCRs and for 52% structure models can only be based on low-homology templates.

To accelerate the determination of GPCR structures and to help assess the quality of the available templates based on the modifications and methods, a recent article in Nature Methods presents “An Online Resource for GPCR Structure Determination and Analysis” [1]. This surveyes the construct engineering and experimental methods and reagens used to produce all available GPCR crystal and cryo-EM structures. Furthermore, it describes and interactive resource integrated in GPCRdb (www.gpcrdb.org) to assist users in designing new constructs and browsing appropriate experimental conditions for structure studies.

Comments by David E. Gloriam, University of Copenhagen (@David_Gloriam)

(1) Munk C et al. (2019). An online resource for GPCR structure determination and analysis. J Med Chem, doi:10.1038/s41592-018-0302-x. [PMID:30664776]

 

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Database Release 2019.1

Our first database release of 2019 (2019.1) is now available. This update contains the following new features and content changes:

Content Updates

GtoPdb contains over 9,400 ligands, with around 7,200 have quantitative interaction data to biological targets. Just over 1,400 of the ligands are approved drugs. The database contains over 1,700 human targets, with near 1,500 of these having quantitative interaction data. Full stats can be found on the About Page.

Targets curated in this release:

GPCRs:

Ion-Channels:

Guide to IMMUNOPHARMACOLOGY

Official Launch, October 2018

At the beginning of October 2018 we held a meeting in Edinburgh focussed on the launch of the IUPHAR Guide to IMMUNOPHARMACOLOGY. Invited speakers contributed to productive discussions on the varying challenges and opportunities in immunopharmacology research. The meeting page has links to the slidesets from the meeting (where permission was granted) and to the meeting report which summarises the presentations, discussions and outcomes.

Immunopaedia

We have begun to incorporate direct links from our ligand pages to the Immunopaedia resource. Immunopaedia promotes education, knowledge and research in immunology globally; it is an immediate source of immunology information for healthcare professionals, students and researchers. Their clinical case studies utilise doctors’ real-world experiences to demonstrate diagnostic methods and treatments as well as explain immunological pathways of diseases. In close collaboration with colleagues at Immunopaedia we have put in place links to relevant case studies from our ligand summary pages (e.g. rituximab).

Guide to Malaria PHARAMCOLOGY

The first public beta-release (v1.0) of the IUPHAR/MMV Guide to Malaria PHARMACOLOGY (www.guidetomalariapharmacology.org) is available in this release! The new portal has been designed to provide tailored routes into browsing the antimalarial data.

At this stage, the data curated in GtoMPdb and viewed on the Antimalarial targets family and the Antimalarial ligands family. In total the are 15 P. falciparum (3D7) targets and 50 ligands tagged as antimalarial in the database. A detailed blog-post on the release will be posted soon.

As well as being able to browse via target or ligand, users can also navigate data via parasite lifecycle stage and via target species. Search tools extended to cover GtoMPdb data, up weighting results relevant to malaria pharmacology.

Other Updates

Drug Approvals

There has been a substantially increased our coverage of European Medicines Agency (EMA) drug approval data in 2019.1. There are 414 approved drugs with EMA marked as a source, up from 274 in 2018.1.   In addition, at about this time of  year  considerable interest is generated from reviews of the previous year’s FDA Drug Approvals (see https://cdsouthan.blogspot.com/2019/01/2018-approved-drugs-in-pubchem.html)

Reaching 59, 2018 was welcomed as a particularly prolific year.  However, for our own capture, we have various exclusion criteria such as antiinfectives (with some exceptions including the antimalarials mentioned above), already-approved mixture components, topicals, non-antibody biologicals, undefined extracts (e.g. fish oil) and inorganics. Thus our scorecard stands at 26 chemical entities that form PubChem Compound Identifiers. We also have database records and PubChem Substance submissions for 11 of the 12 newly-approved antibodies (excepting the anti-HIV one).  Note the exact PubChem CID and SID counts will be linked here in a week or so and reviewed in forthcoming a blog post.

