Protein design and variant prediction using autoregressive generative models
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Abstract
Abstract The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for important applications where multiple sequence alignments are not robust. Such applications include the prediction of variant effects of indels, disordered proteins, and the design of proteins such as antibodies due to the highly variable complementarity determining regions. We introduce a deep generative model adapted from natural language processing for prediction and design of diverse functional sequences without the need for alignments. The model performs state-of-art prediction of missense and indel effects and we successfully design and test a diverse 10 5 -nanobody library that shows better expression than a 1000-fold larger synthetic library. Our results demonstrate the power of the alignment-free autoregressive model in generalizing to regions of sequence space traditionally considered beyond the reach of prediction and design.
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Shin et al.'s trained model was used to generate libraries of nanobodies, fully designing the CDR1, CDR2, and CDR3 regions.
“Their trained model was used to generate libraries of nanobodies, fully designing the CDR1, CDR2, and CDR3 regions.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [56]
Shin et al. applied causal dilated convolutions to learn long-range relationships in sequences in an autoregressive model.
“In an early method, Shin et al. [56] applied causal dilated convolutions to learn long-range relationships in sequences in an autoregressive model (i.e., a model that predicts each amino acid one at a time, using the previous predictions as the next model input).”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [56]
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- Authors
- Shin, J.-E., Riesselman, A. J., Kollasch, A. W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A. C., & Marks, D. S.
- Affiliations
- Harvard University (US)
- University of California, San Francisco (US)
- Broad Institute (US)
- Corresponding author
- Kruse, Andrew C.
- Marks, Debora S.
- ORCID
- Venue
- Nature Communications
- Published
- 2021-04-23
- Volume
- 12
- Issue
- 1
- Pages
- 2403-2403
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC8065141
- OpenAlex
- W3154275519
- Cited by
- 320
- References
- 105
- Citations by year
- 2026: 28 · 2025: 46 · 2024: 90 · 2023: 58 · 2022: 67 · 2021: 29 · 2020: 2
- FWCI
- 19.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (DP5OD023048, DP5OD021345)
- National Cancer Institute (R01CA260415)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- protein design, variant effect prediction, autoregressive generative models, missense mutations, nanobody design, hypervariable region, disordered proteins, protein engineering, deep generative models
- MeSH terms
- Algorithms, Amino Acid Sequence, Antibodies, Antigens, Genotype, Humans, Mutation, Phenotype, Proteins, Protein Engineering, Neural Networks, Computer, Computational Biology
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