Department of Theoretical Chemistry and Biology, School of Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, 106 91, Stockholm, Sweden.
Division of Glycoscience, Department of Chemistry, School of Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.
Sci Rep. 2020 Nov 5;10(1):19125. doi: 10.1038/s41598-020-75762-7.
The current outbreak of Covid-19 infection due to SARS-CoV-2, a virus from the coronavirus family, has become a major threat to human healthcare. The virus has already infected more than 44 M people and the number of deaths reported has reached more than 1.1 M which may be attributed to lack of medicine. The traditional drug discovery approach involves many years of rigorous research and development and demands for a huge investment which cannot be adopted for the ongoing pandemic infection. Rather we need a swift and cost-effective approach to inhibit and control the viral infection. With the help of computational screening approaches and by choosing appropriate chemical space, it is possible to identify lead drug-like compounds for Covid-19. In this study, we have used the Drugbank database to screen compounds against the most important viral targets namely 3C-like protease (3CLpro), papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp) and the spike (S) protein. These targets play a major role in the replication/transcription and host cell recognition, therefore, are vital for the viral reproduction and spread of infection. As the structure based computational screening approaches are more reliable, we used the crystal structures for 3C-like main protease and spike protein. For the remaining targets, we used the structures based on homology modeling. Further, we employed two scoring methods based on binding free energies implemented in AutoDock Vina and molecular mechanics-generalized Born surface area approach. Based on these results, we propose drug cocktails active against the three viral targets namely 3CLpro, PLpro and RdRp. Interestingly, one of the identified compounds in this study i.e. Baloxavir marboxil has been under clinical trial for the treatment of Covid-19 infection. In addition, we have identified a few compounds such as Phthalocyanine, Tadalafil, Lonafarnib, Nilotinib, Dihydroergotamine, R-428 which can bind to all three targets simultaneously and can serve as multi-targeting drugs. Our study also included calculation of binding energies for various compounds currently under drug trials. Among these compounds, it is found that Remdesivir binds to targets, 3CLpro and RdRp with high binding affinity. Moreover, Baricitinib and Umifenovir were found to have superior target-specific binding while Darunavir is found to be a potential multi-targeting drug. As far as we know this is the first study where the compounds from the Drugbank database are screened against four vital targets of SARS-CoV-2 and illustrates that the computational screening using a double scoring approach can yield potential drug-like compounds against Covid-19 infection.
当前由冠状病毒家族的 SARS-CoV-2 病毒引起的新冠病毒感染已成为人类健康的主要威胁。该病毒已感染超过 4400 万人，报告的死亡人数已超过 110 万，这可能归因于缺乏药物。传统的药物发现方法涉及多年的严格研究和开发，需要巨额投资，因此无法用于当前的大流行感染。相反，我们需要一种快速且具有成本效益的方法来抑制和控制病毒感染。借助计算筛选方法并选择适当的化学空间，可以为新冠病毒识别先导药物样化合物。在这项研究中，我们使用 Drugbank 数据库筛选针对最重要的病毒靶标（即 3C 样蛋白酶（3CLpro）、木瓜蛋白酶样蛋白酶（PLpro）、RNA 依赖性 RNA 聚合酶（RdRp）和刺突（S）蛋白）的化合物。这些靶标在复制/转录和宿主细胞识别中起着重要作用，因此对病毒繁殖和感染传播至关重要。由于基于结构的计算筛选方法更为可靠，因此我们使用 3CLpro 和刺突蛋白的晶体结构。对于其余的靶标，我们使用同源建模的结构。此外，我们使用两种基于结合自由能的评分方法，即 AutoDock Vina 和分子力学-广义 Born 表面面积方法。基于这些结果，我们提出了针对三种病毒靶标（3CLpro、PLpro 和 RdRp）的药物鸡尾酒。有趣的是，本研究中鉴定的一种化合物，即巴洛沙韦马博瑞，已在临床试验中用于治疗新冠病毒感染。此外，我们还鉴定了一些化合物，如酞菁、他达拉非、洛那法尼、尼洛替尼、二氢麦角胺、雷地昔韦，它们可以同时与所有三个靶标结合，并可作为多靶标药物。我们的研究还包括计算目前处于药物试验阶段的各种化合物的结合能。在这些化合物中，发现瑞德西韦与 3CLpro 和 RdRp 靶标具有高结合亲和力。此外，发现巴瑞替尼和乌非那韦具有优越的靶标特异性结合，而达鲁那韦被发现是一种潜在的多靶标药物。据我们所知，这是首次从 Drugbank 数据库中筛选化合物以针对 SARS-CoV-2 的四个重要靶标进行的研究，表明使用双评分方法的计算筛选可以产生针对新冠病毒感染的潜在药物样化合物。
