COMPUTATIONAL TOOLS FOR MASS SPECTROMETRY-BASED INTERACTOME DATA

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ABSRACT The analysis of protein complexes and interaction networks, and their dynamic behavior are of central importance in biological research. Affinity purification coupled with mass spectrometry (AP-MS) is now widely used for protein interaction analysis. Our work addresses the critical need to develop robust computational methods and tools for AP-MS data, and all types of shotgun proteomics MS data in general. We have previously developed the Statistical Analysis of INTeractomes (SAINT) framework and a suite of tools for scoring protein interactions in AP-MS studies. We have led an international consortium to comprehensively catalogue the non-specific binding proteins observed in AP-MS experiments, establishing the Contaminant Repository for Affinity Purification (CRAPome). These and other tools developed as part of this project are now used by hundreds of laboratories worldwide. Building upon these advances, we have recently initiated the development of a comprehensive computational resource REPRINT that allows biologist to process their own AP-MS data and to visualize and interactive explore the resulting interaction networks in the context of previously known interactions, pathways, and functional categories. We will further develop this resource, including implementation of advanced network visualization options and methods for integration of user-uploaded experimental AP-MS data with external information. Furthermore, we have recently developed a new data indexing algorithm that enables ultrafast and comprehensive analysis of tandem mass spectra. We will develop a comprehensive computational workflow that will help to shine the light on the “dark matter” of proteomics by enabling unrestricted identification of peptides with different chemical and post-translational modifications (PTMs) in AP-MS datasets. Thus, this work will add a new PTM dimension to the analysis of AP-MS interactome data. This will improve the analysis of AP-MS data by allowing more accurate quantification and detection of interacting partners. It will also allow detection of biologically important PTMs (including rare PTMs) on highly enriched bait proteins and their key interactors, which in turn will assist with uncovering the role of those PTMs on the dynamic and condition-specific interactomes. We will continue providing our widely used computational tools and data resources to the biological community, along with benchmark datasets for further development of computational methods by other scientists.