Format

Send to

Choose Destination
Proteins. 2003;53 Suppl 6:573-8.

Prediction of disordered regions in proteins from position specific score matrices.

Author information

1
Department of Computer Science, Bioinformatics Unit, University College London, London, United Kingdom. dtj@cs.ucl.ac.uk

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.

PMID:
14579348
DOI:
10.1002/prot.10528
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Wiley
Loading ...
Support Center