Abstract
The prediction of globular protein secondary structure is studied by the neural network. The protein secondary structure is allocated to each residue by using Kabsch and Sander's DSSP and the neural network is trained to learn the protein secondary structures. In the input layer of the neural network, the sequence of residues can be allowed to be 24 characters which are 20 amino acides, chain break (!), B, X and Z. The 3, 4 and 8 state classification scheme of secondary structures are considered. In each case, the percent of correct prediction is calculated. Furthermore, the overlearning effect in the protein secondary structure prediction is discussed. In addition, the application of the neural network with the modular architecture to the prediction of the protein secondary structure is presented. The results of the neural network with the modular architecture and thesimple three layer perceptron are compared.
