ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks
Open accessAbstract
Motivation: Contact-map of a protein sequence dictates the global topology of structural fold. Accurate prediction of the contact-map is thus essential to protein 3D structure prediction, which is particularly useful for the protein sequences that do not have close homology templates in the Protein Data Bank. Results: We developed a new method, ResPRE, to predict residue-level protein contacts using inverse covariance matrix (or precision matrix) of multiple sequence alignments (MSAs) through deep residual convolutional neural network training. The approach was tested on a set of 158 non-homologous proteins collected from the CASP experiments and achieved an average accuracy of 50.6% in the top-L long-range contact prediction with L being the sequence length, which is 11.7% higher than the best of other state-of-the-art approaches ranging from coevolution coupling analysis to deep neural network training. Detailed data analyses show that the major advantage of ResPRE lies at the utilization of precision matrix that helps rule out transitional noises of contact-maps compared with the previously used covariance matrix. Meanwhile, the residual network with parallel shortcut layer connections increases the learning ability of deep neural network training. It was also found that appropriate collection of MSAs can further improve the accuracy of final contact-map predictions. The standalone package and online server of ResPRE are made freely available, which should bring important impact on protein structure and function modeling studies in particular for the distant- and non-homology protein targets. Availability and implementation: https://zhanglab.ccmb.med.umich.edu/ResPRE and https://github.com/leeyang/ResPRE. Supplementary information: Supplementary data are available at Bioinformatics online.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Deep neural network-based contact prediction algorithms have greatly enhanced protein structure assembly accuracy.
“the recent development of sequence-derived residue–residue contact prediction algorithms based on deep neural networks27,28 has greatly enhanced the accuracy of protein structure assembly”
Deep learning with residue covariation significantly improved structure prediction.
“Deep learning with residue covariation significantly improved structure prediction.18,19,23–25,33”
From AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function (2021) · cites this paper as [25]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898 · see citing works
Sparse inverse covariance estimation with the graphical lasso
Friedman, J., Hastie, T., & Tibshirani, R. (2008) · Biostatistics · doi:10.1093/biostatistics/kxm045 · Cited by 6,355 · see citing works
XSEDE: Accelerating Scientific Discovery
Towns, J. W., Cockerill, T. M., Dahan, M., Foster, I. T., Gaither, K., Grimshaw, A. S., Hazlewood, V., Lathrop, S. A., Lifka, D. A., Peterson, G. D., et al. (2014) · Computing in Science & Engineering · doi:10.1109/mcse.2014.80 · Cited by 3,114 · see citing works
HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
Remmert, M., Biegert, A., Hauser, A., & Söding, J. (2012) · Nature Methods · doi:10.1038/nmeth.1818 · Cited by 2,288 · see citing works
Morcos, F., Pagnani, A., Lunt, B., Bertolino, A., Marks, D. S., Sander, C., Zecchina, R., Onuchic, J. N., Hwa, T., & Weigt, M. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1111471108 · Cited by 1,434 · see citing works
Identification of direct residue contacts in protein–protein interaction by message passing
Weigt, M., White, R. A., Szurmant, H., Hoch, J. A., & Hwa, T. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0805923106 · Cited by 1,025 · see citing works
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
Wang, S., Sun, S., Li, Z., Zhang, R., & Xu, J. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005324 · Cited by 931 · see citing works
High-dimensional Ising model selection using ℓ1-regularized logistic regression
Ravikumar, P., Wainwright, M. J., & Lafferty, J. D. (2010) · The Annals of Statistics · doi:10.1214/09-aos691 · Cited by 927 · see citing works
Correlated mutations and residue contacts in proteins
Göbel, U., Sander, C., Schneider, R., & Valencia, A. (1994) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340180402 · Cited by 863 · see citing works
Fox, N. K., Brenner, S. E., & Chandonia, J.-M. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1240 · Cited by 762 · see citing works
Jones, D. T., Buchan, D. W. A., Cozzetto, D., & Pontil, M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr638 · Cited by 760 · see citing works
Kamisetty, H., Ovchinnikov, S., & Baker, D. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1314045110 · Cited by 672 · see citing works
Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models
Ekeberg, M., Lövkvist, C., Lan, Y., Weigt, M., & Aurell, E. (2013) · Physical Review E · doi:10.1103/physreve.87.012707 · Cited by 661 · see citing works
Progress and challenges in protein structure prediction
Zhang, Y. (2008) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2008.02.004 · Cited by 507 · see citing works
Self-citationCCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations
Seemayer, S., Gruber, M., & Söding, J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu500 · Cited by 416 · see citing works
An overview of the estimation of large covariance and precision matrices
Fan, J., Liao, Y., & Liu, H. (2016) · Econometrics Journal · doi:10.1111/ectj.12061 · Cited by 390 · see citing works
Jones, D. T., Singh, T., Kościółek, T., & Tetchner, S. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu791 · Cited by 345 · see citing works
Network deconvolution as a general method to distinguish direct dependencies in networks
Feizi, S., Marbach, D., Médard, M., & Kellis, M. (2013) · Nature Biotechnology · doi:10.1038/nbt.2635 · Cited by 287 · see citing works
Using information theory to search for co-evolving residues in proteins
Martin, L. C., Gloor, G. B., Dunn, S. D., & Wahl, L. M. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti671 · Cited by 259 · see citing works
Disentangling Direct from Indirect Co-Evolution of Residues in Protein Alignments
Burger, L., & van Nimwegen, E. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000633 · Cited by 193 · see citing works
Mapping pathways of allosteric communication in GroEL by analysis of correlated mutations
