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Incorporating amino acids composition and functional domains for identifying bacterial toxin proteins.

Su MG, Huang CH, Lee TY, Chen YJ, Wu HY - Biomed Res Int (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan.

ABSTRACT
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.

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Related in: MedlinePlus

Systematic workflow. It consists of the following steps: data collection and preprocessing, feature extraction, model learning and cross validation, and independent testing.
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fig1: Systematic workflow. It consists of the following steps: data collection and preprocessing, feature extraction, model learning and cross validation, and independent testing.

Mentions: Figure 1 presents the systematic workflow of the proposed method. It consists of the following steps: data collection and preprocessing, feature extraction, model learning and cross validation, and independent testing. The details of each process were described as follows.


Incorporating amino acids composition and functional domains for identifying bacterial toxin proteins.

Su MG, Huang CH, Lee TY, Chen YJ, Wu HY - Biomed Res Int (2014)

Systematic workflow. It consists of the following steps: data collection and preprocessing, feature extraction, model learning and cross validation, and independent testing.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4109367&req=5

fig1: Systematic workflow. It consists of the following steps: data collection and preprocessing, feature extraction, model learning and cross validation, and independent testing.
Mentions: Figure 1 presents the systematic workflow of the proposed method. It consists of the following steps: data collection and preprocessing, feature extraction, model learning and cross validation, and independent testing. The details of each process were described as follows.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus