Integration of Multi-Feature Fusion and PLS-DA in Protein Secondary Structure Prediction

Abstract

Protein structure prediction has become one of the central problems in the field of modern computational biology. Protein secondary structure prediction is the basis of the spatial structure prediction of proteins. This paper presents a novel method for protein secondary structure prediction, which integrates multi-feature fusion and partial least square discriminant analysis (PLS-DA). Multi-feature fusion can make full use of the available information of proteins; however, it also leads to high-dimensional and redundant features. Then PLS-DA is utilized to deal with the fused protein data, which can effectively extract features from the protein data and remove the redundant information. Several benchmark datasets are used to verify the performance of the proposed method. The experiment results show that the proposed method gives satisfying prediction results of protein secondary structure compared with existing methods. Therefore the integration of multi-feature fusion and PLS-DA can fully utilize the available protein information, effectively reduce dimension and achieve robust classification in the multi-category analysis of protein secondary structure.