College of Computing, Data Science and Society, University of California, Berkeley, CA 94720, USA.
College of Computing and Software Engineering, Kennesaw State University, Marietta, GA 30060, USA.
Int J Mol Sci. 2024 Jul 23;25(15):8032. doi: 10.3390/ijms25158032.
In late 2019, the emergence of a novel coronavirus led to its identification as SARS-CoV-2, precipitating the onset of the COVID-19 pandemic. Many experimental and computational studies were performed on SARS-CoV-2 to understand its behavior and patterns. In this research, Molecular Dynamic (MD) simulation is utilized to compare the behaviors of SARS-CoV-2 and its Variants of Concern (VOC)-Alpha, Beta, Gamma, Delta, and Omicron-with the hACE2 protein. Protein structures from the Protein Data Bank (PDB) were aligned and trimmed for consistency using Chimera, focusing on the receptor-binding domain (RBD) responsible for ACE2 interaction. MD simulations were performed using Visual Molecular Dynamics (VMD) and Nanoscale Molecular Dynamics (NAMD2), and salt bridges and hydrogen bond data were extracted from the results of these simulations. The data extracted from the last 5 ns of the 10 ns simulations were visualized, providing insights into the comparative stability of each variant's interaction with ACE2. Moreover, electrostatics and hydrophobic protein surfaces were calculated, visualized, and analyzed. Our comprehensive computational results are helpful for drug discovery and future vaccine designs as they provide information regarding the vital amino acids in protein-protein interactions (PPIs). Our analysis reveals that the Original and Omicron variants are the two most structurally similar proteins. The Gamma variant forms the strongest interaction with hACE2 through hydrogen bonds, while Alpha and Delta form the most stable salt bridges; the Omicron is dominated by positive potential in the binding site, which makes it easy to attract the hACE2 receptor; meanwhile, the Original, Beta, Delta, and Omicron variants show varying levels of interaction stability through both hydrogen bonds and salt bridges, indicating that targeted therapeutic agents can disrupt these critical interactions to prevent SARS-CoV-2 infection.
2019 年末，一种新型冠状病毒的出现使其被鉴定为 SARS-CoV-2，引发了 COVID-19 大流行。许多实验和计算研究都针对 SARS-CoV-2 进行，以了解其行为和模式。在这项研究中，利用分子动力学（MD）模拟比较了 SARS-CoV-2 及其关注变种（VOC）-Alpha、Beta、Gamma、Delta 和 Omicron 与 hACE2 蛋白的行为。使用 Chimera 对齐和修剪来自蛋白质数据库（PDB）的蛋白质结构，重点是负责 ACE2 相互作用的受体结合域（RBD）。使用可视化分子动力学（VMD）和纳米尺度分子动力学（NAMD2）进行 MD 模拟，并从这些模拟的结果中提取盐桥和氢键数据。从 10 ns 模拟的最后 5 ns 中提取数据并可视化，提供了关于每个变体与 ACE2 相互作用稳定性的比较信息。此外，还计算、可视化和分析了静电和疏水性蛋白质表面。我们的综合计算结果有助于药物发现和未来疫苗设计，因为它们提供了有关蛋白质-蛋白质相互作用（PPIs）中重要氨基酸的信息。我们的分析表明，原始和奥密克戎变体是两种最相似的蛋白质。Gamma 变体通过氢键与 hACE2 形成最强的相互作用，而 Alpha 和 Delta 形成最稳定的盐桥；Omicron 在结合部位主要由正电势主导，这使其容易吸引 hACE2 受体；同时，原始、Beta、Delta 和奥密克戎变体通过氢键和盐桥表现出不同程度的相互作用稳定性，表明靶向治疗剂可以破坏这些关键相互作用，以防止 SARS-CoV-2 感染。
