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Structure-guided multivalent nanobodies block SARS-CoV-2 infection and suppress mutational escape
Open accessAbstract
A double punch against SARS-CoV-2: Monoclonal antibodies are an important weapon in the battle against COVID-19. However, these large proteins are difficult to produce in the needed quantities and at low cost. Attention has turned to nanobodies, which are aptly named, single-domain antibodies that are easier to produce and have the potential to be administered by inhalation. Koenig et al. describe four nanobodies that bind to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and prevent infection of cells (see the Perspective by Saelens and Schepens). Structures show that the nanobodies target two distinct epitopes on the SARS-CoV-2 spike protein. Multivalent nanobodies neutralize virus much more potently than single nanobodies, and multivalent nanobodies that bind two epitopes prevent the emergence of viral escape mutants. Science , this issue p. eabe6230 ; see also p. 681
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Metadata
- Authors
- Koenig, P.-A., Das, H., Liu, H., Kümmerer, B. M., Gohr, F. N., Jenster, L.-M., Schiffelers, L. D. J., Tesfamariam, Y. M., Uchima, M., Wuerth, J. D., Gatterdam, K., Ruetalo, N., Christensen, M. H., Fandrey, C. I., Normann, S., Tödtmann, J. M. P., Pritzl, S., Hanke, L., Boos, J., Yuan, M., Zhu, X., Schmid-Burgk, J. L., Kato, H., Schindler, M., Wilson, I. A., Geyer, M., Ludwig, K. U., Hällberg, B. M., Wu, N. C., & Schmidt, F. I.
- Venue
- Science
- Published
- 2021-02-12
- Volume
- 371
- Issue
- 6530
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W3119147846
- Cited by
- 396
- References
- 75
- FWCI
- 25.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
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 ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