Kass, I., & Horovitz, A. (2002) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10180 · Cited by 186 · see citing works
Ekeberg, M., Hartonen, T., & Aurell, E. M. (2014) · Journal of Computational Physics · doi:10.1016/j.jcp.2014.07.024 · Cited by 184 · see citing works
Jones, D. T., & Kandathil, S. M. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty341 · Cited by 184 · see citing works
Assessment of contact predictions in CASP12: Co‐evolution and deep learning coming of age
Schaarschmidt, J., Monastyrskyy, B., Kryshtafovych, A., & Bonvin, A. M. J. J. (2018) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25407 · Cited by 177 · see citing works
DNCON2: improved protein contact prediction using two-level deep convolutional neural networks
Adhikari, B., Hou, J., & Cheng, J. J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx781 · Cited by 168 · see citing works
FreeContact: fast and free software for protein contact prediction from residue co-evolution
Kaján, L., Hopf, T. A., Kalaš, M., Marks, D. S., & Rost, B. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-85 · Cited by 152 · see citing works
Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks
Liu, Y., Palmedo, P., Ye, Q., Berger, B., & Peng, J. (2018) · Cell Systems · doi:10.1016/j.cels.2017.11.014 · Cited by 114 · see citing works
Ma, J., Wang, S., Wang, Z., & Xu, J. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv472 · Cited by 107 · see citing works
Zhang, C., Mortuza, S. M., He, B., Wang, Y., & Zhang, Y. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25414 · Cited by 93 · see citing works
Self-citationRidge estimation of inverse covariance matrices from high-dimensional data
van Wieringen, W. N., & Peeters, C. F. W. (2016) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2016.05.012 · Cited by 91 · see citing works
Evaluation of free modeling targets in CASP11 and ROLL
Kinch, L. N., Li, W., Monastyrskyy, B., Kryshtafovych, A., & Grishin, N. V. (2015) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24973 · Cited by 80 · see citing works
He, B., Mortuza, S. M., Wang, Y., Shen, H., & Zhang, Y. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx164 · Cited by 72 · see citing works
Self-citationImproved protein contact predictions with the MetaPSICOV2 server in CASP12
Buchan, D. W. A., & Jones, D. T. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25379 · Cited by 53 · see citing works
Golkov, V., Skwark, M. J., Golkov, A., Dosovitskiy, A., Brox, T., Meiler, J., & Cremers, D. (2016) · neural information processing systems · Cited by 29 · see citing works
Improving accuracy of protein contact prediction using balanced network deconvolution
Sun, H.-P., Huang, Y., Wang, X.-F., Zhang, Y., & Shen, H.-B. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24744 · Cited by 25 · see citing works
Self-citationPrecision Matrix Estimation With ROPE
Kuismin, M., Kemppainen, J., & Sillanpää, M. J. (2017) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2016.1278002 · Cited by 25 · see citing works
Accelerate FPGA macros with one-hot approach.
Knapp, S. K. (1990) · Electronic design · Cited by 20 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Paszke,A. et al. (2017) Automatic differentiation in PyTorch. In: 31st Conference on Neural Information Processing Systems (NIPS 2017) Workshop Autodiff. Paper 8.
Cited by
Sorted by citations
Skolnick, J., Gao, M., Zhou, H., & Singh, S. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01114 · Cited by 251 · see citing works
Deep‐learning contact‐map guided protein structure prediction in CASP13
Zheng, W., Li, Y., Zhang, C., Pearce, R., Mortuza, S. M., & Zhang, Y. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25792 · Cited by 208 · see citing works
Self-citationDeep learning techniques have significantly impacted protein structure prediction and protein design
Pearce, R., & Zhang, Y. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.01.007 · Cited by 127 · see citing works
Self-citationLi, Y., Zhang, C., Bell, E. W., Yu, D., & Zhang, Y. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25798 · Cited by 94 · see citing works
Self-citationProtein design via deep learning
Ding, W., Nakai, K., & Gong, H. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac102 · Cited by 66 · see citing works
Protein sequence‐to‐structure learning: Is this the end(‐to‐end revolution)?
Laine, E., Eismann, S., Elofsson, A., & Grudinin, S. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26235 · Cited by 44 · see citing works
“More recently, some meta-models [87, 88], correlation and precision matrix-based approaches [89, 90, 52], and Overview of X-to-end and end-to-X deep learning approaches for protein structure prediction.”
MethodZhang, C., Zheng, W., Cheng, M., Omenn, G. S., Freddolino, P. L., & Zhang, Y. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00359 · Cited by 27 · see citing works
Self-citationDeep learning methods for 3D structural proteome and interactome modeling
Lee, D., Xiong, D., Wierbowski, S., Li, L., Liang, S., & Yu, H. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102329 · Cited by 26 · see citing works
- Authors
- Li, Y., Hu, J., Zhang, C., Yu, D.-J., & Zhang, Y.
- Affiliations
- University of Michigan (US)
- Nanjing University of Science and Technology (CN)
- Corresponding author
- Yu, Dong-Jun
- Zhang, Yang
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-05-09
- Volume
- 35
- Issue
- 22
- Pages
- 4647-4655
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC6853658
- OpenAlex
- W2944535254
- Cited by
- 158
- References
- 41
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 16 · 2023: 15 · 2022: 30 · 2021: 50 · 2020: 29 · 2019: 10
- FWCI
- 9.85
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Bioinformatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61772273, 61373062, 31628003)
- Fundamental Research Funds for the Central Universities (30916011327)
- National Institute of General Medical Sciences (GM083107, GM116960)
- National Science Foundation (DBI1564756)
- 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 prediction, protein contact maps, precision matrix, inverse covariance matrix, multiple sequence alignment, deep residual convolutional neural network, non-homologous proteins, protein structure prediction
- MeSH terms
- Proteins, Sequence Alignment, Neural Networks, Computer, Computational Biology, Databases, Protein
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
