Predicting changes in neutralizing antibody activity for SARS-CoV-2 XBB.1.5 using in silico protein modeling
Open accessPublished version of a preprint
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 (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 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 andBM.1.1.1). However, noticeable changes in antibody binding affinity were seen due to specific amino acid changes of interest in the newer variants.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
ColabFold - Making protein folding accessible to all
Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391 · see citing works
The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes
van Zundert, G. C. P., Rodrigues, J. P. G. L. M., Trellet, M., Schmitz, C., Kastritis, P. L., Karaca, E., Melquiond, A. S. J., van Dijk, M., de Vries, S. J., & Bonvin, A. M. J. J. (2016) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.09.014 · Cited by 2,785 · see citing works
SARS-CoV-2 neutralizing antibody structures inform therapeutic strategies
Barnes, C. O., Jette, C. A., Abernathy, M. E., Dam, K.-M. A., Esswein, S. R., Gristick, H. B., Malyutin, A. G., Sharaf, N. G., Huey-Tubman, K. E., Lee, Y. E., et al. (2020) · Nature · doi:10.1038/s41586-020-2852-1 · Cited by 1,599 · see citing works
E2F1 Regulates Cellular Growth by mTORC1 Signaling
Real, S., Meo-Evoli, N., Espada, L., & Tauler, A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016163 · Cited by 816 · see citing works
Imprinted SARS-CoV-2 humoral immunity induces convergent Omicron RBD evolution
Cao, Y., Jian, F., Wang, J., Yu, Y., Song, W., Yisimayi, A., Wang, J., An, R., Chen, X., Zhang, N., et al. (2022) · Nature · doi:10.1038/s41586-022-05644-7 · Cited by 801 · see citing works
VanBlargan, L. A., Errico, J. M., Halfmann, P., Zost, S. J., Crowe, J. E., Purcell, L. A., Kawaoka, Y., Corti, D., Fremont, D. H., & Diamond, M. (2022) · Nature Medicine · doi:10.1038/s41591-021-01678-y · Cited by 676 · see citing works
SARS-CoV-2 Omicron variant: Antibody evasion and cryo-EM structure of spike protein–ACE2 complex
Mannar, D., Saville, J. W., Zhu, X., Srivastava, S. S., Berezuk, A. M., Tuttle, K. S., Marquez, A. C., Sekirov, I., & Subramaniam, S. (2022) · Science · doi:10.1126/science.abn7760 · Cited by 630 · see citing works
Contacts-based prediction of binding affinity in protein–protein complexes
Vangone, A., & Bonvin, A. (2015) · eLife · doi:10.7554/elife.07454 · Cited by 563 · see citing works
The neutralizing antibody, LY-CoV555, protects against SARS-CoV-2 infection in nonhuman primates
Jones, B. E., Brown-Augsburger, P., Corbett, K., Westendorf, K., Davies, J., Cujec, T., Wiethoff, C. M., Blackbourne, J., Heinz, B., Foster, D., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abf1906 · Cited by 420 · see citing works
LY-CoV1404 (bebtelovimab) potently neutralizes SARS-CoV-2 variants
Westendorf, K., Žentelis, S., Wang, L., Foster, D., Vaillancourt, P., Wiggin, M., Lovett, E., van der Lee, R., Hendle, J., Pustilnik, A., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110812 · Cited by 398 · see citing works
(2017) · eLife · doi:10.7554/elife · Cited by 367 · see citing works
CPORT: A Consensus Interface Predictor and Its Performance in Prediction-Driven Docking with HADDOCK
de Vries, S. J., & Bonvin, A. M. J. J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017695 · Cited by 323 · see citing works
ACE2 binding and antibody evasion in enhanced transmissibility of XBB.1.5
Yue, C., Song, W., Wang, L., Jian, F., Chen, X., Gao, F., Shen, Z., Wang, Y., Wang, X., & Cao, Y. (2023) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(23)00010-5 · Cited by 311 · see citing works
Genetic and structural basis for SARS-CoV-2 variant neutralization by a two-antibody cocktail
Dong, J., Zost, S. J., Greaney, A. J., Starr, T. N., Dingens, A. S., Chen, E. C., Chen, R. E., Case, J. B., Sutton, R. E., Gilchuk, P., et al. (2021) · Nature Microbiology · doi:10.1038/s41564-021-00972-2 · Cited by 285 · see citing works
Substantial Neutralization Escape by SARS-CoV-2 Omicron Variants BQ.1.1 and XBB.1
Miller, J. J., Hachmann, N. P., Collier, A.-R. Y., Lasrado, N., Mazurek, C. R., Patio, R. C., Powers, O. C., Surve, N., Theiler, J. P., Korber, B., et al. (2023) · New England Journal of Medicine · doi:10.1056/nejmc2214314 · Cited by 210 · see citing works
Potent cross-reactive antibodies following Omicron breakthrough in vaccinees
Nutalai, R., Zhou, D., Tuekprakhon, A., Ginn, H. M., Supasa, P., Liu, C., Huo, J., Mentzer, A. J., Duyvesteyn, H. M. E., Dijokaite-Guraliuc, A., et al. (2022) · Cell · doi:10.1016/j.cell.2022.05.014 · Cited by 164 · see citing works
Coronavirus variant XBB.1.5 rises in the United States — is it a global threat?
