de Souza Anacleto Silva, de Souza Robson Francisco, Guzzo Cristiane Rodrigues
Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.
J Biomol Struct Dyn. 2022;40(21):11339-11356. doi: 10.1080/07391102.2021.1958700. Epub 2021 Aug 9.
The current outbreak of COVID-19 is leading an unprecedented scientific effort focusing on targeting SARS-CoV-2 proteins critical for its viral replication. Herein, we performed high-throughput virtual screening of more than eleven thousand FDA-approved drugs using backpropagation-based artificial neural networks ( = 0.60, = 0.80 and = 0.91), partial-least-square (PLS) regression ( = 0.83, = 0.62 and = 0.70) and sequential minimal optimization (SMO) regression ( = 0.70, = 0.80 and = 0.89). We simulated the stability of Acarbose-derived hexasaccharide, Naratriptan, Peramivir, Dihydrostreptomycin, Enviomycin, Rolitetracycline, Viomycin, Angiotensin II, Angiotensin 1-7, Angiotensinamide, Fenoterol, Zanamivir, Laninamivir and Laninamivir octanoate with 3CL by 100 ns and calculated binding free energy using molecular mechanics combined with Poisson-Boltzmann surface area (MM-PBSA). Our QSAR models and molecular dynamics data suggest that seven repurposed-drug candidates such as Acarbose-derived Hexasaccharide, Angiotensinamide, Dihydrostreptomycin, Enviomycin, Fenoterol, Naratriptan and Viomycin are potential SARS-CoV-2 main protease inhibitors. In addition, our QSAR models and molecular dynamics simulations revealed that His41, Asn142, Cys145, Glu166 and Gln189 are potential pharmacophoric centers for 3CL inhibitors. Glu166 is a potential pharmacophore for drug design and inhibitors that interact with this residue may be critical to avoid dimerization of 3CL. Our results will contribute to future investigations of novel chemical scaffolds and the discovery of novel hits in high-throughput screening as potential anti-SARS-CoV-2 properties.Communicated by Ramaswamy H. Sarma.
当前的新冠疫情引发了一场前所未有的科学努力,聚焦于针对对新冠病毒复制至关重要的蛋白质。在此,我们使用基于反向传播的人工神经网络(准确率分别为0.60、0.80和0.91)、偏最小二乘(PLS)回归(准确率分别为0.83、0.62和0.70)以及序列最小优化(SMO)回归(准确率分别为0.70、0.80和0.89),对一万一千多种美国食品药品监督管理局(FDA)批准的药物进行了高通量虚拟筛选。我们通过100纳秒的分子动力学模拟,研究了阿卡波糖衍生的六糖、那拉曲坦、帕拉米韦、二氢链霉素、恩维霉素、罗利环素、紫霉素、血管紧张素II、血管紧张素1-7、血管紧张素酰胺、非诺特罗、扎那米韦、拉尼米韦和拉尼米韦辛酸酯与3CL蛋白酶的稳定性,并使用分子力学结合泊松-玻尔兹曼表面积(MM-PBSA)计算了结合自由能。我们的定量构效关系(QSAR)模型和分子动力学数据表明,七种重新利用的药物候选物,如阿卡波糖衍生的六糖、血管紧张素酰胺、二氢链霉素、恩维霉素、非诺特罗、那拉曲坦和紫霉素,是潜在的新冠病毒主要蛋白酶抑制剂。此外,我们的QSAR模型和分子动力学模拟显示,His41、Asn142、Cys145、Glu166和Gln189是3CL蛋白酶抑制剂潜在的药效团中心。Glu166是药物设计的潜在药效团,与该残基相互作用的抑制剂可能对避免3CL蛋白酶二聚化至关重要。我们的研究结果将有助于未来对新型化学支架的研究,并在高通量筛选中发现具有潜在抗新冠病毒特性的新型活性分子。由Ramaswamy H.Sarma传达。