Critical assessment of coiled-coil predictions based on protein structure data
Open accessAbstract
Coiled-coil regions were among the first protein motifs described structurally and theoretically. The simplicity of the motif promises that coiled-coil regions can be detected with reasonable accuracy and precision in any protein sequence. Here, we re-evaluated the most commonly used coiled-coil prediction tools with respect to the most comprehensive reference data set available, the entire Protein Data Bank, down to each amino acid and its secondary structure. Apart from the 30-fold difference in minimum and maximum number of coiled coils predicted the tools strongly vary in where they predict coiled-coil regions. Accordingly, there is a high number of false predictions and missed, true coiled-coil regions. The evaluation of the binary classification metrics in comparison with naïve coin-flip models and the calculation of the Matthews correlation coefficient, the most reliable performance metric for imbalanced data sets, suggests that the tested tools’ performance is close to random. This implicates that the tools’ predictions have only limited informative value. Coiled-coil predictions are often used to interpret biochemical data and are part of in-silico functional genome annotation. Our results indicate that these predictions should be treated very cautiously and need to be supported and validated by experimental evidence.
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Socket2 does not rely on, and is not biased by, sequence-based CC predictions.
“Therefore, it does not rely on, and is not biased by sequence‐based CC predictions, which are not always consistent or reliable (Simm et al., 2021).”
From <scp> CC <sup>+</sup> </scp> : A searchable database of validated coiled coils in <scp>PDB</scp> structures and <scp>AlphaFold2</scp> models (2023) · cites this paper as Simm et al., 2021
The Methods section (“Running coiled-coil software”; page unavailable) describes SOCKET detecting coiled coils from structural features and DSSP assignments, while the Results compare its hits with separate sequence-based predictions. This supports independence from those predictions for SOCKET, but the paper does not identify or describe Socket2 specifically. (LLM, conf=0.82)
Experimental — accuracy not yet measuredSequence-based CC predictions are not always consistent or reliable.
“Therefore, it does not rely on, and is not biased by sequence‐based CC predictions, which are not always consistent or reliable (Simm et al., 2021).”
From <scp> CC <sup>+</sup> </scp> : A searchable database of validated coiled coils in <scp>PDB</scp> structures and <scp>AlphaFold2</scp> models (2023) · cites this paper as Simm et al., 2021
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“Therefore, it does not rely on, and is not biased by sequence-based CC predictions, which are not always consistent or reliable (Simm et al., 2021).”
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- Authors
- Simm, D., Hatje, K., Waack, S., & Kollmar, M.
- Affiliations
- Max Planck Institute for Biophysical Chemistry (DE)
- F. Hoffmann-La Roche AG (CH)
- University of Göttingen (DE)
- Corresponding author
- Kollmar, Martin
- Venue
- Scientific Reports
- Published
- 2021-06-14
- Volume
- 11
- Issue
- 1
- Pages
- 12439-12439
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC8203680
- OpenAlex
- W3170731024
- Cited by
- 24
- References
- 71
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 5 · 2023: 4 · 2022: 6 · 2020: 1
- FWCI
- 1.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, cc-by)
- Funders
- Georg-August-Universität Göttingen
- 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, Protein Data Bank, Matthews correlation coefficient, imbalanced classification, false positives, protein secondary structure, protein motifs
- MeSH terms
- Amino Acid Sequence, Models, Molecular, Software, Protein Structure, Secondary, Amino Acid Motifs, Databases, Protein
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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