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Computational anti-COVID-19 drug design: progress and challenges.

ABSTRACT: Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.

SUBMITTER: Wang J

PROVIDER: S-EPMC8690229 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Computational anti-COVID-19 drug design: progress and challenges.

Wang Jinxian J Zhang Ying Y Nie Wenjuan W Luo Yi Y Deng Lei L

Briefings in bioinformatics 20220101 1

Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead dr ...[more]

PMID: 34850817

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Project description:BackgroundIn 2003, the first case of severe acute respiratory syndrome coronavirus (SARS-CoV) was recorded. Coronaviruses (CoVs) have caused a major outbreak of human fatal pneumonia. Currently, there is no specific drug or treatment for diseases caused by SARS CoV 2. Computational approach that adopts dynamic models is widely accepted as indispensable tool in drug design but yet to be exploited in covid-19 in Zaria, Nigeria. In this study, steps were taken to advance on the successful achievements in the field of covid-19 drug, with the aid of in silico drug design technique, to create novel inhibitor drug candidates with better activity. In this study, one thousand human immunodeficiency virus (HIV1) antiviral chemical compounds from www.bindingBD.org were docked on the SARS CoV 2 main protease protein data bank identification number 6XBH (PDB ID: 6XBH) and the molecular docking score were ranked in order to identify the compounds with the highest inhibitory effects, and easy selection for future studies.ResultsThe docking studies showed some interesting results. Inhibitors with Index numbers 331, 741, and 819 had the highest binding affinity. Similarly, inhibitors with Index number 441, 847, and 46 had the lowest hydrogen bond energy. Inhibitor with index number 331 was reported with the lowest value (- 48.38kCal/mol). Five new compounds were designed from the selected six (6) compounds with the best binding score giving a total of thirty (30) novel compounds. The low binding energy of inhibitor with index no. 847b is unique, as most of the interaction energies are of H-bond type with amino acids (Thr26, Gly143, Ser144, Cys145, Glu166, Gln189, Hie164, Met49, Thr26, Thr25, Thr190, Asn142, Met165) resulting in an overall negative value (-16.31 kCal/mol) making it the best of all the newly designed inhibitors.ConclusionsThe novel inhibitor is 2-(2-(5-amino-2-((((3-aminobenzyl)oxy)carbonyl)amino)-5-oxopentanamido)-4-(2-(tert-butyl)-4-oxo-4-(pentan-3-ylamino) butanamido)-3-hydroxybutyl) benzoic acid. The improvement it has over the parent inhibitor is from the primary amine group attached to meta position of first benzene ring and the carboxyl group attached to the ortho position of the second benzene ring. The molecular dynamics studies also show that the novel inhibitor remains stable after the study. This result makes it a better drug candidate against SARS CoV 2 main protease when compared with the co-crystallized inhibitor or any of the 1000 docked inhibitors.Supplementary informationThe online version contains supplementary material available at 10.1186/s42269-022-00892-z.

Dataset Information

Computational anti-COVID-19 drug design: progress and challenges.

Publications

Computational anti-COVID-19 drug design: progress and challenges.

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