A Hybrid Method for Protein Secondary Structure Prediction

Abstract

Protein secondary structure can be used to help determine the tertiary structure via the fold recognition. Predicting the secondary structure from the protein sequence has attracted the attention of many researchers. Support Vector Machine (SVM) is a new learning algorithm based on statistical learning theory that has been successfully applied to the protein secondary structure prediction problem. However, the algorithm takes a long time to train the prediction model with a large data set. It becomes important to revise the method so that the time performance is improved while the accuracy performance is maintained. In this study, we implement a genetic algorithm to cluster the data set before the structure classification is predicted. Using position specific scoring matrix as part of the input, the hybrid method achieves good performances through 7-fold cross validation tests on a set of 513 non-redundant protein sequences (the CB513 data set). The result is comparable to that of the existing best prediction, yet the time spent is substantially reduced.