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Protein interaction prediction for mouse pdz domains using dipeptide composition features

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3 Author(s)
Songyot Nakariyakul ; Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for Pre-Diabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 200233, China ; Zhi-Ping Liu ; Luonan Chen

The PDZ domain is one of the largest families of protein domains that are involved in targeting and routing specific proteins in signaling pathways. PDZ domains mediate protein-protein interactions by binding the C-terminal peptides of their target proteins. Using the dipeptide feature encoding, we develop a PDZ domain interaction predictor using a support vector machine that achieves a high accuracy rate of 82.49%. Since most of the dipeptide compositions are redundant and irrelevant, we propose a new hybrid feature selection technique to select only a subset of these compositions that are useful for interaction prediction. Our experimental results show that only approximately 25% of dipeptide features are needed and that our method increases the accuracy by 3%. The selected dipeptide features are analyzed and shown to have important roles on specificity pattern of PDZ domains.

Published in:

2011 IEEE International Conference on Systems Biology (ISB)

Date of Conference:

2-4 Sept. 2011