New Target: Vanin 1

Novel targets, as defined by first documentation of disease-targeted chemical modulators  (or new probes to explore roles of under-studied proteins) are relatively infrequent.  However, this release sees the first entry of Vanin 1 where inhibition is being explored as a novel mechanism for the treatment of inflammatory diseases.  This Boehringer filing WO2018228934 on Vanin inhibition with SAR for 44 compounds was found via filtered browsing of recent WO patents in SureChEMBL looking for new immunopharmacology indications in particular.

Update to CDK library to v2.2

We have updated the Chemical Development Kit (CDK) library to version 2.2. This is used by the Guide to Pharmacology to calculate molecular properties of ligands curated in the database. As part of this update, we performed a re-calculation of all molecular properties in the database. As a consequence, you may notice some difference between the properties in 2018.4 and 2019.1.

Chemicalize Pro API (Marvin JS update)

A key feature of the IUPHAR Guide to Pharmacology website is the ability to perform searches by chemical structure (http://www.guidetopharmacology.org/GRAC/chemSearch.jsp). The chemical structure search tool utilises Marvin JS by ChemAxon. In the 2019.1 release we have updated the API control to use Chemicalize Pro (https://pro.chemicalize.com/). This update simplifies the integration of Marvin JS into our website.

Page navigation

We have updated our webpages to feature a drop-down navigation bar, which is revealed when users scroll-down on longer pages. Many pages on GtoPdb are quite long, particularly detailed targets pages (e.g. CB1 receptor) – the new drop-down menu keeps key menu items, and most importantly the site search, in focus at all time.

Detailed target pages

Minor adjustments to the top-section of these pages to layout GtoImmuPdb and GtoMPdb icons and toggle button.

Posted in Database updates, Guide to Immunopharmacology, Guide to Malaria Pharmacology

Hot Topics: New Cannabinoid Receptors Structures

Cannabinoid receptors respond to multiple endogenous fatty acid derivatives and are often divided into neuronal-associated CB1 receptors and immune cell-associated CB2 receptors. Both receptors are GPCR, coupled predominantly to Gi, and have cytoprotective properties. The predominant psychotropic agent in Cannabis, THC, acts as a partial agonist at both receptors. CB1 patho/physiological responses are often characterised as analgesic, rewarding, orexigenic, hypothermic and amnestic, while CB2 receptors are mostly associated with anti-inflammatory effects.

In many countries, synthetic cannabinoids have become a social issue, with a prevalence of use amongst the homeless and incarcerated, with even a number of deaths attributed to these agents. Although all the molecular mechanisms of action of these synthetic cannabinoids are yet to be defined, one feature they have in common is a high potency and high efficacy profile at CB1 receptors. Kumar and colleagues [1] report a CB1 receptor:Gi complex, where the receptor is bound to a synthetic cannabinoid, MDMB-FUBINACA. The authors report an agonist binding-evoked conformational switch involving residues in TM3 and TM6, which they suggest underlies the high affinity of this synthetic cannabinoid. Furthermore, they conduct in silico simulations to suggest a lateral path of entry for the synthetic cannabinoid between TM1 and TM7 rather than the ‘traditional’ extracellular point of ingress. This lateral diffusion model has been suggested for a number of lipid-binding GPCR.

There is a second cannabinoid receptor crystal structure in the same journal, which focusses on the CB2 receptor [2]. Based on the primary sequences of the two human receptors, there is limited structural identity between CB1 and CB2 (~40 %), although the overlap is much higher in the transmembrane domains, as might be expected, given they bind a number of structurally-diverse ligands with little discrimination (e.g. CP55940, WIN55212-2 and HU210). Li et al report the first crystal structure of the CB2 receptor. In this version, a novel high affinity antagonist/inverse agonist AM10257 was bound to the receptor for crystallisation. The resultant structure shows a number of similarities with the antagonist-bound structure of the CB1 receptor, although notably the extracellular portions of the two receptors diverged markedly. Slightly surprisingly, a close resemblance to the agonist-bound CB1 receptor was identified, which lead them to investigate CB1 receptor function of the novel CB2 antagonist, which turned out to be a low efficacy CB1 receptor agonist.

