Mukhaleva Elizaveta, Ma Ning, van der Velden Wijnand J C, Gogoshin Grigoriy, Branciamore Sergio, Bhattacharya Supriyo, Rodin Andrei S, Vaidehi Nagarajan
Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, CA 91010.
Irell and Manella Graduate School of Biological Sciences, Beckman Research Institute of the City of Hope, Duarte, CA 91010.
bioRxiv. 2023 Oct 12:2023.10.09.561618. doi: 10.1101/2023.10.09.561618.
Cooperative interactions in protein-protein interfaces demonstrate the interdependency or the linked network-like behavior of interface interactions and their effect on the coupling of proteins. Cooperative interactions also could cause ripple or allosteric effects at a distance in protein-protein interfaces. Although they are critically important in protein-protein interfaces it is challenging to determine which amino acid pair interactions are cooperative. In this work we have used Bayesian network modeling, an interpretable machine learning method, combined with molecular dynamics trajectories to identify the residue pairs that show high cooperativity and their allosteric effect in the interface of G protein-coupled receptor (GPCR) complexes with G proteins. Our results reveal a strong co-dependency in the formation of interface GPCR:G protein contacts. This observation indicates that cooperativity of GPCR:G protein interactions is necessary for the coupling and selectivity of G proteins and is thus critical for receptor function. We have identified subnetworks containing polar and hydrophobic interactions that are common among multiple GPCRs coupling to different G protein subtypes (Gs, Gi and Gq). These common subnetworks along with G protein-specific subnetworks together confer selectivity to the G protein coupling. This work underscores the potential of data-driven Bayesian network modeling in elucidating the intricate dependencies and selectivity determinants in GPCR:G protein complexes, offering valuable insights into the dynamic nature of these essential cellular signaling components.
蛋白质-蛋白质界面中的协同相互作用展示了界面相互作用的相互依赖性或类似链接网络的行为及其对蛋白质偶联的影响。协同相互作用还可能在蛋白质-蛋白质界面中引起远距离的涟漪效应或变构效应。尽管它们在蛋白质-蛋白质界面中至关重要，但确定哪些氨基酸对相互作用是协同的却具有挑战性。在这项工作中，我们使用了贝叶斯网络建模（一种可解释的机器学习方法）并结合分子动力学轨迹，以识别在G蛋白偶联受体（GPCR）与G蛋白的复合物界面中显示出高协同性及其变构效应的残基对。我们的结果揭示了界面GPCR：G蛋白接触形成过程中的强烈共依赖性。这一观察结果表明，GPCR：G蛋白相互作用的协同性对于G蛋白的偶联和选择性是必要的，因此对于受体功能至关重要。我们已经确定了包含极性和疏水相互作用的子网，这些子网在与不同G蛋白亚型（Gs、Gi和Gq）偶联的多个GPCR中是常见的。这些共同的子网与G蛋白特异性子网一起赋予了G蛋白偶联选择性。这项工作强调了数据驱动的贝叶斯网络建模在阐明GPCR：G蛋白复合物中复杂的依赖性和选择性决定因素方面的潜力，为这些重要细胞信号成分的动态性质提供了有价值的见解。
