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Research article2024Peer reviewedOpen access

TermineR: Extracting information on endogenous proteolytic processing from shotgun proteomics data

Cosenza-Contreras, Miguel; Seredynska, Adrianna; Vogele, Daniel; Pinter, Niko; Brombacher, Eva; Cueto, Ruth Fiestas; Dinh, Thien-Ly Julia; Bernhard, Patrick; Rogg, Manuel; Liu, Junwei; Willems, Patrick; Stael, Simon; Huesgen, Pitter F.; Kuehn, E. Wolfgang; Kreutz, Clemens; Schell, Christoph; Schilling, Oliver

Abstract

State-of-the-art mass spectrometers combined with modern bioinformatics algorithms for peptide-to-spectrum matching (PSM) with robust statistical scoring allow for more variable features (i.e., post-translational modifications) being reliably identified from (tandem-) mass spectrometry data, often without the need for biochemical enrichment. Semi-specific proteome searches, that enforce a theoretical enzymatic digestion to solely the N- or C-terminal end, allow to identify of native protein termini or those arising from endogenous proteolytic activity (also referred to as "neo-N-termini" analysis or "N-terminomics"). Nevertheless, deriving biological meaning from these search outputs can be challenging in terms of data mining and analysis. Thus, we introduce TermineR, a data analysis approach for the (1) annotation of peptides according to their enzymatic cleavage specificity and known protein processing features, (2) differential abundance and enrichment analysis of N-terminal sequence patterns, and (3) visualization of neo-N-termini location. We illustrate the use of TermineR by applying it to tandem mass tag (TMT)-based proteomics data of a mouse model of polycystic kidney disease, and assess the semi-specific searches for biological interpretation of cleavage events and the variable contribution of proteolytic products to general protein abundance. The TermineR approach and example data are available as an R package at .

Keywords

data processing; polycystic kidney disease; proteolysis; terminomics

Published in

Proteomics
2024, Publisher: WILEY

    UKÄ Subject classification

    Bioinformatics (Computational Biology)
    Biochemistry and Molecular Biology

    Publication identifier

    DOI: https://doi.org/10.1002/pmic.202300491

    Permanent link to this page (URI)

    https://res.slu.se/id/publ/131804