Recognition of protein transduction domain by support vector machine
10.3760/cma.j.issn.1673-4181.2010.04.004
- VernacularTitle:基于支持向量机预测蛋白质转导域的研究
- Author:
Xiaolin YANG
;
Zhengguo ZHANG
- Publication Type:Journal Article
- Keywords:
Protein transduction domain;
Support vector machine;
Prediction;
Bioinformatics
- From:
International Journal of Biomedical Engineering
2010;33(4):205-208
- CountryChina
- Language:Chinese
-
Abstract:
Objective To predict protein transduction domain (PTD) which is a short peptide with the ability to pull diverse molecules across cell membranes.Methods Every peptide segment including PTDs and peptide sequence from SwissProt database was represented by 68 numerical values,which reflected their physicochemical and conformational properties related to the PTD' s membrane penetrating function.Transductive support vector machine (TSVM) and support vector machine(SVM),combined with cluster method,was introduced to predict new PTDs from the peptide segments,which were extracted from SwissProt database.Results TSVM prediction model achieved 94%±4% accuracy and SVM model achieved 94%±5% accuracy.1210 possible PTDs were predicted using the classifiers based on these two models.Conclusion The research provides a guide to find more PTDs in molecular biology experiments and will be helpful in the understanding of the mechanism of PTDs and their function in vivo.
