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BioMed Research International
Volume 2014 (2014), Article ID 972692, 7 pages
Research Article

Incorporating Amino Acids Composition and Functional Domains for Identifying Bacterial Toxin Proteins

1Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
2Tao-Yuan Hospital, Ministry of Health & Welfare, Taoyuan 320, Taiwan
3Institute of Chemistry, Academia Sinica, 128 Academia Road, Section 2, Nankang District, Taipei 115, Taiwan

Received 11 April 2014; Accepted 11 June 2014; Published 7 July 2014

Academic Editor: Wen-Chi Chang

Copyright © 2014 Min-Gang Su 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.


Aside from pathogenesis, bacterial toxins also have been used for medical purpose such as drugs for cancer and immune diseases. Correctly identifying bacterial toxins and their types (endotoxins and exotoxins) has great impact on the cell biology study and therapy development. However, experimental methods for bacterial toxins identification are time-consuming and labor-intensive, implying an urgent need for computational prediction. Thus, we are motivated to develop a method for computational identification of bacterial toxins based on amino acid sequences and functional domain information. In this study, a nonredundant dataset of 167 bacterial toxins including 77 exotoxins and 90 endotoxins is adopted to learn the predictive model by using support vector machines (SVMs). The cross-validation evaluation shows that the SVM models trained with amino acids and dipeptides composition could yield an accuracy of 96.07% and 92.50%, respectively. For discriminating endotoxins from exotoxins, the SVM models trained with amino acids and dipeptides composition have achieved an accuracy of 95.71% and 92.86%, respectively. After incorporating functional domain information, the predictive performance is further improved. The proposed method has been demonstrated to be able to more effectively identify and classify bacterial toxins than the other two features on independent dataset, which may aid in bacterial biomedical development.