Prediction of the location and type of beta-turns in proteins using neural networks.
1045 - 1055.
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 nor-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).
|Title:||Prediction of the location and type of beta-turns in proteins using neural networks|
|Keywords:||beta-turn prediction, feed-forward networks, neural networks, secondary structure prediction, SECONDARY STRUCTURE, CLASSIFICATION|
|UCL classification:||UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Biosciences (Division of)
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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