Cited by 374Protein Structure and DynamicsMachine Learning in BioinformaticsEnzyme Structure and Function

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End-to-End Differentiable Learning of Protein Structure

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AlQuraishi, Mohammed·2019·Cell Systems

DOI 10.1016/j.cels.2019.03.006PMC PMC6513320

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Abstract

Predicting protein structure from sequence is a central challenge of biochemistry. Co-evolution methods show promise, but an explicit sequence-to-structure map remains elusive. Advances in deep learning that replace complex, human-designed pipelines with differentiable models optimized end to end suggest the potential benefits of similarly reformulating structure prediction. Here, we introduce an end-to-end differentiable model for protein structure learning. The model couples local and global protein structure via geometric units that optimize global geometry without violating local covalent chemistry. We test our model using two challenging tasks: predicting novel folds without co-evolutionary data and predicting known folds without structural templates. In the first task, the model achieves state-of-the-art accuracy, and in the second, it comes within 1-2 Å; competing methods using co-evolution and experimental templates have been refined over many years, and it is likely that the differentiable approach has substantial room for further improvement, with applications ranging from drug discovery to protein design.

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Authors: AlQuraishi, M.
Affiliations: Harvard University (US)
Corresponding author: AlQuraishi, Mohammed
ORCID: AlQuraishi, Mohammed
Venue: Cell Systems
Publisher: Elsevier BV
Published: 2019-04
Volume: 8
Issue: 4
Pages: 292-301.e3
ISSN: 2405-4712 (ISSN-L)
DOI: 10.1016/j.cels.2019.03.006
PMC: PMC6513320
OpenAlex: W2952317511
Cited by: 371
References: 55
Citations by year: 2026: 15 · 2025: 20 · 2024: 33 · 2023: 49 · 2022: 58 · 2021: 87 · 2020: 61 · 2019: 41
FWCI: 20.43
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 389 d)
Locations: Cell Systems (publishedVersion)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: NVIDIA
NIGMS (P50GM107618)
NCI (U54-CA225088)
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, differentiable models, coevolution, protein design
MeSH terms: Machine Learning, Software, Protein Folding, Evolution, Molecular, Sequence Analysis, Protein

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