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<p>Identifying target-specific drugs remains a challenge in pharmacology, especially for highly homologous proteins such as dopamine receptors D<sub>2</sub>R and D<sub>3</sub>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<sub>2</sub>-adrenergic receptor. Further, upon analyzing dopamine receptor dynamics we identify distinct and non-conserved residue communities around the contacts F170<sup>4.62</sup>_F172<sup>ECL2</sup> and S146<sup>4.38</sup>_G141<sup>34.56</sup> that are specific to D<sub>3</sub>R conformational transitions compared to D<sub>2</sub>R. This information can be tapped to design subtype-specific drugs for neuropsychiatric and substance use disorders.</p>

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Temporally resolved and interpretable machine learning model of GPCR conformational transition

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月6日

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

摘要

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 F1704.62_F172ECL2 and S1464.38_G14134.56 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.