Jones, DT and Ward, JJ (2003) Prediction of disordered regions in proteins from position specific score matrices. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS , 53 (6) 573 - 578. 10.1002/prot.10528.
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Abstract
We describe here the results of using a neural network based method (DISOPRED) for predicting disordered regions in 55 proteins in the 5(th) 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. (C) 2003 Wiley-Liss, Inc.
| Type: | Article |
|---|---|
| Title: | Prediction of disordered regions in proteins from position specific score matrices |
| Location: | PACIFIC GROVE, CA |
| DOI: | 10.1002/prot.10528 |
| Keywords: | protein structure prediction, folding, disorder, neural networks, sequence analysis, SECONDARY STRUCTURE |
| UCL classification: | UCL > School of BEAMS > Faculty of Engineering Science > Computer Science |
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