Prediction of protein secondary structures by a neural network with a modular architecture and super-computer

Abstract

The application of a neural network with a modular architecture to prediction of protein secondary structures (α-helix, β -sheet and coil) is presented. Each module is a three layer neural network. We compare the results from the neural network with a modular architecture and with a simple three layer structure. The prediction accuracy by a neural network with a modular architecture is higher than of the ordinary neural network. In addition, the 3, 4 and 8 state classification scheme of secondary structures are considered in the ordinary three layer neural network. The percentage of correct prediction depends on these state classification scheme.