Comments by Steve Alexander (@mqzspa)

[1] Kumar KK et al. (2018). Structure of a Signaling Cannabinoid Receptor 1-G Protein Complex. Cell, pii: S0092-8674(18)31565-4. doi: 10.1016/j.cell.2018.11.040. [Epub ahead of print]. [PMID:30639101].

[2] Li X et al. (2018). Crystal Structure of the Human Cannabinoid Receptor CB2. Cell, pii: S0092-8674(18)31625-8. doi: 10.1016/j.cell.2018.12.011. [Epub ahead of print]. [PMID:30639103].

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GtoPdb at BPS Pharmacology 2018

The IUPHAR/BPS Guide to Pharmacology was represented at the recent BPS Pharmacology 2018 meeting (London, UK, 18-20 Dec 2018).

Tuesday 18th Dec

On Tuesday we had two significant presentations. Firstly, a late-breaking poster on the IUPHAR/MMV Guide to Malaria Pharmacology. This is an under-development extension to the database to curate in anti-malarial ligands and Plasmodium targets for approved drugs.

Poster: Introducing a new resource: the capture of drugs, leads and targets in the IUPHAR/MMV Guide to MALARIA PHARMACOLOGY (Presented by Dr. Chris Southan & Dr. Simon D. Harding)

Secondly, Dr. Southan presented on the challenges and tribulations of curating peptides in the Guide to Pharmacology. His slides are available below.

Oral Presentation: Tribulations of curating published key bioactive peptides for the Guide to PHARMACOLOGY

Thursday 20th Dec

On the Thursday by three more presentations. Firstly, Dr. Harding presented a flash presentation and poster on new features and updates to the Guide to Pharmacology in 2018, which was awarded the daily flash poster prize.

Poster: The IUPHAR/BPS Guide to PHARMACOLOGY in 2018: new features and updates

Also during the poster session Dr. Southan presented his second poster looking at how we can identify the most pharmacologically significant proteins.

Poster: Will the real pharmacologically significant proteins please stand up?

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Posted in Events, Guide to Immunopharmacology, Guide to Malaria Pharmacology

Hot Topics: GPR37/GPR37L1 and the putative pairing with prosaptide/PSAP

Comments by Dr. Nicola J. Smith, National Heart Foundation Future Leader Fellow & Group Leader, Molecular Pharmacology Laboratory, Victor Chang Cardiac Research Institute, Australia

As is often the case with orphan GPCRs, assigning the endogenous ligand has been controversial for the closely related peptide family orphans, GPR37 and GPR37L1. In 2013, Randy Hall and his team (PubMed: 23690594) first reported an association between both centrally-expressed orphan GPCRs and prosaposin (PSAP) and prosaptide (TX14A), the synthetic active epitope of PSAP. Since that time there has been much debate in the field about whether this pairing is correct, with some authors corroborating the findings (PubMed: 24371137; 30010619, 28795439) and others not (PubMed: 23396314; 27072655; 28688853). Note that Head Activator, found in Hydra, was earlier reported as a ligand (PubMed: 16443751) but was quickly discredited (PubMed:28688853; 23686350).

A recent paper by Sergey Kasparov’s laboratory in Bristol has added further fuel to the fire. In a series of well controlled experiments, Liu et al. (PubMed: 30260505) provided convincing evidence that prosaptide is cyto- and neuro-protective and promotes chemotaxis. They are also the first group to demonstrate an effect of prosaptide at a more physiologically plausible potency. At the same time, Bang et al. (PubMed: 30010619) published a ground-breaking paper linking GPR37 expression to macrophage function. Moreover, they proposed a second, more potent ligand for GPR37 (GPR37L1 was not studied): the pro-resolving mediator neuroprotectin D1 (NPD1). Using HEK293 cells expressing GPR37, NPD1 was a potent stimulator of Gαi/o-dependent calcium flux; findings that were corroborated in macrophages isolated from wild type, but not GPR37 knock-out, mice (PubMed: 30010619). Thus, it may be that the endogenous ligand for GPR37 (and perhaps GPR37L1?) is not a peptide after all, but a lipid.

