PreprintCited by 15SARS-CoV-2 and COVID-19 ResearchMonoclonal and Polyclonal Antibodies Researchvaccines and immunoinformatics approaches

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Optimizing a Human Monoclonal Antibody for Better Neutralization of SARS-CoV-2

Open access

Preprint — published as 10.1038/s41467-025-61472-z · Paper page

Abstract

SARS-CoV-2 has largely evolved to resist antibody pressure, with each successive viral variant becoming more and more resistant to serum antibodies in the population. This evolution renders all previously authorized anti-spike therapeutic monoclonal antibodies inactive, and it threatens the remaining pipelines against COVID-19. We report herein the isolation of a human monoclonal antibody with a broad but incomplete SARS-CoV-2 neutralization profile, but structural analyses and mutational scanning lead to the engineering of variants that result in greater antibody flexibility while binding to the viral spike. Three such optimized monoclonal antibodies neutralize all SARS-CoV-2 strains tested with much improved potency and breadth, including against subvariants XEC and LP.8.1. The findings of this study not only present antibody candidates for clinical development against COVID-19, but also introduce a unique engineering approach to improve antibody activity via increasing conformational flexibility.

Overview

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1101/2024.12.04.626800

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

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.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 78

  1. 2021, Antibody resistance of SARS-CoV-2 variants B.1.351 and B.1.1.7, ature, 593, 130
  2. 10.1016/j.chom.2021.03.005 · DOI · Paper page
  3. 10.1016/j.cell.2021.03.028 · DOI · Paper page
  4. 2021, Increased resistance of SARS-CoV-2 variant P.1 to antibody neutralization. Cell Host Microbe, 29, 747-751
  5. 10.1038/s41586-020-2852-1 · DOI · Paper page
  6. 10.1101/2021.05.26.445838 · DOI
  7. 10.1038/s41586-020-2381-y · DOI · Paper page
  8. 10.1126/science.abd0827 · DOI · Paper page
  9. 10.1126/science.abm8143 · DOI
  10. Shu, Y. & McCauley, J . GISAID: Global initiative on sharing all influenza data - from vision to reality. Euro Surveill 22 (2017). · DOI · Paper page
  11. 2021, Track Omicron’s spread with molecular data, Science, 374, 1454, 10.1126/science.abn4543 · DOI
  12. 10.1038/s41586-021-04388-0 · DOI · Paper page
  13. 10.1038/s41586-022-04399-5 · DOI
  14. 10.1038/s41586-021-04386-2 · DOI
  15. 10.1038/s41586-021-04389-z · DOI · Paper page
  16. 10.1038/s41586-021-04385-3 · DOI · Paper page
  17. 10.1038/s41586-021-04387-1 · DOI · Paper page
  18. 10.1038/s41586-020-2548-6 · DOI · Paper page
  19. 2022, Antibody evasion by SARS-CoV-2 Omicron subvariants BA.2.12.1, BA.4 and BA.5. Nature, 608, 603-608
  20. 10.1016/j.cell.2022.12.018 · DOI · Paper page
  21. 10.1038/s41586-020-2349-y · DOI · Paper page
  22. 10.1016/j.celrep.2022.110812 · DOI
  23. 2023, Antibody neutralisation of emerging SARS-CoV-2 subvariants: EG.5.1 and XBC.1.6. Lancet Infect Dis, 23, e397-e398
  24. 2023, ACE2 binding and antibody evasion in enhanced transmissibility of XBB.1.5. Lancet Infect Dis, 23, 278-280
  25. 10.1371/journal.ppat.1011868 · DOI
  26. Wang, Q. et al. Antigenicity and receptor affinity of SARS-CoV-2 BA.2.86 spike. Nature (2023). · DOI
  27. 10.1038/s41586-022-04581-9 · DOI
  28. 10.1073/pnas.2220948120 · DOI
  29. 10.1016/j.immuni.2023.09.003 · DOI
  30. 2023, Covid-19: WHO adds JN.1 as new variant of interest. BMJ, 383, 2975
  31. 2024, XBB.1.5 monovalent mRNA vaccine booster elicits robust neutralizing antibodies against XBB subvariants and JN.1. Cell Host Microbe, 32, 315-321
  32. Wang, Q. et al. Antibody evasiveness of SARS-CoV-2 subvariants KP.3.1.1 and XEC. bioRxiv, 2024.2011.2017.624037 (2024). · DOI
  33. 2024, Activity of Research-Grade Pemivibart against Recent SARS-CoV-2 JN.1 Sublineages. N Engl J Med, 391, 1863-1864
  34. 2025, Antibody evasiveness of SARS-CoV-2 subvariants KP.3.1.1 and XEC. Cell Rep, 44, 115543
  35. Cai, Y. , et al. AZD3152 neutralizes SARS-CoV-2 historical and contemporary variants and is protective in hamsters and well tolerated in adults. Sci Transl Med 16, eado2817 (2024). · DOI
  36. 10.1038/s41586-020-2571-7 · DOI · Paper page
  37. 10.1126/science.abd2321 · DOI · Paper page
  38. 10.1093/nar/gki387 · DOI · Paper page
  39. 10.1016/j.jinf.2023.10.008 · DOI
  40. Cao, Y. et al. Imprinted SARS-CoV-2 humoral immunity induces convergent Omicron RBD evolution. Nature (2022). · DOI
  41. 10.1016/j.cell.2022.05.014 · DOI
  42. 10.1016/j.cell.2020.05.025 · DOI · Paper page
  43. Cao, Y. et al. Rational identification of potent and broad sarbecovirus-neutralizing antibody cocktails from SARS convalescents. Cell Rep 41, 111845 (2022). · DOI
  44. Jian, F. et al. A generalized framework to identify SARS-CoV-2 broadly neutralizing antibodies. bioRxiv, 2024.2004.2016.589454 (2024).
  45. Yu, L. et al. Potent and broadly neutralizing antibodies against sarbecoviruses elicited by single ancestral SARS-CoV-2 infection. bioRxiv, 2024.2006.2006.597720 (2024). · DOI
  46. Yao, T. , Ma, Z. , Lan, K. , Guo, Y. & Liu, L . Neutralizing Activity and Viral Escape of Pemivibart by SARS-CoV-2 JN.1 sublineages. bioRxiv, 2024.2011.2008.622746 (2024). · DOI
  47. Planas, D. et al. Escape of SARS-CoV-2 Variants KP.1.1, LB.1, and KP3.3 From Approved Monoclonal Antibodies. Pathog Immun 10, 1-11 (2024). · DOI
  48. 10.1001/jama.2021.24931 · DOI
  49. 10.1146/annurev-med-061323-073837 · DOI
  50. 2025, Anti-Spike Monoclonal Antibody Monotherapies and Immune Escape Risk Minimization Strategies, Clin Infect Dis, 80, 484, 10.1093/cid/ciae254 · DOI
  51. 10.1038/nbt0792-779 · DOI
  52. 10.1093/jb/mvn009 · DOI
  53. Kim, H.Y. , Stojadinovic, A. & Izadjoo, M.J . Affinity maturation of monoclonal antibodies by multi-site-directed mutagenesis. Methods Mol Biol 1131, 407–420 (2014). · DOI
  54. 10.1038/s41467-023-42098-5 · DOI
  55. Pan, Y. et al. Screening of potent neutralizing antibodies against SARS-CoV-2 using convalescent patients-derived phage-display libraries. Cell Discov 7, 57 (2021). · DOI
  56. 10.1038/nmeth.4038 · DOI
  57. 10.1093/nar/gky550 · DOI
  58. 10.1038/s41589-021-00832-4 · DOI
  59. 10.1038/s41586-024-07385-1 · DOI
  60. 10.1073/pnas.2122954119 · DOI · Paper page
  61. 10.1038/s41467-023-39022-2 · DOI · Paper page
  62. Burn Aschner , C., et al. A multi-specific, multi-affinity antibody platform neutralizes sarbecoviruses and confers protection against SARS-CoV-2 in vivo. Sci Transl Med 15, eadf4549 (2023). · DOI
  63. 10.1038/nbt.2677 · DOI
  64. Ovchinnikov, V. , Louveau, J.E. , Barton, J.P. , Karplus, M. & Chakraborty, A.K . Role of framework mutations and antibody flexibility in the evolution of broadly neutralizing antibodies. Elife 7 (2018). · DOI
  65. 10.1016/S0022-2836(05)80102-0 · DOI
  66. Yanaka, S. , Moriwaki, Y. , Tsumoto, K. & Sugase, K . Elucidation of potential sites for antibody engineering by fluctuation editing. Sci Rep 7, 9597 (2017). · DOI
  67. Liu, L. et al. An antibody class with a common CDRH3 motif broadly neutralizes sarbecoviruses. Sci Transl Med 14, eabn6859 (2022). · DOI
  68. 10.1126/science.abd0826 · DOI · Paper page
  69. 10.1126/science.abc0870 · DOI
  70. 10.1107/S0907444909047374 · DOI
  71. 10.1107/S0907444913000061 · DOI
  72. 10.1107/S0907444910045749 · DOI · Paper page
  73. 10.1107/S0907444906045975 · DOI
  74. 10.1107/S0907444904019158 · DOI · Paper page
  75. 10.1107/S0907444909052925 · DOI · Paper page
  76. 10.1093/nar/gkh398 · DOI
  77. 10.1002/PRO.4792 · DOI
  78. 10.1107/S2059798319011471/HTTPS://GOLDBOOK.IUPAC.ORG/E02002.HTML · DOI

