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Predicting changes in neutralizing antibody activity for SARS-CoV-2 XBB.1.5 using in silico protein modeling

View ORCID ProfileColby T Ford, Shirish Yasa, View ORCID ProfileDenis T Jacob Machado, View ORCID ProfileRichard Allen White III, View ORCID ProfileDaniel A Janies
doi: https://doi.org/10.1101/2023.02.10.528025
Colby T Ford
1 The University of North Carolina at Charlotte;
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  • For correspondence: cford38@uncc.edu
Shirish Yasa
2 University of North Carolina at Charlotte
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Denis T Jacob Machado
2 University of North Carolina at Charlotte
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Richard Allen White III
2 University of North Carolina at Charlotte
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Daniel A Janies
2 University of North Carolina at Charlotte
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Abstract

The SARS-CoV-2 variant XBB.1.5 is of concern as it has high transmissibility. XBB.1.5 currently accounts for upwards of 30% of new infections in the United States. One year after our group published the predicted structure of the Omicron (B.1.1.529) variant's receptor binding domain and antibody binding affinity, we return to investigate the new mutations seen in XBB.1.5 which is a descendant of Omicron. Using in silico modeling approaches against newer neutralizing antibodies that are shown effective against B.1.1.529, we posit the immune consequences of XBB.1.5's mutations and show that there is no statistically significant difference in overall antibody evasion when comparing to the B.1.1.529 and other related variants (e.g. BJ.1 and BM.1.1.1). However, noticeable changes in neutralizing activity were seen due to specific amino acid changes of interest in the newer variants.

Competing Interest Statement

Author CTF is the owner of Tuple, LLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

  • In this revision, we add PRODIGY binding affinity metrics for multiple RBD-antibody complexes in each HADDOCK cluster. We also add considerable Supplementary Materials where we look at active residue prediction in the RBD epitope and compare the structures of the four variant RBD structures.

  • https://github.com/colbyford/SARS-CoV-2_XBB.1.5_Spike-RBD_Predictions

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 20, 2023.
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Predicting changes in neutralizing antibody activity for SARS-CoV-2 XBB.1.5 using in silico protein modeling
Colby T Ford, Shirish Yasa, Denis T Jacob Machado, Richard Allen White III, Daniel A Janies
bioRxiv 2023.02.10.528025; doi: https://doi.org/10.1101/2023.02.10.528025
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Predicting changes in neutralizing antibody activity for SARS-CoV-2 XBB.1.5 using in silico protein modeling
Colby T Ford, Shirish Yasa, Denis T Jacob Machado, Richard Allen White III, Daniel A Janies
bioRxiv 2023.02.10.528025; doi: https://doi.org/10.1101/2023.02.10.528025

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