Single-sequence protein structure prediction using a language model and deep learning
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Abstract
AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain in (1) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated; (2) rapid exploration of designed structures; and (3) understanding the rules governing spontaneous polypeptide folding in solution. Here we report development of an end-to-end differentiable recurrent geometric network (RGN) that uses a protein language model (AminoBERT) to learn latent structural information from unaligned proteins. A linked geometric module compactly represents C α backbone geometry in a translationally and rotationally invariant way. On average, RGN2 outperforms AlphaFold2 and RoseTTAFold on orphan proteins and classes of designed proteins while achieving up to a 10 6-fold reduction in compute time. These findings demonstrate the practical and theoretical strengths of protein language models relative to MSAs in structure prediction.
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Deep learning has been employed to predict protein structure.
“predict protein structure [8]”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [8]
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- Authors
- Chowdhury, R., Bouatta, N., Biswas, S., Floristean, C., Kharkar, A., Roy, K., Rochereau, C., Ahdritz, G., Zhang, J., Church, G. M., Sorger, P. K., & AlQuraishi, M.
- Affiliations
- Harvard University (US)
- Columbia University (US)
- Corresponding author
- Bouatta, Nazim
- Sorger, Peter K.
- AlQuraishi, Mohammed
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2022-10-03
- Volume
- 40
- Issue
- 11
- Pages
- 1617-1623
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC10440047
- OpenAlex
- W4300861364
- Cited by
- 455
- References
- 56
- Citations by year
- 2026: 49 · 2025: 103 · 2024: 135 · 2023: 124 · 2022: 37 · 2021: 7
- FWCI
- 32.66
- Type
- journal article
- Language
- en
- Update notices
- correction (2022-10-17)
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Nvidia
- Defense Advanced Research Projects Agency (HR0011, HR0011-19-2-0022)
- National Cancer Institute (U54CA225088)
- 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
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- protein structure prediction, protein language models, orphan proteins, protein design, computational efficiency
- MeSH terms
- Deep Learning, Language, Protein Conformation, Proteins, Sequence Alignment, Computational Biology
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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