CRNPRED: highly accurate prediction of one-dimensional protein structures by large-scale critical random networks

• Main Authors: Kinjo Akira R, Nishikawa Ken
• Format: Article
• Language: English
• Published: BMC 2006-09-01
• Series: BMC Bioinformatics
• Online Access: http://www.biomedcentral.com/1471-2105/7/401

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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, <it>Q</it>₃= 81% for secondary structure prediction, and correlation coefficients of 0.75 and 0.61 for contact number and residue-wise contact order predictions, respectively.</p> <p>Conclusion</p> <p>CRNPRED will be a useful tool for computational as well as experimental biologists who need accurate one-dimensional protein structure predictions.</p>