Hot topic: Cryo-EM structures of Mucolipin TRP Channels in the Lysosome: Five Together at Once

The mucolipin subfamily of Transient Receptor Potential (TRP) channels, which consist of TRPML1, TRPML2, and TRPML3 (a.k.a. MCOLN1- 3), are Ca2+-permeable cation channels localized in intracellular endosomes and lysosomes. In response to cellular stimulation, TRPMLs mediate Ca2+ release from the lysosome lumen, triggering Ca2+-dependent lysosomal membrane trafficking events involved in a variety of basic cell biological processes, including lysosomal exocytosis, autophagy, and membrane repair [1]. In humans, loss-of-function mutations of TRPML1 cause type IV Mucolipidosis (ML-IV), a lysosome storage neurodegenerative disease (LSD). In mice, gain-of-function mutations of TRPML3 cause pigmentation and hearing defects [1]. Phosphatidylinositol 3,5-bisphosphate (PI(3,5)P2), an endolysosome-specific phosphoinositide, may serve as an endogenous agonist of TRPMLs [2]. In addition, mucolipin-specific synthetic agonists (ML-SAs) have been identified and shown to regulate various TRPML-dependent lysosome functions by mimicking endogenous agonists [3]. Now, five independent studies, led by Youxing Jiang, Xiaochun Li, Soek-yong Lee, Maojun Yang, and Jian Yang, respectively, report a total of three TRPML1 and two TRPML3 Cryo-EM structures, all at atomic resolution, and in both closed and agonist-bound open conformations [4-8]. The general features of these channels are consistent across all five studies. Consistent with previous work [2], positively-charged amino acid residues in the cytoplasmic N–terminus are found to be responsible for channel activation by PI(3,5)P2 [7, 8]. In contrast, the synthetic agonist ML-SA1 binds to a separate site at an intriguing location. TRPML1 and TRPML3 are six-transmembrane (6TM) channel proteins with an overall topology similar to many other tetrameric cation channels, including KV channels. ML-SA1 binds to residues in the S5 and S6 [4, 6], domains that are known to form the “activation gate”. These five studies have provided a structural foundation for studying TRPML channel regulation, pharmacology, and lysosome chemical biology, which in turn may help develop new therapeutic strategies for a spectrum of lysosome-related diseases, including ML-IV, other LSDs, and common neurodegenerative diseases.

Comments by Haoxing Xu, NC-IUPHAR subcommittee Chair of the Transient Receptor Potential Channels and Professor, the University of Michigan


1. Xu, H. and D. Ren, Lysosomal physiology. Annu Rev Physiol, 2015. 77: p. 57-80. [PMID:25668017]

2. Dong, X.P., et al., PI(3,5)P(2) Controls Membrane Traffic by Direct Activation of Mucolipin Ca Release Channels in the Endolysosome. Nat Commun, 2010. 1(4). [PMID:20802798]

3. Shen, D., et al., Lipid storage disorders block lysosomal trafficking by inhibiting a TRP channel and lysosomal calcium release. Nat Commun, 2012. 3: p. 731. [PMID:22415822]

4. Zhou, X., et al., Cryo-EM structures of the human endolysosomal TRPML3 channel in three distinct states. Nat Struct Mol Biol, 2017. [PMID:29106414]

5. Zhang, S., et al., Cryo-EM structures of the mammalian endo-lysosomal TRPML1 channel elucidate the combined regulation mechanism. Protein Cell, 2017. 8(11): p. 834-847. [PMID:28936784]

6. Schmiege, P., et al., Human TRPML1 channel structures in open and closed conformations. Nature, 2017. 550(7676): p. 366-370. [PMID:29019983]

7. Hirschi, M., et al., Cryo-electron microscopy structure of the lysosomal calcium-permeable channel TRPML3. Nature, 2017. 550(7676): p. 411-414. [PMID:29019979]

8. Chen, Q., et al., Structure of mammalian endolysosomal TRPML1 channel in nanodiscs. Nature, 2017. 550(7676): p. 415-418. [PMID:29019981]

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The Concise Guide to PHARMACOLOGY 2017/18


Concise Guide to Pharmacology Simplifies Drug Discovery Research

The Concise Guide to PHARMACOLOGY 2017/18 (CGTP), which is produced from a subset of the data contained in the IUPHAR/BPS Guide to PHARMACOLOGY database, is now available in the British Journal of Pharmacology. Published by Wiley on behalf of the British Pharmacological Society, the 440 page guide includes overviews of key properties for close to 1,700 human drug targets, identifies 3,500 ligands including more than 2,400 synthetic organic molecules and over 50 antibodies. Over 4,000 interactions between ligands and targets are quantified, allowing researchers to assess the potency of these interactions.

