Multivariate mining of an alpaca immune repertoire identifies potent cross-neutralising SARS-CoV-2 nanobodies
Open accessPreprint — published as 10.1126/sciadv.abm0220 · Paper page
Abstract
Conventional approaches to isolate and characterize nanobodies are laborious and cumbersome. Here we combine phage display, multivariate enrichment, and novel sequence analysis techniques to annotate an entire nanobody repertoire from an immunized alpaca. We combine this approach with a streamlined screening strategy to identify numerous anti-SARS-CoV-2 nanobodies, and use neutralization assays and Hydrogen/Deuterium exchange coupled to mass spectrometry (HDX-MS) epitope mapping to characterize their potency and specificity. Epitope mapping revealed that the binding site is a key determinant of neutralization potency, rather than affinity alone. The most potent nanobodies bind to the receptor binding motif of the RBD, directly preventing interaction with the host cell receptor ACE2, and we identify two exceptionally potent members of this category (with monomeric IC50s around 13 and 16 ng/ml). Other nanobodies bind to a more conserved epitope on the side of the RBD, and are able to potently neutralize the SARS-CoV-2 founder virus (42 ng/ml), the beta variant (B.1.351/501Y.V2) (35 ng/ml), and also cross-neutralize the more distantly related SARS-CoV-1 (0.46 μg/ml). The approach presented here is well suited for the screening of phage libraries to identify functional nanobodies for various biomedical and biochemical applications.
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
Rogers, T. F., Zhao, F., Huang, D., Beutler, N., Burns, A., He, W.-T., Limbo, O., Smith, C., Song, G., Woehl, J., et al. (2020) · Science · doi:10.1126/science.abc7520 · Cited by 1,230 · see citing works
Flagged first, then by citations
- Authors
- Hanke, L., Sheward, D. J., Pankow, A., Vidakovics, L. P., Karl, V., Kim, C., Urgard, E., Smith, N. L., Astorga-Wells, J., Ekström, S., Coquet, J. M., McInerney, G. M., & Murrell, B.
- Affiliations
- Karolinska Institutet (SE)
- University of Cape Town (ZA)
- Lund University (SE)
- Corresponding author
- McInerney, Gerald M.
- Murrell, Ben
- ORCID
- Venue
- Science Advances
- Publisher
- openRxiv
- Published
- 2021-07-26
- Volume
- 8
- Issue
- 12
- Pages
- eabm0220-eabm0220
- ISSN
- 2375-2548 (ISSN-L)
- PMC
- PMC8956255
- OpenAlex
- W4220841727
- Cited by
- 38
- References
- 2
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 14 · 2023: 8 · 2022: 4
- FWCI
- 3.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Science Advances (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- Funders
- European Commission (101003653)
- Vetenskapsrådet (2018-03914, 2018-02381)
- Topics
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- SARS-CoV-2, nanobodies, cross-neutralizing antibodies, receptor binding domain, phage display, neutralization potency, Beta variant, SARS-CoV-1, epitope mapping, hydrogen-deuterium exchange mass spectrometry
- MeSH terms
- COVID-19, SARS-CoV-2, Animals, Antibodies, Monoclonal, Antibodies, Viral, Camelids, New World, Humans, Membrane Glycoproteins, Neutralization Tests, Viral Envelope Proteins, Single-Domain Antibodies, 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