2006/04/12 by Akira R. Kinjo, Akira R Kinjo, Ken Nishikawa · 1 citation
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #Biomolecular structure #CASP #DNA microarray #Network structure #Protein Structure and Dynamics #Protein structure #Protein structure prediction #Protein–protein interaction #q-bio.BM
paper · pdf · doi:10.1186/1471-2105-7-401
published as BMC Bioinformatics, 7:401 (2006) · 10 pages, 1 figure, 2 tables
arxiv created 2006/04/12 · openalex publication_date 2006/09/05 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
BACKGROUND: 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.