Poster Presentation 2014 International Biophysics Congress

Based on structural and pseudo-amino acid composition information to predict the anti- and pro-apoptosis proteins (#377)

Yingli Chen 1 , Miao Yu
  1. Inner Mongolia University, Hohhot, China
Apoptosis protein is a kind of protein with specific functions, play an important role in the growth and homeostasis of organisms. During apoptosis the anti-apoptosis and pro-apoptosis plays a different role in the regulation of apoptosis. If the inactivation of anti-apoptosis proteins or pro-apoptosis proteins, it will lead to the occurrence of cancer and other diseases. So the classification of anti-apoptotic proteins and pro-apoptotic proteins will help us understand the pathogenic mechanism of the apoptosis proteins better. In this paper, a new apoptosis proteins data set is built, and the two kinds of apoptosis in the new data set are predicted by using the increment of diversity(ID)and support vector machine(SVM)algorithm based on the structural and pseudo-amino acid composition information. According to the biological and physicochemical characteristics of apoptosis protein, we extracted several feature information shown as follow: protein sequence information, amino acid hydrophilic-hydrophobic properties, protein block information, evolutionary, chemical shifts and protein n-terminal sequence component information, and furthermore, the impact of the single feature and multi-feature fusion models on predictive results was analyzed. The results show that the selection of more useful feature information for improving the success rate is very important factor. This proposed effective algorithm will be able to predict the classification of anti-apoptotic and pro-apoptotic proteins, to further improve the predictive capability, enhance prediction credibility, and to predict unknown function of apoptosis proteins.
  1. Qianzhong Li and Zhiqing Lu. The Prediction of the Structural Class of Proein: Application of the Measure of Diversity,Journal of Theoretical Biology 213: 493-502.
  2. Yingli Chen and Qianzhong Li. Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo amino acid composition, Journal of Theoretical Biology, 248(2): 377-381.