Improved protein structural class prediction using genetic algorithm and artificial immune system

Predicting the structure of a protein from primary sequence is one of the challenging problems in Molecular biology. In this context, protein structural class information provides a key idea of their structure and also other features related to the biological function. In this paper we present a new optimization approach based on Genetic algorithm (GA) and artificial immune system (AIS) for predicting the protein structural class. It uses the maximum component coefficient principle in association with the amino acid composition feature vector to efficiently classify the protein structures. The effectiveness is evaluated by comparing the results with that obtained from other existing methods using a standard database. Especially for all ¿ and ¿ + ß class protein, the rate of accurate prediction by the proposed methods is much higher than their counterparts.