RT Journal Article SR Electronic T1 Predicting changes in neutralizing antibody activity for SARS-CoV-2 XBB.1.5 using in silico protein modeling JF bioRxiv FD Cold Spring Harbor Laboratory SP 2023.02.10.528025 DO 10.1101/2023.02.10.528025 A1 Colby T. Ford A1 Shirish Yasa A1 Denis Jacob Machado A1 Richard Allen White III A1 Daniel Janies YR 2023 UL http://biorxiv.org/content/early/2023/03/20/2023.02.10.528025.abstract AB 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 (RBD) 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 ico modeling approaches against newer neutralizing antibodies that are shown effective against B.1.1.529, we predict 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 antibody binding affinity were seen due to specific amino acid changes of interest in the newer variants.Competing Interest StatementAuthor 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.