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Virtual screening of molecular databases for potential inhibitors of the NSP16/NSP10 methyltransferase from SARS-CoV-2

COVID-19 is a disease caused by the SARS-CoV-2 virus and represents one of the greatest health problems that humanity faces at the moment. Therefore, efforts have been made with the objective of seeking therapies that could be effective in combating this problematic. In the search for ligands, compu...

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Autores principales: Gomes, João Pedro Agra, Rocha, Larissa de Oliveira, Leal, Cíntia Emi Yanaguibashi, Filho, Edilson Beserra de Alencar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8958854/
https://www.ncbi.nlm.nih.gov/pubmed/35369609
http://dx.doi.org/10.1016/j.molstruc.2022.132951
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author Gomes, João Pedro Agra
Rocha, Larissa de Oliveira
Leal, Cíntia Emi Yanaguibashi
Filho, Edilson Beserra de Alencar
author_facet Gomes, João Pedro Agra
Rocha, Larissa de Oliveira
Leal, Cíntia Emi Yanaguibashi
Filho, Edilson Beserra de Alencar
author_sort Gomes, João Pedro Agra
collection PubMed
description COVID-19 is a disease caused by the SARS-CoV-2 virus and represents one of the greatest health problems that humanity faces at the moment. Therefore, efforts have been made with the objective of seeking therapies that could be effective in combating this problematic. In the search for ligands, computational chemistry plays an essential role, since it allows the screening of thousands of molecules on a given target, in order to save time and money for the in vitro or in vivo pharmacological stage. In this paper, we perform a virtual screening by docking looking for potential inhibitors of the NSP16-NSP10 protein dimer (methyltransferase) from SARS-CoV-2, by evaluating a homemade databank of molecules found in plants of the Caatinga Brazilian biome, compounds from ZINC online molecular database, as well as structural analogues of the enzymatic cofactor s-adenosylmethionine (SAM) and a known inhibitor in the literature, sinefungin (SFG), provided at PubChem database. All the evaluated sets presented molecules that deserve attention, highlighting four compounds from ZINC as the most promising ligands. These results contribute to the discovery of new molecular hits, in the search of potential agents against SARS-CoV-2 virus, still unveiling a pathway that can be used in combined therapies.
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spelling pubmed-89588542022-03-29 Virtual screening of molecular databases for potential inhibitors of the NSP16/NSP10 methyltransferase from SARS-CoV-2 Gomes, João Pedro Agra Rocha, Larissa de Oliveira Leal, Cíntia Emi Yanaguibashi Filho, Edilson Beserra de Alencar J Mol Struct Article COVID-19 is a disease caused by the SARS-CoV-2 virus and represents one of the greatest health problems that humanity faces at the moment. Therefore, efforts have been made with the objective of seeking therapies that could be effective in combating this problematic. In the search for ligands, computational chemistry plays an essential role, since it allows the screening of thousands of molecules on a given target, in order to save time and money for the in vitro or in vivo pharmacological stage. In this paper, we perform a virtual screening by docking looking for potential inhibitors of the NSP16-NSP10 protein dimer (methyltransferase) from SARS-CoV-2, by evaluating a homemade databank of molecules found in plants of the Caatinga Brazilian biome, compounds from ZINC online molecular database, as well as structural analogues of the enzymatic cofactor s-adenosylmethionine (SAM) and a known inhibitor in the literature, sinefungin (SFG), provided at PubChem database. All the evaluated sets presented molecules that deserve attention, highlighting four compounds from ZINC as the most promising ligands. These results contribute to the discovery of new molecular hits, in the search of potential agents against SARS-CoV-2 virus, still unveiling a pathway that can be used in combined therapies. Elsevier B.V. 2022-08-05 2022-03-28 /pmc/articles/PMC8958854/ /pubmed/35369609 http://dx.doi.org/10.1016/j.molstruc.2022.132951 Text en © 2022 Elsevier B.V. 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
Gomes, João Pedro Agra
Rocha, Larissa de Oliveira
Leal, Cíntia Emi Yanaguibashi
Filho, Edilson Beserra de Alencar
Virtual screening of molecular databases for potential inhibitors of the NSP16/NSP10 methyltransferase from SARS-CoV-2
title Virtual screening of molecular databases for potential inhibitors of the NSP16/NSP10 methyltransferase from SARS-CoV-2
title_full Virtual screening of molecular databases for potential inhibitors of the NSP16/NSP10 methyltransferase from SARS-CoV-2
title_fullStr Virtual screening of molecular databases for potential inhibitors of the NSP16/NSP10 methyltransferase from SARS-CoV-2
title_full_unstemmed Virtual screening of molecular databases for potential inhibitors of the NSP16/NSP10 methyltransferase from SARS-CoV-2
title_short Virtual screening of molecular databases for potential inhibitors of the NSP16/NSP10 methyltransferase from SARS-CoV-2
title_sort virtual screening of molecular databases for potential inhibitors of the nsp16/nsp10 methyltransferase from sars-cov-2
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8958854/
https://www.ncbi.nlm.nih.gov/pubmed/35369609
http://dx.doi.org/10.1016/j.molstruc.2022.132951
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