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Nearly all proteins undergo chemical modifications after translation. These post-translational modifications (PTMs) play crucial roles in functional proteomics, regulating the protein structure, activity, and expression. PTMs regulate interaction with cellular molecules such as nucleic acids, lipids and cofactors, as well as other proteins. PTMs can occur at any moment in the "life cycle" of a protein, influencing their biological function in processes such as initiating catalytic activity, governing protein-protein interactions, or causing protein degradation. Glycosylation and phosphorylation are of particular interest to researchers because they are critical pathways for signaling, activation, and often give insight into disease states.
Analysis of PTMs by mass spectrometry using multiple fragmentation techniques yields the most comprehensive structural characterization of modified proteins. Here we describe useful workflows for analysis of glycosylated and phosphorylated proteins.
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Overview
Workflow Overview for Phosphorylation
Reversible protein phosphorylation occurring on serine, threonine, or tyrosine residues is one of the most important and most-studied PTMs. Phosphorylation plays a central role in regulating many cellular processes including cell cycle, growth and apoptosis, as well as participating in signal transduction pathways. Given the influence that phosphorylation has on biological processes, a huge emphasis has been placed on understanding the biological role of protein phosphorylation in the context of human disease. Using sample enrichment followed by MS analysis with complementary fragmentation techniques CID, HCD and ETD, sensitive and conclusive structural elucidation of phosphorylation sites can be achieved.
Literature Highlights
Large-scale phosphorylation analysis of mouse liver
Villén J, Beausoleil SA, et al.
Proc Natl Acad Sci U S A. 2007 Jan 30;104(5):1488-93.
Proteomic investigations reveal a role for RNA processing factor THRAP3 in the DNA damage response
Beli P, Lukashchuk N, et al.
Mol Cell. 2012 Apr 27;46(2):212-25.
Sample Preparation
Sample Preparation Workflow for Phosphorylation
To achieve robust MS results, enrichment of phosphopeptide samples is essential due to their low stoichiometric abundance and poorer ionization efficiency relative to non-phosphorylated peptides. The presence of multiple phosphorylated amino acids within a single peptide can also contribute to the complexity of phosphopeptide analysis but can be mitigated by sample enrichment. The most common and favored enrichment strategies involve metal-based affinity (1-11)
The Thermo Scientific Pierce TiO2 Phosphopeptide Enrichment and Clean-up Kit enables fast, selective enrichment of phosphorylated peptides for mass spectrometry using TiO2 spin tips, graphite spin columns and optimized buffers. The complete kit includes 24 TiO2 spin tips and graphite spin columns with buffers to facilitate preparation of enriched and desalted phosphopeptides for analysis by MS.
Thermo Scientific Pierce Fe-NTA Phosphopeptide Enrichment Kit complements Thermo Scientific Pierce TiO2 kit by enriching a unique set of phosphopeptides. The Thermo Scientific Pierce Fe-NTA Phosphopeptide Enrichment Kit works in complex samples by using iron-chelating resin in spin columns. These columns enrich a higher percentage of phosphopeptides than other resins and with an overall higher number of total and unique phosphopeptides. The complete kit is easy to use and requires less than one hour to process protein digests or strong cation-exchange peptide fractions for analysis by MS.
References
1. Isolation of phosphoproteins by immobilized metal (Fe3+) affinity chromatography
Andersson L, Porath J.
Anal Biochem. 1986 Apr;154(1):250-4.
2. Large-scale phosphorylation analysis of mouse liver
Villén J, Beausoleil SA, et al.
Proc Natl Acad Sci U S A. 2007 Jan 30;104(5):1488-93.
Swaney DL, Wenger CD, et al.
Proc Natl Acad Sci U S A. 2009 Jan 27;106(4):995-1000.
4. Phosphoproteome analysis by mass spectrometry and its application to Saccharomyces cerevisiae
Ficarro SB, McCleland ML, et al.
Nat Biotechnol. 2002 Mar;20(3):301-5.
5. Mitochondrial phosphoproteome revealed by an improved IMAC method and MS/MS/MS
Lee J, Xu Y, et al.
Mol Cell Proteomics. 2007 Apr;6(4):669-76.