This open access knowledgebase of major drug targets is completely linked and divided into eight major areas of research focus:

  • G protein-coupled receptors
  • Ligand-gated ion channels
  • Voltage-gated ion channels
  • Other ion channels
  • Nuclear hormone receptors
  • Catalytic receptors
  • Enzymes
  • Transporters

It also includes an Overview chapter with additional information on other protein targets.

“As a pharmacologist, being able to access freely information on current human drug targets is vital to discovering new therapeutics,” said Steve Alexander, Associate Professor of Molecular Pharmacology, Faculty of Medicine & Health Sciences at the University of Nottingham and Lead Editor of the Concise Guide.

The Concise Guide provides an authoritative voice on nomenclature of these pharmacological targets through close links with NC-IUPHAR. It offers summary information on the best available pharmacological tools, alongside key references and suggestions for further reading.

“The Concise Guide to PHARMACOLOGY is the drug discovery researchers’ bible,” said Amrita Ahluwalia, Co-Director, The William Harvey Research Institute, Professor of Vascular Pharmacology at Barts & The London School of Medicine & Dentistry, and Editor-in-Chief of the British Journal of Pharmacology. “We are pleased to once again make the Concise Guide freely available to our colleagues around the globe at”

This edition of the Concise Guide was compiled with the help of over 150 collaborators representing industry and academia from 22 countries across four continents. The British Pharmacological Society and the Guide to PHARMACOLOGY database team would like to thank the CGTP editors, contributors, and colleagues at the Universities of Cambridge, Edinburgh, Nottingham in the UK, and Monash, Australia for their contributions to updating the Concise Guide to PHARMACOLOGY.

The Concise Guide is a handy starting point for teaching and researching on specific pharmacological targets. All the targets and ligands are also linked directly to the online database for further details. Please share this URL widely with your students and colleagues.


Alexander SPH, Kelly E, Marrion NV, Peters JA, Faccenda E, Harding SD, Pawson AJ, Sharman JL, Southan C, Buneman OP, Cidlowski JA, Christopoulos A, Davenport AP, Fabbro D, Spedding M, Striessnig J, Davies JA; CGTP Collaborators. (2017) The Concise Guide to PHARMACOLOGY 2017/18. Br J Pharmacol. 174 (Suppl 1): S1-S446. [PMIDs: 29055037, 29055040, 29055033, 29055038, 29055036, 29055035, 29055034, 29055039, 29055032]

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GtoPdb NAR database issue 2018: Journal to database connectivity and journal to GtoPdb links

The following blog post acts as supplementary data to the 2018 NAR Database Issue

Journal to Database connectivity

The citation provenance of all entity records and contextual comments selected by the curators and NC-IUPHAR members in GtoPdb is supported by four document types. These are journal papers with PubMed Identifiers (PMIDs, 30,894), journal papers without PMIDs (246), book references (72) and patent numbers (412). We also have 109 URL-only citations we have judged of good reputation and expected stability (some of which will get displaced when appropriate journal papers appear). The key axis of connectivity that we facilitate is PubChem-to-PubMed reciprocal linking. The importance of this overall has been described by the PubChem team in some detail, including the contribution of GtoPdb as one of the mapping sources [1].

The set of curated ligand references (for quantitative activity data at targets as well as selected ancillary references, such as completed clinical trial reports) form part of the SID records we submit to PubChem, which has a number of linking consequences. Note also that we uniquely specify the explicit location of the ligand structure within the reference. For example, ligand id: 8135 is named “compound 21 [PMID: 23312943]” and can thus be discriminated from no less than seven other “compound 21”s in the database by their specific PMID suffixes. Figure 1 illustrates the link between GtoPdb compounds and “Depositor Provided PubMed Citations” (DPPMC) both in the SID from us and merged in the CID from other submitters. Crucially, this relationship is reciprocal as we can see in the lower panel of Figure 1. This means that any user coming in to the NCBI Entrez system [2], either via PubMed or PubChem, can connect the paper to the structure or vice versa. In this example, we are the only source that has submitted a connection and the structure can be located in the paper (i.e. as compound 21). Conversely, popular compounds (e.g. approved drugs) may have PubMed connections in their CIDs from many submitters, but ours will include the quantitative binding data reference which may be before the drug was awarded an International Nonpropietary Name (INN).


