1097-0134/asset/PROT_centre.gif?v=1&s=77b56b1f2cdaba74cb3bb149bd9b029cd8803cdb)
Prediction of Disorder
Prediction of disordered regions in proteins from position specific score matrices
Article first published online: 15 OCT 2003
DOI: 10.1002/prot.10528
Copyright © 2003 Wiley-Liss, Inc.
Issue
1097-0134/asset/cover.gif?v=1&s=d817e79b67ba6cacf8bdcce1a819c04de300a7e3 "Proteins: Structure, Function, and Bioinformatics")
Proteins: Structure, Function, and Bioinformatics
Supplement: Fifth Meeting on the Critical Assessment of Techniques for Protein Structure Prediction
Volume 53, Issue Supplement 6, pages 573–578, 2003
Additional Information
How to Cite
Jones, D. T. and Ward, J. J. (2003), Prediction of disordered regions in proteins from position specific score matrices. Proteins, 53: 573–578. doi: 10.1002/prot.10528
Publication History
- Issue published online: 15 OCT 2003
- Article first published online: 15 OCT 2003
- Manuscript Accepted: 17 JUN 2003
- Manuscript Received: 26 FEB 2003
Funded by
- Medical Research Council Studentship
- Abstract
- Article
- References
- Cited By
Keywords:
- protein structure prediction;
- folding;
- disorder;
- neural networks;
- sequence analysis
Abstract
We describe here the results of using a neural network based method (DISOPRED) for predicting disordered regions in 55 proteins in the 5th CASP experiment. A set of 715 highly resolved proteins with regions of disorder was used to train the network. The inputs to the network were derived from sequence profiles generated by PSI-BLAST. A post-filter was applied to the output of the network to prevent regions being predicted as disordered in regions of confidently predicted alpha helix or beta sheet structure. The overall two-state prediction accuracy for the method is very high (90%) but this is highly skewed by the fact that most residues are observed to be ordered. The overall Matthews' correlation coefficient for the submitted predictions is 0.34, which gives a more realistic impression of the overall accuracy of the method, though still indicates significant predictive power. Proteins 2003;53:573–578. © 2003 Wiley-Liss, Inc.