Exploring potential therapeutic candidates against COVID-19: a molecular docking study
S K Erfanul Haque, Sukanta Bhadra, Nishith Kumar Pal
Discover Molecules, doi:10.1007/s44345-024-00005-5
The COVID-19 pandemic, caused by SARS-CoV-2, has made it urgently necessary to develop effective therapeutic options, and in recent years, computational biological tool assisted to achieve this goal. We conducted MD simulations using six SARS-CoV-2 proteins and 31 ligands to identify possible inhibitors as well as evaluate the binding efficiency of them. Post simulation study showed that 6VSB-Dactinomycin complex reveals the most stable binding, protein flexibility, and robust structural integrity, making it a promising model for therapeutic studies. Our study assessed the therapeutic potential of antiviral candidates against covid-19 virus using computational and experimental method, including the flexibility and stability of twelve docked protein-ligand complexes using normal mode analysis (NMA) and molecular dynamics (MD) simulations. After MD simulation, Dactinomycin and Ivermectin showed significant deformability and high binding affinities for Spike protein and RNA-dependent RNA polymerase, respectively. Remarkably, Dactinomycin showed greater binding to the Helicase protein, but Hesperidin and Epigallocatechin gallate (EGCG) exhibited encouraging interactions with the Nucleocapsid protein and Main protease. Analysis of docking experiments and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) showed the toxicity profiles and binding affinity of various drugs with key viral proteins. Toxicity of major drugs exhibited low to moderate although Dactinomycin, Ivermectin and vitamin-D exhibited higher degree of toxicity. Carcinogenicity observed in Quercetin, Baricitinib, Luteolin, Berberine, and Favipiravir while hepatotoxicity was not found in most case. Although EGCG and Hesperidin exhibit potential, more studies are required to evaluate effectiveness against approved drugs such as Remdesivir. This integrated approach shows that an integration of computational predictions with experimental data can help to support the development of antiviral drugs by adding novel perspectives in the safety profiles and efficiency of potential drugs.
Author contributions E.H. wrote the main manuscript text and prepared all figures. S. B. prepared figures, revised and supervised and N.K conceptualized the work. All authors reviewed the manuscript.
Declarations Competing Interests The authors declare no competing interests. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by-nc-nd/4. 0/. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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