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.
Keywords
CASPFilter (signal processing)Protein structure predictionArtificial neural networkPosition (finance)Data setSet (abstract data type)Computer scienceAlgorithmPattern recognition (psychology)Artificial intelligenceProtein structurePhysicsNuclear magnetic resonance
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Publication Info
- Year
- 2003
- Type
- article
- Volume
- 53
- Issue
- S6
- Pages
- 573-578
- Citations
- 210
- Access
- Closed
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Cite This
David T. Jones,
Jonathan J. Ward
(2003).
Prediction of disordered regions in proteins from position specific score matrices.
Proteins Structure Function and Bioinformatics
, 53
(S6)
, 573-578.
https://doi.org/10.1002/prot.10528
Identifiers
- DOI
- 10.1002/prot.10528