Using Chou’s Pseudo Amino Acid Composition for Protein Remote Homology Detection

Bin Liu, Xiaolong Wang

School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, Guangdong, China Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, Guangdong, China Shanghai Key Laboratory of Intelligent Information Processing, Shanghai, China.
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, Guangdong, China Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, Guangdong, China.

DOI: 10.4236/eng.2013.510B032                

Abstract

Protein remote homology detection is a key problem in bioinformatics. Currently, the discriminative methods, such as Support Vector Machine (SVM), can achieve the best performance. The most efficient approach to improve the performance of the SVM-based methods is to find a general protein representation method that is able to convert proteins with different lengths into fixed length vectors and captures the different properties of the proteins for the discrimination. The bottleneck of designing the protein representation method is that native proteins have different lengths. Motivated by the success of the pseudo amino acid composition (PseAAC) proposed by Chou, we applied this approach for protein remote homology detection. Some new indices derived from the amino acid index (AAIndex) database are incorporated into the PseAAC to improve the generalization ability of this method. Our experiments on a well-known benchmark show this method achieves superior or comparable performance with current state-of-the-art methods.

Keywords

Protein Remote Homology; Support Vector Machine; Pseudo Amino Acid Composition; Protein Representation

Conflicts of Interest

The authors declare no conflicts of interest.

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