These two studies, while exciting, do little to help us resolve the conundrum that is PSAP/prosaptide and GPR37/GPR37L1. At the very least, it seems likely that prosaptide, if not the highest affinity endogenous ligand at GPR37, is certainly capable of signalling through GPR37 to stimulate Gαi/o signal transduction (whether it is the most potent endogenous agonist will be shown in time as independent groups seek to validate the actions of NPD1).

But what of GPR37L1? This is harder to answer as a number of studies linking GPR37L1 to PSAP/prosaptide have been performed in double GPR37/GPR37L1 knock-out backgrounds or inappropriate tissue models. For example, in the original paper connecting prosaptide to the receptors, the authors claimed prosaptide acted through both GPR37 and GPR37L1 in primary astrocytes, despite the fact that their Western blots demonstrated marked GPR37 expression in comparison to GPR37L1 in the cells (PubMed: 23690594). More recently, they failed to recapitulate this initial pairing in a HEK293 model (PubMed: 28688853). The absence of GPR37L1 in primary astrocytes is consistent with the animal knock-out work of Marazziti et al. (PubMed: 24062445), who showed that GPR37L1 protein was barely detectable before post-natal day 15, which is after the window for isolating primary astrocyte cultures (confirmed by PubMed: 28795439). Coleman et al. (PubMed: 27072655) overcame this expression issue, with difficulty, by using cerebellar slice cultures in vitro to examine Gαs, but not prosaptide, signalling in wild type and knock-out tissue.

In the neuroprotection paper by Liu et al. (PubMed: 30260505) it is clear that prosaptide or PSAP are exhibiting an effect on the cells. By depleting astrocytes of PSAP and then reintroducing prosaptide exogenously, there is an obvious effect on cell migration, cytotoxicity and neuroprotection – phenotypes that are all lost when shRNA knocking down expression of both GPR37 and GPR37L1 are used. Frustratingly, though, the use of a double knock-down approach makes it impossible to ascribe a specific effect to GPR37L1. While GPR37 and GPR37L1 are very closely related by phylogeny and have highly similsar binding sites (PubMed: 27992882), this does not mean that prosaptide is a ligand at both receptors, nor that both receptors signal via the same G proteins (another area of controversy for GPR37L1, where contradictory studies including two by the same team show either Gαi/o or Gαs signaling: PubMed: 23690594, 30260505 vs 27072655, 28688853). Thus, the failure to use single receptor knock-out/knock-downs, or isolate cells with endogenous expression of GPR37L1, represent major limitations in these studies.

Other than the confounding effects of both GPR37 and GPR37L1 deletion in tested cells, what are other reasons that could explain the inconsistencies between studies? Kasparov and colleagues (PubMed: 30260505) attribute this to cellular background, stating that previous studies that failed to confirm prosaptide/GPR37L1 coupling (PubMed: 27072655, 28688853) used HEK293 cells that must be lacking in the necessary endogenous machinery for signal transduction (PubMed: 30260505). To support this claim, they turned to the PRESTO-Tango assay in HEK293 cells to demonstrate prosaptide stimulation could not lead to GPR37L1-dependent recruitment of beta-arrestin. The assay choice is surprising because previous beta-arrestin-based screens at GPR37L1 have failed to show that the receptor can indeed recruit arrestins (PubMed: 23396314, 25895059), and Liu et al. (PubMed: 30260505) did not provide evidence that recruitment was intact in a more physiologically relevant cellular background. Most puzzling though is that the original paper that identified prosaptide and PSAP as GPR37/37L1 ligands used HEK293 cells to make the original pairing (PubMed: 23690594). They also refute the physiological relevance of high constitutive Gαs signalling reported by Coleman et al., even though Coleman et al. demonstrated higher cAMP accumulation in cerebellar brain slices from wild type mice when compared to GPR37L1-/- (PubMed: 27072655).

