Virtual and In Vitro Screening of Natural Products Identifies Indole and Benzene Derivatives as Inhibitors of SARS-CoV-2 Main Protease (Mpro)

被引:4
作者
Ang, Dony [1 ,2 ]
Kendall, Riley [1 ,2 ]
Atamian, Hagop S. [2 ,3 ]
机构
[1] Chapman Univ, Computat & Data Sci Program, Orange, CA 92866 USA
[2] Chapman Univ, Schmid Coll Sci & Technol, Orange, CA 92866 USA
[3] Chapman Univ, Biol Sci Program, Orange, CA 92866 USA
来源
BIOLOGY-BASEL | 2023年 / 12卷 / 04期
关键词
COVID-19; computation; structure-based virtual screening; protease inhibitor; ligand-based virtual screening; in vitro; drug-target interaction; NONSTEROIDAL ANTIINFLAMMATORY DRUGS; PLANT; CAPIVASERTIB; SILVESTROL; ALKALOIDS; POTENT; ENTRY;
D O I
10.3390/biology12040519
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Simple Summary The coronavirus disease 2019 (COVID-19) pandemic caused more than 6.7 million deaths worldwide. Certain groups of individuals are still at a high risk of severe illness. The availability of drugs to treat COVID-19 symptoms will save many lives. The main protease (M-pro) of SARS-CoV2, the causal agent of COVID-19, is a promising target for drug discovery. Natural products have been used for thousands of years to treat diseases and represent valuable resources for drug discovery. While the process of experimentally screening chemicals for drug discovery has often been very long and expensive, recent advances in virtual screening have made it possible to screen millions of potential chemicals in a very short time using computers. In this research, around 400,000 natural products were virtually screened within a month and narrowed down to 20 products that could potentially bind to the SARS-CoV2 M-pro. In vitro experimental testing of seven natural products demonstrated that the virtual screening approach used in this study had a significantly high rate of accuracy since more than 50% of the experimentally tested natural products (four out of seven) were able to inhibit the function of the M-pro in real-life consistent with the computer predictions. Our results show that with further research, beta-carboline, N-alkyl indole, and Benzoic acid ester types of natural products could be used in treating COVID-19 in the future. The rapid spread of the coronavirus disease 2019 (COVID-19) resulted in serious health, social, and economic consequences. While the development of effective vaccines substantially reduced the severity of symptoms and the associated deaths, we still urgently need effective drugs to further reduce the number of casualties associated with SARS-CoV-2 infections. Machine learning methods both improved and sped up all the different stages of the drug discovery processes by performing complex analyses with enormous datasets. Natural products (NPs) have been used for treating diseases and infections for thousands of years and represent a valuable resource for drug discovery when combined with the current computation advancements. Here, a dataset of 406,747 unique NPs was screened against the SARS-CoV-2 main protease (M-pro) crystal structure (6lu7) using a combination of ligand- and structural-based virtual screening. Based on 1) the predicted binding affinities of the NPs to the M-pro, 2) the types and number of interactions with the M-pro amino acids that are critical for its function, and 3) the desirable pharmacokinetic properties of the NPs, we identified the top 20 candidates that could potentially inhibit the M-pro protease function. A total of 7 of the 20 top candidates were subjected to in vitro protease inhibition assay and 4 of them (4/7; 57%), including two beta carbolines, one N-alkyl indole, and one Benzoic acid ester, had significant inhibitory activity against M-pro protease. These four NPs could be developed further for the treatment of COVID-19 symptoms.
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页数:18
相关论文
共 72 条
  • [11] Structure-based design of antiviral drug candidates targeting the SARS-CoV-2 main protease
    Dai, Wenhao
    Zhang, Bing
    Jiang, Xia-Ming
    Su, Haixia
    Li, Jian
    Zhao, Yao
    Xie, Xiong
    Jin, Zhenming
    Peng, Jingjing
    Liu, Fengjiang
    Li, Chunpu
    Li, You
    Bai, Fang
    Wang, Haofeng
    Cheng, Xi
    Cen, Xiaobo
    Hu, Shulei
    Yang, Xiuna
    Wang, Jiang
    Liu, Xiang
    Xiao, Gengfu
    Jiang, Hualiang
    Rao, Zihe
    Zhang, Lei-Ke
    Xu, Yechun
    Yang, Haitao
    Liu, Hong
    [J]. SCIENCE, 2020, 368 (6497) : 1331 - +
  • [12] Screening of an FDA-Approved Compound Library Identifies Four Small-Molecule Inhibitors of Middle East Respiratory Syndrome Coronavirus Replication in Cell Culture
    de Wilde, Adriaan H.
    Jochmans, Dirk
    Posthuma, Clara C.
    Zevenhoven-Dobbe, Jessika C.
    van Nieuwkoop, Stefan
    Bestebroer, Theo M.
    van den Hoogen, Bernadette G.
    Neyts, Johan
    Snijder, Eric J.
    [J]. ANTIMICROBIAL AGENTS AND CHEMOTHERAPY, 2014, 58 (08) : 4875 - 4884
  • [13] Benzoic acid and its derivatives as naturally occurring compounds in foods and as additives: Uses, exposure, and controversy
    del Olmo, Ana
    Calzada, Javier
    Nunez, Manuel
    [J]. CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION, 2017, 57 (14) : 3084 - 3103
  • [15] Inhibition of Zika Virus Replication by Silvestrol
    Elgner, Fabian
    Sabino, Catarina
    Basic, Michael
    Ploen, Daniela
    Gruenweller, Arnold
    Hildt, Eberhard
    [J]. VIRUSES-BASEL, 2018, 10 (04):
  • [16] Faria NR, 2021, SCIENCE, V372, P815, DOI [10.1126/science.abh2644, 10.1101/2021.02.26.21252554, 10.1126/science.abh2644Article]
  • [17] An introduction to ROC analysis
    Fawcett, Tom
    [J]. PATTERN RECOGNITION LETTERS, 2006, 27 (08) : 861 - 874
  • [18] Observational Study of Hydroxychloroquine in Hospitalized Patients with Covid-19
    Geleris, Joshua
    Sun, Yifei
    Platt, Jonathan
    Zucker, Jason
    Baldwin, Matthew
    Hripcsak, George
    Labella, Angelena
    Manson, Daniel K.
    Kubin, Christine
    Barr, R. Graham
    Sobieszczyk, Magdalena E.
    Schluger, Neil W.
    [J]. NEW ENGLAND JOURNAL OF MEDICINE, 2020, 382 (25) : 2411 - 2418
  • [19] Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery
    Gentile, Francesco
    Agrawal, Vibudh
    Hsing, Michael
    Ton, Anh-Tien
    Ban, Fuqiang
    Norinder, Ulf
    Gleave, Martin E.
    Cherkasov, Artem
    [J]. ACS CENTRAL SCIENCE, 2020, 6 (06) : 939 - 949
  • [20] BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology
    Gilson, Michael K.
    Liu, Tiqing
    Baitaluk, Michael
    Nicola, George
    Hwang, Linda
    Chong, Jenny
    [J]. NUCLEIC ACIDS RESEARCH, 2016, 44 (D1) : D1045 - D1053