New Preprint on in silico analyses of the SAR-CoV-2 Pirola variant
New Preprint: Predicting Antibody and ACE2 Affinity for SARS-CoV-2 BA.2.86 with In Silico Protein Modeling and Docking
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-CoV-2 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.
The preprint was authored by UNC Charlotte students (Shirish Yasa and Sayal Guirales-Medrano) with CIPHER faculty (Drs. Denis Jacob Machado, Colby Ford, and Daniel Janies).
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