Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14
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Abstract
Substantial progresses in protein structure prediction have been made by utilizing deep‐learning and residue‐residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein structure prediction system by incorporating three new components: (a) a new deep learning‐based protein inter‐residue distance predictor to improve template‐free (ab initio) tertiary structure prediction, (b) an enhanced template‐based tertiary structure prediction method, and (c) distance‐based model quality assessment methods empowered by deep learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked seventh out of 146 predictors in tertiary structure prediction and ranked third out of 136 predictors in inter‐domain structure prediction. The results demonstrate that the template‐free modeling based on deep learning and residue‐residue distance prediction can predict the correct topology for almost all template‐based modeling targets and a majority of hard targets (template‐free targets or targets whose templates cannot be recognized), which is a significant improvement over the CASP13 MULTICOM predictor. Moreover, the template‐free modeling performs better than the template‐based modeling on not only hard targets but also the targets that have homologous templates. The performance of the template‐free modeling largely depends on the accuracy of distance prediction closely related to the quality of multiple sequence alignments. The structural model quality assessment works well on targets for which enough good models can be predicted, but it may perform poorly when only a few good models are predicted for a hard target and the distribution of model quality scores is highly skewed. MULTICOM is available at https://github.com/jianlin-cheng/MULTICOM_Human_CASP14/tree/CASP14_DeepRank3 and https://github.com/multicom-toolbox/multicom/tree/multicom_v2.0 .
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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 [19]
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- Authors
- Liu, J., Wu, T., Guo, Z., Hou, J., & Cheng, J.
- Affiliations
- University of Missouri (US)
- Saint Louis University (US)
- Corresponding author
- Cheng, Jianlin
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2021-07-27
- Volume
- 90
- Issue
- 1
- Pages
- 58-72
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- PMC
- PMC8671168
- OpenAlex
- W3186340132
- Cited by
- 32
- References
- 59
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 4 · 2023: 7 · 2022: 8 · 2021: 8
- FWCI
- 2.12
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy (DE‐SC0020400, DE‐SC0021303, DE‐AC05‐00OR22725)
- National Institute of General Medical Sciences (R01GM093123)
- National Science Foundation (DBI1759934, IIS1763246)
- 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 tertiary structure prediction, template-based structure prediction, model quality assessment, CASP14, deep learning
- MeSH terms
- Deep Learning, Algorithms, Humans, Models, Molecular, Proteins, Software, Sequence Alignment, Protein Structure, Tertiary, Sequence Analysis, Protein
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