TITLE:
Using Chou’s Pseudo Amino Acid Composition for Protein Remote Homology Detection
AUTHORS:
Bin Liu, Xiaolong Wang
KEYWORDS:
Protein Remote Homology; Support Vector Machine; Pseudo Amino Acid Composition; Protein Representation
JOURNAL NAME:
Engineering,
Vol.5 No.10B,
October
30,
2013
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.