COMPUTATIONAL TOOLS FOR MASS SPECTROMETRY-BASED INTERACTOME DATA

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ABSTRACT Our overarching aim is to address the critical need for robust computational methods and tools for mass spectrometry (MS)-based proteomics data. Here, we are focusing on three key areas of biological research: 1) MS-based characterization of “interactomes”, i.e., analysis of protein-protein interaction networks and complexes using affinity purification – mass spectrometry (AP-MS) and related technologies. 2) Post-translational modifications (PTMs), which have a profound effect on a myriad of cellular process, including how proteins interact with other proteins and assemble into multi-functional complexes. 3) Single-cell proteomics that is critical for obtaining more complete (in addition to single-cell transcriptomics) insights into cellular heterogeneity. Furthermore, in recent years the field of proteomics has witness tremendous advances in MS technologies and sample preparation protocols, including addition of the trapped ion mobility separation (TIMS) dimension to time- of-flight MS (timsTOF technology), new fragmentation mechanisms, new chemical labeling strategies, and improved multiplex quantitative technologies. Under this grant, we have developed the Statistical Analysis of Interactomes (SAINT) framework which, together with the Contaminant Repository for Affinity Purification (CRAPome), formed the basis for a widely used Resource for Evaluation of PRotein INTeractions (REPRINT). Our DIA-Umpire and IonQuant algorithms have advanced the field of label-free protein quantification. The novel indexing algorithm of MSFragger has led to a 100-fold increase in the computational speed of peptide identification from MS spectra, enabling the new “open search” and “mass offset” strategies for comprehensive identification of PTMs. We will continue our innovative work by developing new functionalities and algorithms in our interactome analysis resources, including transitioning REPRINT to our new MSFragger-based pipeline, further expansion of the CRAPome repository of non-specific background proteins, and improved interactome scoring. Leveraging our recent computational advances, we will develop novel tools to identify PTMs in large- scale MS-based interactome data, and search for PTMs that correlate with changes in the interactomes across different conditions. Building on our new localization-aware open search algorithm of MSFragger, we will develop new algorithms for comprehensive PTM profiling and chemical proteomics. Furthermore, working closely with the leaders in the field, we will develop new algorithms, tools, and benchmarking strategies for the analysis of MS-based single-cell proteomics data. We will continue providing our widely used computational tools and data resources to the biological community.