Software

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

Akira R Kinjo^1,2,3* and Ken Nishikawa^1,2

• * Corresponding author: Akira R Kinjo akinjo@protein.osaka-u.ac.jp

Author Affiliations

¹ Center for Information Biology and DNA Data Bank of Japan, National Institute of Genetics, Mishima, 411-8540, Japan

² Department of Genetics, The Graduate University for Advanced Studies (SOKENDAI), Mishima 411-8540, Japan

³ Research Center for Structural and Functional Proteomics, Institute for Protein Research, Osaka University, 3-2 Suita, 565-0871, Japan

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BMC Bioinformatics 2006, 7:401 doi:10.1186/1471-2105-7-401

Published: 5 September 2006

Abstract

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.