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BioMed Research International
Volume 2016, Article ID 9480276, 10 pages
Research Article

QuaBingo: A Prediction System for Protein Quaternary Structure Attributes Using Block Composition

1Department of Bioinformatics, Chung-Hua University, Room S116, No. 707, Section 2, WuFu Road, Hsinchu 30012, Taiwan
2Institute of Genomics and Bioinformatics, National Chung Hsing University, 250 Kuo Kuang Road, Taichung 402, Taiwan
3Department of Computer Science, Universiti Tunku Abdul Rahman, Jalan Universiti, 31900 Kampar, Malaysia
4Biotechnology Center, Agricultural Biotechnology Center, Institute of Molecular Biology, Graduate Institute of Biotechnology, National Chung Hsing University, 250 Kuo Kuang Road, Taichung 402, Taiwan

Received 23 February 2016; Revised 30 June 2016; Accepted 20 July 2016

Academic Editor: Ryuji Hamamoto

Copyright © 2016 Chi-Hua Tung et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. Quaternary structures of proteins are closely relevant to gene regulation, signal transduction, and many other biological functions of proteins. In the current study, a new method based on protein-conserved motif composition in block format for feature extraction is proposed, which is termed block composition. Results. The protein quaternary assembly states prediction system which combines blocks with functional domain composition, called QuaBingo, is constructed by three layers of classifiers that can categorize quaternary structural attributes of monomer, homooligomer, and heterooligomer. The building of the first layer classifier uses support vector machines (SVM) based on blocks and functional domains of proteins, and the second layer SVM was utilized to process the outputs of the first layer. Finally, the result is determined by the Random Forest of the third layer. We compared the effectiveness of the combination of block composition, functional domain composition, and pseudoamino acid composition of the model. In the 11 kinds of functional protein families, QuaBingo is 23% of Matthews Correlation Coefficient (MCC) higher than the existing prediction system. The results also revealed the biological characterization of the top five block compositions. Conclusions. QuaBingo provides better predictive ability for predicting the quaternary structural attributes of proteins.