Prediction of disordered regions in proteins from position specific score matrices.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
573 - 578.
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.
|Title:||Prediction of disordered regions in proteins from position specific score matrices|
|Location:||PACIFIC GROVE, CA|
|Keywords:||protein structure prediction, folding, disorder, neural networks, sequence analysis, SECONDARY STRUCTURE|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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