Phosphoproteomics

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Studies of the yeast pheromone pathway using
quantitative proteomics Episode IV
Phosphoproteomes
Biological Sciences Division Pacific Northwest
National Laboratory
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Initial goals of PNNL efforts

• Identify phosphorylated species and quantify
  their changes in response to alpha factor
  treatment
• Provide kinetic data for pathway modeling (e.g.
  time course studies)

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(No Transcript)
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Identification of phosphorylated proteins by
nanoLC-MS/MS of tryptic peptides
(K.FQSEEQQQTEDELQDK.I)
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Challenges

• Low abundance of pathway proteins
• - Ste5 500 molecules/cell and Fus3 20,000,
  molecules/cell vs. millions/cell for abundant
  proteins
• Unknown number of pathway player modifications
• Often low phosphorylation stoichiometry
• Desire to make many quantitative measurements

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Focused analysis of affinity selected alpha
pathway players and their complexes

• Tandem affinity purification (TAP) to expand
  results obtained from global view (identify other
  binding partners, verify and identify additional
  low level sites of modification, etc.)

LC-MS
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To be continued
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A first look via a seemingly strange path
Protein
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IMAC(Immobilized Metal ion Affinity
Chromatography)
Esterification
of carboxylic groups
O

To reduce interaction between COO
-
and metal ion in IMAC enrichment
process
aspartic acid (D)
glutamic
acid (E)
C
-
terminus
O
H
H
O
OH
H
H
O
N
O
C
N
C
O
C
N
C
C
O
C
H
OR
Fe3
C
H
2
P
2
C
H
O
C
O
2
O
C
O
O
O
O
OH2

O
O
H


C
X
O
H
O
H
C
C
3
2
O
H
C
X
O
3
X H or D
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Direct vs. IMAC enriched nanoLC-MS analyses
K.FQSEEQQQTEDELQDK.I
50.50
Before IMAC enrichment
28.83
22.35
33.70
62.87
42.56
57.13
20.17
41.08
Relative Abundance
29.16
K.FQSEEQQQTEDELQDK.I
After IMAC enrichment
61.97
50.60
39.27
74.59
36.65
27.90
0
10
20
30
40
50
60
70
80
90
100
Time (min)
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Yeast Strain SUB592

• Obtained from Dan Finleys lab (Harvard)
• (Peng et al., Nature Biotech vol. 21, 2003)
• Endogenous ubiquitin genes knocked out
• NH2-His-tagged ubiquitin gene supplied by
  plasmid
• Cells grow and respond to alpha factor normally
• (in plate assay)

Initial studies Treat Yeast strain 592 with
alpha factor, recover His-tagged proteins,
greatly enrich phosphopeptides using IMAC, and
identify peptides/proteins using nanoLC-MS/MS
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Proteins identified after dual His affinity
purification and IMAC enrichment
Untreated (757 total)
Alpha treated (703 total)
384
373
330
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Alpha pathway phosphorylated proteins identified
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Use of Accurate Mass and Time (AMT) tags
Shotgun peptide identification and generation of
AMT tag look-up table
High throughput analyses
Proteins
Proteins
Digestion
Digestion
SCX LC fractionation
Nano LC-FTICR MS
ID using AMT tags
nanoLC-MS/MS
Peptides (or features) identified by their
accurate masses and LC separation times,
abundances determined
Peptide identification
Set of AMT tags providing a look-up table of
peptides identified by their accurate masses and
LC separation times
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The application of peptide AMT tags
Single LC-FTICR analysis
Locations of peptides identified from multiple
shotgun LC-MS/MS analyses
Peptides identified using AMT tags
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Quantitation using stable-isotope labeling with
nanoLC-FTICR AMT tag approach

• Ability to quantify modified peptides
  independent of unmodified species
• Ability to accurately detect and quantify at low
  stoichiometric ratios

IMAC selected phosphopeptides
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A path forward

• Technology improvements for the masses
• - New metal free high resolution nanoLC system
  optimized for phosphoproteomics
• - Improved characterization of modifications
  (using ECD/ETD, intact protein top-down
  approaches)
• - Much higher throughput e.g. for time course
  studies and fishing with an adjustable net
• Deliniation of phosphorylation sites, and
  abundances, for all known alpha pathway players
  (in progress)
• Characterization of other modifications and
  other possible (fringe?) players
• Studies of selected perturbations, time
  courses, etc

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Acknowledgements
Robert Maxwell Orna Resnekov Kirsten
Benjamin David Pincus Roger Brent

PNNL Proteomics Team
David Camp Mary Lipton Joshua Adkins
Sample processing and automation Eric Livesay
Kim Hixson Heather Mottaz Carrie
Goddard Marina Gritsenko Therese Clauss Dave
Prior Data processing, software development and
statistics Gordon Anderson Matt Monroe Mary
Powers Dave Clark Angela Norbeck Nikola
Tolic Gary Kiebel Eric Strittmater Ken
Auberry Sam Purvine Kerry Steele Steve
Callister Deep Jaitly Niksa Blonder
Separations Yufeng Shen Kostas Petritis Rui
Zhao David Simpson Alex Shvartsburg Quanzhou
Luo Mass spectrometry Ljiljana Pasa-Tolic Keqi
Tang Harold Udseth Anil Shukla Tom Metz Tao
Liu Ron Moore David Anderson Aleksey
Tolmachev Rui Zhang Fumin Li Jon Jacobs Charley
Langley Feng Yang Jason Page Weijun Qian Hyak
Kang