SECAT: Quantifying differential protein-protein interaction states by network-centric analysis

Protein-protein interactions (PPIs) play critical functional and regulatory roles in virtually all cellular processes. They are essential for the formation of macromolecular complexes, which in turn constitute the basis for extended protein interaction networks that determine the functional state of a cell. We and others have previously shown that chromatographic fractionation of native protein complexes in combination with bottom-up mass spectrometric analysis of consecutive fractions supports the multiplexed characterization and detection of state-specific changes of protein complexes. In this study, we describe a computational approach that extends the analysis of data from the co-fractionation / mass spectrometric analysis of native complexes to the level of PPI networks, thus enabling a qualitative and quantitative comparison of the proteome organization between samples and states. The Size-Exclusion Chromatography Algorithmic Toolkit (SECAT) implements a novel, network-centric strategy for the scalable and robust differential analysis of PPI networks. SECAT and its underlying statistical framework elucidate differential quantitative abundance and stoichiometry attributes of proteins in the context of their PPIs. We validate algorithm predictions using publicly available datasets and demonstrate that SECAT represents a more scalable and effective methodology to assess protein-network state and that our approach thus obviates the need to explicitly infer individual protein complexes. Further, by differential analysis of PPI networks of HeLa cells in interphase and mitotic state, respectively, we demonstrate the ability of the algorithm to detect PPI network differences and to thus suggest molecular mechanisms that differentiate cellular states.