Learning a prediction model for protein-protein recognition

Authors:
Huang-Cheng Kuo;Kuan-Yu Su;Ping-Lin Ong;Jen-Peng Huang
Affiliations:
National Chiayi University, Chia-Yi City, Taiwan;National Chiayi University, Chia-Yi City, Taiwan;National Chiayi University, Chia-Yi City, Taiwan;Southern Taiwan University, Tainan County, Taiwan
Venue:
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Year:
2009

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Abstract

Study on protein-protein interaction is important for understanding the protein function in cell activity. Protein-protein recognition plays a crucial role of biology. Therefore, we use the properties of protein interface for protein recognition prediction because the interface offers important clues in biological functions. Genetic Programming (GP), one of artificial intelligence technologies, has been proposed in data classification research in biology. In this paper, we present a prediction method with GP for protein-protein recognition based on protein binding site features. We successfully predict recognition proteins with an average accuracy rate of 78% with ten-fold cross validation.