Ensembling multiple raw coevolutionary features with deep residual neural networks for contact‐map prediction in CASP13
Open accessAbstract
We report the results of residue‐residue contact prediction of a new pipeline built purely on the learning of coevolutionary features in the CASP13 experiment. For a query sequence, the pipeline starts with the collection of multiple sequence alignments (MSAs) from multiple genome and metagenome sequence databases using two complementary Hidden Markov Model (HMM)‐based searching tools. Three profile matrices, built on covariance, precision, and pseudolikelihood maximization respectively, are then created from the MSAs, which are used as the input features of a deep residual convolutional neural network architecture for contact‐map training and prediction. Two ensembling strategies have been proposed to integrate the matrix features through end‐to‐end training and stacking, resulting in two complementary programs called TripletRes and ResTriplet, respectively. For the 31 free‐modeling domains that do not have homologous templates in the PDB, TripletRes and ResTriplet generated comparable results with an average accuracy of 0.640 and 0.646, respectively, for the top L/5 long‐range predictions, where 71% and 74% of the cases have an accuracy above 0.5. Detailed data analyses showed that the strength of the pipeline is due to the sensitive MSA construction and the advanced strategies for coevolutionary feature ensembling. Domain splitting was also found to help enhance the contact prediction performance. Nevertheless, contact models for tail regions, which often involve a high number of alignment gaps, and for targets with few homologous sequences are still suboptimal. Development of new approaches where the model is specifically trained on these regions and targets might help address these problems.
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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”
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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
Comparative Protein Modelling by Satisfaction of Spatial Restraints
Šali, A., & Blundell, T. L. (1993) · Journal of Molecular Biology · doi:10.1006/jmbi.1993.1626 · Cited by 12,977 · see citing works
Accelerated Profile HMM Searches
Eddy, S. R. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002195 · Cited by 6,975 · see citing works
The PSIPRED protein structure prediction server
McGuffin, L. J., Bryson, K., & Jones, D. T. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.4.404 · Cited by 3,841 · 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
HH-suite3 for fast remote homology detection and deep protein annotation
Steinegger, M., Meier, M., Mirdita, M., Vöhringer, H., Haunsberger, S. J., & Söding, J. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3019-7 · Cited by 1,145 · see citing works
Computer simulation of protein folding
Levitt, M., & Warshel, A. (1975) · Nature · doi:10.1038/253694a0 · Cited by 1,051 · 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
Clustering huge protein sequence sets in linear time
Steinegger, M., & Söding, J. (2018) · Nature Communications · doi:10.1038/s41467-018-04964-5 · Cited by 875 · 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
LOMETS: A local meta-threading-server for protein structure prediction
Wu, S., & Zhang, Y. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm251 · Cited by 774 · see citing works
Self-citationJones, D. T., Buchan, D. W. A., Cozzetto, D., & Pontil, M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr638 · Cited by 760 · see citing works
Uniclust databases of clustered and deeply annotated protein sequences and alignments
Mirdita, M., von den Driesch, L., Galiez, C., Martin, M. J., Söding, J., & Steinegger, M. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1081 · Cited by 710 · 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
Browne, W. J., North, A. C. T., Phillips, D. C., Brew, K., VANAMAN, T. C., & Hill, R. L. (1969) · Journal of Molecular Biology · doi:10.1016/0022-2836(69)90487-2 · Cited by 525 · see citing works
Protein structure determination using metagenome sequence data
Ovchinnikov, S., Park, H., Varghese, N., Huang, P.-S., Pavlopoulos, G. A., Kim, D. E., Kamisetty, H., Kyrpides, N. C., & Baker, D. (2017) · Science · doi:10.1126/science.aah4043 · Cited by 520 · see citing works
CCMpred—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
Jones, D. T., Singh, T., Kościółek, T., & Tetchner, S. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu791 · Cited by 345 · see citing works
Shindyalov, I. N., Kolchanov, N. A., & Sander, C. (1994) · "Protein Engineering, Design and Selection" · doi:10.1093/protein/7.3.349 · Cited by 297 · 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
Zhang, C., Zheng, W. H., Mortuza, S. M., Li, Y., & Zhang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz863 · Cited by 182 · see citing works
Self-citationDNCON2: 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
Li, Y., Hu, J., Zhang, C., Yu, D.-J., & Zhang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz291 · Cited by 158 · see citing works
Self-citationEnhancing 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
Ovchinnikov, S., Kim, D. E., Wang, R. Y., Liu, Y., DiMaio, F., & Baker, D. A. (2015) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24974 · Cited by 111 · 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-citationAbriata, L. A., Tamò, G. E., Monastyrskyy, B., Kryshtafovych, A., & Peraro, M. D. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25423 · Cited by 90 · see citing works
HH-suite3 for fast remote homology detection and deep protein annotationPreprint
Steinegger, M., Meier, M., Mirdita, M., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/560029 · Cited by 89 · see citing works
ThreaDom: extracting protein domain boundary information from multiple threading alignments
Xue, Z., Xu, D. H., Wang, Y., & Zhang, Y. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt209 · Cited by 84 · see citing works
Self-citationEvaluation 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-citationImproving Protein Structure Prediction Using Multiple Sequence-Based Contact Predictions
Wu, S., Szilágyi, A., & Zhang, Y. (2011) · Structure · doi:10.1016/j.str.2011.05.004 · Cited by 70 · 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
Flagged first, then by citations
Research works printed but not linked: 3
- [32]Remmert M, Biegert A, Hauser A, Soding J. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat Methods 2011; 9(2): 173– 175. 22198341
- [36]Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv: 151107122 2015.
- [38]Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv: 14126980 2014.
Cited by
Sorted by citations
Improved protein structure prediction using potentials from deep learning
Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1923-7 · Cited by 3,378 · see citing works
AlphaFold and the amyloid landscape
Pinheiro, F., Santos, J., & Ventura, S. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167059 · Cited by 88 · see citing works
Zhang, 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
Kandathil, S. M., Greener, J. G., Lau, A. M., & Jones, D. T. (2020) · doi:10.1101/2020.11.27.401232 · Cited by 8 · see citing works
- Authors
- Li, Y., Zhang, C., Bell, E. W., Yu, D., & Zhang, Y.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Yu, Dong‐Jun
- Zhang, Yang
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2019-08-22
- Volume
- 87
- Issue
- 12
- Pages
- 1082-1091
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- PMC
- PMC6851483
- OpenAlex
- W2968295962
- Cited by
- 94
- References
- 37
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 5 · 2023: 7 · 2022: 22 · 2021: 36 · 2020: 16 · 2019: 6
- FWCI
- 6.62
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Funders
- Fundamental Research Funds for the Central Universities (30918011104)
- National Institute of General Medical Sciences (GM083107, GM116960)
- National Natural Science Foundation of China (31628003, 61373062, 61772273)
- National Science Foundation (DBI1564756, ACI‐1548562)
- 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
- residue-residue contact prediction, multiple sequence alignment, deep residual convolutional neural network, stacking ensemble, CASP13, domain splitting, alignment gaps, long-range contacts
- MeSH terms
- Machine Learning, Algorithms, Models, Molecular, Protein Conformation, Proteins, Sequence Alignment, Neural Networks, Computer, Computational Biology, Sequence Analysis, Protein, Databases, Protein, Metagenome
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