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Insights from a computational analysis of the SARS-CoV-2 Omicron variant: Host–pathogen interaction, pathogenicity, and possible drug therapeutics
Parvez et al., Immunity, Inflammation and Disease, doi:10.1002/iid3.639 (date from earlier preprint) (Preprint)
Parvez et al., Insights from a computational analysis of the SARS-CoV-2 Omicron variant: Host–pathogen interaction,.., Immunity, Inflammation and Disease, doi:10.1002/iid3.639 (date from earlier preprint) (Preprint)
Jan 2022   Source   PDF  
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In Silico analysis of the omicron variant and 10 treatments reported effective for previous variants, predicting that all will be effective for omicron, with ivermectin showing the best results.
Parvez et al., 20 Jan 2022, preprint, 7 authors.
Contact: ohtsuki.gen.7w@kyoto-u.ac.jp, jakir-gen@sust.edu.
In Silico studies are an important part of preclinical research, however results may be very different in vivo.
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Abstract: Received: 8 February 2022 | Revised: 18 April 2022 | Accepted: 20 April 2022 DOI: 10.1002/iid3.639 ORIGINAL ARTICLE Insights from a computational analysis of the SARS‐CoV‐2 Omicron variant: Host–pathogen interaction, pathogenicity, and possible drug therapeutics Md Sorwer Alam Parvez1,2 | Manash Kumar Saha2 | Md. Ibrahim2 | Yusha Araf2 | Md. Taufiqul Islam2 | Gen Ohtsuki1 | Mohammad Jakir Hosen2 1 Department of Drug Discovery Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan 2 Department of Genetic Engineering & Biotechnology, Shahjalal University of Science & Technology, Sylhet, Bangladesh Correspondence Gen Ohtsuki, Department of Drug Discovery Medicine, Kyoto University Graduate School of Medicine, 53 Shogoin‐Kawahara‐cho, Sakyo‐ku, Kyoto 606‐8507, Japan. Email: ohtsuki.gen.7w@kyoto-u.ac.jp Mohammad Jakir Hosen, Department of Genetic Engineering & Biotechnology, Shahjalal University of Science & Technology, Sylhet‐3114, Bangladesh. Email: jakir-gen@sust.edu Funding information Takeda Science Foundation; Mitsubishi Foundation Abstract Introduction: Prominently accountable for the upsurge of COVID‐19 cases as the world attempts to recover from the previous two waves, Omicron has further threatened the conventional therapeutic approaches. The lack of extensive research regarding Omicron has raised the need to establish correlations to understand this variant by structural comparisons. Here, we evaluate, correlate, and compare its genomic sequences through an immunoinformatic approach to understand its epidemiological characteristics and responses to existing drugs. Methods: We reconstructed the phylogenetic tree and compared the mutational spectrum. We analyzed the mutations that occurred in the Omicron variant and correlated how these mutations affect infectivity and pathogenicity. Then, we studied how mutations in the receptor‐binding domain affect its interaction with host factors through molecular docking. Finally, we evaluated the drug efficacy against the main protease of the Omicron through molecular docking and validated the docking results with molecular dynamics simulation. Results: Phylogenetic and mutational analysis revealed the Omicron variant is similar to the highly infectious B.1.620 variant, while mutations within the prominent proteins are hypothesized to alter its pathogenicity. Moreover, docking evaluations revealed significant differences in binding affinity with human receptors, angiotensin‐converting enzyme 2 and NRP1. Surprisingly, most of the tested drugs were proven to be effective. Nirmatrelvir, 13b, and Lopinavir displayed increased effectiveness against Omicron. Conclusion: Omicron variant may be originated from the highly infectious B.1.620 variant, while it was less pathogenic due to the mutations in the prominent proteins. Nirmatrelvir, 13b, and Lopinavir would be the most effective, compared to other promising drugs that were proven effective. KEYWORDS ACE2, COVID‐19, drugs efficacy, host–pathogen interaction, NRP1, Omicron variant This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2022 The Authors. Immunity, Inflammation and Disease published by John Wiley & Sons Ltd. Immun Inflamm Dis. 2022;10:e639. https://doi.org/10.1002/iid3.639 wileyonlinelibrary.com/journal/iid3 | 1 of 15 2 of 15 1 | | PARVEZ ET..
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