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

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 https://github.com/MiguelCos/TermineR.