Hall-Swan Sarah, Antunes Dinler A, Devaurs Didier, Rigo Mauricio M, Kavraki Lydia E, Zanatta Geancarlo
bioRxiv. 2021 Jan 22:2021.01.21.427315. doi: 10.1101/2021.01.21.427315.
Recent efforts to computationally identify inhibitors for SARS-CoV-2 proteins have largely ignored the issue of receptor flexibility. We have implemented a computational tool for ensemble docking with the SARS-CoV-2 proteins, including the main protease (Mpro), papain-like protease (PLpro) and RNA-dependent RNA polymerase (RdRp).
Ensembles of other SARS-CoV-2 proteins are being prepared and made available through a user-friendly docking interface. Plausible binding modes between conformations of a selected ensemble and an uploaded ligand are generated by DINC, our parallelized meta-docking tool. Binding modes are scored with three scoring functions, and account for the flexibility of both the ligand and receptor. Additional details on our methods are provided in the supplementary material.
dinc-covid.kavrakilab.org.
Details on methods for ensemble generation and docking are provided as supplementary data online.
最近通过计算识别严重急性呼吸综合征冠状病毒2（SARS-CoV-2）蛋白抑制剂的努力在很大程度上忽略了受体灵活性的问题。我们已经实现了一种用于与SARS-CoV-2蛋白（包括主要蛋白酶（Mpro）、木瓜样蛋白酶（PLpro）和RNA依赖性RNA聚合酶（RdRp））进行整合对接的计算工具。
正在制备其他SARS-CoV-2蛋白的整合构象，并通过用户友好的对接界面提供。我们的并行化元对接工具DINC生成所选整合构象与上传配体之间合理的结合模式。结合模式用三种评分函数进行评分，并考虑了配体和受体的灵活性。补充材料中提供了我们方法的更多详细信息。
dinc-covid.kavrakilab.org。
整合构象生成和对接方法的详细信息作为在线补充数据提供。
