Accurate single-domain scaffolding of three nonoverlapping protein epitopes using deep learning
Open accessPublished version of a preprint
Abstract
De novo protein design has seen major success in scaffolding single functional motifs; however, in nature, most proteins present multiple functional sites. Here, we describe an approach to simultaneously scaffold multiple functional sites in a single-domain protein using deep learning. We designed small single-domain immunogens, under 130 residues, that present three distinct and irregular motifs from respiratory syncytial virus. These motifs together comprise nearly half of the designed proteins; hence, the overall folds are quite unusual with little global similarity to proteins in the Protein Data Bank. Despite this, X-ray crystal structures confirmed the accuracy of presentation of each of the motifs and the multiepitope design yields improved cross-reactive titers and neutralizing response compared to a single-epitope immunogen. The successful presentation of three distinct binding surfaces in a small single-domain protein highlights the power of generative deep learning methods to solve complex protein design problems.
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Efficient protein structure generation with sparse denoising models
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- Authors
- Castro, K. M., Watson, J. L., Wang, J., Southern, J., Ayardulabi, R., Georgeon, S., Rosset, S., Baker, D., & Correia, B. E.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- University of Washington (US)
- Imperial College London (GB)
- Corresponding author
- Baker, David
- Correia, Bruno E.
- ORCID
- Venue
- Nature Chemical Biology
- Published
- 2025-12-05
- Volume
- 22
- Issue
- 4
- Pages
- 604-611
- ISSN
- 1552-4450 (ISSN-L)
- 1552-4469
- PMC
- PMC13038408
- OpenAlex
- W4417031708
- Cited by
- 13
- References
- 9
- Citations by year
- 2026: 8 · 2025: 3 · 2024: 2
- FWCI
- 3.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Chemical Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DORA PSI (Paul Scherrer Institute) (submittedVersion, cc-by)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Funders
- National Science Foundation
- Bill and Melinda Gates Foundation (INV-010680)
- Open Philanthropy Project
- École Polytechnique Fédérale de Lausanne
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (197724, 310030)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Keywords
- de novo protein design, single-domain proteins, immunogen, respiratory syncytial virus, epitope presentation, deep learning protein design, generative models, cross-reactive neutralizing antibodies
- MeSH terms
- Deep Learning, Epitopes, Humans, Models, Molecular, Respiratory Syncytial Viruses, Protein Engineering, Crystallography, X-Ray
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