Identifying Protein Complexes Using Hybrid Properties

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Protein complexes, integrating multiple gene products, perform all sorts of fundamental biological functions in cells. Much effort has been put into identifying protein complexes using computational approaches. A vast majority attempt to research densely connected regions in protein-protein interaction (PPI) network/graph. In this research, we try an alterative approach to analyze protein complexes using hybrid features and present a method to determine whether multiple (more than two) proteins from yeast can form a protein complex. The data set consists of 493 positive protein complexes and 9878 negative protein complexes. Every complex is represented by graph features, where proteins in the complex form a graph (web) of interactions, and features derived from biological properties including protein length, biochemical properties and physicochemical properties. These features are filtered and optimized by Minimum Redundancy Maximum Relevance method, Incremental Feature Selection and Forward Feature Selection, established through a prediction/identification model called Nearest Neighbor Algorithm. Jackknife cross-validation test is employed to evaluate the identification accuracy. As a result, the highest accuracy for the identification of the real protein complexes using filtered features is 69.17%, and feature analysis shows that, among the adopted features, graph features play the main roles in the determination of protein complexes.