Figure 1.  GtoPdb to PubChem to PubMed connectivity for ligand 8135.

Our overall PubMed statistics are shown in Table 1.

Table 1. GtoPdb PubMed statistics


All PMIDs curated into GtoPdb


Associated with target annotation


Associated with ligand annotation


Ligand SIDs (from 8978) that have PMID links


Total PMID links


Associated with ligand interactions or comments in PubChem


Associated with quantitative ligand interactions in PubChem



The majority of PMIDs (22,060) are associated with individual targets as well as commentaries on families, accumulated from curation and committee updates over 14 years. Internally we can attribute 9,673 PMIDs to ligand-specific references. From our 8978 SIDs, 82% have at least one DPPMC making a total of 9,086 PMID links. Of these, 6,011 refer to the quantitative interaction. We have analysed the journal breakdown for our ligands as shown in Figure 2 which reflects our empirical primary, secondary and tertiary reference classifications. For example, primary citations as first reports of binding data between ligands and targets are often selected from the Journal of Medicinal Chemistry, while we generally cite the British Journal of Pharmacology (BJP) in relation to in vivo rodent pharmacology, and occasionally the British Journal of Clinical Pharmacology (BJCP) for clinical trial reports. To discern if there was an immunopharmacological curation signal in our literature we compared Figure 2 with the PMIDs only from GtoImmuPdb. It was interesting to note that for the primary references we selected for quantitative ligand interactions, the overall pattern was similar. Notably, however, Journal of Immunology had moved up from a ranking of 17th in Figure 2 to 6th in the GtoImmuPdb references.


Figure 12.  Top-twenty journals from the 8,756 PMIDs cited in the interaction comments.

Journal-to-GtoPdb links

Our engagement with the BJP in the provision of live out-links has been described previously [3]. The major enhancement for this year is that Wiley have transitioned to in-line links in the text (at first mention), rather than the previous method of adding separate tables to the manuscripts. Taking the recent BJP papers from Volume 174, Issue 18 September 2017 as an example, the 12 papers therein have 134 out-links to GtoPdb. This year has also produced our first “circular” example where GtoPdb team members are co-authors on a Systems Pharmacology study, partly derived from the database for which we have added a set of links “back in” [4]. This year Wiley have also introduced the same GtoPdb out-links for the BJCP.

1. Kim, S., Thiessen, P.A., Cheng, T., Yu, B., Shoemaker, B.A., Wang, J., Bolton, E.E., Wang, Y. and Bryant, S.H. (2016) Literature information in PubChem: associations between PubChem records and scientific articles. J Cheminform, 8, 32. PMID: 27293485

2. Gibney, G. and Baxevanis, A.D. (2011) Searching NCBI databases using Entrez. Current protocols in bioinformatics, Chapter 1, Unit 1 3. PMID: 21975942

3. McGrath, J.C., Pawson, A.J., Sharman, J.L. and Alexander, S.P. (2015) BJP is linking its articles to the IUPHAR/BPS Guide to PHARMACOLOGY. Br J Pharmacol, 172, 2929-2932. PMID: 25965085

4. Benson, H., Watterson, S., Sharman, J., Mpamhanga, C., Parton, A., Southan, C., Harmar, A. and Ghazal, P. (2017) Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway. Br J Pharmacol. [Epub ahead of print] PMID: 28910500

Posted in Chemical curation

GtoPdb NAR database issue 2018: PubChem Content

The following blog post acts as supplementary data to the 2018 NAR Database Issue

GtoPdb PubChem Content

The GtoPdb PubChem integration strategy has been previously outlined (1). Since 2015 we have made nine PubChem submissions for new releases of our database. For 2017.5 (see release notes for version 2017.5) we now have 8978 Substance Identifiers (SIDs) (PubChem query “IUPHAR/BPS Guide to PHARMACOLOGY”[SourceName]). We submit within days of our public release but users should note that it can take PubChem a few days to complete the processing of a new submission and several weeks to complete the more computationally intensive relationship mappings (e.g. 3D neighbours).

It is valuable for users to be able to seamlessly navigate between bioactive chemistry content in these two resources. We therefore pay close attention to the correspondence between our internal ligand entries and the external PubChem records. For a range of technical reasons, we observe small discrepancies not only between inside and outside counts (e.g. for Compound Identifiers (CIDs)) but also the exact numbers associated with our content from derivative searches in PubChem (i.e. executed via several steps) which may depend on how the query is executed. We are in the process of investigating these minor but complex differences (including consulting with the PubChem team). In the interim we are being transparent in declaring differences between the internal counts in Table 1 and the external counts dealt with in this section.

