BITS - Introduction to proteomics

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This is the second presentation of the BITS training on 'Mass spec data processing'.

It reviews the methods for separating protein mixtures prior to further analysis.

Thanks to the Compomics Lab of the VIB for contribution.

Published in: Technology, Business
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BITS - Introduction to proteomics

  1. 1. http://www.bits.vib.be/training
  2. 2. introduction to proteomics kenny helsens kenny.helsens@ugent.be Lennart MARTENS lennart.martens@ebi.ac.uk Computational Omics and Systems Biology Group Proteomics Services Group European Bioinformatics Institute Department of Medical Protein Research, VIB Hinxton, Cambridge United Kingdom Department of Biochemistry, Ghent University www.ebi.ac.ukKenny Helsens Ghent, Belgium BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  3. 3. The central paradigm - Primary structure (sequence) …YSFVATAER… - Secondary structure (structural elements) - Tertiairy structure (3D shape) - Modifications (dynamic, function) phosphorylation - Processing (targetting, activation) trypsin Adapted from the NCBI Science Primer http://www.ncbi.nih.gov/About/primer/genetics_cell.html platelet activityKenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  4. 4. 2D-PAGE separation of proteins (Est. 1975) Principle Protein A Protein B cell lysis protein extraction Protein C Protein D cells protein mixture pI Chemistry Mr toolbox 2D-PAGEKenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  5. 5. 2D-PAGE separation of proteins (Est. 1975) protein complex protein mixture extraction http://www.akh-wien.ac.at/biomed-research/htx/platweb1.htm 2D-PAGE separation MS/MS analysis 100 % pI 0 m/z 100 300 500 700 900 1100 1300 1500 1700 1900 2100 fragmentation 100 MS analysis tryptic % digest 0 m/z 300 400 500 600 700 800 900 1000 1100 MWKenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  6. 6. Overall gel-free proteomics workflow protein complex protein mixture extraction enzymatic http://www.akh-wien.ac.at/biomed-research/htx/platweb1.htm digest Data-dependent MS/MS analyses extremely complex peptide mixture100 100 100% % % 0 100 300 500 700 900 1100 1300 1500 1700 1900 2100 m/z 0 100 300 500 700 900 1100 1300 1500 1700 1900 2100 m/z 0 100 300 500 700 900 1100 1300 1500 1700 1900 2100 m/z separation selection MS analysis less complex peptide fractionsKenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  7. 7. Going gel-free in the new millennium • ICAT (Gygi et al., 1999) • MudPIT (Washburn et al., 2001) • Accurate Mass Tags for proteome analysis (Conrads et al., 2000) • Signature Peptides approach for proteomics (Ji et al., 2000) • AA-based covalent chromatography peptide selection (Wang & Regnier, 2001) • Affinity-based enrichment of phosphopeptides (Oda et al., 2001) • ICAT for phosphopeptides (Zhou et al., 2001) • Reversible biotinylation of Cys-peptides (Spahr et al., 2000) • COFRADIC (Gevaert et al., 2002)Kenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  8. 8. An overview of the pro’s and cons • Massive increase in mixture redundancy (eg. membrane proteins)  Corresponding increase in mixture complexity (from a few thousand proteins to hundreds of thousands of peptides!) • Easier seperation of peptides instead of proteins  Loss of protein-level information (pI, MW, isoforms) • Mixture complexity can be reduced by peptide selection (Cys- peptides, Met-peptides, N-terminal peptides, phospho-peptides, …)  Again leading to reduced redundancy of the mixture • Choice of selection technique, depending on circumstances/analyte  Massive amounts of data generated (10.000 spectra per hour) • Additional processing information (N-terminal peptides)  Unadapted database search engines (N-terminal processing)Kenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  9. 9. AN INCOMPLETE OVERVIEW OF GEL-FREE TECHNIQUESKenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  10. 10. MudPIT: that which we call a rose… Strong cation Reverse-phase exchanger resin SCX RP ESI-based MS • Orthogonal, 2D separation of peptides • 2D analogon: pI = SCX, Mr = RPKenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  11. 