End-to-End Differentiable Learning of Protein Structure
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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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Machine learning-based structure prediction methods explicitly incorporate constraints on protein geometries.
“significant work has been done in the opposite direction of machine learning-based structure prediction methods that explicitly incorporate constraints on protein geometries 3,5,26,87-89.”
From Learning the protein language: Evolution, structure, and function (2021) · cites this paper as [87]
End-to-end differentiable coordinate generation for protein structures was first demonstrated in the Recurrent Geometric Network.
“The process of end-to-end differentiable coordinate generation for protein structures, where atomic coordinates are produced as a direct output of a neural network, was first demonstrated in the Recurrent Geometric Network (18)”
From Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterised proteins (2020) · cites this paper as [18]
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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)
- 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 · 2018: 6 · 2017: 1
- FWCI
- 20.43
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- 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
- Physical Sciences › Materials Science › Materials Chemistry › 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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