Development of New Methods for Enhanced Conformational Sampling of GPCRs

Abstract

G protein coupled receptors (GPCRs) are integral membrane proteins that enable a cell to respond to extracellular stimuli like light, small molecules, peptides, and proteins. These receptors have evolved as highly flexible proteins that possess multiple functionally important conformational states, which enable these receptors to activate pleiotropic signaling events inside the cells. Recent progress in membrane protein structural biology techniques and cryo‐electron microscopy is providing structural evidence of two or more distinct receptor conformations for these receptors. Computational structural modeling approaches can complement and supplement these studies by predicting all functionally important states of a receptor, however, they run into the classical protein conformational sampling problem. We are developing a computational biophysical method called Enhanced Conformational Markov‐state Sampling in Membrane BiLayer Environment (EnCoMSeMBLE) to solve this sampling problem for GPCRs. We are utilizing Markov‐State‐Models to combine the Boltzmann sampling of receptor conformations from molecular dynamics (MD) with the brute‐force sampling of receptor conformations from our previously developed ActiveGEnSeMBLE method. The method was applied to the A2A receptor starting from the active state by only using traditional MD simulations. This is not surprising because the pre‐activce states is a downhill direction on the energy landscape. However, applying both MD and ActivateGEnSeMBLE methods was able to predict pre‐active states from the inactive states which, classical MD cannot achieve. Our results indicated that our methods provide an unbiased method to sample the conformational landscape. This can protentional sample unexplored regions of the conformational landscape and find novel conformations that can elucidate the mechanistic properties that GPCRs possess.Support or Funding InformationThis project was funded through a grant from the National Institutes of Health (NIH) Building Infrastructure Leading to Diversity (BUILD) #5TL4GM118977 or # 5RL5GM118975.