Assessment of contact predictions in CASP12: Co‐evolution and deep learning coming of age
Open accessAbstract
Following up on the encouraging results of residue‐residue contact prediction in the CASP11 experiment, we present the analysis of predictions submitted for CASP12. The submissions include predictions of 34 groups for 38 domains classified as free modeling targets which are not accessible to homology‐based modeling due to a lack of structural templates. CASP11 saw a rise of coevolution‐based methods outperforming other approaches. The improvement of these methods coupled to machine learning and sequence database growth are most likely the main driver for a significant improvement in average precision from 27% in CASP11 to 47% in CASP12. In more than half of the targets, especially those with many homologous sequences accessible, precisions above 90% were achieved with the best predictors reaching a precision of 100% in some cases. We furthermore tested the impact of using these contacts as restraints in ab initio modeling of 14 single‐domain free modeling targets using Rosetta. Adding contacts to the Rosetta calculations resulted in improvements of up to 26% in GDT_TS within the top five structures.
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Recent CASPs have seen a dramatic reduction in the dependence of model accuracy on sequence alignment depth.
“On the first of these, recent CASPs have seen a dramatic reduction in the dependence of model accuracy on sequence alignment depth, a key ingredient in the classical contact prediction methods that preceded deep learning33 and also a usual input into deep learning networks.”
From Critical assessment of methods of protein structure prediction (CASP)—Round <scp>XIV</scp> (2021) · cites this paper as [33]
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- Authors
- Schaarschmidt, J., Monastyrskyy, B., Kryshtafovych, A., & Bonvin, A. M. J. J.
- Affiliations
- Faculty of Science ‐ Chemistry Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University Utrecht The Netherlands
- University of California, Davis (US)
- Utrecht University (NL)
- Corresponding author
- Bonvin, Alexandre M.J.J.
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2017-11-07
- Volume
- 86
- Issue
- S1
- Pages
- 51-66
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- PMC
- PMC5820169
- OpenAlex
- W2765742208
- Cited by
- 177
- References
- 71
- Citations by year
- 2025: 2 · 2024: 6 · 2023: 11 · 2022: 20 · 2021: 35 · 2020: 34 · 2019: 50 · 2018: 15 · 2017: 3
- FWCI
- 11.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc)
- Funders
- National Institute of General Medical Sciences (GM100482)
- 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, caspase-12, coevolution, deep learning, ab initio protein structure prediction, Rosetta, GDT_TS
- MeSH terms
- Machine Learning, Algorithms, Humans, Models, Molecular, Protein Conformation, Proteins, Software, Protein Folding, Crystallography, X-Ray, Computational Biology, Databases, Protein, Protein Interaction Domains and Motifs
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