对接(动物)
严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)
分子动力学
化学
碳-13核磁共振
2019年冠状病毒病(COVID-19)
计算化学
蛋白酶
2019-20冠状病毒爆发
组合化学
计算生物学
生物系统
立体化学
计算机科学
酶
病毒学
生物化学
生物
医学
爆发
护理部
疾病
病理
传染病(医学专业)
作者
Valentin A. Semenov,Leonid B. Krivdin
标识
DOI:10.1021/acs.jpcb.1c10489
摘要
In continuation of the search for potential drugs that inhibit the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), in this work, a combined approach based on the modeling of NMR chemical shifts and molecular docking is suggested to identify the possible suppressors of the main protease of this virus among a number of natural products of diverse nature. Primarily, with the aid of an artificial neural network, the problem of the reliable determination of the stereochemical structure of a number of studied compounds was solved. Complementary to the main goal of this study, theoretical modeling of NMR spectral parameters made it feasible to perform a number of signal reassignments together with introducing some missing NMR data. Finally, molecular docking formalism was applied to the analysis of several natural products that could be chosen as prospective candidates for the role of potential inhibitors of the main protease. The results of this study are believed to assist in further research aimed at the development of specific drugs based on the natural products against COVID-19.
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