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What can we learn from conformational profiling of GPCRs?
Published on January 30, 2020 68 min
Other Talks in the Series: G Protein-Coupled Receptors (GPCRs) signaling in health and disease
Computational modelling of GPCR signalling dynamics
- Dr. Graham Ladds
- University of Cambridge, UK
Super-resolution imaging of GPCR oligomers: applications and functional roles
- Dr. Aylin Hanyaloglu
- Imperial College London, UK
Structure and activation of endothelin ETB receptor by endothelin
- Dr. Tomoko Doi
- Kyoto University, Japan
Structural and mechanistic insights into the neurotensin receptor
- Dr. Reinhard Grisshammer
- National Cancer Institute, USA
Adhesion GPCRs in nervous system development and disease
- Prof. Tobias Langenhan
- Leipzig University, Germany
My name is Terry Hebert. I'm a professor in the Department of Pharmacology and Therapeutics at McGill University in Montreal, Quebec. What I'm going to talk to you about today is what we can learn from conformational profiling of GPCRs. So the idea is that GPCRs are coupled through multiple signaling pathways. We're learning to capture a lot more of that signaling complexity. But I think there's something also to be gained from understanding them as dynamic conformational machine and what I'm going to talk to you about is both of those aspects of screening and how they can combine to get us at drug candidate in a more efficient manner.
We can see from this slide that yes, there are multiple proteins that a given GPCR will interact with as part of its working life. That includes other GPCRs in the context of receptor heterodimers, homodimers, homo-and-hetero-oligomers. There are multiple proteins that the intracellular domain of G proteins interact with. The canonical heterotrimeric G proteins on the left, alpha, beta, and gamma subunits, lots of adaptor proteins or other signaling molecules like beta-Arrestin, multiple kinases, multiple small GTPases, multiple scaffolding proteins. So the question becomes that in the drug discovery contexts, how do we capture this complexity when we think about GPCRs as drug targets?
Let's bring this out in a pharmacological problem. The field has become intensely interested in an idea called biased signaling. That says that if there are multiple pathways downstream of a receptors. So on this graph you can see pathway A, B, and C, and there are three logins for that receptor, but they drive different effects. So obviously the one you want is ligand 2 because it will activate a pathway which is associated with therapeutic efficacy. The model here and the paper referred to here talks about opioid receptors. So one of the problems with opioid receptors is that if there are other pathways engaged, so pathway B on the left in addition to pathway A, we could get some efficacy but the patient or the receptor developed tolerance. So you need more and more of the drug, like one in this case, to maintain the clinical effect. Then on the far right, there's another ligand which is also an opioid receptor ligand, but it mainly drives the pathways that lead to tolerance and adverse effects. So things like gastrointestinal disorders, respiratory depression, and again, tolerance, and sometimes addiction. So if we could devise ligands that favored pathway A and spared pathways B and C, we would call that a biased ligand and we would see that that particular drug was much more effective than say the drugs we currently use now. So that's the idea in pharma, that's what people are thinking about doing, is trying to bias signalling toward therapeutically relevant pathways and away from pathways that cause side effects or adverse effects.