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基于蛋白质动力学分析指导的基于活性的化合物库筛选构建高亲和力SARS-CoV-2 Mpro抑制剂蓝图

A Blueprint for High Affinity SARS-CoV-2 Mpro Inhibitors from Activity-Based Compound Library Screening Guided by Analysis of Protein Dynamics.

作者信息

Gossen Jonas, Albani Simone, Hanke Anton, Joseph Benjamin P, Bergh Cathrine, Kuzikov Maria, Costanzi Elisa, Manelfi Candida, Storici Paola, Gribbon Philip, Beccari Andrea R, Talarico Carmine, Spyrakis Francesca, Lindahl Erik, Zaliani Andrea, Carloni Paolo, Wade Rebecca C, Musiani Francesco, Kokh Daria B, Rossetti Giulia

机构信息

Institute for Neuroscience and Medicine (INM-9), Forschungszentrum Jülich, Jülich, 52425, Germany.

Institute for Advanced Simulations (IAS-5) "Computational biomedicine", Forschungszentrum Jülich, Jülich, 52425, Germany.

出版信息

ACS Pharmacol Transl Sci. 2021 Mar 16;4(3):1079-1095. doi: 10.1021/acsptsci.0c00215. eCollection 2021 Jun 11.

Abstract

The SARS-CoV-2 coronavirus outbreak continues to spread at a rapid rate worldwide. The main protease (Mpro) is an attractive target for anti-COVID-19 agents. Unexpected difficulties have been encountered in the design of specific inhibitors. Here, by analyzing an ensemble of ∼30 000 SARS-CoV-2 Mpro conformations from crystallographic studies and molecular simulations, we show that small structural variations in the binding site dramatically impact ligand binding properties. Hence, traditional druggability indices fail to adequately discriminate between highly and poorly druggable conformations of the binding site. By performing ∼200 virtual screenings of compound libraries on selected protein structures, we redefine the protein's druggability as the consensus chemical space arising from the multiple conformations of the binding site formed upon ligand binding. This procedure revealed a unique SARS-CoV-2 Mpro blueprint that led to a definition of a specific structure-based pharmacophore. The latter explains the poor transferability of potent SARS-CoV Mpro inhibitors to SARS-CoV-2 Mpro, despite the identical sequences of the active sites. Importantly, application of the pharmacophore predicted novel high affinity inhibitors of SARS-CoV-2 Mpro, that were validated by assays performed here and by a newly solved X-ray crystal structure. These results provide a strong basis for effective rational drug design campaigns against SARS-CoV-2 Mpro and a new computational approach to screen protein targets with malleable binding sites.

摘要

严重急性呼吸综合征冠状病毒2(SARS-CoV-2)引发的新冠疫情仍在全球迅速蔓延。主要蛋白酶(Mpro)是抗新冠病毒药物的一个有吸引力的靶点。在设计特异性抑制剂时遇到了意想不到的困难。在这里,通过分析来自晶体学研究和分子模拟的约30000个SARS-CoV-2 Mpro构象集合,我们发现结合位点的微小结构变化会显著影响配体结合特性。因此,传统的可成药指数无法充分区分结合位点的高可成药构象和低可成药构象。通过对选定的蛋白质结构进行约200次化合物库虚拟筛选,我们将蛋白质的可成药性重新定义为配体结合时形成的结合位点多种构象所产生的共识化学空间。这一过程揭示了一个独特的SARS-CoV-2 Mpro蓝图,从而定义了一个基于结构的特定药效团。尽管活性位点序列相同,但这解释了强效SARS-CoV Mpro抑制剂对SARS-CoV-2 Mpro的转移性较差的原因。重要的是,该药效团的应用预测了SARS-CoV-2 Mpro的新型高亲和力抑制剂,本文所进行的实验以及新解析的X射线晶体结构验证了这些抑制剂。这些结果为针对SARS-CoV-2 Mpro开展有效的合理药物设计活动提供了有力依据,并为筛选具有可塑性结合位点的蛋白质靶点提供了一种新的计算方法。

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