Abstract
BackgroundIdentifying 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.ResultsThis 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.ConclusionThe 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.
Highlights
Identifying protein complexes from protein-protein interaction network is fundamental for understanding the mechanism of cellular component and protein function
Based on the protein complex formation model and the properties of known complexes, we propose an effective method to refine the complexes predicted by existing methods
Refining method To improve the accuracy of complex prediction, we propose a method to refine the protein complexes predicted by existing methods based on protein activity and the protein complex formation model with the just-in-time mechanism
Summary
Identifying protein complexes from protein-protein interaction network is fundamental for understanding the mechanism of cellular component and protein function. Based on the assumption that the members in the same protein complex and functional module strongly bind each other, a cluster can be referred as a densely connected subgraph within a PPIN. Wang et al [24] propose a new topological model by extending the definition of k-clique community of algorithm CPM and introducing distance restriction, and develop a novel algorithm called CP-DR based on the new topological model to identify protein complexes. Hierarchical clustering algorithms are based on similarity or distance to identify protein complexes, with the idea that the majority of proteins within a same protein complex tend to have similar or identical functions [31]. HC-PIN method [26] uses the weighted edge clustering coefficient to perform fast hierarchical clustering
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