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.

1. http://www.bits.vib.be/training

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.uk
Kenny Helsens Ghent, Belgium
BITS MS Data Processing – Protein Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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 activity
Kenny Helsens BITS MS Data Processing – Protein Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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-PAGE
Kenny Helsens BITS MS Data Processing – Protein Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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
MW
Kenny Helsens BITS MS Data Processing – Protein Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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 mixture
100 100 100
% % %
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 fractions
Kenny Helsens BITS MS Data Processing – Protein Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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 Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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 Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

9. AN INCOMPLETE OVERVIEW
OF GEL-FREE TECHNIQUES
Kenny Helsens BITS MS Data Processing – Protein Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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 = RP
Kenny Helsens BITS MS Data Processing – Protein Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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 Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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 aspect
Kenny Helsens BITS MS Data Processing – Protein Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

13. The explorative aspect
Complete coverage
2010
2006
2002
Kenny Helsens BITS MS Data Processing – Protein Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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 peptides
Kenny Helsens BITS MS Data Processing – Protein Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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 analysis
Kenny Helsens BITS MS Data Processing – Protein Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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, 1999
Kenny Helsens BITS MS Data Processing – Protein Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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, 2003
Kenny Helsens BITS MS Data Processing – Protein Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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
time
Kenny Helsens BITS MS Data Processing – Protein Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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
NO2
Kenny Helsens BITS MS Data Processing – Protein Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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-cysteine
Kenny Helsens BITS MS Data Processing – Protein Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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 Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

22. Thank you!
Questions?
Kenny Helsens BITS MS Data Processing – Protein Inference
kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011