Deep‐learning contact‐map guided protein structure prediction in CASP13
Open accessAbstract
We report the results of two fully automated structure prediction pipelines, “Zhang‐Server” and “QUARK”, in CASP13. The pipelines were built upon the C‐I‐TASSER and C‐QUARK programs, which in turn are based on I‐TASSER and QUARK but with three new modules: (a) a novel multiple sequence alignment (MSA) generation protocol to construct deep sequence‐profiles for contact prediction; (b) an improved meta‐method, NeBcon, which combines multiple contact predictors, including ResPRE that predicts contact‐maps by coupling precision‐matrices with deep residual convolutional neural‐networks; and (c) an optimized contact potential to guide structure assembly simulations. For 50 CASP13 FM domains that lacked homologous templates, average TM‐scores of the first models produced by C‐I‐TASSER and C‐QUARK were 28% and 56% higher than those constructed by I‐TASSER and QUARK, respectively. For the first time, contact‐map predictions demonstrated usefulness on TBM domains with close homologous templates, where TM‐scores of C‐I‐TASSER models were significantly higher than those of I‐TASSER models with a P‐value <.05. Detailed data analyses showed that the success of C‐I‐TASSER and C‐QUARK was mainly due to the increased accuracy of deep‐learning‐based contact‐maps, as well as the careful balance between sequence‐based contact restraints, threading templates, and generic knowledge‐based potentials. Nevertheless, challenges still remain for predicting quaternary structure of multi‐domain proteins, due to the difficulties in domain partitioning and domain reassembly. In addition, contact prediction in terminal regions was often unsatisfactory due to the sparsity of MSAs. Development of new contact‐based domain partitioning and assembly methods and training contact models on sparse MSAs may help address these issues.
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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 [12]
C-I-TASSER assembles significantly more accurate structure models than traditional threading/homology approaches for nonhomologous protein sequences, as shown in CASP13.
“As shown in the recent CASP13 experiment,29 C-I-TASSER is capable of assembling significantly more accurate structure models than traditional threading/homology approaches for the nonhomologous protein sequences”
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- Authors
- Zheng, W., Li, Y., Zhang, C., Pearce, R., Mortuza, S. M., & Zhang, Y.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Zhang, Yang
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2019-08-14
- Volume
- 87
- Issue
- 12
- Pages
- 1149-1164
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- PMC
- PMC6851476
- OpenAlex
- W2966057938
- Cited by
- 208
- References
- 52
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 14 · 2023: 29 · 2022: 42 · 2021: 63 · 2020: 43 · 2019: 7
- FWCI
- 12.82
- 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
- Division of Biological Infrastructure (DBI1564756)
- National Institute of Allergy and Infectious Diseases (AI134678)
- National Institute of General Medical Sciences (GM083107, GM116960)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein structure prediction, contact map prediction, CASP13, charm quark, quarks, I-TASSER, multiple sequence alignment, contact potential difference, TM-score, quaternary structure, domain partitioning, terminal region
- MeSH terms
- Deep Learning, Algorithms, Amino Acid Sequence, Models, Molecular, Protein Conformation, Proteins, Software, Sequence Alignment, Neural Networks, Computer, Protein Folding, Computational Biology, Databases, Protein
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