Exploring potential therapeutic candidates against COVID-19: a molecular docking study

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.

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Publication Details

Journal
Discover Molecules
Published
2024-11-21
DOI
https://doi.org/10.1007/s44345-024-00005-5
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.36
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article

Exploring potential therapeutic candidates against COVID-19: a molecular docking study

S M Taiabul Haque, Sukanta Bhadra, Nishith Kumar Pal
1 citations
Discover Molecules
Computational Drug Discovery Methods
0.36
article

Exploring potential therapeutic candidates against COVID-19: a molecular docking study

S M Taiabul Haque, Sukanta Bhadra, Nishith Kumar Pal
article en
1 citations

Abstract

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.

Discover MoleculesVol. 1(1)
Techno India University (IN), Budge Budge Institute of Technology (IN), Institute of Medical Sciences (IN)
Openalex Percentile: Top 34%
Computational Drug Discovery Methods
0.36
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