Activity Map and Transition Pathways of G Protein Coupled Receptor Revealed by Machine Learning

Abstract

Approximately, one-third of all FDA-approved drugs target G protein-coupled receptors (GPCRs). However, more knowledge of protein structure-activity correlation is required to improve the efficacy of the drugs targeting GPCRs. In this study, we developed a machine learning (ML) model to predict activation state and activity level of the receptors with high prediction accuracy. Furthermore, we applied this model to thousands of molecular dynamics trajectories to correlate residue-level conformational changes of a GPCR to its activity level. Finally, the most probable transition pathway between activation states of a receptor can be identified by using the state-activity information. In addition, with this model, we can associate the contribution of each amino acid to the activation process. Using this method we will be able to design drugs that mainly target principal amino acids driving the transition between activation states of GPCRs. Our advanced method is generalizable to all GPCR classes and provides mechanistic insight into the activation mechanism in the receptors.