In Silico Identification of Potential Inhibitors of SARS-CoV-2 Main Protease (Mpro)
The ongoing Coronavirus Disease 19 (COVID-19) pandemic has had a profound impact on the global healthcare system. As the SARS-CoV-2 virus, responsible for this pandemic, continues to spread and develop mutations in its genetic material, new variants of interest (VOIs) and variants of concern (VOCs) are emerging. These outbreaks lead to a decrease in the efficacy of existing treatments such as vaccines or drugs, highlighting the urgency of new therapies for COVID-19. Therefore, in this study, we aimed to identify potential SARS-CoV-2 antivirals using a virtual screening protocol and molecular dynamics simulations. These techniques allowed us to predict the binding affinity of a database of compounds with the virus M<sup>pro</sup> protein. This in silico approach enabled us to identify twenty-two chemical structures from a public database (QSAR Toolbox Ver 4.5 ) and ten promising molecules from our in-house database. The latter molecules possess advantageous qualities, such as two-step synthesis, cost-effectiveness, and long-lasting physical and chemical stability. Consequently, these molecules can be considered as promising alternatives to combat emerging SARS-CoV-2 variants.
Authors
- Manuel Alejandro Hernández‐Serda (ORCID: https://orcid.org/0009-0009-0702-1502)
- Levon Abrahamyan (ORCID: https://orcid.org/0000-0002-0849-0134)
- R Falfán-Valencia
- Enrique Ángeles (ORCID: https://orcid.org/0000-0003-0068-322X)
- Ana María Velázquez‐Sánchez
- Pablo Aguirre-Vidal
- Pablo A. Martínez‐Soriano (ORCID: https://orcid.org/0000-0002-4635-8425)
- Nathan M. Markarian (ORCID: https://orcid.org/0000-0001-8072-4268)
- José L. Medina‐Franco (ORCID: https://orcid.org/0000-0003-4940-1107)
- Víctor H. Vázquez-Valadez (ORCID: https://orcid.org/0000-0002-5014-0023)
- Aldo Yoshio Alarcón‐López (ORCID: https://orcid.org/0009-0008-1700-8582)
- Luis Alfonso Cárdenas‐Granados (ORCID: https://orcid.org/0009-0004-8687-4143)
Institutions
- Universidad de Cuautitlán Izcalli (MX)
- Université de Montréal (CA)
- Universidad Nacional Autónoma de México (MX)
Publication Details
- Journal
- Pathogens
- Published
- 2024-10-11
- DOI
- https://doi.org/10.3390/pathogens13100887
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00