Further muddying the waters, the physiological role of GPR37L1 itself remains enigmatic. For example, Min et al. (PubMed: 20100464) initially reported GPR37L1 null mice to have a staggering 62 mmHg increase in systolic blood pressure when compared to a cardiac-specific overexpressing model, with the presence of concomitant left ventricular hypertrophy. However, Coleman et al. (PubMed: 29625592) found a far more marginal cardiovascular phenotype, with a small increase in blood pressure evident in female mice only. Notably, male GPR37L1 knock-out mice appeared to be more susceptible to cardiovascular stressors, while females were cardioprotected (PubMed: 29625592). In terms of a developmental phenotype, Marazziti et al. (PubMed: 24062445) found that GPR37L1 null mice displayed precocious cerebellar development with enhanced performance in a rotarod test up to 1 year of age. More recently, though, Jolly et al. (PubMed: 28795439) failed to confirm a behavioural difference in their own GPR37L1 knock out mice. The links between GPR37L1 and neurological defects are also confounded by the fact that GPR37 also needs to be deleted in mice for a clear phenotype to be evident. For example, while a single point mutation in GPR37L1 (K349N) in a highly consanguineous family appeared to be causative of fatal progressive myoclonus epilepsy, the mouse phenotype was most pronounced in double GPR37/GPR37L1 knock-out animals (PubMed: 28688853). In vitro studies of the GPR37L1 K349N mutant found no difference between it and the wild type receptor in terms of receptor expression, processing, signalling or ubiquitination (PubMed: 28688853). In the absence of a transgenic K349N mutant mouse, or any confirmed synthetic agonists or antagonists, the authors then assessed seizure susceptibility in knock-out mice of either GPR37L1, GPR37 or both receptors. Interestingly, using the 6Hz-induced seizure model, the GPR37-/- mice appeared to have a more pronounced phenotype than the GPR37L1-/- mice, while double KO mice were extremely susceptible to seizures at all frequencies tested. GPR37 and double KO mice both displayed more spontaneous seizures, although curiously in the flurothyl-induced seizure model only GPR37L1-/- differed from wild type. Thus, conclusive links between GPR37L1 and a specific physiological or pathophysiological state remain to be provided and it seems in general that we are far from understanding the true biology and pharmacology of the receptor.

 

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Hot Topics: Somatic APP gene recombination in normal and Alzheimer’s disease neurons

A new facet of the human brain has been reported [1] involving a first example of somatic gene recombination in neurons, representing a normal neural mechanism whose disruption could underlie the most common (sporadic) forms of Alzheimer’s disease. Mosaic and somatic recombination of Amyloid Precursor Protein (APP) was identified in this well-known Alzheimer’s disease gene, where increased copies and mutations in rare families or Down syndrome are considered causal. Recombination generates thousands of previously unknown gene variants characterized as “genomic complementary DNAs” or “gencDNAs” that could show identical sequences to cDNAs copied from brain-specific spliced RNAs, as well as myriad truncated forms characterized by exonic deletions and “intraexonic junctions” to produce novel sequences that become “retro-inserted” into the genome of single neurons, with neurons showing from 0 to 13 copies. Recombination appeared to require gene transcription, reverse transcriptase activity and DNA strand breaks. Both forms and numbers of APP gencDNAs were altered and increased in sporadic Alzheimer’s disease. Recombination might normally provide a way to alter post-mitotic neuronal genomes to “record” preferred gene variants for later “playback” without a need for gene splicing, towards optimizing or fine-tuning gene expression, representing a form of memory. The involvement of reverse transcriptase activities implicate potential Alzheimer’s disease therapeutics using reverse transcriptase inhibitors, a possibility supported epidemiologically by relatively rare cases of Alzheimer’s disease in aged HIV patients. Recombination could generate new therapeutic targets. Other recombined genes and affected diseases are possible.

Comments by Jerold Chun, Sanford Burnham Prebys Medical Discovery Institute

(1) Lee MH et al. (2018). Somatic APP gene recombination in Alzheimer’s disease and normal neurons. Nature, doi: 10.1038/s41586-018-0718-6. [Epub ahead of print]. [PMID:30464338].

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