Temporally resolved and interpretable machine learning model of GPCR conformational transition.

Abstract

Identifying target-specific drugs remains a challenge in pharmacology, especially for highly homologous proteins such as dopamine receptors D₂R and D₃R. Differences in target-specific cryptic druggable sites for such receptors arise from the distinct conformational ensembles underlying their dynamic behavior. While Molecular Dynamics (MD) simulations has emerged as a powerful tool for dissecting protein dynamics, the sheer volume of MD data requires scalable and unbiased data analysis strategies to pinpoint residue communities regulating conformational state ensembles. We present the Dynamically Resolved Universal Model for BayEsiAn network Tracking (DRUMBEAT) interpretable machine learning algorithm and validate it by identifying residue communities that enable the deactivation of the β₂-adrenergic receptor. Further, upon analyzing dopamine receptor dynamics we identify distinct and non-conserved residue communities around the contacts F170^4.62_F172^ECL2 and S146^4.38_G141^34.56 that are specific to D₃R conformational transitions compared to D₂R. This information can be tapped to design subtype-specific drugs for neuropsychiatric and substance use disorders.