Coevolution-driven method for efficiently simulating conformational changes in proteins reveals molecular details of ligand effects in the β2AR receptor

Abstract

AbstractWith the advent of AI-powered structure prediction, the scientific community is inching ever closer to solving protein folding. An unresolved enigma, however, is to accurately, reliably and deterministically predict alternative conformational states that are crucial for the function of e.g. transporters, receptors or ion channels where conformational cycling is innately coupled to protein function. Accurately discovering and exploring all conformational states of membrane proteins has been challenging due to the need to retain atomistic detail while enhancing the sampling along interesting degrees of freedom. The challenges include but are not limited to finding which degrees of freedom are relevant, how to accelerate the sampling along them, and then quantifying the populations of each micro- and macrostate. In this work, we present a methodology that finds the relevant degrees of freedom by combining evolution and physics through machine learning and apply it to the β2 adrenergic receptor conformational sampling. In addition to predicting new conformations that are beyond the training set, we have computed free energy surfaces associated with the protein’s conformational landscape. We then show that the methodology is able to quantitatively predict the effect of an array of ligands on the β2 adrenergic receptor activation, and that the full conformational landscape, including states related to biased signaling, is discovered using this procedure. Lastly, we also stake out the structural determinants of activation and inactivation pathway signaling through different ligands.