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Tutorials

Tutorials

Combined proximity labeling and affinity purification−mass spectrometry workflow for mapping and visualizing protein interaction networks

Affinity purification coupled with mass spectrometry (AP–MS) and proximity-dependent biotinylation identification (BioID) methods have made substantial contributions to interaction proteomics studies. Whereas AP−MS results in the identification of proteins that are in a stable complex, BioID labels and identifies proteins that are in close proximity to the bait, resulting in overlapping yet distinct protein identifications. Integration of AP–MS and BioID data has been shown to comprehensively characterize a protein’s molecular context, but interactome analysis using both methods in parallel is still labor and resource intense with respect to cell line generation and protein purification. Therefore, we developed the Multiple Approaches Combined (MAC)-tag workflow, which allows for both AP–MS and BioID analysis with a single construct and with almost identical protein purification and mass spectrometry (MS) identification procedures. We have applied the MAC-tag workflow to a selection of subcellular markers to provide a global view of the cellular protein interactome landscape. This localization database is accessible via our online platform (http://proteomics.fi) to predict the cellular localization of a protein of interest (POI) depending on its identified interactors. In this protocol, we present the detailed three-stage procedure for the MAC-tag workflow: (1) cell line generation for the MAC-tagged POI; (2) parallel AP–MS and BioID protein purification followed by MS analysis; and (3) protein interaction data analysis, data filtration and visualization with our localization visualization platform. The entire procedure can be completed within 25 d.

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Phosphopeptide enrichment for phosphoproteomic analysis – A tutorial and review of novel materials

Significant technical advancements in phosphopeptide enrichment have enabled the identification of thousands of p-peptides (mono and multiply phosphorylated) in a single experiment. However, it is still not possible to enrich all p-peptide species in a single step. A range of new techniques and materials has been developed, with the potential to provide a step-change in phosphopeptide enrichment. The first half of this review contains a tutorial for new potential phosphoproteomic researchers; discussing the key steps of a typical phosphoproteomic experiment used to investigate canonical phosphorylation sites (serine, threonine and tyrosine). The latter half then show-cases the latest developments in p-peptide enrichment including: i) Strategies to mitigate non-specific binding in immobilized metal ion affinity chromatography and metal oxide affinity chromatography protocols; ii) Techniques to separate multiply phosphorylated peptides from monophosphorylated peptides (including canonical from non-canonical phosphorylated peptides), or to simultaneously co-enrich other post-translational modifications; iii) New hybrid materials and methods directed towards enhanced selectivity and efficiency of metal-based enrichment; iv) Novel materials that hold promise for enhanced phosphotyrosine enrichment. A combination of well-understood techniques and materials is much more effective than any technique in isolation; but the field of phosphoproteomics currently requires benchmarking of novel materials against current methodologies to fully evaluate their utility in peptide based proteoform analysis.

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A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane

Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow.

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