Institute of Chemical Sciences and Technologies "Giulio Natta" (SCITEC) - CNR, Rome, 00168, Italy.
Mol Inform. 2021 Jun;40(6):e2060080. doi: 10.1002/minf.202060080. Epub 2021 Apr 27.
The spike glycoprotein (S) of the SARS-CoV-2 virus surface plays a key role in receptor binding and virus entry. The S protein uses the angiotensin converting enzyme (ACE2) for entry into the host cell and binding to ACE2 occurs at the receptor binding domain (RBD) of the S protein. Therefore, the protein-protein interactions (PPIs) between the SARS-CoV-2 RBD and human ACE2, could be attractive therapeutic targets for drug discovery approaches designed to inhibit the entry of SARS-CoV-2 into the host cells. Herein, with the support of machine learning approaches, we report structure-based virtual screening as an effective strategy to discover PPIs inhibitors from ZINC database. The proposed computational protocol led to the identification of a promising scaffold which was selected for subsequent binding mode analysis and that can represent a useful starting point for the development of new treatments of the SARS-CoV-2 infection.
新型冠状病毒表面的刺突糖蛋白（S）在受体结合和病毒进入中起着关键作用。S 蛋白利用血管紧张素转换酶（ACE2）进入宿主细胞，ACE2 与 S 蛋白的受体结合域（RBD）结合。因此，新型冠状病毒 RBD 与人类 ACE2 之间的蛋白质-蛋白质相互作用（PPIs），可能是设计用于抑制新型冠状病毒进入宿主细胞的药物发现方法的有吸引力的治疗靶点。在此，在机器学习方法的支持下，我们报告了基于结构的虚拟筛选，这是一种从 ZINC 数据库中发现 PPIs 抑制剂的有效策略。所提出的计算方案导致了一种有前途的支架的识别，该支架被选择用于进一步的结合模式分析，并且可以作为开发新型冠状病毒感染治疗方法的有用起点。
