Journal Article

Temporally resolved and interpretable machine learning model of GPCR conformational transition

Manookian, Babgen, Vaidehi, Nagarajan, Sivaramakrishnan, Sivaraj, Bhattacharya, Supriyo, Gogoshin, Grigoriy, Rodin, Andrei S., Branciamore, Sergio, Mukhaleva, Elizaveta

2025

Overview

Identifying target-specific drugs remains a challenge in pharmacology, especially for highly homologous proteins such as dopamine receptors D 2 R and D 3 R. 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 β 2 -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 D 3 R conformational transitions compared to D 2 R. This information can be tapped to design subtype-specific drugs for neuropsychiatric and substance use disorders. Differences among homologous receptor proteins complicate target-specific drug design. Here, authors develop an interpretable dynamic machine learning model DRUMBEAT to identify amino acid residues enabling distinct conformational transitions in proteins.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 119/118 / 631/114/1305 / 631/45/612/194 / 631/535/1267 / 631/57/2266 / Adrenergic receptors / Algorithms / Amino acids / Artificial intelligence / Bayes Theorem / Bayesian analysis / Data analysis / Dopamine / Dopamine D2 receptors / Dopamine D3 receptors / Dopamine receptors / Drug development / Drugs / Feature selection / G protein-coupled receptors / Humanities and Social Sciences / Humans / Learning algorithms / Machine Learning / Mental disorders / Molecular dynamics / Molecular Dynamics Simulation / multidisciplinary / Pharmacology / Protein Conformation / Proteins / Receptors (physiology) / Receptors, Adrenergic, beta-2 - chemistry / Receptors, Adrenergic, beta-2 - metabolism / Receptors, Dopamine D2 - chemistry / Receptors, Dopamine D2 - metabolism / Receptors, Dopamine D3 - chemistry / Receptors, Dopamine D3 - metabolism / Residues / Science / Science (multidisciplinary) / Signal transduction / Simulation / Substance use