Abstract：
Understanding protein-protein interactions (PPIs) is fundamental to infer how different molecular systems work. A major component to model molecular recognition is the buried surface area (BSA), that is, the area that becomes inaccessible to solvent upon complex formation. To date, many attempts tried to connect BSA to molecular recognition principles, and in particular, to the underlying binding affinity. However, the most popular approach to calculate BSA is to use a single (or in some cases few) bound structures, consequently neglecting a wealth of structural information of the interacting proteins derived from ensembles corresponding to their unbound and bound states. Moreover, the most popular method inherently assumes the component proteins to bind as rigid entities. To address the above shortcomings, we developed a Monte Carlo method-based Interface Residue Assessment Algorithm (IRAA), to calculate a combined distribution of BSA for a given complex. Further, we apply our algorithm to human ACE2 and SARS-CoV-2 Spike protein complex, a system of prime importance. Results show a much broader distribution of BSA compared to that obtained from only the bound structure or structures and extended residue members of the interface with implications to the underlying biomolecular recognition. We derive that specific interface residues of ACE2 and of S-protein are consistently highly flexible, whereas other residues systematically show minor conformational variations. In effect, IRAA facilitates the use of all available structural data for any biomolecular complex of interest, extracting quantitative parameters with statistical significance, thereby providing a deeper biophysical understanding of the molecular system under investigation.
摘要：
了解蛋白质-蛋白质相互作用（PPIs）是推断不同分子系统如何工作的基础。模拟分子识别的主要组成部分是埋藏表面积(BSA)，即，复合物形成后溶剂无法进入的区域。迄今为止，许多尝试试图将BSA与分子识别原理联系起来，特别是,与潜在的结合亲和力。然而,计算BSA的最流行的方法是使用单个（或在某些情况下很少）绑定结构，因此，忽略了来自与其未结合和结合状态相对应的集合的相互作用蛋白质的大量结构信息。此外，最流行的方法固有地假定组分蛋白质作为刚性实体结合。针对上述不足,我们开发了一种基于蒙特卡罗方法的界面残差评估算法(IRAA)，计算给定复合物的BSA组合分布。Further,我们将我们的算法应用于人类ACE2和SARS-CoV-2尖峰蛋白复合物，一个最重要的系统。结果表明，与仅从界面的一个或多个结合结构和扩展残基成员获得的BSA相比，BSA的分布要广得多，这与潜在的生物分子识别有关。我们得出，ACE2和S蛋白的特定界面残基始终具有高度灵活性，而其他残基系统地显示微小的构象变化。实际上，IRAA有助于使用所有可用的结构数据，用于任何感兴趣的生物分子复合物，提取具有统计意义的定量参数，从而为研究中的分子系统提供了更深入的生物物理理解。本文受版权保护。保留所有权利。