The largest of our PubChem entries is the antisense polynucleotide mipomersen (ligand 7364) with a molecular weight (MW) of 7158. Our largest peptide entry (ligand 7387) is lixisenatide, with 44 amino acids and MW of 4858. We established that 2156 of our SIDs could not form CIDs (i.e. they had no representation in Simplified Molecular-Input Line-Entry System (SMILES) form) because they were proteins (i.e. mapped to an intact UniProtKB, large peptides or antibodies. Over the last two years we have been converting more curated peptides, and a limited number of therapeutic polynucleotides, without pre-existing CIDs, into SMILES. This enhances intra-PubChem connectivity for these increasingly important classes of ligands. To form a CID, these must be within the current upper limit of 1000 atoms, approximating to 70 residues for a peptide (Dr P Theissen, personal communication). For this reason, we have introduced the Sugar & Splice program (NextMove Software, Cambridge, UK) to facilitate our conversions of peptides to SMILES and Hierarchical Editing Language for Macromolecules (HELM) notation (15). While we have reached 273 peptide CID entries, we are continually coming up against the problem of authors insufficiently defining peptide modifications (e.g. by correct International Union of Pure and Applied Chemistry (IUPAC) terminology) for unequivocal translation to SMILES.

In Figure 1 we show an analysis of our content in PubChem.


Figure 1. Category breakdown at the SID (A) and CID(B) level for GtoPdb PubChem entries

For the SIDs (Figure 1A), we have introduced new annotation categories into our SID comment lines for users to be able to retrieve two important subsets. These are “approved drug true” (with “true” suffixed for technical reasons; most approvals have been passed by the FDA and/or European Medicines Agency (EMA)), and “immunopharmacology” for ligands specifically curated as part of GtoImmuPdb. For PubMed links, the connections have been made by us as a source. Note also that the intersect between approved drug and immunopharmacology is derived from our curation of publications suggesting the association but are not necessarily approved for immunological clinical indications. For the CIDs, the categories in Figure 1B are as described previously (1) except for the two new ones explained above. The CID counts for these are lower than their SID counts by 160 and 334 respectively because of the antibody component of both but also peptide content of the latter. The general pattern is approximately in proportion to our 10% ligand growth over two years, with the largest increase in the PubMed coverage (expanded on below).

One of the powerful consequences of our submitting to PubChem is to be able to compare between different sources, using filters for “slicing and dicing” (2). This is already introduced in Figure 1 by showing the ChEMBL overlap, but also, in terms of complementarity, to indicate we have 1595 CID structures ChEMBL does not.

The subject of the correctness of chemical structure representation within the pharmacological domain in general and GtoPdb is too extensive to be addressed here but we have an NC-IUPHAR committee specially to advise us on this important topic. Notwithstanding we use PubChem statistics as direct quality control for the structures we submit. This can be seen in Figure 1 where we have 326 structures no other source has submitted. The converse is reassuring in that just over 95% of our structures are supported by at least one other of the 545 sources in PubChem. While this is an argument for correctness there are caveats. The first of these is that two sources can independently submit an incorrect structure. The second is that all databases have an element of circularity where records can be re-cycled between sources. Inspection of our unique structures establishes that they include extractions from the literature that (for public sources) only we have made. An example is AZ13102909 (, where we derived the structure of a kinase inhibitor from an image in the paper ( Thus, we have introduced the additional triage of checking our unique 326 with the PubChem “same connectivity” operator to check relationships with other CIDs.

As a more detailed utility example, we generated CID comparisons to two other sources of similar size that also manually curate drugs and other pharmacologically active compounds. These are the well-established DrugBank (3) and the more recent DrugCentral (4). The former captures biochemical and pharmacological information about drugs, mechanisms and targets with recent expansion into absorption, distribution, metabolism, excretion and toxicity (ADMET). The emphasis of the latter is on active ingredients in all pharmaceutical formulations approved by the FDA and other regulatory agencies; in addition to structure and bioactivity the compounds are linked to drug label annotations and other regulatory information. The result is shown in Figure 2.


Figure 2. Intra-PubChem content comparison between GtoPdb, DugBank and DrugCentral. The union of all three is 14892. The PubChem latest submission dates for the sources were 23rd Aug 2017, 10th Feb 2016 and 2nd Sept 2017, respectively.

