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This article is part of the supplement: Selected articles from the Eighth Asia-Pacific Bioinformatics Conference (APBC 2010)

Open Access Research

Predicting the protein-protein interactions using primary structures with predicted protein surface

Darby Tien-Hao Chang*, Yu-Tang Syu and Po-Chang Lin

Author Affiliations

Department of Electrical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan

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BMC Bioinformatics 2010, 11(Suppl 1):S3  doi:10.1186/1471-2105-11-S1-S3

Published: 18 January 2010

Abstract

Background

Many biological functions involve various protein-protein interactions (PPIs). Elucidating such interactions is crucial for understanding general principles of cellular systems. Previous studies have shown the potential of predicting PPIs based on only sequence information. Compared to approaches that require other auxiliary information, these sequence-based approaches can be applied to a broader range of applications.

Results

This study presents a novel sequence-based method based on the assumption that protein-protein interactions are more related to amino acids at the surface than those at the core. The present method considers surface information and maintains the advantage of relying on only sequence data by including an accessible surface area (ASA) predictor recently proposed by the authors. This study also reports the experiments conducted to evaluate a) the performance of PPI prediction achieved by including the predicted surface and b) the quality of the predicted surface in comparison with the surface obtained from structures. The experimental results show that surface information helps to predict interacting protein pairs. Furthermore, the prediction performance achieved by using the surface estimated with the ASA predictor is close to that using the surface obtained from protein structures.

Conclusion

This work presents a sequence-based method that takes into account surface information for predicting PPIs. The proposed procedure of surface identification improves the prediction performance with an F-measure of 5.1%. The extracted surfaces are also valuable in other biomedical applications that require similar information.