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Detection of Binding Sites on SARS-CoV-2 Spike Protein Receptor-Binding Domain by Molecular Dynamics Simulations in Mixed Solvents
IEEE/ACM Transactions on Computational Biology and Bioinformatics ( IF 3.6 ) Pub Date : 2021-04-29 , DOI: 10.1109/tcbb.2021.3076259
Elmeri M. Jokinen , Krishnasamy Gopinath , Sami T. Kurkinen , Olli T. Pentikainen

The novel SARS-CoV-2 uses ACE2 (Angiotensin-Converting Enzyme 2) receptor as an entry point. Insights on S protein receptor-binding domain (RBD) interaction with ACE2 receptor and drug repurposing has accelerated drug discovery for the novel SARS-CoV-2 infection. Finding small molecule binding sites in S protein and ACE2 interface is crucial in search of effective drugs to prevent viral entry. In this study, we employed molecular dynamics simulations in mixed solvents together with virtual screening to identify small molecules that could be potential inhibitors of S protein –ACE2 interaction. Observation of organic probe molecule localization during the simulations revealed multiple sites at the S protein surface related to small molecule, antibody, and ACE2 binding. In addition, a novel conformation of the S protein was discovered that could be stabilized by small molecules to inhibit attachment to ACE2. The most promising binding site on RBD-ACE2 interface was targeted with virtual screening and top-ranked compounds (DB08248, DB02651, DB03714, and DB14826) are suggested for experimental testing. The protocol described here offers an extremely fast method for characterizing key proteins of a novel pathogen and for the identification of compounds that could inhibit or accelerate spreading of the disease.

中文翻译:


通过混合溶剂中的分子动力学模拟检测 SARS-CoV-2 刺突蛋白受体结合域的结合位点



新型 SARS-CoV-2 使用 ACE2(血管紧张素转换酶 2)受体作为切入点。对 S 蛋白受体结合域 (RBD) 与 ACE2 受体相互作用和药物再利用的见解加速了针对新型 SARS-CoV-2 感染的药物发现。寻找S蛋白和ACE2界面中的小分子结合位点对于寻找有效药物阻止病毒进入至关重要。在这项研究中,我们采用混合溶剂中的分子动力学模拟和虚拟筛选来识别可能成为 S 蛋白 -ACE2 相互作用的潜在抑制剂的小分子。模拟过程中对有机探针分子定位的观察揭示了 S 蛋白表面与小分子、抗体和 ACE2 结合相关的多个位点。此外,还发现了 S 蛋白的一种新构象,可以通过小分子稳定该构象,从而抑制与 ACE2 的附着。 RBD-ACE2 界面上最有希望的结合位点被虚拟筛选为目标,并建议排名靠前的化合物(DB08248、DB02651、DB03714 和 DB14826)进行实验测试。这里描述的协议提供了一种极其快速的方法来表征新型病原体的关键蛋白质以及识别可以抑制或加速疾病传播的化合物。
更新日期:2021-04-29
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