The overlaps and differences between these three sources quantify their complementarity. However, exact numbers can be confounded by minor differences in chemistry rules for their independent submissions (e.g. salts, parents or both) as well as different connectivity choices for the same compound skeleton (e.g. R versus S isomer). Notwithstanding, Figure 2 makes it clear the three sources have substantially different capture. The results also establish pairwise cross-corroboration (e.g. GtoPdb overlaps with 334 and 239 structures for which DrugBank and DrugCentral, respectively, diverge between each other). It should also be noted that GtoPdb was one of the sources used in the compilation of DrugCentral which would thus contribute to the 1276 overlap (4). The three-way intersect of 1037 should correspond to those approved drugs that can form CIDs. This is lower than expected (i.e. for the FDA would be predicted to be closer to 1500) but possible reasons for this have been discussed previously (5).


1. Southan, C., Sharman, J.L., Benson, H.E., Faccenda, E., Pawson, A.J., Alexander, S.P., Buneman, O.P., Davenport, A.P., McGrath, J.C., Peters, J.A. et al. (2016) The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands. Nucleic Acids Res, 44, D1054-1068. PMID: 26464438

2. Southan, C., Sitzmann, M. and Muresan, S. (2013) Comparing the chemical structure and protein content of ChEMBL, DrugBank, Human Metabolome Database and the Therapeutic Target Database. Molecular Informatics, 32 (11-12), 881-897. PMID: 24533037

3. Law, V., Knox, C., Djoumbou, Y., Jewison, T., Guo, A.C., Liu, Y., Maciejewski, A., Arndt, D., Wilson, M., Neveu, V. et al. (2014) DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res, 42, D1091-1097. PMID: 24203711

4. Ursu, O., Holmes, J., Knockel, J., Bologa, C.G., Yang, J.J., Mathias, S.L., Nelson, S.J. and Oprea, T.I. (2017) DrugCentral: online drug compendium. Nucleic Acids Res, 45, D932-D939. PMID: 27789690

5. Southan, C., Varkonyi, P. and Muresan, S. (2009) Quantitative assessment of the expanding complementarity between public and commercial databases of bioactive compounds. J Cheminform, 1, 10. PMID: 20298516

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Hot topic: A new research avenue investigating mitochondrial GPCR biology

As one of the first propositions for GPCRs being present in mitochondrial membranes, a recent report from Robert Friedlander and colleagues [1] follows on from previous work characterising synaptic and extrasynaptic mitochondria in human cortex (post-mortem samples) and their role in neuroprotection. This work, if reproduced, opens up new vistas, and has many implications for neurodegenerative diseases. Taken together, Suofu et al. show that melatonin is synthesised in mitochondria, that MT1 receptors are present in mitochondrial membranes, and that MT1 receptor stimulation reduces cytochrome c and caspase secretion caused by calcium overload. The authors propose that this is a mechanism for the neuroprotective effects of melatonin in hypoxic-ischaemic brain injury in neonatal and in models of Huntington’s disease, where there is mitochondrial impairment.

Comments by Michael Spedding, Secretary General, IUPHAR, and CEO, Spedding Research Solutions SARL, France

(1) Suofu Y et al. (2017). Dual role of mitochondria in producing melatonin and driving GPCR signaling to block cytochrome c release. Proc Natl Acad Sci U S A., pii: 201705768. doi: 10.1073/pnas.1705768114. [Epub ahead of print] [PMID:28874589]

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Hot topic: Crystal structure of LPA6, a receptor for lysophosphatidic acid, at 3.2A

Lysophospholipids (LPs) have myriad roles as extracellular signals that activate cognate G protein-coupled receptors (GPCRs) (2). LPs for which receptors have been reported include lysophosphatidic acid (LPA) (receptors: LPA1-6), sphingosine 1-phosphate (S1P1-5), lysophosphatidyl serine (LPS1-3, 2L (2L is a pseudogene in humans)) and lysophosphatidyl inositol/glucose (LPI/LPG), all of which are Class A GPCRs. Of these 15 LP receptors, crystal structures of two have been previously reported for S1P1 (2.8-3.35A) (3) and LPA1 (2.9-3.0A) (4) both of which utilized human cDNA sequences bound in the presence of antagonists. The new structure (1), from the laboratories of Junken Aoki and Osamu Nureki, elucidates a zebrafish receptor – with 80% amino acid similarity to human LPA6, in the transmembrane (TM) region – in the absence of a ligand, which nonetheless crystalized. This contrasts with the prior 2 antagonist-bound human structures. All 3 receptors were chimeric proteins stabilized by T4-lysozyme (S1P1 and LPA6) or thermostabilized apocytochrome b562RIL (LPA1) fused to the 3rd intracellular loop, but all were capable of responding to native ligands.

