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

Protein-protein interactions (PPIs) play a critical role in virtually all cellular processes. Their context-dependent characterization is thus a key objective of proteomic research. We and others have previously shown that chromatographic fractionation of native protein complexes (e.g. through size-exclusion chromatography, SEC) can be effectively combined with high-throughput, bottom-up mass-spectrometry-based proteomics (e.g. data-independent acquisition-based SWATH-MS), to support proteome-wide characterization of protein complexes. To enable qualitative and quantitative comparison of the proteome organization encoded in these datasets, across multiple experimental conditions, scalable and robust analysis strategies are required. To address this need, we developed the Size-Exclusion Chromatography Algorithmic Toolkit (SECAT), a novel network-centric strategy for the quantitation of protein complex profiles. SECAT elucidates proteins and their context-specific PPIs in terms of both abundance and connectivity. We validate algorithm predictions using publicly available datasets and compare them to established strategies to demonstrate that SECAT represents a more scalable and effective methodology to assess protein-network state, obviating the need to infer individual protein complexes. Further, by comparing PPI-networks in interphase and mitotic HeLa cells, we demonstrate SECAT’s ability to provide novel insight about context-specific molecular mechanisms that differentiate cellular states.