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建立模型以快速预测 SARS-CoV 疾病中对 3CL 酶的抑制活性。

modeling for quick prediction of inhibitory activity against 3CL enzyme in SARS CoV diseases.

机构信息

Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India.

Center for Informatics, Shiv Nadar University, Dadri, Uttar Pradesh, India.

出版信息

J Biomol Struct Dyn. 2022 Feb;40(3):1010-1036. doi: 10.1080/07391102.2020.1821779. Epub 2020 Sep 21.

Abstract

As of 2 September 2020, the 2019 novel coronavirus or SARS CoV-2 has been responsible for more than 2,56,02,665 infections and 8,52,768 deaths worldwide. There has been an urgent need of newer drug discovery to tackle the situation. Severe acute respiratory syndrome-associated coronavirus 3C-like protease (or 3CL) is a potential target as anti-SARS agents as it plays a vital role in the viral life cycle. This study aims at developing a quantitative structure-activity relationship (QSAR) model against a group of 3CL inhibitors to study their structural requirements for their inhibitory activity. Further, molecular docking studies were carried out which helped in the justification of the QSAR findings. Moreover, molecular dynamics simulation study was performed for selected compounds to check the stability of interactions as suggested by the docking analysis. The current QSAR model was further used in the prediction and screening of large databases within a short time.Communicated by Ramaswamy H. Sarma.

摘要

截至 2020 年 9 月 2 日,2019 年新型冠状病毒(SARS-CoV-2)已在全球范围内导致超过 25602665 例感染和 852768 例死亡。迫切需要发现新药来应对这种情况。严重急性呼吸综合征相关冠状病毒 3C 样蛋白酶(或 3CL)是一种有希望的抗 SARS 药物靶点,因为它在病毒生命周期中起着至关重要的作用。本研究旨在开发针对一组 3CL 抑制剂的定量构效关系(QSAR)模型,以研究其抑制活性的结构要求。此外,还进行了分子对接研究,这有助于对 QSAR 发现进行解释。此外,还对选定的化合物进行了分子动力学模拟研究,以检查对接分析建议的相互作用的稳定性。目前的 QSAR 模型进一步用于在短时间内对大型数据库进行预测和筛选。由 Ramaswamy H. Sarma 传达。

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