The DOI matches its Crossref record.
An algorithm for protein secondary structure prediction based on class prediction
Abstract
An algorithm has been developed to improve the success rate in the prediction of the secondary structure of proteins by taking into account the predicted class of the proteins. This method has been called the 'double prediction method' and consists of a first prediction of the secondary structure from a new algorithm which uses parameters of the type described by Chou and Fasman, and the prediction of the class of the proteins from their amino acid composition. These two independent predictions allow one to optimize the parameters calculated over the secondary structure database to provide the final prediction of secondary structure. This method has been tested on 59 proteins in the database (i.e. 10,322 residues) and yields 72% success in class prediction, 61.3% of residues correctly predicted for three states (helix, sheet and coil) and a good agreement between observed and predicted contents in secondary structure.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
NPS@: Network Protein Sequence Analysis
Combet, C., Blanchet, C., Geourjon, C., & Deléage, G. (2000) · Trends in Biochemical Sciences · doi:10.1016/s0968-0004(99)01540-6 · Cited by 1,757
Self-citationCombining evolutionary information and neural networks to predict protein secondary structure
Rost, B., & Sander, C. (1994) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.340190108 · Cited by 1,452
Improvements in protein secondary structure prediction by an enhanced neural network
Kneller, D. G., Cohen, F. E., & Langridge, R. (1990) · Journal of Molecular Biology · doi:10.1016/0022-2836(90)90154-e · Cited by 698
Bishop, N., & Woodman, P. (2001) · Journal of Biological Chemistry · doi:10.1074/jbc.m009863200 · Cited by 187
SIRT3 substrate specificity determined by peptide arrays and machine learning
Smith, B. C., Settles, B., Hallows, W. C., Craven, M. W., & Denu, J. M. (2011) · ACS Chemical Biology · doi:10.1021/cb100218d · Cited by 80
Metadata
- Authors
- Deléage, G., & Roux, B.
- Venue
- "Protein Engineering, Design and Selection"
- Publisher
- Oxford University Press (OUP)
- Published
- 1987
- Volume
- 1
- Issue
- 4
- Pages
- 289-294
- ISSN
- 0269-2139 (ISSN-L)
- 1741-0126
- 1741-0134
- OpenAlex
- W2075651068
- Cited by
- 351
- References
- 0
- FWCI
- 1.19
- Type
- journal article
- Language
- en
- Open access
- closed
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An algorithm for protein secondary structure prediction based on class prediction. https://app.cochat.ai/papers/10.1093/protein/1.4.289
- The paper
- Deléage, G., & Roux, B. (1987). An algorithm for protein secondary structure prediction based on class prediction. "Protein Engineering, Design and Selection". https://doi.org/10.1093/protein/1.4.289
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.