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一种使用超大型虚拟筛选靶向严重急性呼吸综合征冠状病毒2(SARS-CoV-2)蛋白的多管齐下方法。

A Multi-Pronged Approach Targeting SARS-CoV-2 Proteins Using Ultra-Large Virtual Screening.

作者信息

Gorgulla Christoph, Padmanabha Das Krishna M, Leigh Kendra E, Cespugli Marco, Fischer Patrick D, Wang Zi-Fu, Tesseyre Guilhem, Pandita Shreya, Shnapir Alec, Calderaio Anthony, Gechev Minko, Rose Alexander, Lewis Noam, Hutcheson Colin, Yaffe Erez, Luxenburg Roni, Herce Henry D, Durmaz Vedat, Halazonetis Thanos D, Fackeldey Konstantin, Patten Justin J, Chuprina Alexander, Dziuba Igor, Plekhova Alla, Moroz Yurii, Radchenko Dmytro, Tarkhanova Olga, Yavnyuk Irina, Gruber Christian, Yust Ryan, Payne Dave, Näär Anders M, Namchuk Mark N, Davey Robert A, Wagner Gerhard, Kinney Jamie, Arthanari Haribabu

机构信息

Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Harvard University, Boston, USA.

Department of Physics, Faculty of Arts and Sciences, Harvard University, Cambridge, USA.

出版信息

ChemRxiv. 2020 Jul 24:12682316. doi: 10.26434/chemrxiv.12682316.v1.

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), previously known as 2019 novel coronavirus (2019-nCoV), has spread rapidly across the globe, creating an unparalleled global health burden and spurring a deepening economic crisis. As of July 7th, 2020, almost seven months into the outbreak, there are no approved vaccines and few treatments available. Developing drugs that target multiple points in the viral life cycle could serve as a strategy to tackle the current as well as future coronavirus pandemics. Here we leverage the power of our recently developed screening platform, VirtualFlow, to identify inhibitors that target SARS-CoV-2. VirtualFlow is able to efficiently harness the power of computing clusters and cloud-based computing platforms to carry out ultra-large scale virtual screens. In this unprecedented structure-based multi-target virtual screening campaign, we have used VirtualFlow to screen an average of approximately 1 billion molecules against each of 40 different target sites on 17 different potential viral and host targets in the cloud. In addition to targeting the active sites of viral enzymes, we also target critical auxiliary sites such as functionally important protein-protein interaction interfaces. This multi-target approach not only increases the likelihood of finding a potent inhibitor, but could also help identify a collection of anti-coronavirus drugs that would retain efficacy in the face of viral mutation. Drugs belonging to different regimen classes could be combined to develop possible combination therapies, and top hits that bind at highly conserved sites would be potential candidates for further development as coronavirus drugs. Here, we present the top 200 hits for each target site. While in-house experimental validation of some of these compounds is currently underway, we want to make this array of potential inhibitor candidates available to researchers worldwide in consideration of the pressing need for fast-tracked drug development.

摘要

严重急性呼吸综合征冠状病毒2(SARS-CoV-2),以前称为2019新型冠状病毒(2019-nCoV),已在全球迅速传播,造成了前所未有的全球健康负担,并引发了日益严重的经济危机。截至2020年7月7日,疫情爆发已近七个月,目前尚无获批的疫苗,可用的治疗方法也很少。开发针对病毒生命周期多个环节的药物可作为应对当前及未来冠状病毒大流行的一种策略。在此,我们利用我们最近开发的筛选平台VirtualFlow来识别针对SARS-CoV-2的抑制剂。VirtualFlow能够有效地利用计算集群和基于云的计算平台的能力,以进行超大规模的虚拟筛选。在这次前所未有的基于结构的多靶点虚拟筛选活动中,我们利用VirtualFlow在云端针对17个不同的潜在病毒和宿主靶点上的40个不同靶点位点,平均对约10亿个分子进行筛选。除了针对病毒酶的活性位点外,我们还针对关键的辅助位点,如功能重要的蛋白质-蛋白质相互作用界面。这种多靶点方法不仅增加了找到有效抑制剂的可能性,还可能有助于识别一系列在面对病毒突变时仍能保持疗效的抗冠状病毒药物。属于不同治疗类别的药物可以联合起来开发可能的联合疗法,而在高度保守位点结合的顶级命中物将是作为冠状病毒药物进一步开发的潜在候选物。在此,我们展示了每个靶点位点的前200个命中物。虽然目前正在对其中一些化合物进行内部实验验证,但考虑到快速推进药物开发的迫切需求,我们希望将这一系列潜在的抑制剂候选物提供给全球的研究人员。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab04/7668741/5e80f45c09a7/nihpp-12682316-f0001.jpg

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