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Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Author

Listed:
  • Sheng Wang
  • Siqi Sun
  • Zhen Li
  • Renyu Zhang
  • Jinbo Xu

Abstract

Motivation: Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. Method: This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Results: Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact-assisted models also have much better quality than template-based models especially for membrane proteins. The 3D models built from our contact prediction have TMscore>0.5 for 208 of the 398 membrane proteins, while those from homology modeling have TMscore>0.5 for only 10 of them. Further, even if trained mostly by soluble proteins, our deep learning method works very well on membrane proteins. In the recent blind CAMEO benchmark, our fully-automated web server implementing this method successfully folded 6 targets with a new fold and only 0.3L-2.3L effective sequence homologs, including one β protein of 182 residues, one α+β protein of 125 residues, one α protein of 140 residues, one α protein of 217 residues, one α/β of 260 residues and one α protein of 462 residues. Our method also achieved the highest F1 score on free-modeling targets in the latest CASP (Critical Assessment of Structure Prediction), although it was not fully implemented back then. Availability: http://raptorx.uchicago.edu/ContactMap/ Author Summary: Protein contact prediction and contact-assisted folding has made good progress due to direct evolutionary coupling analysis (DCA). However, DCA is effective on only some proteins with a very large number of sequence homologs. To further improve contact prediction, we borrow ideas from deep learning, which has recently revolutionized object recognition, speech recognition and the GO game. Our deep learning method can model complex sequence-structure relationship and high-order correlation (i.e., contact occurrence patterns) and thus, improve contact prediction accuracy greatly. Our test results show that our method greatly outperforms the state-of-the-art methods regardless how many sequence homologs are available for a protein in question. Ab initio folding guided by our predicted contacts may fold many more test proteins than the other contact predictors. Our contact-assisted 3D models also have much better quality than homology models built from the training proteins, especially for membrane proteins. One interesting finding is that even trained mostly with soluble proteins, our method performs very well on membrane proteins. Recent blind CAMEO test confirms that our method can fold large proteins with a new fold and only a small number of sequence homologs.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1005324
    DOI: 10.1371/journal.pcbi.1005324
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    References listed on IDEAS

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    1. Marcin J Skwark & Daniele Raimondi & Mirco Michel & Arne Elofsson, 2014. "Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns," PLOS Computational Biology, Public Library of Science, vol. 10(11), pages 1-14, November.
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    Cited by:

    1. Juan A Morales-Cordovilla & Victoria Sanchez & Martin Ratajczak, 2018. "Protein alignment based on higher order conditional random fields for template-based modeling," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-14, June.
    2. Zhiye Guo & Jian Liu & Jeffrey Skolnick & Jianlin Cheng, 2022. "Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Rui Fa & Domenico Cozzetto & Cen Wan & David T Jones, 2018. "Predicting human protein function with multi-task deep neural networks," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-16, June.
    4. Shivangi & Laxman S Meena & Md Amjad Beg, 2018. "Insights of Rv2921c (Ftsy) Gene of Mycobacterium tuberculosis H37Rv To Prove Its Significance by Computational Approach," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 12(2), pages 9147-9157, December.
    5. Peicong Lin & Yumeng Yan & Huanyu Tao & Sheng-You Huang, 2023. "Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    6. 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.
    7. Lei Wang & Jiangguo Zhang & Dali Wang & Chen Song, 2022. "Membrane contact probability: An essential and predictive character for the structural and functional studies of membrane proteins," PLOS Computational Biology, Public Library of Science, vol. 18(3), pages 1-27, March.
    8. Nicolae Sapoval & Amirali Aghazadeh & Michael G. Nute & Dinler A. Antunes & Advait Balaji & Richard Baraniuk & C. J. Barberan & Ruth Dannenfelser & Chen Dun & Mohammadamin Edrisi & R. A. Leo Elworth &, 2022. "Current progress and open challenges for applying deep learning across the biosciences," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    9. 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.
    10. 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.
    11. Claudio Mirabello & Björn Wallner, 2019. "rawMSA: End-to-end Deep Learning using raw Multiple Sequence Alignments," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-15, August.

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