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Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (M(pro)) using molecular docking and deep learning methods
Highly transmissive and rapidly evolving Coronavirus disease-2019 (COVID-19), a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), triggered a global pandemic, which is one of the most researched viruses in the academia. Effective drugs to treat people with COVID-1...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10008098/ https://www.ncbi.nlm.nih.gov/pubmed/36931201 http://dx.doi.org/10.1016/j.compbiomed.2023.106785 |
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author | Hossain, Alomgir Rahman, Md Ekhtiar Rahman, Md Siddiqur Nasirujjaman, Khondokar Matin, Mohammad Nurul Faruqe, Md Omar Rabbee, Muhammad Fazle |
author_facet | Hossain, Alomgir Rahman, Md Ekhtiar Rahman, Md Siddiqur Nasirujjaman, Khondokar Matin, Mohammad Nurul Faruqe, Md Omar Rabbee, Muhammad Fazle |
author_sort | Hossain, Alomgir |
collection | PubMed |
description | Highly transmissive and rapidly evolving Coronavirus disease-2019 (COVID-19), a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), triggered a global pandemic, which is one of the most researched viruses in the academia. Effective drugs to treat people with COVID-19 have yet to be developed to reduce mortality and transmission. Studies on the SARS-CoV-2 virus identified that its main protease (M(pro)) might be a potential therapeutic target for drug development, as this enzyme plays a key role in viral replication. In search of potential inhibitors of M(pro), we developed a phytochemical library consisting of 2431 phytochemicals from 104 Korean medicinal plants that exhibited medicinal and antioxidant properties. The library was screened by molecular docking, followed by revalidation by re-screening with a deep learning method. Recurrent Neural Networks (RNN) computing system was used to develop an inhibitory predictive model using SARS coronavirus M(pro) dataset. It was deployed to screen the top 12 compounds based on their docked binding affinity that ranged from −8.0 to −8.9 kcal/mol. The top two lead compounds, Catechin gallate and Quercetin 3-O-malonylglucoside, were selected depending on inhibitory potency against M(pro). Interactions with the target protein active sites, including His41, Met49, Cys145, Met165, and Thr190 were also examined. Molecular dynamics simulation was performed to analyze root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RG), solvent accessible surface area (SASA), and number of hydrogen bonds. Results confirmed the inflexible nature of the docked complexes. Absorption, distribution, metabolism, excretion, and toxicity (ADMET), as well as bioactivity prediction confirmed the pharmaceutical activities of the lead compound. Findings of this research might help scientists to optimize compatible drugs for the treatment of COVID-19 patients. |
format | Online Article Text |
id | pubmed-10008098 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100080982023-03-13 Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (M(pro)) using molecular docking and deep learning methods Hossain, Alomgir Rahman, Md Ekhtiar Rahman, Md Siddiqur Nasirujjaman, Khondokar Matin, Mohammad Nurul Faruqe, Md Omar Rabbee, Muhammad Fazle Comput Biol Med Article Highly transmissive and rapidly evolving Coronavirus disease-2019 (COVID-19), a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), triggered a global pandemic, which is one of the most researched viruses in the academia. Effective drugs to treat people with COVID-19 have yet to be developed to reduce mortality and transmission. Studies on the SARS-CoV-2 virus identified that its main protease (M(pro)) might be a potential therapeutic target for drug development, as this enzyme plays a key role in viral replication. In search of potential inhibitors of M(pro), we developed a phytochemical library consisting of 2431 phytochemicals from 104 Korean medicinal plants that exhibited medicinal and antioxidant properties. The library was screened by molecular docking, followed by revalidation by re-screening with a deep learning method. Recurrent Neural Networks (RNN) computing system was used to develop an inhibitory predictive model using SARS coronavirus M(pro) dataset. It was deployed to screen the top 12 compounds based on their docked binding affinity that ranged from −8.0 to −8.9 kcal/mol. The top two lead compounds, Catechin gallate and Quercetin 3-O-malonylglucoside, were selected depending on inhibitory potency against M(pro). Interactions with the target protein active sites, including His41, Met49, Cys145, Met165, and Thr190 were also examined. Molecular dynamics simulation was performed to analyze root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RG), solvent accessible surface area (SASA), and number of hydrogen bonds. Results confirmed the inflexible nature of the docked complexes. Absorption, distribution, metabolism, excretion, and toxicity (ADMET), as well as bioactivity prediction confirmed the pharmaceutical activities of the lead compound. Findings of this research might help scientists to optimize compatible drugs for the treatment of COVID-19 patients. Elsevier Ltd. 2023-05 2023-03-11 /pmc/articles/PMC10008098/ /pubmed/36931201 http://dx.doi.org/10.1016/j.compbiomed.2023.106785 Text en © 2023 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Hossain, Alomgir Rahman, Md Ekhtiar Rahman, Md Siddiqur Nasirujjaman, Khondokar Matin, Mohammad Nurul Faruqe, Md Omar Rabbee, Muhammad Fazle Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (M(pro)) using molecular docking and deep learning methods |
title | Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (M(pro)) using molecular docking and deep learning methods |
title_full | Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (M(pro)) using molecular docking and deep learning methods |
title_fullStr | Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (M(pro)) using molecular docking and deep learning methods |
title_full_unstemmed | Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (M(pro)) using molecular docking and deep learning methods |
title_short | Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (M(pro)) using molecular docking and deep learning methods |
title_sort | identification of medicinal plant-based phytochemicals as a potential inhibitor for sars-cov-2 main protease (m(pro)) using molecular docking and deep learning methods |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10008098/ https://www.ncbi.nlm.nih.gov/pubmed/36931201 http://dx.doi.org/10.1016/j.compbiomed.2023.106785 |
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