Abstract
Post-translational modifications (PTMs) can occur soon after translation or at any stage in the lifecycle of a given protein, and they may help regulate protein folding, stability, cellular localisation, activity, or the interactions proteins have with other proteins or biomolecular species. PTMs are crucial to our functional understanding of biology, and new quantitative mass spectrometry (MS) and bioinformatics workflows are maturing both in labelled multiplexed and label-free techniques, offering increasing coverage and new opportunities to study human health and disease. Techniques such as Data Independent Acquisition (DIA) are emerging as promising approaches due to their re-mining capability. Many bioinformatics tools have been developed to support the analysis of PTMs by mass spectrometry, from prediction and identifying PTM site assignment, open searches enabling better mining of unassigned mass spectra—many of which likely harbour PTMs—through to understanding PTM associations and interactions. The remaining challenge lies in extracting functional information from clinically relevant PTM studies. This review focuses on canvassing the options and progress of PTM analysis for large quantitative studies, from choosing the platform, through to data analysis, with an emphasis on clinically relevant samples such as plasma and other body fluids, and well-established tools and options for data interpretation.
Highlights
The ability to analyse protein post-translational modifications (PTMs) occurring on a large scale in a biological system yields insight into their roles and relevance to disease states and confers proteomics a unique edge
A detailed description of MS methodology including chromatography and mass spectrometry instrumentation is included in the recent phosphoproteomics review of Riley and Coon [87], while data acquisition methods were very recently and comprehensively reviewed in the context of PTM analysis [12], and details on sequence-specific identifications of PTMs including example spectra are provided in the classic review of Choudhary and Mann [88]
Modern MS instrumentation allows for increasingly fast data acquisition rates, providing ever increasing numbers of peptide-spectrum matches (PSMs), Dependent Acquisition (DDA) is stochastic in nature, and missing values in individual DDA experiments lead to a degree of incompleteness in large data sets
Summary
The ability to analyse protein post-translational modifications (PTMs) occurring on a large scale in a biological system yields insight into their roles and relevance to disease states and confers proteomics a unique edge. An immense variety of biological responses occur in this manner—in Bill Bryson’s memorable turn of phrase “depending on mood and metabolic circumstance, (proteins) will allow themselves to be phosphorylated, glycosylated, acetylated, ubiquitinated, farnesylated, sulphated and linked to GPI anchors, among rather a lot else” [1] The wealth of these changes and their importance in cell signalling and disease led to modified proteins being the focus of clinical and pharmaceutical research as potential drug targets [2,3,4]. Glycan changes were shown to mediate disease progression and influence overall survival rates [35] Supporting these studies, the mRNA and protein levels of core 1 β-1,3-galactosyltransferase (C1GALT1) were observed to be elevated in hepatocellular carcinoma [36]. Histone deacetylase inhibitors have been shown to provide neuroprotection to the retinal ganglion cells in experimental model of optic nerve injury [59]
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