Protein Secondary Structure Prediction by the Gamma Neural Model

Abstract

One way to understand a protein's function is to infer from its structure. However, it is not easy to obtain the three-dimensional structure directly. So to predict substructures like alpha helices, beta sheets, and coils from the protein sequence is the first step to decode the mystery of a protein. It is straightforward and was investigated, but improvement is still needed in this problem. In this paper, the gamma neural model for context analysis is used. Also, a new encoding method for the protein sequence is proposed and discussed. The results are compared with the traditionally-used time-delay neural networks and another encoding method in the field. The gamma neural model not only can reduce both space complexity and time complexity but also improves the overall prediction accuracy. The proposed encoding method, on the other hand, shows its usefulness in improving the prediction rate of beta sheets.