11. But what about the complexity? e.g., Escherichia coli 4,349 predicted proteins if 100% expressed 109,934 detectable tryptic peptides if 50% expressed 54,967 detectable tryptic peptides Sample complexity increased one order of magnitude!Kenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  12. 12. A thought experiment seems appropriate What happens when there are 100.000 peptides present? How often do we need to repeat an analysis of an identical sample in order to obtain reasonable coverage? The explorative aspectKenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  13. 13. The explorative aspect Complete coverage 2010 2006 2002Kenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  14. 14. More coverage by reducing population size Tissue • Laser capture microdissection • Flow cytometry cells one cell-type • Differential Detergent compartments fractionation one organel / • Differential centrifugation compartment • Gel-filtration proteins subset of • 1D-gel electrophoresis • Ion-exchange proteins • ICAT-method peptides subset of • COmbined FRActional peptides Diagonal Chromatography Preselected, representative peptidesKenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  15. 15. Peptide selection techniques: ICAT  Isotope Coded Affinity Tag 1) Modify cysteine residues using a molecule consisting of 3 parts: • a thiol reactive group • a biotin label • a linker that may contain light or heavy atoms 2) Digest proteins 3) Affinity isolation of labeled cysteine-peptides 4) Use cysteine-peptides for LC-MS/MS analysisKenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  16. 16. The ICAT molecule biotin thiol-specific O heavy reagent: X = deuterium light reagent: X = hydrogen reactive group HN NH O X X O X O X I N XO OX N S H H X X The linker allows differential proteome analysis! Evoked mass difference = 8 amu’s. From: Gygi SP et al., Nature Biotechnology, 1999Kenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  17. 17. Peptide selection techniques: COFRADIC  COmbined FRActional DIagonal Chromatography • Selection technique based on diagonal chromatography • Versatile – requires only a specific modification that changes chromatographic properties • Already applied to methionine, cysteine, N-terminal, nitrosylated, glycosylated, phosphorylated and ATP- binding peptides • N-terminal analysis is well-suited for detecting proteolytic events From: Gevaert et al., Molecular & Cellular Proteomics, 2002 Gevaert et al., Nature Biotechnology, 2003Kenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  18. 18. COFRADIC in principle AU gradient Chemical (or enzymatic) alteration of subset of peptides in separate or combined fractions time =0 Separate and collect in fractions AU gradient - + Altered peptides display changed chromatographic properties (-, +) Alternatively: selected peptides are not altered (=0), while non selected peptides are altered timeKenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  19. 19. COFRADIC in practice (I) primary run secondary run H H O H H O ... N C C ... ... N C C ... Methionine COFRADIC CH2 H2O2-oxidation CH2 (Gevaert et al., 2002) CH2 CH2 S S O CH3 CH3 methionine methionine-sulfoxide primary run secondary run Ac AA1 AA2 AA3 AA4 ... Arg Ac AA1 AA2 AA3 AA4 ... Arg N-terminal Ac AA1 Lys AA3 AA4 ... Arg peptides Ac AA1 Lys AA3 AA4 ... Arg  NH-Ac  NH-Ac NO2 N-terminal COFRADIC TNBS modification H2N AA1 AA2 AA3 AA4 ... Arg NO2 N AA1 AA2 AA3 AA4 ... Arg (Gevaert et al., 2003) H NO2 internal NO2 peptides H2N AA1 AA2 Lys AA4 ... Arg NO2 N AA1 AA2 Lys AA4 ... Arg H  NH-Ac  NH-Ac NO2Kenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  20. 20. COFRADIC in practice (II) primary run secondary run H H O H H O H H O ... N C C ... ... N C C ... ... N C C ... CH2 Ellman’s reagent CH2 TCEP reduction CH2 Cysteine COFRADIC SH S SH S (Gevaert et al., 2004) cysteine cysteine HOOC NO2 TNB-cysteineKenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  21. 21. COFRADIC in practice (III) ~60% Detectable! log10(Mass N-terminal Peptide) ~60% Detectable! log10(Mass C-terminal Peptide)Kenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  22. 22. Thank you! Questions?Kenny Helsens BITS MS Data Processing – Protein Inferencekenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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