Prediction of disordered regions in proteins from position specific score matrices.
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.
Department of Computer Science, Bioinformatics Unit, University College London, London, United Kingdom. dtj@cs.ucl.ac.uk
David T
DT
Jones
Jonathan J
JJ
Ward
D019295:Computational Biology / Q000379:methods*; D009682:Magnetic Resonance Spectroscopy; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results
D011506:Proteins