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bioRxiv · 10.1101/2023.11.22.568364

Predicting Antibody and ACE2 Affinity forSARS-CoV-2 BA.2.86 with In Silico ProteinModeling and Docking

Yasa, S. R.·Guirales-Medrano, S.·Jacob Machado, D.·Ford, C. T.·Janies, D. A.
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Abstract

The emergence of SARS-CoV-2 lineages derived from Omicron, including BA.2.86 (nicknamed "Pirola") and its relative, JN.1, has raised concerns about their potential impact on public and personal health due to numerous novel mutations. Despite this, predicting their implications based solely on mutation counts proves challenging. Empirical evidence of JN.1s increased immune evasion capacity in relation to previous variants is mixed. To improve predictions beyond what is possible based solely on mutation counts, we conducted extensive in silico analyses on the binding affinity between the RBD of different SARS-CoV-2 variants (Wuhan-Hu-1, BA.1/B.1.1.529, BA.2, XBB.1.5, BA.2.86, and JN.1) and neutralizing antibodies from vaccinated or infected individuals, as well as the human angiotensin-converting enzyme 2 (ACE2) receptor. We observed no statistically significant difference in binding affinity between BA.2.86 or JN.1 and other variants. Therefore, we conclude that the new SARS-CoV-2 variants have no pronounced immune escape or infection capacity compared to previous variants. However, minor reductions in binding affinity for both the antibodies and ACE2 were noted for JN.1. We discuss the implications of the in silico 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. More-over, the fitness benefits of genomic variation outside of the RBD of BA.2.86 and JN.1 need to be investigated. This research contributes to understanding the BA.2.86 and JN.1 variants potential impact on public health. Taken together, this work introduces a paradigm for functional genomic epidemiology in ongoing efforts to combat the evolving SARS-CoV-2 pandemic and prepare for other hazards.

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Yasa, S. R., Guirales-Medrano, S., Jacob Machado, D., Ford, C. T., Janies, D. A.. 2023-11-23. Predicting Antibody and ACE2 Affinity forSARS-CoV-2 BA.2.86 with In Silico ProteinModeling and Docking. https://doi.org/10.1101/2023.11.22.568364

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