Prediction of disordered regions in proteins from position specific score matrices

Proteins. 2003;53 Suppl 6:573-8. doi: 10.1002/prot.10528.

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.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology / methods*
  • Magnetic Resonance Spectroscopy
  • Models, Molecular
  • Neural Networks, Computer*
  • Protein Conformation*
  • Proteins / chemistry*
  • Reproducibility of Results

Substances

  • Proteins