Metadata

Authors
Wang, Q., Guo, Y., Casner, R. G., Yu, J., Nair, M. S., Ho, J., Reddem, E. R., Mellis, I. A., Wu, M., Tzang, C. C., Hong, H., Huang, Y., Shapiro, L., Liu, L., & Ho, D. D.
Affiliations
  • Aaron Diamond AIDS Research Center (US)
  • Mortimer B. Zuckerman Mind Brain Behavior Institute (US)
Corresponding author
  • Shapiro, Lawrence
  • Liu, Lihong
  • Ho, David D.
Venue
Nature Communications
Publisher
openRxiv
Published
2024-12-05
Volume
16
Issue
1
Pages
6195-6195
ISSN
2041-1723 (ISSN-L)
OpenAlex
W4412020829
Cited by
15
References
1
Citations by year
2026: 8 · 2025: 7
FWCI
11.54
Type
journal article
Language
en
Open access
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Bill and Melinda Gates Foundation (INV-019355)
Topics
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
  • Life Sciences › Immunology and Microbiology › Immunology › Monoclonal and Polyclonal Antibodies Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords
SARS-CoV-2, human monoclonal antibodies, antibody engineering, neutralizing antibodies, spike protein, Omicron subvariants, COVID-19 treatment, mutation scanning
MeSH terms
COVID-19, SARS-CoV-2, Animals, Antibodies, Monoclonal, Antibodies, Viral, Humans, Mutation, Neutralization Tests, Antibodies, Neutralizing, Spike Glycoprotein, Coronavirus

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