An effective method for refining predicted protein complexes based on protein activity and the mechanism of protein complex formation

BMC Systems Biology

Yi Pan²  Min Li¹  Qianghua Xiao¹  Xiaoqing Peng¹  Jianxin Wang¹ 

[1] School of Information Science and Engineering, Central South University, Changsha 410083, China;Department of Computer Science, Georgia State University, Atlanta, GA 30302-4110, USA

关键词: Gene expression;    Refining;    Just-in-time;    Protein complex formation model;    Protein activity;   

Others  :  1142946
DOI  :  10.1186/1752-0509-7-28

 received in 2012-10-09, accepted in 2013-03-14,  发布年份 2013

【 摘 要 】

Background

Identifying protein complexes from protein-protein interaction network is fundamental for understanding the mechanism of cellular component and protein function. At present, many methods to identify protein complexes are mainly based on the topological characteristics or the functional similarity features, neglecting the fact that proteins must be in their active forms to interact with others and the formation of protein complex is following a just-in-time mechanism.

Results

This paper firstly presents a protein complex formation model based on the just-in-time mechanism. By investigating known protein complexes combined with gene expression data, we find that most protein complexes can be formed in continuous time points, and the average overlapping rate of the known complexes during the formation is large. A method is proposed to refine the protein complexes predicted by clustering algorithms based on the protein complex formation model and the properties of known protein complexes. After refinement, the number of known complexes that are matched by predicted complexes, Sensitivity, Specificity, and f-measure are significantly improved, when compared with those of the original predicted complexes.

Conclusion

The refining method can discard the spurious proteins by protein activity and generate new complexes by just-in-time assemble mechanism, which can enhance the ability to predict complex.

【 授权许可】

   
2013 Wang et al.; licensee BioMed Central Ltd.

【 图 表 】

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