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De novo protein design enables the precise induction of RSV-neutralizing antibodies
Open accessAbstract
TopoBuilding precision vaccines: To induce strong and targeted neutralizing antibody (nAb) responses against vaccine targets, one strategy has been to use computationally designed immunogens. However, the structural complexity of many known neutralization epitopes has posed a major challenge for the design of accurate epitope mimetics. Sesterhenn et al. created a protein design algorithm called TopoBuilder to design scaffolds for irregular and discontinuous neutralization epitopes. As a proof of principle, the authors generated epitope-focused immunogens based on the prefusion conformation of the respiratory syncytial virus (RSV) fusion protein. When these immunogens were used to vaccinate mice and nonhuman primates in RSV infection models, they generated targeted nAb responses to RSV and boosted site-specific nAb responses in heterologous prime-boost vaccination schemes. Science , this issue p. eaay5051
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“we established an in silico benchmark test (Supplementary Table 9) comprising 25 motif-scaffolding design problems addressed in six recent publications encompassing several design methodologies4,5,29,38–40”
From De novo design of protein structure and function with RFdiffusion (2023) · cites this paper as [38]
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“Previous methods can scaffold functional sites made up of one or two contiguous chain segments (6–10), but with the exception of helical bundles (8) these do not extend readily to more complex sites composed of three or more chain segments, and the generated backbones are not guaranteed to be designable (encodable by some amino acid sequence).”
From Scaffolding protein functional sites using deep learning (2022) · cites this paper as [9]
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“Previous methods can scaffold functional sites made up of one or two contiguous chain segments (6–10), but with the exception of helical bundles (8) these do not extend readily to more complex sites composed of three or more chain segments, and the generated backbones are not guaranteed to be designable (encodable by some amino acid sequence).”
From Scaffolding protein functional sites using deep learning (2022) · cites this paper as [9]
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“Previous methods can scaffold functional sites made up of one or two contiguous chain segments (6–10), but with the exception of helical bundles (8) these do not extend readily to more complex sites composed of three or more chain segments, and the generated backbones are not guaranteed to be designable (encodable by some amino acid sequence).”
From Scaffolding protein functional sites using deep learning (2022) · cites this paper as [9]
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Self-citation
Metadata
- Authors
- Sesterhenn, F., Yang, C., Bonet, J., Cramer, J. T., Wen, X., Wang, Y., Chiang, C.-I., Abriata, L. A., Kucharska, I., Castoro, G., Vollers, S. S., Galloux, M., Dheilly, E., Rosset, S., Corthésy, P., Georgeon, S., Villard, M., Richard, C.-A., Descamps, D., Delgado, T., Oricchio, E., Rameix-Welti, M.-A., Más, V., Ervin, S., Eléouët, J.-F., Riffault, S., Bates, J. T., Julien, J.-P., Li, Y., Jardetzky, T., Krey, T., & Correia, B. E.
- Affiliations
- SIB Swiss Institute of Bioinformatics (CH)
- Medizinische Hochschule Hannover (DE)
- Stanford Medicine (US)
- Institute for Bioscience and Biotechnology Research (US)
- University of Toronto (CA)
- Université de Versailles Saint-Quentin-en-Yvelines (FR)
- École Polytechnique Fédérale de Lausanne (CH)
- Instituto de Salud Carlos III (ES)
- UMR1173, INSERM, Université de Versailles St. Quentin, 78180 Montigny le Bretonneux, France.
- Atrium Health Wake Forest Baptist (US)
- University of Mississippi Medical Center (US)
- University of Maryland, Baltimore (US)
- Corresponding author
- Sesterhenn, Fabian
- Yang, Che
- Bonet, Jaume
- Cramer, Johannes T.
- Correia, Bruno E.
- ORCID
- Sesterhenn, Fabian
- Yang, Che
- Bonet, Jaume
- Cramer, Johannes T.
- Wen, Xiaolin
- Wang, Yimeng
- Chiang, Chi-I
- Abriata, Luciano A.
- Kucharska, Iga
- Vollers, Sabrina S.
- Galloux, Marie
- Rosset, Stéphane
- Georgeon, Sandrine
- Villard, Mélanie
- Richard, Charles-Adrien
- Descamps, Delphyne
- Oricchio, Elisa
- Rameix-Welti, Marie-Anne
- Más, Vicente
- Ervin, Sean
- Eléouët, Jean-François
- Riffault, Sabine
- Bates, John T.
- Julien, Jean-Philippe
- Li, Yuxing
- Jardetzky, Theodore
- Krey, Thomas
- Correia, Bruno E.
- Venue
- Science
- Published
- 2020-05-15
- Volume
- 368
- Issue
- 6492
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC7391827
- OpenAlex
- W3024848554
- Cited by
- 191
- References
- 85
- Citations by year
- 2026: 19 · 2025: 20 · 2024: 30 · 2023: 26 · 2022: 35 · 2021: 44 · 2020: 17
- FWCI
- 9.90
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- HAL (submittedVersion, other-oa)
- DORA PSI (Paul Scherrer Institute) (submittedVersion)
- Funders
- European Research Council (716058)
- Swiss National Science Foundation (310030_163139)
- Topics
- 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
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Keywords
- de novo protein design, respiratory syncytial virus, neutralizing antibodies, vaccine design, non-human primates, therapeutic antibodies
- MeSH terms
- Humans, Protein Conformation, Recombinant Fusion Proteins, Protein Engineering, Immunodominant Epitopes, Respiratory Syncytial Virus, Human, Computational Biology, Amino Acid Motifs, Respiratory Syncytial Virus Vaccines, Antibodies, Neutralizing, Single-Domain Antibodies
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