CRNPRED: Highly Accurate Prediction of One-dimensional Protein Structures by Large-scale Critical Random Networks

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dc.creatorKinjo, Akira R.
dc.creatorNishikawa, Ken
dc.date2006-04-12
dc.date.accessioned2026-07-07T08:14:26Z
dc.date.available2026-07-07T08:14:26Z
dc.descriptionBackground: One-dimensional protein structures such as secondary structures or contact numbers are useful for three-dimensional structure prediction and helpful for intuitive understanding of the sequence-structure relationship. Accurate prediction methods will serve as a basis for these and other purposes. Results: We implemented a program CRNPRED which predicts secondary structures, contact numbers and residue-wise contact orders. This program is based on a novel machine learning scheme called critical random networks. Unlike most conventional one-dimensional structure prediction methods which are based on local windows of an amino acid sequence, CRNPRED takes into account the whole sequence. CRNPRED achieves, on average per chain, Q3 = 81% for secondary structure prediction, and correlation coefficients of 0.75 and 0.61 for contact number and residue-wise contact order predictions, respectively. Conclusion: CRNPRED will be a useful tool for computational as well as experimental biologists who need accurate one-dimensional protein structure predictions.
dc.description10 pages, 1 figure, 2 tables
dc.identifierhttps://arxiv.org/abs/q-bio/0604013
dc.identifierhttp://arxiv.org/abs/q-bio/0604013
dc.identifierBMC Bioinformatics, 7:401 (2006)
dc.identifierdoi:10.1186/1471-2105-7-401
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/133123
dc.subjectBiomolecules
dc.titleCRNPRED: Highly Accurate Prediction of One-dimensional Protein Structures by Large-scale Critical Random Networks
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