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Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints

Author

Listed:
  • Joe G. Greener

    (University College London
    The Francis Crick Institute)

  • Shaun M. Kandathil

    (University College London
    The Francis Crick Institute)

  • David T. Jones

    (University College London
    The Francis Crick Institute)

Abstract

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.

Suggested Citation

  • Joe G. Greener & Shaun M. Kandathil & David T. Jones, 2019. "Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11994-0
    DOI: 10.1038/s41467-019-11994-0
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    Cited by:

    1. Julia Koehler Leman & Pawel Szczerbiak & P. Douglas Renfrew & Vladimir Gligorijevic & Daniel Berenberg & Tommi Vatanen & Bryn C. Taylor & Chris Chandler & Stefan Janssen & Andras Pataki & Nick Carrier, 2023. "Sequence-structure-function relationships in the microbial protein universe," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Andrew J McGehee & Sutanu Bhattacharya & Rahmatullah Roche & Debswapna Bhattacharya, 2020. "PolyFold: An interactive visual simulator for distance-based protein folding," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-11, December.
    3. Rahmatullah Roche & Sutanu Bhattacharya & Debswapna Bhattacharya, 2021. "Hybridized distance- and contact-based hierarchical structure modeling for folding soluble and membrane proteins," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-31, February.
    4. Yang Li & Chengxin Zhang & Eric W Bell & Wei Zheng & Xiaogen Zhou & Dong-Jun Yu & Yang Zhang, 2021. "Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-19, March.

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