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


CONCLUSIONS
CRNPRED will be a useful tool for computational as well as experimental biologists who need accurate one-dimensional protein structure predictions.
Kinjo
Akira R
AR
Center for Information Biology and DNA Data Bank of Japan, National Institute of Genetics, Mishima, 411-8540, Japan. akinjo@protein.osaka-u.ac.jp
Nishikawa
Ken
K
D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D008969:Molecular Sequence Data; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000648:ultrastructure; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
D011506:Proteins