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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/8392| Predicting Antibody and ACE2 Affinity for SARS-CoV-2 BA.2.86 with In Silico Protein Modeling and Docking | |
| Shirish Yasa Sayal Guirales-Medrano Denis Jacob Machado Colby Ford Daniel Janies | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2023.11.22.568364 | |
| https://www.biorxiv.org/content/10.1101/2023.11.22.568364v2 | |
| The emergence of the Omicron sublineage of SARS-CoV-2 virus BA.2.86 (nicknamed “Pirola”) has raised concerns about its potential impact on public health and personal health as it has many mutations with respect to previous variants. We conducted an in silico analysis of neutralizing antibody binding to BA.2.86. Selected antibodies came from patients who were vaccinated and/or infected. We predicted binding affinity between BA.2.86 and antibodies. We also predicted the binding affinity between the same antibodies and several previous SARS-CoV2 variants (Wuhan and Omicron descendants BA.1, BA.2, and XBB.1.5). Additionally, we examined binding affinity between BA.2.86 and human angiotensin converting enzyme 2 (ACE2) receptor, a cell surface protein crucial for viral entry. We found no statistically significant difference in binding affinity between BA.2.86 and other variants, indicating a similar immune response. These findings contradict media reports of BA.2.86’s high immune evasion potential based on its mutations. We discuss the implications of our findings and highlight the need for modeling and docking studies to go above and beyond mutation and basic serological neutralization analysis. Future research in this area will benefit from increased structural analyses of memory B-cell derived antibodies and should emphasize the importance of choosing appropriate samples for in silico studies to assess protection provided by vaccination and infection. This research contributes to understanding the BA.2.86 variant’s potential impact on public health. Moreover, we introduce new methodologies for predictive medicine in ongoing efforts to combat the evolving SARS-CoV-2 pandemic and prepare for other hazards. | |
| bioRxiv | |
| 24-11-2023 | |
| Preimpreso | |
| Inglés | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Materiales de Consulta y Comunicados Técnicos |
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| Predicting Antibody and ACE2 Affinity for SARS-CoV-2 BA.2.86 with In Silico Protein Modeling and Docking.pdf | 2.1 MB | Adobe PDF | Visualizar/Abrir |