Network Approaches for Shotgun Proteomics Data Analysis

Shotgun proteomics has emerged as a powerful technology for protein identification with remarkable applications in discovering disease biomarkers. Protein assembly and biological interpretation of the assembled protein lists are critical steps in shotgun proteomics data analysis. Although most biological functions arise from interactions among proteins, current protein assembly pipelines treat proteins as independent entities. Usually, only individual proteins with strong experimental evidence (confident proteins) are reported, while many possible proteins of potential biological interest are eliminated. In biomarker studies, this conservative assembly may prevent us from identifying important biomarker candidates. In this study, we have developed a protein interaction network-assisted complex-enrichment approach (CEA) to improve protein identification by taking into consideration the functional relationship among proteins as embedded in protein interaction networks. CEA is based on the assumption that an eliminated protein is more likely to be present in the original sample if it is a member of a complex for which other members have been confidently identified in the same sample.