Key features of LPA6 included a surprisingly large distance between TM4 and 5, which suggests lateral entry of LPA via membrane translocation into the LPA6 binding pocket. Such a mechanism contrasts with that of LPA1 in which TM1 and 7 distances are comparatively small, and whose structure includes a barrel opening flexibly covered by an unstabilized N-terminal helix that contrasts with a stabilized helix in S1P1 that could inhibit ligand entry from extracellular space. LPA1’s structure is further consistent with LPA entry from the extracellular environment that could include its biosynthetic enzyme, autotaxin. By comparision, both S1P1 and LPA6 – despite being of distinct gene sub-families (EDG and P2Y, respectively) – show receptor entry of ligands from within the membrane plane, suggesting parallel evolution of membrane access for these gene sub-families. LPA6 prefers unsaturated LPAs (e.g., 18:2) that appear to enter a hydrophobic cleft and central cavity binding site that supports unsaturated LPA species based upon docking models. Modeling also supports LPA-binding that produces a shift of TM6 and 7 to allow more favorable interactions with LPA’s phosphate headgroup. Membrane access of LPA into LPA6 is further supported by actions of the phospholipase PA-PLA1α that was shown to increase membrane LPA without extracellular secretion, thus providing membrane ligand that could translocate into LPA6.

Comments by Jerold Chun, MD, PhD, Professor & Senior Vice President, Neuroscience Drug Discovery, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA

  1. Taniguchi, R. et al. (2017) Structural insights into ligand recognition by the lysophosphatidic acid receptor LPA6. Nature, 548, 356-360, doi:10.1038/nature23448. [PMID:28792932]
  2. Kihara, Y. et al. (2014) Lysophospholipid receptor nomenclature review: IUPHAR Review 8. Br J Pharmacol, 171, 3575-3594, doi:10.1111/bph.12678 . [PMID:24602016]
  3. Hanson, M. A. et al. (2012) Crystal structure of a lipid G protein-coupled receptor. Science, 335, 851-855, doi:10.1126/science.1215904 . [PMID:22344443]
  4. Chrencik, J. E. et al. (2015) Crystal Structure of Antagonist Bound Human Lysophosphatidic Acid Receptor 1. Cell, 161, 1633-1643, doi:10.1016/j.cell.2015.06.002 . [PMID:26091040]
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Hot topic: FZD6 dimers dissociate after stimulation – briefly

GPCRs of all classes are widely thought to form homodimers, heterodimers and higher-order oligomers. The functional significance of dimerization is well understood for Class C receptors but less certain for the other GPCR classes, including the rather unconventional class F or Frizzled (FZD) receptors. Although the relationship between receptor activity and quaternary structure is often unclear, across classes it is generally found that ligand binding does not dramatically influence dimerization. A recent report by Gunnar Schulte and his colleagues suggests that in this respect class F receptors may once again be somewhat different [1]. Using an impressive combination of live-cell imaging, biochemical and modeling techniques the group presents evidence that FZD6 forms relatively stable dimers that dissociate when stimulated with the activating ligand WNT-5A. Remarkably, FZD6 protomers reassociate at the cell surface after 20 minutes of continuous stimulation, a timing which coincides with termination of ERK1/2 phosphorylation. Taken together with previous results from the Schulte group [2] the data are consistent with a model where FZD6 dimers are constitutively associated with G proteins and the phosphoprotein Disheveled (DVL) in an inactive state complex that must dissociate in order to generate downstream signals. Although it remains to be seen how representative this model will be for other GPCRs, including other class F receptors, the report sets an important standard for studies aimed at linking receptor activity and quaternary structure.

Comments by Nevin A. Lambert, PhD, Department of Pharmacology and Toxicology Medical College of Georgia, Augusta University, USA

[1] Petersen, J., Wright, S.C. et al. (2017) Agonist-induced dimer dissociation as a macromolecular step in G protein-coupled receptor signaling. Nat Commun. 8(1):226.  [PMID: 28790300]

[2] Kilander, M.B.C., Petersen, J. et al. (2014) Disheveled regulates precoupling of heterotrimeric G proteins to Frizzled 6. FASEB J. 28(5):2293-305. [PMID: 24500924]

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