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

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

Lennart MARTENSlennart.martens@ebi.ac.uk

Proteomics Services GroupEuropean Bioinformatics Institute

Hinxton, CambridgeUnited Kingdomwww.ebi.ac.uk

kenny helsens

kenny.helsens@ugent.be

Computational Omics and Systems Biology Group

Department of Medical Protein Research, VIBDepartment of Biochemistry, Ghent University

Ghent, Belgium

introduction to proteomics

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

Adapted from the NCBI Science Primerhttp://www.ncbi.nih.gov/About/primer/genetics_cell.html

- Primary structure (sequence)

- Secondary structure (structural elements)

- Tertiairy structure (3D shape)

- Modifications (dynamic, function)

- Processing (targetting, activation)

…YSFVATAER…

phosphorylation

trypsinplatelet activity

The central paradigm

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

Principle

Protein A Protein B

Protein C Protein Dcells protein mixture

cell lysisprotein extraction

2D-PAGE

pI

MrChemistrytoolbox

2D-PAGE separation of proteins (Est. 1975)

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

100 300 500 700 900 1100 1300 1500 1700 1900 2100m/z0

100

%

100 300 500 700 900 1100 1300 1500 1700 1900 2100m/z0

100

%

300 400 500 600 700 800 900 1000 1100m/z0

100

%

300 400 500 600 700 800 900 1000 1100m/z0

100

%

protein extraction complex protein mixture

2D-PAGE separation

MS analysis

MS/MS analysis

pI

MW

http://www.akh-wien.ac.at/biomed-research/htx/platweb1.htm

fragmentation

tryptic

digest

2D-PAGE separation of proteins (Est. 1975)

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

100 300 500 700 900 1100 1300 1500 1700 1900 2100m/z0

100

%

100 300 500 700 900 1100 1300 1500 1700 1900 2100m/z0

100

%

enzymaticdigest

extremely complexpeptide mixture

Data-dependent MS/MS analyses

separationselection

MS analysis

protein extraction complex protein mixture

http://www.akh-wien.ac.at/biomed-research/htx/platweb1.htm

less complexpeptide fractions

100 300 500 700 900 1100 1300 1500 1700 1900 2100m/z0

100

%

100 300 500 700 900 1100 1300 1500 1700 1900 2100m/z0

100

%

100 300 500 700 900 1100 1300 1500 1700 1900 2100m/z0

100

%

100 300 500 700 900 1100 1300 1500 1700 1900 2100m/z0

100

%

Overall gel-free proteomics workflow

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

• 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)

Going gel-free in the new millennium

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

• 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)

An overview of the pro’s and cons

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

AN INCOMPLETE OVERVIEW

OF GEL-FREE TECHNIQUES

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

RPSCX ESI-based MS

Strong cationexchanger

Reverse-phaseresin

• Orthogonal, 2D separation of peptides

• 2D analogon: pI = SCX, Mr = RP

MudPIT: that which we call a rose…

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

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!

But what about the complexity?

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

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

A thought experiment seems appropriate

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

The explorative aspect

2002

2006

2010

Complete coverage

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

Tissue

one cell-type

one organel /

compartment

subset of

proteins

subset of

peptides

cells

compartments

proteins

peptides

Preselected, representative peptides

• Laser capture microdissection• Flow cytometry

• Differential Detergent fractionation

• Differential centrifugation

• Gel-filtration• 1D-gel electrophoresis• Ion-exchange

• ICAT-method• COmbined FRActional

Diagonal Chromatography

More coverage by reducing population size

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

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

Peptide selection techniques: ICAT

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

O

NH NH

SNH

O

OOX

XO N

H

X

X

X

X

X

X

IO

biotinheavy reagent: X = deuteriumlight reagent: X = hydrogen

thiol-specificreactive group

The linker allows differential proteome analysis!

Evoked mass difference = 8 amu’s.

From: Gygi SP et al., Nature Biotechnology, 1999

The ICAT molecule

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

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, 2002Gevaert et al., Nature Biotechnology, 2003

Peptide selection techniques: COFRADIC

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

AU

time

gradient

Separate and collect in fractions

Chemical (or enzymatic) alteration of subset of peptides

in separate or combined fractions

Altered peptides display changed chromatographic properties

(-, +)Alternatively: selected peptides are not altered (=0), while non selected peptides are altered

AU

time

gradient

- +

=0

COFRADIC in principle

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

Methionine COFRADIC(Gevaert et al., 2002)

N-terminal COFRADIC(Gevaert et al., 2003)

... N C C ...O

CH2

CH2

SCH3

HH... N C C ...

O

CH2

CH2

SCH3

O

HH

H2O2-oxidation

methionine methionine-sulfoxide

primary run secondary run

... N C C ...O

CH2

CH2

SCH3

HH... N C C ...

O

CH2

CH2

SCH3

O

HH

H2O2-oxidation

methionine methionine-sulfoxide

primary run secondary run

Ac AA1 AA2 AA3 AA4 ... Arg

NH2 AA1 AA2 AA3 AA4 ... Arg

NH2 AA1 AA2 Lys AA4 ... Arg

NH-Ac

Ac AA1 Lys AA3 AA4 ... Arg

NH-Ac

Ac AA1 AA2 AA3 AA4 ... Arg

Ac AA1 Lys AA3 AA4 ... Arg

NH-Ac

NH

AA1 AA2 AA3 AA4 ... Arg

NO2

NO2

NO2

NH

AA1 AA2 Lys AA4 ... Arg

NH-Ac

NO2

NO2

NO2

primary run secondary run

TNBS modification

N-terminalpeptides

internalpeptides

Ac AA1 AA2 AA3 AA4 ... Arg

NH2 AA1 AA2 AA3 AA4 ... Arg

NH2 AA1 AA2 Lys AA4 ... Arg

NH-Ac

Ac AA1 Lys AA3 AA4 ... Arg

NH-Ac

Ac AA1 AA2 AA3 AA4 ... Arg

Ac AA1 Lys AA3 AA4 ... Arg

NH-Ac

NH

AA1 AA2 AA3 AA4 ... Arg

NO2

NO2

NO2

NH

AA1 AA2 Lys AA4 ... Arg

NH-Ac

NO2

NO2

NO2

primary run secondary run

TNBS modification

N-terminalpeptides

internalpeptides

COFRADIC in practice (I)

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

... N C C ...O

CH2

SH

HH... N C C ...

O

CH2

S

HH

S

NO2

HOOC

... N C C ...O

CH2

SH

HH

primary run secondary run

cysteine cysteine

TNB-cysteine

Ellman’s reagent TCEP reduction

Cysteine COFRADIC(Gevaert et al., 2004)

COFRADIC in practice (II)

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

COFRADIC in practice (III)~60% Detectable!

log1

0(M

ass

N-te

rmin

al P

eptid

e)

log10(Mass C-terminal Peptide)

~60% Detectable!

BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny Helsenskenny.helsens@UGent.be

Thank you!

Questions?