Modeling of ACE2 and antibodies bound to SARS-CoV-2 provides insights into infectivity and immune evasion
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Abstract
Given the COVID-19 pandemic, there is interest in understanding ligand-receptor features and targeted antibody-binding attributes against emerging SARS-CoV-2 variants. Here, we developed a large-scale structure-based pipeline for analysis of protein-protein interactions regulating SARS-CoV-2 immune evasion. First, we generated computed structural models of the Spike protein of 3 SARS-CoV-2 variants (B.1.1.529, BA.2.12.1, and BA.5) bound either to a native receptor (ACE2) or to a large panel of targeted ligands ( n = 282), which included neutralizing or therapeutic monoclonal antibodies. Moreover, by using the Barnes classification, we noted an overall loss of interfacial interactions (with gain of new interactions in certain cases) at the receptor-binding domain (RBD) mediated by substituted residues for neutralizing complexes in classes 1 and 2, whereas less destabilization was observed for classes 3 and 4. Finally, an experimental validation of predicted weakened therapeutic antibody binding was performed in a cell-based assay. Compared with the original Omicron variant (B.1.1.529), derivative variants featured progressive destabilization of antibody-RBD interfaces mediated by a larger set of substituted residues, thereby providing a molecular basis for immune evasion. This approach and findings provide a framework for rapidly and efficiently generating structural models for SARS-CoV-2 variants bound to ligands of mechanistic and therapeutic value.
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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
Antibody resistance of SARS-CoV-2 variants B.1.351 and B.1.1.7
Wang, P., Nair, M. S., Liu, L., Iketani, S., Luo, Y., Guo, Y., Wang, M., Yu, J., Zhang, B., Kwong, P. D., et al. (2021) · Nature · doi:10.1038/s41586-021-03398-2 · Cited by 2,312 · see citing works
Omicron escapes the majority of existing SARS-CoV-2 neutralizing antibodies
Cao, Y., Wang, J., Jian, F., Xiao, T., Song, W., Yisimayi, A., Huang, W., Li, Q., Wang, P., An, R., et al. (2022) · Nature · doi:10.1038/s41586-021-04385-3 · Cited by 2,006 · see citing works
Data, disease and diplomacy: GISAID's innovative contribution to global health
Elbe, S., & Buckland‐Merrett, G. (2017) · Global Challenges · doi:10.1002/gch2.1018 · Cited by 1,928 · cited 1× · see citing works
“In our pipeline, residue substitutions observed in sequenced SARS-CoV-2 genomes obtained from the GISAID database (53, 54) are introduced in available PDB structures or CSMs of proteins and their energetic impact is qualitatively evaluated.”
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
Striking antibody evasion manifested by the Omicron variant of SARS-CoV-2
Liu, L., Iketani, S., Guo, Y., Chan, J. F.-W., Wang, M., Liu, L., Luo, Y., Chu, H., Huang, Y., Nair, M. S., et al. (2022) · Nature · doi:10.1038/s41586-021-04388-0 · Cited by 1,290 · see citing works
Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa
Pulliam, J. R. C., van Schalkwyk, C., Govender, N., Gottberg, A. V., Cohen, C., Groome, M. J., Dushoff, J., Mlisana, K., & Moultrie, H. (2021) · doi:10.1101/2021.11.11.21266068 · Cited by 1,037 · see citing works
Greaney, A. J., Starr, T. N., Gilchuk, P., Zost, S. J., Binshtein, E., Loes, A. N., Hilton, S. K., Huddleston, J., Eguia, R., Crawford, K. H. D., et al. (2021) · Cell Host & Microbe · doi:10.1016/j.chom.2020.11.007 · Cited by 1,016 · see citing works
Structural basis of a shared antibody response to SARS-CoV-2
Yuan, M., Liu, H., Wu, N. C., Lee, C.-C. D., Zhu, X., Zhao, F., Huang, D., Yu, W., Hua, Y., Tien, H., et al. (2020) · Science · doi:10.1126/science.abd2321 · Cited by 589 · see citing works
Structural basis of SARS-CoV-2 Omicron immune evasion and receptor engagement
McCallum, M., Czudnochowski, N., Rosen, L. E., Zepeda, S. K., Bowen, J. E., Walls, A. C., Hauser, K., Joshi, A., Stewart, C., Dillen, J. R., et al. (2022) · Science · doi:10.1126/science.abn8652 · Cited by 535 · see citing works
Mapping mutations to the SARS-CoV-2 RBD that escape binding by different classes of antibodies
Greaney, A. J., Starr, T. N., Barnes, C. O., Weisblum, Y., Schmidt, F., Caskey, M., Gaebler, C., Cho, A., Agudelo, M., Finkin, S., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24435-8 · Cited by 433 · see citing works
Structural and functional ramifications of antigenic drift in recent SARS-CoV-2 variants
Yuan, M., Huang, D., Lee, C.-C. D., Wu, N. C., Jackson, A. M., Zhu, X., Liu, H., Peng, L., van Gils, M. J., Sanders, R. W., et al. (2021) · doi:10.1101/2021.02.16.430500 · Cited by 364 · see citing works
Using AlphaFold to predict the impact of single mutations on protein stability and function
Pak, M. A., Markhieva, K. A., Novikova, M. S., Petrov, D. S., Vorobyev, I. S., Maksimova, E. S., Kondrashov, F. A., & Ivankov, D. N. (2023) · PLOS One · doi:10.1371/journal.pone.0282689 · Cited by 322 · see citing works
Copin, R., Baum, A., Wloga, E., Pascal, K. E., Giordano, S., Fulton, B. O., Zhou, A., Negron, N., Lanza, K., Chan, N., et al. (2021) · Cell · doi:10.1016/j.cell.2021.06.002 · Cited by 212 · see citing works
Where did ‘weird’ Omicron come from?
Kupferschmidt, K. (2021) · Science · doi:10.1126/science.acx9738 · Cited by 154 · see citing works
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COVID-19 and cellular senescence
Schmitt, C. A., Tchkonia, T., Niedernhofer, L. J., Robbins, P. D., Kirkland, J. L., & Lee, S. (2023) · Nature Reviews Immunology · doi:10.1038/s41577-022-00785-2 · Cited by 153 · see citing works
- Authors
- Lubin, J. H., Markosian, C., Balamurugan, D., Ma, M. T., Chen, C.-H., Liu, D., Pasqualini, R., Arap, W., Burley, S. K., & Khare, S. D.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Rutgers Cancer Institute (US)
- Rutgers New Jersey Medical School (US)
- Corresponding author
- Arap, Wadih
- ORCID
- Venue
- JCI Insight
- Published
- 2023-07-10
- Volume
- 8
- Issue
- 13
- ISSN
- 2379-3708 (ISSN-L)
- PMC
- PMC10371346
- OpenAlex
- W4379056128
- Cited by
- 26
- References
- 15
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 4 · 2022: 16
- FWCI
- 2.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- JCI Insight (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- Longenbaugh-Levy Donor-Advised Fund (N/A)
- National Institutes of Health (AI130197,CA267368,GM132565,R01GM133198)
- National Science Foundation (CBET-1929237,DBI-1832184)
- US Department of Energy (DE-SC0019749)
- 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 › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- SARS-CoV-2, ACE2, spike protein, receptor binding domain, immune evasion, neutralizing antibodies, therapeutic antibodies, Omicron variant, BA.2, BA.5, protein-protein interaction, antibody affinity, structural modeling, ligand-receptor binding, COVID-19 pandemic
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