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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

April 2022 Sosei Heptares Confirms Senior Leadership Changes to Drive the Company Through the Next Stage Sosei Group Corporation (“the Company”; TSE: 4565) today confirms that a series of Executive Management changes

Obesity-induced changes in human islet G protein-coupled receptor expression: Implications for metabolic

Adapted from: Zhang Y, Tang H, Chen W, Zhang J. is most effective at studying interactions between large proteins and small ligands thanks to this change

associate with the active parathyroid hormone 1 receptor (PTH1R) in different complex configurations ("hanging Moreover, we assess β-arrestin conformational changes that are induced specifically by proximal and distal Here, we show differences between conformational changes that are induced by P-R* or R* receptor states

Conformational changes between inactive and active states are facilitated by a "microswitch network" chemokine antagonist ([5P7]CCL5), and a small-molecule inverse agonist (maraviroc) (Tan, Zhu et al. 2013, Zheng , Han et al. 2017, Isaikina, Tsai et al. 2021, Zhang, Chen et al. 2021). , exhibits bias toward G-protein signaling, which has been structurally related to a conformational change

Xiaoyu Zhang showed great insight into new ligands for new E3 ligases for PROTAC development.

While her work there focused on immunological changes in pregnancy—not addiction—it was a valuable chapter Catherine's experience serves as a reminder that it is never too late to change direction and pursue Embracing Change and Uncertainty Change can be daunting, especially when it involves stepping away from illustrates the power of following one's passion and the importance of being adaptable in the face of change Catherine's experience serves as an inspiration for anyone considering a career change or seeking to

Zhang, A.J.M. Molina, T.C. Südhof, and R.C. Malenka. 2013. Zhang, and L. Ma. 2008.

And that shift changed everything. This accidental tool changed that. Collaboration, Chemistry, and the Pivot That Changed the Project Goal:  Develop a photo-switchable GPCR What Changed After This Data This imaging tool is now being used to: Re-evaluate where GLP-1 and GIP

the lipid composition of intracellular membranes may influence GPCR dynamics and signaling outcomes, changing Z., Wilderman, A., Katakia, T., McCann, T., Yokouchi, H., Zhang, L., Corriden, R., Liu, D., Feigin, M

These receptors respond to a variety of signals by undergoing structural changes that activate internal brief sequential intervals following GTP addition, the research team identified the conformational changes The captured structures reveal a dynamic of conformational changes initiated by the binding of an agonist This early interaction sets the stage for a cascade of significant conformational changes. Concurrently, the α1 helix extends, propagating structural changes throughout the G protein.

This process is a binding event that facilitates a change in the shape of a macromolecule which impacts post-translational modification, or protein interactions, allowing proteins to sense and respond to changes structure, its molecular interactions with various ligands and canonical Gαq transducer, and conformational changes Lu S, He X, Ni D, Zhang J.

structural perspective, the -4 position of αH5 was key for the selectivity of G-protein coupling, since the change , Z., Liu, C., Li, X., Zhu, X., Wang, N., Xu, Z., Xia, R., Liang, J., Duan, Y., Yin, H., Xiong, Y., Zhang

These probes shift brightness and fluorescence lifetime as pH changes. “You’re not changing the spectrum. You’re just changing how bright it is—and how long it glows,” said Dr. Eric Trinquet. To hear the full story of how pHSense came to life—and why the GLP-1 data changed everything— 🎧 Listen

receptors under diverse states Fan Liu , Han Zhou , Xiaonong Li , Liangliang Zhou , Chungong Yu , Haicang Zhang

Cambridge Fellowship  — Precision meets scale Leadership at MIPS  — Turning questions into impact What Changed Why GPCR Spatial Signaling Is Changing Drug Discovery Today, Michelle leads the Spatial Organization laboratory, asking a deceptively simple question: where  do GPCR signals happen—and how does location change

Mutagenesis involves deliberately altering the DNA sequence of a gene to study the resulting changes framework that combined mutagenesis and structural data to contextualise ligand-induced structural changes datasets, allowing researchers to determine the functional consequences of every possible amino acid change ligand-receptor interactions, mutagenesis fills crucial knowledge gaps by revealing how specific amino acid changes B., Chang, B., & Peisajovich, S. G. (2017).

But one academic spark—and the right mentor—changed his trajectory forever. —Alessandro Nicoli This perspective changed everything.

Zhang, H., et al., Autocrine selection of a GLP-1R G-protein biased agonist with potent antidiabetic

Kenakin’s latest lecture delivers a game-changing framework for teams grappling with the gap between This lecture is a guide to understanding why intracellular drug access changes everything: from target scaffold permeability using modern, cost-effective pharmacokinetic assays   Why Intracellular GPCR Drugs Change

In this eye-opening module, Terry Kenakin explores a concept that could change how you interpret pharmacological

This wasn’t just a clever idea on paper — it changed how science happened, day to day. This changed not just what got funded, but what was possible . What Changed After This Data The pooled funding model turned the lab into a magnet: postdocs, visiting

lanthanide probes, the team realized they could tune these molecules to become exquisitely sensitive to pH changes : Endogenous GLP-1 internalization shown in beta cells 🚀 2025: Revvity launches pHSense A Day That Changed questions about constitutive activity, agonist-induced internalization, and cellular dynamics. 🔄 What Changed

. 📚 Whether you're new to pharmacology or brushing up on basics, this lesson will change how you think The receptor may change shape upon binding, affecting how the drug interacts and how effective it will

Allostery and Biased Signaling Change the Game The most profound change in GPCR drug discovery is our discovery teams, pharmacologists refining core skills, and R&D leads who need clear reasoning in a rapidly changing

Medicinal Chemistry   2020 , 188 , 112037. https://doi.org/10.1016/j.ejmech.2020.112037 . (15)      Zhang Discovery   2010 , 15 (10), 1248–1259. https://doi.org/10.1177/1087057110384611 . (25)      Fulp, A.; Zhang

Fluorescence Anisotropy and BBV – The theory behind the assays FA is based on measuring the change in Figure 1.Time course of FA change caused by CELT-419 binding to D3 receptor on the BBV particles. The insert shows the Log(IC50) ± SE change in time of corresponding displacement curves. The Log(IC50) change in time was fitted with equation  3  and k given is the weighted average from 3  H.; Bates, M.; Zhuang, X.

these ligands are orthosteric agonists such as alkylpyrimidine‐4,6‐diol derivatives (Liu et al., 2016; Zhang