This article is part of the supplement: Selected articles from the Twelfth Asia Pacific Bioinformatics Conference (APBC 2014): Genomics
Proposing a highly accurate protein structural class predictor using segmentation-based features
- Equal contributors
1 Institute for Integrated and Intelligent Systems (IIIS), Griffith University, Kessels Road, Brisbane, Australia
2 National ICT Australia (NICTA), Kessels Road, Brisbane, Australia
3 School of Engineering, Griffith University, Kessels Road, Brisbane, Australia
4 School of Engineering, The University of the South Pacific, Suva, Fiji, Fiji
BMC Genomics 2014, 15(Suppl 1):S2 doi:10.1186/1471-2164-15-S1-S2Published: 24 January 2014
Prediction of the structural classes of proteins can provide important information about their functionalities as well as their major tertiary structures. It is also considered as an important step towards protein structure prediction problem. Despite all the efforts have been made so far, finding a fast and accurate computational approach to solve protein structural class prediction problem still remains a challenging problem in bioinformatics and computational biology.
In this study we propose segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins. By applying SVM to our extracted features, for the first time we enhance the protein structural class prediction accuracy to over 90% and 85% for two popular low-homology benchmarks that have been widely used in the literature. We report 92.2% and 86.3% prediction accuracies for 25PDB and 1189 benchmarks which are respectively up to 7.9% and 2.8% better than previously reported results for these two benchmarks.
By proposing segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins, we are able to enhance the protein structural class prediction performance significantly.