Modeling Active-State Conformations of G-Protein-Coupled Receptors Using AlphaFold2 via Template Bias and Explicit Protein Constrains.

Abstract

AlphaFold2 and other deep learning tools represent the state of the art for protein structure prediction; however, they are still limited in their ability to model multiple protein conformations. Since the function of many proteins depends on their ability to assume different stable conformational states, different approaches are required to access these alternative conformations. G-protein-coupled receptors regulate intracellular signaling by assuming two main conformational states: an active state able to bind G-protein and an inactive state. Receptor activation is characterized by large conformational changes at the intracellular region, where the G-protein interacts, accompanied by more subtle structural rearrangements at the extracellular ligand-binding site. Retrospective studies have demonstrated that, for many receptors, the inactive state is the favored conformation generated by AlphaFold2 when the receptor is modeled alone, while active-state structures can only be modeled by introducing a conformational bias in the template information used for the prediction or by explicitly incorporating the binding of a ligand into the modeled system. This benchmarking study extends previous analyses, confirming the opportunities of deep learning tools for modeling G-protein complexed to the active state of receptor, while also revealing limitations in the modeling of allosteric effects, particularly the reduced accuracy of predictions at the receptor extracellular site, which may impact their applicability in structure-based drug design.