phosphoproteomics and motif mining martin miller ph.d. student cbs dtu [email protected]

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Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU [email protected]

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Page 1: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

Phosphoproteomics and motif mining

Martin MillerPh.d. studentCBS [email protected]

Page 2: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

Outline

MS-based phosphoproteomicssubstrate-motifs in intracellular signallingresearch project: motif decomposition of the phosphotyrosine proteome

Page 3: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

Mass spectrometry-based proteomics

Aebersold & Mann, Nature 422: 198-207, 2004.Aebersold & Mann, Nature 422: 198-207, 2004.

Select one peptide species

Collide

Separate fragments

Y3

Y4

Y5

Y6

Y7

Page 4: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

Mass spectrometry-based proteomics

Page 5: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

PTM detection using MS

Page 6: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

Quantitative phosphoproteomicsusing SILAC

Page 7: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

Growth media lacking the SILAC labeling amino acid (e.g. Arg)

Stable Isotope Labeled Amino Acids:

Δm=6 Da Δm=10 Da

Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC)

Page 8: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC)

Page 9: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

State A State B

Light Isotope Heavy Isotope

Mix 1:1

Optional Protein Fractionation

Digest with Trypsin

Protein Identification and Quantitation by LC-MS

Ong Ong et alet al., Mol. Cell. Proteomics 1, 2002.., Mol. Cell. Proteomics 1, 2002.

Typical SILAC experiment workflow

Upregulated protein - Peptide ratio >1

Background protein - Peptide ratio 1:1

Arg-12C6

Arg-13C6

m/z

Arg-12C6

Arg-13C6

m/z

m/z

Arg-12C6

Arg-13C6

m/z

Arg-12C6

Arg-13C6

Page 10: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

SILAC labeling for quantitation

ConvenientNo extra step introduced to experiment, just slightly different growth medium

All identified proteins are - in principle – quantifyableQuantitation of proteins affected by different stimuli, disruption of genes, etc.Quantitation of post-translational modifications (phosphorylation, etc.)

Page 11: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

Fishing for modification-dependent interactors using a bait sequence

Asp-Ser-Trp-Ala-Arg-Leu-His-Gly-Tyr-Met-Ile-Met-Glu-Pro-Lyssolidsupport

Asp-Ser-Trp-Ala-Arg-Leu-His-Gly-Tyr-Met-Ile-Met-Glu-Pro-Lyssolidsupport

P

Page 12: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

phosphorylation specific pull-down experiments

Schulze W and Mann M. (2004)JBC 2004

Page 13: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

Advantages of the SILAC pull-down method

No overexpression – no taggingStraightforward separation between specific interactors and background bindersDetection of low abundance and moderate affinity interactorsEspecially suited for PTM interaction studiesDetermination of the exact interaction site within the proteinImportant protein-protein interactions in cell signaling are frequently mediated by short, unstructured sequences – linear motifs

Page 14: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

sequence motifs in intracellular signalling

linear peptides sequence motifs guide signalling• kinases• phosphorylation-

dependent interaction domains (SH2, PTB, 14-3-3 etc.)

directionality and specificity

Page 15: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

Kinome tree and kinase substrates

Linding et al, Cell, accepted

Page 16: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

SH2 domain tree

Page 17: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

A specific branch of the SH2 domain tree

Page 18: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

The Phospho.ELM database currently contains 13614 phosphorylation sites in 4421 eukaryotic proteins. However, only ~23% of have know function.

Thus there is a unique opportunity to mine for novel phosphorylation motifs

“The Widening Gap”

Page 19: Phosphoproteomics and motif mining Martin Miller Ph.d. student CBS DTU miller@cbs.dtu.dk

Research protect

motif decomposition of the phosphotyrosine proteome

A new method for clustering uncharacterized phosphopeptides and mining for novel phosphorylation motifs

following slides are erased because the data is confidential since results are not published yet