Journal
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
Volume 53, Issue -, Pages 573-578Publisher
WILEY
DOI: 10.1002/prot.10528
Keywords
protein structure prediction; folding; disorder; neural networks; sequence analysis
Categories
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available