DESTINI: A deep-learning approach to contact-driven protein structure prediction
Open accessAbstract
The amino acid sequence of a protein encodes the blueprint of its native structure. To predict the corresponding structural fold from the protein’s sequence is one of most challenging problems in computational biology. In this work, we introduce DESTINI (de ep st ructural in ference for prote i ns), a novel computational approach that combines a deep-learning algorithm for protein residue/residue contact prediction with template-based structural modelling. For the first time, the significantly improved predictive ability is demonstrated in the large-scale tertiary structure prediction of over 1,200 single-domain proteins. DESTINI successfully predicts the tertiary structure of four times the number of “hard” targets (those with poor quality templates) that were previously intractable, viz, a “glass-ceiling” for previous template-based approaches, and also improves model quality for “easy” targets (those with good quality templates). The significantly better performance by DESTINI is largely due to the incorporation of better contact prediction into template modelling. To understand why deep-learning accomplishes more accurate contact prediction, systematic clustering reveals that deep-learning predicts coherent, native-like contact patterns compared to co-evolutionary analysis. Taken together, this work presents a promising strategy towards solving the protein structure prediction problem.
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DESTINI CNN deep learning learned contact map patterns of secondary structure packing and eliminated nonphysical, isolated residue pairs.
“The DESTI-NI convolutional neural network (CNN) deep learning approach to structure prediction23 demonstrated that deep learning learned the contact map patterns of secondary structure packing and eliminated nonphysical, isolated residue pairs.”
From AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function (2021) · cites this paper as [23]
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 [23]
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- Authors
- Gao, M., Zhou, H., & Skolnick, J.
- Affiliations
- Georgia Institute of Technology (US)
- Corresponding author
- Skolnick, Jeffrey
- Venue
- Scientific Reports
- Published
- 2019-03-05
- Volume
- 9
- Issue
- 1
- Pages
- 3514-3514
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6401133
- OpenAlex
- W2918296775
- Cited by
- 61
- References
- 52
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 1 · 2023: 4 · 2022: 6 · 2021: 19 · 2020: 14 · 2019: 8
- FWCI
- 3.89
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R35-118039)
- 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 structure prediction, protein contact prediction, deep learning, protein tertiary structure prediction, single-domain proteins
- MeSH terms
- Deep Learning, Proteins, Protein Structure, Tertiary, Computational Biology
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