Callaway, E. (2023) · Nature · doi:10.1038/d41586-023-00014-3 · Cited by 74 · see citing works
Ford, C. T., Jacob Machado, D., & Janies, D. A. (2022) · Frontiers in Virology · doi:10.3389/fviro.2022.830202 · Cited by 52 · see citing works
Self-citationLiu, H., Yuan, M., Huang, D.-L., Bangaru, S., Zhao, F.-Z., Lee, C. D., Peng, L., Barman, S., Zhu, X.-Y., Nemazee, D., et al. (2021) · Cell Host and Microbe · doi:10.1016/j.chom.2021.04.005 · Cited by 46 · see citing works
Li, T., Han, X., Gu, C., Guo, H., Zhang, H., Wang, Y., Hu, C., Wang, K., Liu, F., Luo, F., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26539-7 · Cited by 45 · see citing works
Waning Immunity Against XBB.1.5 Following Bivalent mRNA BoostersPreprint
Lasrado, N., Collier, A.-R. Y., Miller, J. J., Hachmann, N. P., Liu, J., Sciacca, M., Wu, C., Anand, T., Bondzie, E. A., Fisher, J. L., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.22.525079 · Cited by 44 · see citing works
Fenwick, C. W., Turelli, P., Ni, D., Perez, L. H., Lau, K., Hérate, C., Marlin, R., Lana, E., Pellaton, C., Raclot, C., et al. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01198-6 · Cited by 37 · see citing works
Karyakarte, R. P., Das, R., Dudhate, S., Agarasen, J., Pillai, P., Chandankhede, P. M., Labhshetwar, R. S., Gadiyal, Y., Rajmane, M. V., Kulkarni, P. P., et al. (2023) · Cureus · doi:10.7759/cureus.35261 · Cited by 26 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Large-scale computational modelling of H5 influenza variants against HA1-neutralising antibodies
Ford, C. T., Yasa, S., Obeid, K., Jaimes, R., Tomezsko, P. J., Guirales-Medrano, S., White, R. A., III, & Janies, D. A. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.105632 · Cited by 15 · see citing works
Self-citationHuman cytokine and coronavirus nucleocapsid protein interactivity using large-scale virtual screens
Tomezsko, P. J., Ford, C. T., Meyer, A. E., Michaleas, A. M., & Jaimes, R. (2024) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2024.1397968 · Cited by 9 · see citing works
Self-citationPD-1 Targeted Antibody Discovery Using AI Protein Diffusion
Ford, C. T. (2024) · Technology in Cancer Research & Treatment · doi:10.1177/15330338241275947 · Cited by 7 · see citing works
Self-citationUnlocking the potential of in silico approach in designing antibodies against SARS-CoV-2
Subramaniam, T. R., Mualif, S. A., Chan, W. H., & Halim, K. B. A. (2025) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2025.1533983 · Cited by 4 · see citing works
Yasa, S., Guirales-Medrano, S., Machado, D. J., Ford, C. T., & Janies, D. A. (2024) · Frontiers in Virology · doi:10.3389/fviro.2024.1419276 · Cited by 4 · see citing works
Self-citationAI and Healthcare Disparities: Lessons from a Cautionary Tale in Knee Radiology
Hull, G. (2025) · The Journal of Medicine and Philosophy A Forum for Bioethics and Philosophy of Medicine · doi:10.1093/jmp/jhaf020 · Cited by 2 · see citing works
AI-based antibody design targeting recent H5N1 avian influenza strains
Santolla, N., & Ford, C. T. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.06.026 · Cited by 1 · see citing works
Self-citationDarsono, A., Giri-Rachman, E. A., Artarini, A., Chen, D. V., Lusiany, T., Natalia, D., Naully, P. G., Ismanto, H. S., Pratama, D., Ihsanawati, I., et al. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.144412
- Authors
- Ford, C. T., Yasa, S., Jacob Machado, D., White, R. A., & Janies, D. A.
- Affiliations
- University of North Carolina at Charlotte (US)
- Corresponding author
- Ford, Colby T.
- Venue
- Frontiers in Virology
- Publisher
- Frontiers Media SA
- Published
- 2023-04-26
- Volume
- 3
- ISSN
- 2673-818X (ISSN-L)
- OpenAlex
- W4367054712
- Cited by
- 9
- References
- 24
- Citations by year
- 2025: 5 · 2024: 3 · 2023: 1
- FWCI
- 1.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Virology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- British Columbia Centre for Disease Control
- University of North Carolina at Charlotte
- Topics
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Life Sciences › Agricultural and Biological Sciences › Animal Science and Zoology › Animal Virus Infections Studies
- Keywords
- SARS-CoV-2, XBB.1.5, Omicron variant, neutralizing antibodies, receptor binding domain, antibody affinity, antibody escape, SARS-CoV-2 variants, Omicron subvariants
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
