A multi-tier computational screening framework to effectively search the mutational space of SARS-CoV-2 receptor binding motif to identify mutants with enhanced ACE2 binding abilities.

IF 2.8 4区 医学 Q3 CHEMISTRY, MEDICINAL Molecular Informatics Pub Date : 2023-10-01 Epub Date: 2023-08-31 DOI:10.1002/minf.202300055
Sandipan Chakraborty, Chiranjeet Saha
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Abstract

SARS-CoV-2 gained crucial mutations at the receptor binding domain (RBD) that often changed the course of the pandemic leading to new waves with increased case fatality. Variants are observed with enhanced transmission and immune invasion abilities. Thus, predicting future variants with enhanced transmission ability is a problem of utmost research interest. Here, we have developed a multi-tier exhaustive SARS-CoV-2 mutation screening platform combining MM/GBSA, extensive molecular dynamics simulations, and steered molecular dynamics to identify RBD mutants with enhanced ACE2 binding capability. We have identified four RBM mutations (F490K, S494K, G504F, and the P499L) with significantly higher ACE2 binding abilities than wild-type RBD. Compared to wild-type RBD, they all form stable complexes with more hydrogen bonds and salt-bridge interactions with ACE2. Our simulation data suggest that these mutations allosterically alter the packing of the RBM interface of the RBD-ACE2 complex. As a result, the rupture force required to break the RBD-ACE2 contacts is significantly higher for these mutants.

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一种有效搜索严重急性呼吸系统综合征冠状病毒2型变异空间的多层计算筛选框架 受体结合基序以鉴定具有增强的ACE2结合能力的突变体。
严重急性呼吸系统综合征冠状病毒2型在受体结合域(RBD)上获得了关键突变,这往往会改变疫情的进程,导致新一波的病死率增加。观察到变异具有增强的传播和免疫入侵能力。因此,预测具有增强传播能力的未来变体是一个极具研究兴趣的问题。在这里,我们开发了一个多层详尽的严重急性呼吸系统综合征冠状病毒2型突变筛查平台,该平台结合了MM/GBSA、广泛的分子动力学模拟和分子动力学,以识别具有增强的ACE2结合能力的RBD突变体。我们已经确定了四种RBM突变(F490K、S494K、G504F和P499L),其ACE2结合能力显著高于野生型RBD。与野生型RBD相比,它们都形成了稳定的复合物,具有更多的氢键和与ACE2的盐桥相互作用。我们的模拟数据表明,这些突变变构地改变了RBD-ACE2复合物的RBM界面的堆积。因此,对于这些突变体,破坏RBD-ACE2接触所需的断裂力显著更高。
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来源期刊
Molecular Informatics
Molecular Informatics CHEMISTRY, MEDICINAL-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.30
自引率
2.80%
发文量
70
审稿时长
3 months
期刊介绍: Molecular Informatics is a peer-reviewed, international forum for publication of high-quality, interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design. Molecular Informatics succeeded QSAR & Combinatorial Science in 2010. Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation. The journal''s scope includes but is not limited to the fields of drug discovery and chemical biology, protein and nucleic acid engineering and design, the design of nanomolecular structures, strategies for modeling of macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, computer-assisted screening strategies, as well as novel technologies for the de novo design of biologically active molecules. As a unique feature Molecular Informatics publishes so-called "Methods Corner" review-type articles which feature important technological concepts and advances within the scope of the journal.
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