A neural network has been used to predict both
the location and the type of β-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 β-turn or not-β-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 β-turn prediction methods. Our method also
distinguishes the two most numerous and well-defined types
of β-turn, types I and II, with a significant level
of accuracy (MCCs 0.22 and 0.26, respectively).