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 these data. We have demonstrated the great utility of label-free quantitative protein abundance information that can be extracted from AP-MS data, and developed the Statistical Analysis of INTeractomes (SAINT) framework for scoring protein interactions in AP-MS studies. We have also initiated an international consortium to comprehensively catalogue the non-specific binding proteins observed in AP-MS experiments - the Contaminant Repository for Affinity Purification (CRAPome.org). Building upon these advances, we will continue toward our goal of developing a comprehensive computational resource for scoring protein interaction data applicable to most commonly used experimental protocols and MS platforms. We will also gain a better understanding of the underlying mechanisms of non-specific binding - generating knowledge useful both for retrospective analysis of previously published data and for the design of future experiments. By integrating the experimental AP-MS data with external information such as structure-based protein interaction predictions, we will further improve the sensitivity of detection of low abundance and transient interactions. It has also become apparent that charting a complete interaction map for an organism like human is a community-wide effort, with multiple groups contributing separate portions of the entire interactome. We will develop a novel computational framework for consistent integration of AP-MS datasets from different studies, leading to more complete and accurate quantitative interaction networks. Lastly, one important problem that has yet to be fully addressed is the quantitative analysis protein complexes and interaction networks dynamics. The emergence of highly sensitive multiplex MS techniques presents such an opportunity, and we will develop advanced computational algorithms and tools for differential and dynamic interactome analysis using multiplex MS data. 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.
The proposed computational work will enable statistically robust and quantitative analysis of protein-protein interactions and protein complexes using affinity purification - mass spectrometry (AP/MS) approach. The bioinformatics methods will allow establishing a computational framework for quality assessment, analysis, modelling, and cross-laboratory comparison of AP/MS data. The tools and methods will be of great utility for both large collaborative interactome projects and small scale studies. All computational tools developed as a part of this proposal will be made freely available to the research community.
|Meyer, Jesse G; Mukkamalla, Sushanth; Steen, Hanno et al. (2017) PIQED: automated identification and quantification of protein modifications from DIA-MS data. Nat Methods 14:646-647|
|Xu, Tao; Park, Sung-Soo; Giaimo, Benedetto Daniele et al. (2017) RBPJ/CBF1 interacts with L3MBTL3/MBT1 to promote repression of Notch signaling via histone demethylase KDM1A/LSD1. EMBO J 36:3232-3249|
|da Veiga Leprevost, Felipe; Grüning, Björn A; Alves Aflitos, Saulo et al. (2017) BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics 33:2580-2582|
|Kong, Andy T; Leprevost, Felipe V; Avtonomov, Dmitry M et al. (2017) MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat Methods 14:513-520|
|Perez-Riverol, Yasset; Bai, Mingze; da Veiga Leprevost, Felipe et al. (2017) Discovering and linking public omics data sets using the Omics Discovery Index. Nat Biotechnol 35:406-409|
|Rosenberger, George; Bludau, Isabell; Schmitt, Uwe et al. (2017) Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses. Nat Methods 14:921-927|
|Santos, Renata M; Nogueira, Fabio C S; Brasil, Aline A et al. (2017) Quantitative proteomic analysis of the Saccharomyces cerevisiae industrial strains CAT-1 and PE-2. J Proteomics 151:114-121|
|Rolland, Delphine C M; Basrur, Venkatesha; Jeon, Yoon-Kyung et al. (2017) Functional proteogenomics reveals biomarkers and therapeutic targets in lymphomas. Proc Natl Acad Sci U S A 114:6581-6586|
|Navarro, Pedro; Kuharev, Jörg; Gillet, Ludovic C et al. (2016) A multicenter study benchmarks software tools for label-free proteome quantification. Nat Biotechnol 34:1130-1136|
|Avtonomov, Dmitry M; Raskind, Alexander; Nesvizhskii, Alexey I (2016) BatMass: a Java Software Platform for LC-MS Data Visualization in Proteomics and Metabolomics. J Proteome Res 15:2500-9|
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