CRNPRED: Highly Accurate Prediction of One-dimensional Protein Structures by Large-scale Critical Random Networks

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.

10 pages, 1 figure, 2 tables

References in corpus (3)

• Protein secondary structure: Entropy, correlations and prediction
• Recoverable One-dimensional Encoding of Three-dimensional Protein Structures
• Predicting Secondary Structures, Contact Numbers, and Residue-wise Contact Orders of Native Protein Structure from Amino Acid Sequence by Critical Random Networks

Cited by in corpus (3)

• Nature of protein family signatures: Insights from singular value analysis of position-specific scoring matrices
• On the optimal contact potential of proteins
• Stochastic reconstruction of protein structures from effective connectivity profiles