Identification of Potential TMPRSS2 Inhibitors for COVID-19 Treatment in Chinese Medicine by Computational Approaches and Surface Plasmon Resonance Technology

IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL
Rong Yang, Linhua Liu, Dansheng Jiang, Lei Liu, Huili Yang, Hongling Xu, Meirong Qin, Ping Wang*, Jiangyong Gu* and Yufeng Xing*, 
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引用次数: 1

Abstract

Background: Coronavirus disease-19 (COVID-19) pneumonia continues to spread in the entire globe with limited medication available. In this study, the active compounds in Chinese medicine (CM) recipes targeting the transmembrane serine protease 2 (TMPRSS2) protein for the treatment of COVID-19 were explored. Methods: The conformational structure of TMPRSS2 protein (TMPS2) was built through homology modeling. A training set covering TMPS2 inhibitors and decoy molecules was docked to TMPS2, and their docking poses were re-scored with scoring schemes. A receiver operating characteristic (ROC) curve was applied to select the best scoring function. Virtual screening of the candidate compounds (CCDs) in the six highly effective CM recipes against TMPS2 was conducted based on the validated docking protocol. The potential CCDs after docking were subject to molecular dynamics (MD) simulations and surface plasmon resonance (SPR) experiment. Results: A training set of 65 molecules were docked with modeled TMPS2 and LigScore2 with the highest area under the curve, AUC, value (0.886) after ROC analysis selected to best differentiate inhibitors from decoys. A total of 421 CCDs in the six recipes were successfully docked into TMPS2, and the top 16 CCDs with LigScore2 higher than the cutoff (4.995) were screened out. MD simulations revealed a stable binding between these CCDs and TMPS2 due to the negative binding free energy. Lastly, SPR experiments validated the direct combination of narirutin, saikosaponin B1, and rutin with TMPS2. Conclusions: Specific active compounds including narirutin, saikosaponin B1, and rutin in CM recipes potentially target and inhibit TMPS2, probably exerting a therapeutic effect on COVID-19.

Abstract Image

基于计算方法和表面等离子体共振技术的中药治疗新冠肺炎潜在TMPRSS2抑制剂鉴定
背景:冠状病毒病-19 (COVID-19)肺炎继续在全球传播,但可用药物有限。本研究旨在探索针对跨膜丝氨酸蛋白酶2 (TMPRSS2)蛋白的中药配方中治疗COVID-19的活性成分。方法:通过同源性建模构建TMPRSS2蛋白(TMPS2)的构象结构。将包含TMPS2抑制剂和诱饵分子的训练集与TMPS2对接,并使用评分方案对其对接姿势进行重新评分。采用受试者工作特征(ROC)曲线选择最佳评分函数。基于验证的对接协议,对6种高效抗TMPS2的CM配方中的候选化合物(CCDs)进行虚拟筛选。对对接后的潜在ccd进行了分子动力学(MD)模拟和表面等离子体共振(SPR)实验。结果:一个由65个分子组成的训练集与模拟的TMPS2和LigScore2进行了停靠,经ROC分析选择的曲线下面积(AUC)值最高(0.886),可以最好地区分抑制剂和诱饵。6个配方中共421个ccd成功对接到TMPS2,筛选出了LigScore2高于截止点(4.995)的前16个ccd。MD模拟表明,由于束缚自由能为负,这些ccd与TMPS2之间具有稳定的结合。最后,SPR实验验证了纳瑞芦丁、柴胡皂苷B1和芦丁与TMPS2的直接联合作用。结论:中药配方中含有纳瑞芦丁、柴草皂苷B1和芦丁等特异性活性物质,可靶向抑制TMPS2,可能对COVID-19有治疗作用。
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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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