Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints
Open accessAbstract
The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently, deep learning has shown promise in allowing accurate residue-residue contact prediction even for shallow sequence alignments. Here we introduce DMPfold, which uses deep learning to predict inter-atomic distance bounds, the main chain hydrogen bond network, and torsion angles, which it uses to build models in an iterative fashion. DMPfold produces more accurate models than two popular methods for a test set of CASP12 domains, and works just as well for transmembrane proteins. Applied to all Pfam domains without known structures, confident models for 25% of these so-called dark families were produced in under a week on a small 200 core cluster. DMPfold provides models for 16% of human proteome UniProt entries without structures, generates accurate models with fewer than 100 sequences in some cases, and is freely available.
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DMPfold2 obtains predictions of comparable or better accuracy than DMPfold1 while taking under a second to a few minutes to produce one structure on readily-available hardware.
“We show that despite the relatively simple composition of the input feature set, we are able to obtain predictions of comparable or better accuracy than DMPfold (9), while taking under a second to a few minutes to produce one structure on readily-available hardware.”
From Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterised proteins (2020) · cites this paper as [9]
Iteration of the prediction process can make significant improvements in model quality.
“As we first showed with DMPfold1 (9), iteration of the prediction process, where the final model is fed back in as an input to the network, can make significant improvements in model quality.”
From Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterised proteins (2020) · cites this paper as [9]
DMPfold2 produces structural models of comparable or greater accuracy than DMPfold1.
“When supplemented with an approximate precision matrix, the learned representation can be used to produce structural models of comparable or greater accuracy as compared to our original DMPfold method, while requiring less than a second to produce a typical model.”
From Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterised proteins (2020) · cites this paper as [9]
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“For example, QDeep QA predictions(60) are based on distance estimations from DMPfold.(21) In GalaxyRefine2,(61) RefineD,(62) and Baker suite,(63) the QA is incorporated into a model refinement pipeline.”
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Self-citation
- Authors
- Greener, J. G., Kandathil, S. M., & Jones, D. T.
- Affiliations
- The Francis Crick Institute (GB)
- Corresponding author
- Jones, David T.
- Venue
- Nature Communications
- Published
- 2019-09-04
- Volume
- 10
- Issue
- 1
- Pages
- 3977-3977
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6726615
- OpenAlex
- W2971733377
- Cited by
- 178
- References
- 52
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 17 · 2023: 19 · 2022: 25 · 2021: 66 · 2020: 28 · 2019: 5
- FWCI
- 10.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University)
- The Francis Crick Institute (cc-by)
- Funders
- European Research Council Advanced Grant “ProCovar”
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- de novo protein structure prediction, Pfam domains, hydrogen bond network, transmembrane proteins, human proteome, deep learning
- MeSH terms
- Deep Learning, Algorithms, Animals, Binding Sites, Humans, Models, Molecular, Protein Conformation, Reproducibility of Results, Computational Biology, Proteome, Proteomics
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