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DeepCDpred: Inter-residue distance and contact prediction for improved prediction of protein structure

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  • Shuangxi Ji
  • Tuğçe Oruç
  • Liam Mead
  • Muhammad Fayyaz Rehman
  • Christopher Morton Thomas
  • Sam Butterworth
  • Peter James Winn

Abstract

Rapid, accurate prediction of protein structure from amino acid sequence would accelerate fields as diverse as drug discovery, synthetic biology and disease diagnosis. Massively improved prediction of protein structures has been driven by improving the prediction of the amino acid residues that contact in their 3D structure. For an average globular protein, around 92% of all residue pairs are non-contacting, therefore accurate prediction of only a small percentage of inter-amino acid distances could increase the number of constraints to guide structure determination. We have trained deep neural networks to predict inter-residue contacts and distances. Distances are predicted with an accuracy better than most contact prediction techniques. Addition of distance constraints improved de novo structure predictions for test sets of 158 protein structures, as compared to using the best contact prediction methods alone. Importantly, usage of distance predictions allows the selection of better models from the structure pool without a need for an external model assessment tool. The results also indicate how the accuracy of distance prediction methods might be improved further.

Suggested Citation

  • Shuangxi Ji & Tuğçe Oruç & Liam Mead & Muhammad Fayyaz Rehman & Christopher Morton Thomas & Sam Butterworth & Peter James Winn, 2019. "DeepCDpred: Inter-residue distance and contact prediction for improved prediction of protein structure," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-15, January.
    • Handle: RePEc:plo:pone00:0205214
    DOI: 10.1371/journal.pone.0205214
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    References listed on IDEAS

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    1. Sheng Wang & Siqi Sun & Zhen Li & Renyu Zhang & Jinbo Xu, 2017. "Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-34, January.
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