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计算研究 SARS-CoV-2 谱系 B.1.1.7 毒力和发病机制增强的原因。

Computational investigation of the increased virulence and pathogenesis of SARS-CoV-2 lineage B.1.1.7.

机构信息

Department of Computer Science, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, S-100 44, Stockholm, Sweden.

Department of Bioinformatics, Alagappa University, Karaikudi, Tamilnadu, India.

出版信息

Phys Chem Chem Phys. 2022 Aug 31;24(34):20371-20380. doi: 10.1039/d2cp00469k.

DOI:10.1039/d2cp00469k
PMID:35983778
Abstract

New variants of SARS-CoV-2 are being reported worldwide. The World Health Organization has reported Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2) and Omicron (B.1.1.529) as the variants of concern. There are speculations that the variants might evade the host immune responses induced by currently available vaccines and develop resistance to drugs under consideration. The first step of viral infection in COVID-19 occurs through the interaction of the spike protein's receptor-binding domain (RBD) with the peptidase domain of the human ACE-2 (hACE-2) receptor. This study aims to get a molecular-level understanding of the mechanism behind the increased infection rate in the alpha variant. We have computationally studied the spike protein interaction in both the wild-type and B.1.1.7 variant with the hACE-2 receptor using molecular dynamics and MM-GBSA based binding free energy calculations. The binding free energy difference shows that the mutant variant of the spike protein has increased binding affinity for the hACE-2 receptor ( ΔG(N501Y,A570D) is in the range -7.2 to -7.6 kcal mol) and the results were validated using Density functional theory. We demonstrate that with the use of state-of-the-art computational approaches, we can, in advance, predict the virulent nature of variants of SARS-CoV-2 and alert the world healthcare system.

摘要

新型 SARS-CoV-2 变种正在全球范围内被报道。世界卫生组织已将 Alpha(B.1.1.7)、Beta(B.1.351)、Gamma(P.1)、Delta(B.1.617.2)和 Omicron(B.1.1.529)列为关注变种。有推测称,这些变种可能逃避了目前可用疫苗诱导的宿主免疫反应,并对正在考虑的药物产生耐药性。COVID-19 病毒感染的第一步是通过刺突蛋白的受体结合域(RBD)与人类 ACE-2(hACE-2)受体的肽酶结构域相互作用来实现的。本研究旨在从分子水平上了解 alpha 变体感染率增加的背后机制。我们使用分子动力学和基于 MM-GBSA 的结合自由能计算,对野生型和 B.1.1.7 变体的刺突蛋白与 hACE-2 受体的相互作用进行了计算研究。结合自由能差异表明,刺突蛋白的突变变体对 hACE-2 受体的结合亲和力增加(ΔG(N501Y,A570D)的范围在-7.2 到-7.6 kcal/mol 之间),并使用密度泛函理论进行了验证。我们证明,通过使用最先进的计算方法,我们可以提前预测 SARS-CoV-2 变种的毒力性质,并向世界卫生系统发出警报。

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