Department of Systems Biology, Columbia University Medical Center, New York, NY 10032, USA. Electronic address: https://twitter.com/AlinaSergeeva.
Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA.
J Mol Biol. 2023 Aug 1;435(15):168187. doi: 10.1016/j.jmb.2023.168187. Epub 2023 Jun 22.
The strength of binding between human angiotensin converting enzyme 2 (ACE2) and the receptor binding domain (RBD) of viral spike protein plays a role in the transmissibility of the SARS-CoV-2 virus. In this study we focus on a subset of RBD mutations that have been frequently observed in infected individuals and probe binding affinity changes to ACE2 using surface plasmon resonance (SPR) measurements and free energy perturbation (FEP) calculations. Our SPR results are largely in accord with previous studies but discrepancies do arise due to differences in experimental methods and to protocol differences even when a single method is used. Overall, we find that FEP performance is superior to that of other computational approaches examined as determined by agreement with experiment and, in particular, by its ability to identify stabilizing mutations. Moreover, the calculations successfully predict the observed cooperative stabilization of binding by the Q498R N501Y double mutant present in Omicron variants and offer a physical explanation for the underlying mechanism. Overall, our results suggest that despite the significant computational cost, FEP calculations may offer an effective strategy to understand the effects of interfacial mutations on protein-protein binding affinities and, hence, in a variety of practical applications such as the optimization of neutralizing antibodies.
人类血管紧张素转换酶 2(ACE2)与病毒刺突蛋白受体结合域(RBD)之间的结合强度在 SARS-CoV-2 病毒的传染性中起作用。在这项研究中,我们专注于经常在感染个体中观察到的 RBD 突变亚组,并使用表面等离子体共振(SPR)测量和自由能微扰(FEP)计算来探测对 ACE2 的结合亲和力变化。我们的 SPR 结果与以前的研究基本一致,但由于实验方法和方案的差异,即使使用相同的方法,也会出现差异。总体而言,我们发现 FEP 的性能优于其他检查的计算方法,这是通过与实验的一致性以及其识别稳定突变的能力来确定的。此外,这些计算成功地预测了在 Omicron 变体中存在的 Q498R N501Y 双突变体对结合的协同稳定作用,并为潜在机制提供了物理解释。总体而言,我们的研究结果表明,尽管计算成本很高,但 FEP 计算可能是一种有效的策略,可以理解界面突变对蛋白质-蛋白质结合亲和力的影响,因此可以在各种实际应用中,如中和抗体的优化。