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Congrats to: Ya-Tzu Li , our notable contributor, for her superb undergrad paper along with Hao-Jen Hsu , Chun-Chun Chang , et al.   Drugs like Ozempic will change the world GPCR Events, Meetings, and Webinars November 5 - 7, 2024 | 16th

Watch Episode 171 What happens when your protein has no known ligands, no structure, and very little data? For most researchers, that’s a dead end. For Alessandro Nicoli, it’s an opportunity. In this post, we explore how computational tools—especially AlphaFold —are helping crack the mystery of olfactory GPCRs , one of the most elusive receptor families in the human body. The Problem: Hundreds of Receptors, Almost No Ligands Alessandro’s work focuses on olfactory GPCRs—nearly 400 distinct receptors that play key roles in smell but remain largely uncharacterized . Most have only one known ligand, if any. Their structures are hard to determine experimentally due to poor expression and the volatility of odorant molecules. That’s where computational chemistry steps in. Enter AlphaFold: Predicting the “Face” of a Receptor When Alessandro began his PhD, structural models of olfactory GPCRs were essentially nonexistent. The main challenge was simple but daunting: “The challenge was to get a face to those proteins—the structure. AlphaFold has, of course, as we know, revolutionized the world.”  —Alessandro Nicoli For the first time, researchers had a reliable set of predicted structures to work from. That meant simulations, ligand screening, and experimental design could move forward with confidence. “When they released the first structure of the odorant receptors… AlphaFold already had it, without any prior information, and the match was very close to experimental error.”  —Alessandro Nicoli A New Era of GPCR Research AlphaFold didn’t just fill a gap—it shifted the focus of computational biology. Instead of struggling to predict structures from scratch, Alessandro and others could now use AI-generated models as starting points  for deeper questions. “…now you have a plethora of 400 models that you can start with molecular dynamics, docking, virtual screening.”  —Alessandro Nicoli The result? More accurate hypotheses, faster ligand discovery, and new strategies to tackle one of biology’s most complex receptor families. From Prediction to Discovery One of Alessandro’s projects focused on receptor R5VK1 , where his team tested computational models against a set of experimentally validated active and inactive ligands. By iteratively refining the models with docking and mutagenesis data, they developed predictive pipelines that can help identify new odorant ligands . This case study highlights why computational chemistry is no longer a side tool—it’s a driver of discovery , especially when experimental data is scarce. Want to level up your modeling skills? Start with our GPCR training program and get hands-on with virtual tools shaping the future of drug discovery. ________ Keyword Cloud: # AlphaFold #GPCRdata #DrugDiscovery #OlfactoryReceptors #StructuralBiology #ArtificialIntelligence #MolecularDynamics #ComputationalBiology #MolecularModeling

allosteric modulators with built-in selectivity and context sensitivity Why GPCR Allosteric Thinking Changes Ligands don’t just “bind”—they change  the receptor. These changes can alter how the receptor talks to G proteins, arrestins, or other receptors. muscarinic receptors, CCR5 chemokine programs, and NMDA receptors, where ligand context fundamentally changes But without understanding how GPCR state changes  influence that affinity—or vice versa—drug discovery

New cryo-EM structures from Chen et al. New cryo-EM structures from Chen et al.

About John Stagg "John Stagg is a Professor of Pharmacy at Université de Montréal and researcher at the CHUM

the course included Marc Chabre , Harden McConnell , Richard Henderson , Martin Rodbell , Jean-Pierre Changeux Recently, Tom’s lab discovered, along with Yu Chen and Ping Chi , that a mutant of CYSLTR2 is a driver