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BMCBI
2006

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

8 years 5 months ago
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. Conclusion: CRNPRED will be a useful tool for computational as well as experimental bio...
Akira R. Kinjo, Ken Nishikawa
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2006
Where BMCBI
Authors Akira R. Kinjo, Ken Nishikawa
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