6. Improved immobilized metal affinity chromatography for large-scale phosphoproteomics applications
Ndassa YM, Orsi C, et al.
J Proteome Res. 2006 Oct;5(10):2789-99.
7. Immobilized gallium(III) affinity chromatography of phosphopeptides
Posewitz MC, Tempst P.
Anal Chem. 1999 Jul 15;71(14):2883-92.
Ficarro SB, Zhang Y, et al.
Anal Chem. 2009 May 1;81(9):3440-7.
Jensen SS, Larsen MR.
Rapid Commun Mass Spectrom. 2007;21(22):3635-45.
Sugiyama N, Masuda T, et al.
Mol Cell Proteomics. 2007 Jun;6(6):1103-9.
11. Integrated workflow for characterizing intact phosphoproteins from complex mixtures
Wu S, Yang F, et al.
Anal Chem. 2009 Jun 1;81(11):4210-9.
Mass Spectrometry
Mass Spectrometry Workflow for Phosphorylation
Similarly to glycopeptides analysis, CID and ETD provide highly complementary information for phosphopeptide analysis. Studies have shown that ETD is better suited for phosphopeptides containing precursors with low m/z and charge states >2, while CID is ideal for phosphopeptides that are doubly charged and/or have a high m/z (1). In order to achieve comprehensive analysis of phosphopeptides, an approach utilizing both methods of fragmentation best covers the mass range and charge states. The decision-tree-driven tandem mass spectrometry strategy devised by Swaney et al.(1) implements these rules to drive the best MS2 fragmentation type for each detected peptide in real time.
Data-dependent decision tree (DDDT) logic is available on all Thermo Scientific hybrid linear ion trap-Orbitrap mass spectrometers. The DDDT method improves phosphopeptide identifications and increases analysis throughput compared to separate runs using CID and ETD.
For phosphopeptide analysis, LC columns at least 15 cm in length with gradients greater than one hour are recommended. Thermo Scientific provides HPLC/UHPLC systems that perform at nano, micro, and standard flow rates to meet a wide variety of experimental needs.
Reference
1. Decision tree-driven tandem mass spectrometry for shotgun proteomics
Swaney DL, McAlister GC, et al.
Nat Methods. 2008 Nov;5(11):959-64.
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Thermo Scientific provides UPLC/HPLC systems that perform at low nano, micro, and high flow rate regimes to meet a wide variety of experimental needs. Thermo Scientific EASY-nLC and Dionex UltiMate® 3000 RSLCnano LC systems use split-free designs to achieve exceptional stability and reproducibility and they easily couple to all Thermo Scientific mass spectrometers.
Data Analysis
Data Analysis Workflow for Phosphorylation
Due to the large volume of data generated by mass spectrometers, automation is an important consideration in data analysis. Fortunately, many of the tools that are developed for conventional proteomics can be used for phosphoproteomics. A key strategy involves the use of a target decoy database search. Here, MS spectral data is searched against protein primary sequence databases to identify peptides and proteins. The biggest advantage of this approach is that false discovery rate (FDR) for analysis can be tabulated to provide means of validating the searched data sets. However, it should be noted that phosphopeptide data analysis can be much more challenging compared to analysis of unmodified peptide data due to the larger databases produced by phosphorylation. Phosphorylation can potentially occur at every serine, threonine and tyrosine. These challenges can be overcome by using mass spectrometers with high resolution and accurate mass. The more accurate data greatly reduces FDRs (1).
Thermo Scientific Proteome Discoverer has all the tools that are necessary for data mining of mixed raw files containing CID and ETD spectra. A novel feature within Proteome Discoverer is the implementation of PhosphoRS, an algorithm for phosphorylation site confidence measurement (2). This algorithm within Proteome Discoverer yields increased phosphoproteome coverage from LC-MS/MS data sets.
References
Bakalarski CE, Haas W, et al.
Anal Bioanal Chem. 2007 Nov;389(5):1409-19.
2. Universal and confident phosphorylation site localization using phosphoRS
Taus T, Köcher T, et al.
J Proteome Res. 2011 Dec 2;10(12):5354-62.
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