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 d

SUBMITTER: Manookian B 

PROVIDER: S-EPMC12783649 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature