Protein Remote Homology Detection Based on an Ensemble Learning Approach.

Junjie Chen,Dong Huang,Bingquan Liu

doi:10.1155/2016/5813645

Abstract

Protein remote homology detection is one of the central problems in bioinformatics. Although some computational methods have been proposed, the problem is still far from being solved. In this paper, an ensemble classifier for protein remote homology detection, called SVM-Ensemble, was proposed with a weighted voting strategy. SVM-Ensemble combined three basic classifiers based on different feature spaces, including Kmer, ACC, and SC-PseAAC. These features consider the characteristics of proteins from various perspectives, incorporating both the sequence composition and the sequence-order information along the protein sequences. Experimental results on a widely used benchmark dataset showed that the proposed SVM-Ensemble can obviously improve the predictive performance for the protein remote homology detection. Moreover, it achieved the best performance and outperformed other state-of-the-art methods.

Highlights

In computational biology, protein remote homology detection is the classification of proteins into structural and functional classes given their amino acid sequences, especially, with low sequence identities
Protein remote homology detection is a critical step for basic research and practical application, which can be applied to the protein 3D structure and function prediction [1, 2]
In this study, inspired by the success of ensemble classifier in the other fields, we proposed an ensemble classifier for protein remote homology detection, called Support vector machine (SVM)-Ensemble, which combined three state-of-the-art discriminative methods with a weighted voting strategy

Summary

Introduction

Protein remote homology detection is the classification of proteins into structural and functional classes given their amino acid sequences, especially, with low sequence identities. Protein remote homology detection is a critical step for basic research and practical application, which can be applied to the protein 3D structure and function prediction [1, 2]. Remote homology proteins have similar structures and functions, they lack detectable sequence similarities, because the protein structures are more conserved than protein sequences. It is often a failure to detect protein remote homology by computational approaches only based on protein sequence features. To improve the specificity and sensitivity of the detection, we proposed an ensemble learning method, which can combine basic classifiers based on different feature spaces

Methods

Results

Conclusion