Identifying multiple active conformations in the G protein-coupled receptor activation landscape using computational methods

G protein-coupled receptors (GPCRs) are membrane proteins critical in cellular signaling, making them important targets for therapeutics. The activation of GPCRs is central to their function, requiring multiple conformations of the GPCRs in their activation landscape. To enable rational design of GPCR-targeting drugs, it is essential to obtain the ensemble of atomistic structures of GPCRs along their activation pathways. This is most challenging for structure determination experiments, making it valuable to develop reliable computational structure 'prediction methods. In particular, since the active-state conformations are higher in energy (less stable) than inactive-state conformations, they are difficult to stabilize. In addition, the computational methods are generally biased toward lowest energy structures by design and miss these high energy but functionally important conformations. To address this problem, we have developed a computationally efficient ActiveGEnSeMBLE method that systematically predicts multiple conformations that are likely in the GPCR activation landscape, including multiple active-and inactive-state conformations. ActiveGEnSeMBLE starts with a systematic coarse grid sampling of helix tilts/rotations (similar to 13 trillion transmembrane domain conformations) and identifies multiple potential active-state energy wells, using the TM3TM6 intracellular distance as a surrogate activation coordinate. These energy wells are then sampled locally using a finer grid in conformational space to find a locally minimized conformation in each energy well, which can be further relaxed using molecular dynamics (MD) simulations. This method, combining homology modeling, hierarchical complete conformational sampling, and nanosecond scale MD, provides one of the very few computational methods that predict multiple candidates for active-state conformations and is one of the most computationally affordable.