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Updated: Aug 9, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
Prediction of the location and type of beta-turns in proteins using neural networks
A J Shepherd1, D Gorse, J M Thornton
1Department 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).
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