Department of Neurology and Neurological Sciences, School of Medicine, Stanford University, 1201 Welch Road, MSLS, P259, Stanford, CA 94305, USA.
Biomolecules. 2022 Nov 10;12(11):1665. doi: 10.3390/biom12111665.
With its fast-paced mutagenesis, the SARS-CoV-2 Omicron variant has threatened many societies worldwide. Strategies for predicting mutagenesis such as the computational prediction of SARS-CoV-2 structural diversity and its interaction with the human receptor will greatly benefit our understanding of the virus and help develop therapeutics against it. We aim to use protein structure prediction algorithms along with molecular docking to study the effects of various mutations in the Receptor Binding Domain (RBD) of the SARS-CoV-2 and its key interactions with the angiotensin-converting enzyme 2 (ACE-2) receptor. The RBD structures of the naturally occurring variants of SARS-CoV-2 were generated from the WUHAN-Hu-1 using the trRosetta algorithm. Docking (HADDOCK) and binding analysis (PRODIGY) between the predicted RBD sequences and ACE-2 highlighted key interactions at the Receptor-Binding Motif (RBM). Further mutagenesis at conserved residues in the Original, Delta, and Omicron variants (P499S and T500R) demonstrated stronger binding and interactions with the ACE-2 receptor. The predicted T500R mutation underwent some preliminary tests in vitro for its binding and transmissibility in cells; the results correlate with the in-silico analysis. In summary, we suggest conserved residues P499 and T500 as potential mutation sites that could increase the binding affinity and yet do not exist in nature. This work demonstrates the use of the trRosetta algorithm to predict protein structure and future mutations at the RBM of SARS-CoV-2, followed by experimental testing for further efficacy verification. It is important to understand the protein structure and folding to help develop potential therapeutics.
SARS-CoV-2 奥密克戎变异株具有快速的突变能力，对全球许多社会构成了威胁。预测突变的策略，如 SARS-CoV-2 结构多样性的计算预测及其与人类受体的相互作用，将极大地有助于我们了解病毒，并帮助开发针对它的治疗方法。我们旨在使用蛋白质结构预测算法和分子对接来研究 SARS-CoV-2 的受体结合域（RBD）中各种突变及其与血管紧张素转换酶 2（ACE-2）受体的关键相互作用的影响。使用 trRosetta 算法从 WUHAN-Hu-1 生成 SARS-CoV-2 的自然发生变异的 RBD 结构。对接（HADDOCK）和 ACE-2 之间预测的 RBD 序列之间的结合分析（PRODIGY）突出了受体结合基序（RBM）的关键相互作用。在原始、德尔塔和奥密克戎变异株（P499S 和 T500R）中的保守残基上进一步进行突变，证明与 ACE-2 受体的结合和相互作用更强。预测的 T500R 突变在体外进行了一些初步测试，以评估其在细胞中的结合和传染性；结果与计算机分析相关。总之，我们建议将保守残基 P499 和 T500 作为潜在的突变位点，这些突变位点可以增加结合亲和力，但在自然界中不存在。这项工作展示了使用 trRosetta 算法预测 SARS-CoV-2 的 RBM 中的蛋白质结构和未来突变，然后进行实验测试以进一步验证功效。了解蛋白质结构和折叠对于帮助开发潜在的治疗方法非常重要。
