Alshahrani Mohammed, Parikh Vedant, Foley Brandon, Verkhivker Gennady
bioRxiv. 2025 Jul 1:2025.06.28.662162. doi: 10.1101/2025.06.28.662162.
The ongoing evolution of SARS-CoV-2 variants has underscored the need to understand not only the structural basis of antibody recognition but also the dynamic and allosteric mechanisms that could underlie complexity of broad and escape-resistant neutralization. In this study, we employed a multi-scale approach integrating structural analysis, hierarchical molecular simulations, mutational scanning and network-based allosteric modeling to dissect how Class 4 antibodies (represented by S2X35, 25F9, and SA55) and Class 5 antibodies (represented by S2H97, WRAIR-2063 and WRAIR-2134) can modulate conformational behavior, binding energetics, allosteric interactions and immune escape patterns of the SARS-CoV-2 spike protein. Using hierarchical simulations of the antibody complexes with the spike protein and ensemble-based mutational scanning of binding interactions we showed that these antibodies through targeting conserved cryptic sites can exert allosteric effects that influence global conformational dynamics in the RBD functional regions. The ensemble-based mutational scanning of binding interactions revealed an excellent agreement with experimentally derived deep mutational scanning (DMS) data accurately recapitulating the known binding hotspots and escape mutations across all studied antibodies. The predicted destabilization values in functional sites are consistent with experimentally observed reductions in antibody binding affinity and immune escape profiles demonstrating that computational models can robustly reproduce and forecast mutation-induced immune escape trends. Using dynamic network modeling we characterized the antibody-induced changes in residue interaction networks and long-range interactions. The results revealed that class 4 antibodies can exhibit distinct patterns of allosteric influence despite targeting overlapping regions, while class 5 antibodies elicit consistently dense and broadly distributed allosteric networks and long-range stabilization of the RBD conformations. Dynamic network analysis identifies a conserved allosteric network core that mediates long-range interactions and incudes antibody specific allosteric extensions that connect the binding interface hotspots with allosteric hubs. This study suggests that mechanisms of binding and immune escape for classes of antibodies targeting cryptic binding sites may be determined by confluence of multiple factors including high-affinity binding and long-range allosteric effects that modulate RBD adaptability and propagation of dynamic constraints which can influence mechanisms of viral evolution and escape.
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）变体的不断演变凸显了不仅要了解抗体识别的结构基础，还要了解可能构成广泛且抗逃逸中和复杂性基础的动态和变构机制的必要性。在本研究中，我们采用了一种多尺度方法，整合结构分析、分层分子模拟、突变扫描和基于网络的变构建模，以剖析4类抗体（以S2X35、25F9和SA55为代表）和5类抗体（以S2H97、WRAIR-2063和WRAIR-2134为代表）如何调节SARS-CoV-2刺突蛋白的构象行为、结合能、变构相互作用和免疫逃逸模式。通过对抗体与刺突蛋白复合物的分层模拟以及基于整体的结合相互作用突变扫描，我们表明这些抗体通过靶向保守的隐蔽位点可以发挥变构效应，影响RBD功能区域的全局构象动力学。基于整体的结合相互作用突变扫描与实验得出的深度突变扫描（DMS）数据显示出极好的一致性，准确地概括了所有研究抗体中已知的结合热点和逃逸突变。功能位点的预测去稳定值与实验观察到的抗体结合亲和力降低和免疫逃逸谱一致，表明计算模型可以稳健地再现和预测突变诱导的免疫逃逸趋势。使用动态网络建模，我们表征了抗体诱导的残基相互作用网络和长程相互作用的变化。结果表明，尽管4类抗体靶向重叠区域，但它们可以表现出不同的变构影响模式，而5类抗体则引发一致密集且广泛分布的变构网络以及RBD构象的长程稳定。动态网络分析确定了一个保守的变构网络核心，该核心介导长程相互作用，并包括将结合界面热点与变构枢纽连接起来的抗体特异性变构延伸。这项研究表明，针对隐蔽结合位点的各类抗体的结合和免疫逃逸机制可能由多种因素共同决定，包括高亲和力结合和长程变构效应，这些效应调节RBD的适应性以及动态约束的传播，进而可能影响病毒进化和逃逸的机制。
