Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States.
Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75275, United States.
J Phys Chem B. 2024 May 16;128(19):4696-4715. doi: 10.1021/acs.jpcb.4c01341. Epub 2024 May 2.
In this study, we combined AlphaFold-based atomistic structural modeling, microsecond molecular simulations, mutational profiling, and network analysis to characterize binding mechanisms of the SARS-CoV-2 spike protein with the host receptor ACE2 for a series of Omicron XBB variants including XBB.1.5, XBB.1.5+L455F, XBB.1.5+F456L, and XBB.1.5+L455F+F456L. AlphaFold-based structural and dynamic modeling of SARS-CoV-2 Spike XBB lineages can accurately predict the experimental structures and characterize conformational ensembles of the spike protein complexes with the ACE2. Microsecond molecular dynamics simulations identified important differences in the conformational landscapes and equilibrium ensembles of the XBB variants, suggesting that combining AlphaFold predictions of multiple conformations with molecular dynamics simulations can provide a complementary approach for the characterization of functional protein states and binding mechanisms. Using the ensemble-based mutational profiling of protein residues and physics-based rigorous calculations of binding affinities, we identified binding energy hotspots and characterized the molecular basis underlying epistatic couplings between convergent mutational hotspots. Consistent with the experiments, the results revealed the mediating role of the Q493 hotspot in the synchronization of epistatic couplings between L455F and F456L mutations, providing a quantitative insight into the energetic determinants underlying binding differences between XBB lineages. We also proposed a network-based perturbation approach for mutational profiling of allosteric communications and uncovered the important relationships between allosteric centers mediating long-range communication and binding hotspots of epistatic couplings. The results of this study support a mechanism in which the binding mechanisms of the XBB variants may be determined by epistatic effects between convergent evolutionary hotspots that control ACE2 binding.
在这项研究中,我们结合了基于 AlphaFold 的原子结构建模、微秒分子模拟、突变分析和网络分析,以表征一系列奥密克戎 XBB 变体(包括 XBB.1.5、XBB.1.5+L455F、XBB.1.5+F456L 和 XBB.1.5+L455F+F456L)中 SARS-CoV-2 刺突蛋白与宿主受体 ACE2 的结合机制。基于 AlphaFold 的 SARS-CoV-2 刺突 XBB 谱系的结构和动态建模可以准确预测实验结构,并描绘刺突蛋白与 ACE2 复合物的构象集合。微秒分子动力学模拟确定了 XBB 变体构象景观和平衡集合的重要差异,这表明结合 AlphaFold 对多种构象的预测和分子动力学模拟可以为功能蛋白状态和结合机制的表征提供一种互补方法。使用基于集合的蛋白质残基突变分析和基于物理的结合亲和力严格计算,我们确定了结合能热点,并描绘了趋同突变热点之间上位性耦合的分子基础。与实验结果一致的是,结果揭示了 Q493 热点在 L455F 和 F456L 突变之间上位性耦合同步中的介导作用,为 XBB 谱系之间结合差异的能量决定因素提供了定量见解。我们还提出了一种基于网络的变构通讯突变分析方法,并揭示了介导长程通讯的变构中心与上位性耦合结合热点之间的重要关系。这项研究的结果支持了这样一种机制,即 XBB 变体的结合机制可能由控制 ACE2 结合的趋同进化热点之间的上位性效应决定。