Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
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Abstract
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 extremely 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 model contact occurring patterns and very complex sequence-structure relationship and thus, obtain high-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 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 by only non-membrane proteins, our deep learning method works very well on membrane protein contact prediction. In the recent blind CAMEO benchmark, our fully-automated web server implementing this method successfully folded 5 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 and one α protein of 217 residues and one α/β of 260 residues. 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 occurring 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 with only soluble proteins, our method performs very well on membrane proteins. Recent blind test in CAMEO confirms that our method can fold large proteins with a new fold and only a small number of sequence homologs.
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Existing protein structure prediction methods fall far short of atomic or experimental accuracy, especially when no homologous structure is available, limiting their utility for many biological applications.
“Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available.”
From Highly accurate protein structure prediction with AlphaFold (2021) · cites this paper as [11]
The Introduction (page unavailable) says that state-of-the-art contact predictors often produce contacts too poor for accurate folding, especially for proteins with few sequence homologs. The Results (page unavailable) also describe poor template-based predictions for a novel-fold target. But the paper does not establish the broader claim that existing structure-prediction methods fall far short of atomic or experimental accuracy, or that this limits their utility for many biological applications; few sequence homologs is not the same condition as no homologous structure. (LLM, conf=0.84)
Experimental — accuracy not yet measuredDeep learning successes in computational biology include accurate contact prediction in proteins, improved diagnostics for several diseases, molecular design and simulation.
“Deep learning has begun to permeate every scientific discipline, and in computational biology its successes include accurate contact prediction in proteins [61], improved diagnostics for several diseases [128], molecular design [129] and simulation [130,131].”
From The prospects of quantum computing in computational molecular biology (2021) · cites this paper as [61]
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- Authors
- Wang, S., Sun, S., Li, Z., Zhang, R., & Xu, J.
- Affiliations
- Toyota Technological Institute at Chicago (US)
- Corresponding author
- Wang, Sheng
- Sun, Siqi
- Li, Zhen
- Zhang, Renyu
- Xu, Jinbo
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-01-05
- Volume
- 13
- Issue
- 1
- Pages
- e1005324
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC5249242
- OpenAlex
- W2949867299
- Cited by
- 931
- References
- 54
- Citations by year
- 2026: 28 · 2025: 53 · 2024: 71 · 2023: 83 · 2022: 127 · 2021: 182 · 2020: 114 · 2019: 145 · 2018: 72 · 2017: 55 · 2016: 1
- FWCI
- 42.90
- Type
- journal article
- Language
- en
- Update notices
- new_version (2017-01-20)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- 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, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- National Institute of General Medical Sciences (R01GM089753)
- Directorate for Biological Sciences (DBI-1564955)
- 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 contact map prediction, de novo protein structure prediction, evolutionary coupling, sequence conservation, deep residual neural network, membrane proteins, web server, pair potential, sequence-structure relationship
- MeSH terms
- Machine Learning, Algorithms, Protein Conformation, Proteins, Neural Networks, Computer, Computational Biology, Sequence Analysis, Protein, Databases, Protein
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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