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支持严重急性呼吸综合征冠状病毒2型木瓜样蛋白酶药物研发:方法与基准测试

Supporting SARS-CoV-2 Papain-Like Protease Drug Discovery: Methods and Benchmarking.

作者信息

Ibrahim Tamer M, Ismail Muhammad I, Bauer Matthias R, Bekhit Adnan A, Boeckler Frank M

机构信息

Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Kafrelsheikh University, Kafrelsheikh, Egypt.

Department of Pharmaceutical Chemistry, Faculty of Pharmacy, The British University in Egypt, Cairo, Egypt.

出版信息

Front Chem. 2020 Nov 5;8:592289. doi: 10.3389/fchem.2020.592289. eCollection 2020.

Abstract

The coronavirus disease 19 (COVID-19) is a rapidly growing pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Its papain-like protease (SARS-CoV-2 PLpro) is a crucial target to halt virus replication. SARS-CoV PLpro and SARS-CoV-2 PLpro share an 82.9% sequence identity and a 100% sequence identity for the binding site reported to accommodate small molecules in SARS-CoV. The flexible key binding site residues Tyr269 and Gln270 for small-molecule recognition in SARS-CoV PLpro exist also in SARS-CoV-2 PLpro. This inspired us to use the reported small-molecule binders to SARS-CoV PLpro to generate a high-quality DEKOIS 2.0 benchmark set. Accordingly, we used them in a cross-benchmarking study against SARS-CoV-2 PLpro. As there is no SARS-CoV-2 PLpro structure complexed with a small-molecule ligand publicly available at the time of manuscript submission, we built a homology model based on the ligand-bound SARS-CoV structure for benchmarking and docking purposes. Three publicly available docking tools FRED, AutoDock Vina, and PLANTS were benchmarked. All showed better-than-random performances, with FRED performing best against the built model. Detailed performance analysis via pROC-Chemotype plots showed a strong enrichment of the most potent bioactives in the early docking ranks. Cross-benchmarking against the X-ray structure complexed with a peptide-like inhibitor confirmed that FRED is the best-performing tool. Furthermore, we performed cross-benchmarking against the newly introduced X-ray structure complexed with a small-molecule ligand. Interestingly, its benchmarking profile and chemotype enrichment were comparable to the built model. Accordingly, we used FRED in a prospective virtual screen of the DrugBank database. In conclusion, this study provides an example of how to harness a custom-made DEKOIS 2.0 benchmark set as an approach to enhance the virtual screening success rate against a vital target of the rapidly emerging pandemic.

摘要

新型冠状病毒肺炎(COVID-19)是由严重急性呼吸综合征冠状病毒2(SARS-CoV-2)引起的迅速蔓延的大流行病。其木瓜样蛋白酶(SARS-CoV-2 PLpro)是阻止病毒复制的关键靶点。SARS-CoV PLpro与SARS-CoV-2 PLpro的序列同一性为82.9%,对于据报道可容纳SARS-CoV中小分子的结合位点,二者的序列同一性为100%。SARS-CoV PLpro中用于小分子识别的灵活关键结合位点残基Tyr269和Gln270在SARS-CoV-2 PLpro中也存在。这促使我们使用已报道的与SARS-CoV PLpro结合的小分子化合物来生成高质量的DEKOIS 2.0基准集。因此,我们将它们用于针对SARS-CoV-2 PLpro的交叉基准研究。由于在提交稿件时没有公开的与小分子配体复合的SARS-CoV-2 PLpro结构,我们基于与配体结合的SARS-CoV结构构建了一个同源模型,用于基准测试和对接。对三种公开可用的对接工具FRED、AutoDock Vina和PLANTS进行了基准测试。所有工具的表现均优于随机水平,其中FRED针对构建模型的表现最佳。通过pROC-化学型图进行的详细性能分析表明,在对接早期排名中,最有效的生物活性物质有很强的富集。与与肽样抑制剂复合的X射线结构进行交叉基准测试证实,FRED是表现最佳的工具。此外,我们对新引入的与小分子配体复合 的X射线结构进行了交叉基准测试。有趣的是,其基准测试概况和化学型富集与构建模型相当。因此,我们在DrugBank数据库的前瞻性虚拟筛选中使用了FRED。总之,本研究提供了一个示例,展示了如何利用定制的DEKOIS 2.0基准集作为一种方法来提高针对迅速出现的大流行病的重要靶点的虚拟筛选成功率。

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