Predicting Antibody and ACE2 Affinity for SARS-CoV-2 BA.2.86 and JN.1 with In Silico Protein Modeling and Docking

Citation Author(s):
Jacob Machado
Submitted by:
Shirish Yasa
Last updated:
Tue, 03/19/2024 - 09:45
Research Article Link:
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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.1’s 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. This research contributes to understanding the BA.2.86 and JN.1 variants’ 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.


Data is available on Github alongside this location. The folder RBD_structures, ace2_structures, and antibody_structures contain our HADDOCK Input files. The folder HADDOCK_Results contains out HADDOCK Output files. The Folder FinalTables contains our raw data. 

Github Link For Your Easy Use:



If you have any questions about the dataset please email me at

Submitted by Shirish Yasa on Tue, 03/19/2024 - 09:46