Cited by 457Protein Structure and DynamicsMachine Learning in BioinformaticsEnzyme Structure and Function

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Single-sequence protein structure prediction using a language model and deep learning

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Chowdhury, Ratul, Bouatta, Nazim, Biswas, Surojit, Floristean, Christina, Kharkar, Anant, Roy, Koushik, Rochereau, Charlotte, Ahdritz, Gustaf, Zhang, Joanna, Church, George M., Sorger, Peter K., AlQuraishi, Mohammed·2022·Nature Biotechnology

DOI 10.1038/s41587-022-01432-wPMC PMC10440047

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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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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: Chowdhury, Ratul
Bouatta, Nazim
Biswas, Surojit
Floristean, Christina
Roy, Koushik
Rochereau, Charlotte
Ahdritz, Gustaf
Zhang, Joanna
Church, George M.
Sorger, Peter K.
AlQuraishi, Mohammed
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2022-10-03
Volume: 40
Issue: 11
Pages: 1617-1623
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/s41587-022-01432-w
PMC: PMC10440047
OpenAlex: W4300861364
Cited by: 455
References: 56
Citations by year: 2026: 49 · 2025: 103 · 2024: 135 · 2023: 124 · 2022: 37
FWCI: 32.66
Type: journal article
Language: en
Update notices: correction (2022-10-17)
Open access: green
doi.org
Licence: cc-by-nc-nd
www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
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
Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › 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

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Single-sequence protein structure prediction using a language model and deep learning

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