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In-Silico Lead Druggable Compounds Identification against SARS COVID-19 Main Protease Target from In-House, Chembridge and Zinc Databases by Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations

Pharmacological strategies to lower the viral load among patients suffering from severe diseases were researched in great detail during the SARS-CoV-2 outbreak. The viral protease M(pro) (3CLpro) is necessary for viral replication and is among the main therapeutic targets proposed, thus far. To stop...

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Autores principales: Ghufran, Mehreen, Ullah, Mehran, Khan, Haider Ali, Ghufran, Sabreen, Ayaz, Muhammad, Siddiq, Muhammad, Abbas, Syed Qamar, Hassan, Syed Shams ul, Bungau, Simona
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854631/
https://www.ncbi.nlm.nih.gov/pubmed/36671672
http://dx.doi.org/10.3390/bioengineering10010100
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author Ghufran, Mehreen
Ullah, Mehran
Khan, Haider Ali
Ghufran, Sabreen
Ayaz, Muhammad
Siddiq, Muhammad
Abbas, Syed Qamar
Hassan, Syed Shams ul
Bungau, Simona
author_facet Ghufran, Mehreen
Ullah, Mehran
Khan, Haider Ali
Ghufran, Sabreen
Ayaz, Muhammad
Siddiq, Muhammad
Abbas, Syed Qamar
Hassan, Syed Shams ul
Bungau, Simona
author_sort Ghufran, Mehreen
collection PubMed
description Pharmacological strategies to lower the viral load among patients suffering from severe diseases were researched in great detail during the SARS-CoV-2 outbreak. The viral protease M(pro) (3CLpro) is necessary for viral replication and is among the main therapeutic targets proposed, thus far. To stop the pandemic from spreading, researchers are working to find more effective M(pro) inhibitors against SARS-CoV-2. The 33.8 kDa M(pro) protease of SARS-CoV-2, being a nonhuman homologue, has the possibility of being utilized as a therapeutic target against coronaviruses. To develop drug-like compounds capable of preventing the replication of SARS-main CoV-2’s protease (M(pro)), a computer-aided drug design (CADD) approach is extremely viable. Using MOE, structure-based virtual screening (SBVS) of in-house and commercial databases was carried out using SARS-CoV-2 proteins. The most promising hits obtained during virtual screening (VS) were put through molecular docking with the help of MOE. The virtual screening yielded 3/5 hits (in-house database) and 56/66 hits (commercial databases). Finally, 3/5 hits (in-house database), 3/5 hits (ZINC database), and 2/7 hits (ChemBridge database) were chosen as potent lead compounds using various scaffolds due to their considerable binding affinity with M(pro) protein. The outcomes of SBVS were then validated using an analysis based on molecular dynamics simulation (MDS). The complexes’ stability was tested using MDS and post-MDS. The most promising candidates were found to exhibit a high capacity for fitting into the protein-binding pocket and interacting with the catalytic dyad. At least one of the scaffolds selected will possibly prove useful for future research. However, further scientific confirmation in the form of preclinical and clinical research is required before implementation.
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spelling pubmed-98546312023-01-21 In-Silico Lead Druggable Compounds Identification against SARS COVID-19 Main Protease Target from In-House, Chembridge and Zinc Databases by Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations Ghufran, Mehreen Ullah, Mehran Khan, Haider Ali Ghufran, Sabreen Ayaz, Muhammad Siddiq, Muhammad Abbas, Syed Qamar Hassan, Syed Shams ul Bungau, Simona Bioengineering (Basel) Article Pharmacological strategies to lower the viral load among patients suffering from severe diseases were researched in great detail during the SARS-CoV-2 outbreak. The viral protease M(pro) (3CLpro) is necessary for viral replication and is among the main therapeutic targets proposed, thus far. To stop the pandemic from spreading, researchers are working to find more effective M(pro) inhibitors against SARS-CoV-2. The 33.8 kDa M(pro) protease of SARS-CoV-2, being a nonhuman homologue, has the possibility of being utilized as a therapeutic target against coronaviruses. To develop drug-like compounds capable of preventing the replication of SARS-main CoV-2’s protease (M(pro)), a computer-aided drug design (CADD) approach is extremely viable. Using MOE, structure-based virtual screening (SBVS) of in-house and commercial databases was carried out using SARS-CoV-2 proteins. The most promising hits obtained during virtual screening (VS) were put through molecular docking with the help of MOE. The virtual screening yielded 3/5 hits (in-house database) and 56/66 hits (commercial databases). Finally, 3/5 hits (in-house database), 3/5 hits (ZINC database), and 2/7 hits (ChemBridge database) were chosen as potent lead compounds using various scaffolds due to their considerable binding affinity with M(pro) protein. The outcomes of SBVS were then validated using an analysis based on molecular dynamics simulation (MDS). The complexes’ stability was tested using MDS and post-MDS. The most promising candidates were found to exhibit a high capacity for fitting into the protein-binding pocket and interacting with the catalytic dyad. At least one of the scaffolds selected will possibly prove useful for future research. However, further scientific confirmation in the form of preclinical and clinical research is required before implementation. MDPI 2023-01-11 /pmc/articles/PMC9854631/ /pubmed/36671672 http://dx.doi.org/10.3390/bioengineering10010100 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ghufran, Mehreen
Ullah, Mehran
Khan, Haider Ali
Ghufran, Sabreen
Ayaz, Muhammad
Siddiq, Muhammad
Abbas, Syed Qamar
Hassan, Syed Shams ul
Bungau, Simona
In-Silico Lead Druggable Compounds Identification against SARS COVID-19 Main Protease Target from In-House, Chembridge and Zinc Databases by Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations
title In-Silico Lead Druggable Compounds Identification against SARS COVID-19 Main Protease Target from In-House, Chembridge and Zinc Databases by Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations
title_full In-Silico Lead Druggable Compounds Identification against SARS COVID-19 Main Protease Target from In-House, Chembridge and Zinc Databases by Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations
title_fullStr In-Silico Lead Druggable Compounds Identification against SARS COVID-19 Main Protease Target from In-House, Chembridge and Zinc Databases by Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations
title_full_unstemmed In-Silico Lead Druggable Compounds Identification against SARS COVID-19 Main Protease Target from In-House, Chembridge and Zinc Databases by Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations
title_short In-Silico Lead Druggable Compounds Identification against SARS COVID-19 Main Protease Target from In-House, Chembridge and Zinc Databases by Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations
title_sort in-silico lead druggable compounds identification against sars covid-19 main protease target from in-house, chembridge and zinc databases by structure-based virtual screening, molecular docking and molecular dynamics simulations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854631/
https://www.ncbi.nlm.nih.gov/pubmed/36671672
http://dx.doi.org/10.3390/bioengineering10010100
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