Application of PBIL algorithm to prediction of protein secondary structure

Prediction of protein secondary structure has not been resolved in bioinformatics for over thirty years. Numerous methods have been developed to conquer this problem so far, but the results of most methods are not satisfactory. The Chou-Fasman method is simple, straightforward, and instructive to biologists and chemists, although its prediction accuracy is not as good as some newly developed learning algorithms such as neural network and SVM. This article presents the first attempt to predict protein secondary structure by means of PBIL algorithm. The idea is to predict the secondary structure by statistically optimal functions based on rules derived from the sequence-structure data. These rules, as part of optimal or tabu functions, are quite important to the success of this algorithm. The concept of probability of secondary structure corresponding to amino acids in sequence has been successfully applied to calculating the optimal function, providing a new approach to prediction of protein secondary structure.