The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
Open accessPreprint — published as 10.1038/s41467-022-34600-2
Abstract
Residue-residue distance information is useful for predicting the tertiary structures of protein monomers or the quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but very few methods can accurately predict inter-chain residue-residue distances of protein complexes. We develop a new deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network architecture to address the gap. CDPred predicts the inter-chain distance maps of dimers (homodimers or heterodimers) from the features extracted from multiple sequence alignments (MSAs) and the intra-chain distance maps of predicted tertiary structures of monomers. Tested on two homodimer test datasets, CDPred achieves the precision of 61.56% and 43.26% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, which is substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. And tested on the two heterodimer test datasets, the top L/5 inter-chain contact prediction precision (L: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, which surpasses GLINTER’s 23.24% and 13.49%. Moreover, we demonstrate that the residue-residue co-evolutionary features calculated from multiple sequence alignments by a deep learning language model are more informative for the inter-chain contact prediction than the traditional statistical optimization approach of maximizing direct co-evolutionary signals, and large intra-chain distances in the intra-chain distance maps of monomers are more useful for the inter-chain distance prediction than small intra-chain distances.
Overview
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
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Flagged first, then by citations
Research works printed but not linked: 36
- 10.1186/s12859-018-2032-6 · DOI
- 2018, DNCON2: improved protein contact prediction using two-level deep convolutional neural networks, Bioinformatics, 34, 1466, 10.1093/bioinformatics/btx781 · DOI
- Bhagwat, M. and Aravind, L. Psi-blast tutorial. In, Comparative genomics. Springer; 2007. p. 177–186. · DOI
- 2022, Improved prediction of protein-protein interactions using AlphaFold2, Nature Communications, 13, 1
- 10.1093/bioinformatics/bts598 · DOI
- Evans, R. , et al. Protein complex prediction with AlphaFold-Multimer. BioRxiv 2021. · DOI · Paper page
- 2022, AF2Complex predicts direct physical interactions in multimeric proteins with deep learning, Nature Communications, 13, 1
- 10.1093/bioinformatics/btt024 · DOI
- 2013, Maxout networks, arXiv preprint
- 2021, Large-scale discovery of protein interactions at residue resolution using co-evolution calculated from genomic sequences, Nature communications, 12, 1
- 2021, Improving deep learning-based protein distance prediction in CASP14, Bioinformatics, 37, 3190, 10.1093/bioinformatics/btab355 · DOI
- He, K. , et al. Deep residual learning for image recognition. In, Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. p. 770–778. · DOI · Paper page
- Hu, J. , Shen, L. and Sun, G. Squeeze-and-excitation networks. In, Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. p. 7132–7141. · DOI · Paper page
- Kandathil, S.M. , et al. Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins. Proceedings of the National Academy of Sciences 2022;119(4). · DOI
- 2019, ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks, Bioinformatics, 35, 4647, 10.1093/bioinformatics/btz291 · DOI · Paper page
- 2019, Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13, Proteins: Structure, Function, and Bioinformatics, 87, 1082, 10.1002/prot.25798 · DOI · Paper page
- Mao, W. , et al. AmoebaContact and GDFold as a pipeline for rapid de novo protein structure prediction. Nature Machine Intelligence 2019:1–9. · DOI
- 10.1093/nar/gkw1081 · DOI
- Rao, R.M. , et al. MSA transformer. In, International Conference on Machine Learning. PMLR; 2021. p. 8844-8856.
- 2012, HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment, Nature methods, 9, 173, 10.1038/nmeth.1818 · DOI · Paper page
- Rohl, C.A. , et al. Protein structure prediction using Rosetta. In, Methods in enzymology. Elsevier; 2004. p. 66–93. · DOI
- Roy, R.S. , et al. A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers. bioRxiv 2021. · DOI · Paper page
- 10.1093/bioinformatics/btu500 · DOI
- 10.1073/pnas.2032324100 · DOI
- 2019, Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold, Nature methods, 16, 603, 10.1038/s41592-019-0437-4 · DOI · Paper page
- 2016, Instance normalization: The missing ingredient for fast stylization, arXiv preprint
- 1997, Structure-Function Relationships, Food proteins and their applications, 80, 257
- 2017, Accurate de novo prediction of protein contact map by ultra-deep learning model, PLoS computational biology, 13, e1005324, 10.1371/journal.pcbi.1005324 · DOI · Paper page
- Woo, S. , et al. Cbam: Convolutional block attention module. In, Proceedings of the European conference on computer vision (ECCV). 2018. p. 3–19. · DOI · Paper page
- Wu, T. , et al. DeepDist: real-value inter-residue distance prediction with deep residual network. bioRxiv 2020. · DOI
- 2022, Deep graph learning of inter-protein contacts, Bioinformatics, 38, 947, 10.1093/bioinformatics/btab761 · DOI · Paper page
- 2019, Analysis of distance-based protein structure prediction by deep learning in CASP13, Proteins: Structure, Function, and Bioinformatics, 87, 1069, 10.1002/prot.25810 · DOI
- 2021, Accurate prediction of inter-protein residue–residue contacts for homo-oligomeric protein complexes, Briefings in bioinformatics, 22, bbab038, 10.1093/bib/bbab038 · DOI
- 10.1073/pnas.1914677117 · DOI · Paper page
- 10.1093/nar/gkg571 · DOI · Paper page
- 2004, Scoring function for automated assessment of protein structure template quality, Proteins: Structure, Function, and Bioinformatics, 57, 702, 10.1002/prot.20264 · DOI · Paper page
Metadata
- Authors
- Guo, Z., Liu, J., Skolnick, J., & Cheng, J.
- Affiliations
- University of Missouri (US)
- Georgia Institute of Technology (US)
- Corresponding author
- Cheng, Jianlin
- Venue
- Nature Communications
- Publisher
- openRxiv
- Published
- 2022-06-20
- Volume
- 13
- Issue
- 1
- Pages
- 6963-6963
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC9666547
- OpenAlex
- W4309190357
- Cited by
- 57
- References
- 1
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 12 · 2023: 21 · 2022: 2
- FWCI
- 3.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- National Science Foundation (1763246, 1759934, R01GM093123)
- U.S. Department of Energy (DEAR0001213, DESC0021303, DESC0020400)
- National Institutes of Health
- National Institute of General Medical Sciences (R01GM093123, R01GM146340, R35GM118039)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- protein complexes, ResNet, homodimer, heterodimer, quaternary structure, AlphaFold2-Multimer
- MeSH terms
- Proteins, Neural Networks, Computer, Computational Biology
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
