Evaluation of model refinement in CASP14
Open accessAbstract
We report here an assessment of the model refinement category of the 14th round of Critical Assessment of Structure Prediction (CASP14). As before, predictors submitted up to five ranked refinements, along with associated residue‐level error estimates, for targets that had a wide range of starting quality. The ability of groups to accurately rank their submissions and to predict coordinate error varied widely. Overall, only four groups out‐performed a “naïve predictor” corresponding to the resubmission of the starting model. Among the top groups, there are interesting differences of approach and in the spread of improvements seen: some methods are more conservative, others more adventurous. Some targets were “double‐barreled” for which predictors were offered a high‐quality AlphaFold 2 (AF2)‐derived prediction alongside another of lower quality. The AF2‐derived models were largely unimprovable, many of their apparent errors being found to reside at domain and, especially, crystal lattice contacts. Refinement is shown to have a mixed impact overall on structure‐based function annotation methods to predict nucleic acid binding, spot catalytic sites, and dock protein structures.
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In CASP14, only four methods on average improve models over the starting structures provided, and no method improves much more than half of the targets.
“In CASP14, only four methods on average improve models over the starting structures provided, and no method improves much more than half of the targets6.”
From Critical assessment of methods of protein structure prediction (CASP)—Round <scp>XIV</scp> (2021) · cites this paper as [6]
That strategy led to improved performance on bigger targets in CASP14.
“That strategy led to improved performance on bigger targets in CASP146.”
From Critical assessment of methods of protein structure prediction (CASP)—Round <scp>XIV</scp> (2021) · cites this paper as [6]
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- Authors
- Simpkin, A. J., Sánchez Rodríguez, F., Mesdaghi, S., Kryshtafovych, A., & Rigden, D. J.
- Affiliations
- University of Liverpool (GB)
- Institute of Systems, Molecular and Integrative Biology University of Liverpool Liverpool United Kingdom
- University of California, Davis (US)
- Corresponding author
- Rigden, Daniel J.
- ORCID
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2021-07-29
- Volume
- 89
- Issue
- 12
- Pages
- 1852-1869
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- PMC
- PMC8616799
- OpenAlex
- W3164905191
- Cited by
- 28
- References
- 37
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 3 · 2023: 7 · 2022: 8 · 2021: 5
- FWCI
- 1.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- Biotechnology and Biological Sciences Research Council (BB/S007105/1)
- National Institute of General Medical Sciences (GM100482)
- 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
- model refinement, CASP14, structure prediction, AlphaFold2, protein structure, nucleic acid binding, protein docking
- MeSH terms
- Models, Molecular, Protein Conformation, Proteins, Software, Computational Biology, Sequence Analysis, Protein
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