Prediction of the location and type of beta-turns in proteins using neural networks.
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).
Shepherd
A J
AJ
Department of Biochemistry and Molecular Biology, University College London, United Kingdom. a.shepherd@biochem.ucl.ac.uk
Gorse
D
D
Thornton
J M
JM
D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D016208:Databases, Factual; D015233:Models, Statistical; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary