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Protein Sci. 1999 May;8(5):1045-55.

Prediction of the location and type of beta-turns in proteins using neural networks.

Author information

1
Department of Biochemistry and Molecular Biology, University College London, United Kingdom. a.shepherd@biochem.ucl.ac.uk

Abstract

A neural network has been used to predict both the location and the type of beta-turns in a set of 300 nonhomologous protein domains. A substantial improvement in prediction accuracy compared with previous methods has been achieved by incorporating secondary structure information in the input data. The total percentage of residues correctly classified as beta-turn or not-beta-turn is around 75% with predicted secondary structure information. More significantly, the method gives a Matthews correlation coefficient (MCC) of around 0.35, compared with a typical MCC of around 0.20 using other beta-turn prediction methods. Our method also distinguishes the two most numerous and well-defined types of beta-turn, types I and II, with a significant level of accuracy (MCCs 0.22 and 0.26, respectively).

PMID:
10338015
PMCID:
PMC2144340
DOI:
10.1110/ps.8.5.1045
[Indexed for MEDLINE]
Free PMC Article

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