Protein Mass Spectrometry Applications
Katheryn Resing & Natalie Ahn
• Proteomics profiling
• Deuterium exchange MS
Lewis, T.S., et al. (2001) Identification of Novel MAP kinase pathway signaling targets
by functional proteomics and mass spectrometry. Molecular Cell. 6:1-20.
Identifying Markers in Cancer
Progression – Melanoma
Analysis of melanocyte and melanoma lines by 2D-PAGE/MS
Cell Line
Melanocyte 71
78
AJCC/Pathology/Level/thickness
Normal
Normal
Status
Stage X, RGP-VGP
DOD
1.3%
Early
primary
melanoma
RGP
WM3208V
SBCL2
WM35
WM1789
WM1552C
Stage I, RGP-VGP/ Level II/0.69
Stage I, RGP/ Level III/0.82
Stage 3, RGP-VGP/ Level IV/5.9
NED
NED
DOD
1.0%
0.1%
1.0%
Primary
melanoma
VGP
WM75
WM278
WM115
WM793B
Stage 3, RGP-VGP/ Level IV/6.3
Stage 2, VGP/ Level IV/3.7
Stage N/A, VGP/ Level III/2.2
Stage 1, VGP/ Level II/0.55
DOD
DOD
DOD
NED
10.8%
4.2%
6.0%
3.7%
Metastatic
Lu451
WM1232
WM852
WM1617
WM239A
1205Lu
HS294T
A375
Soft Agar
2%
17%
23%
90%
25%
Cell lines from Meenhard Herlyn (Wistar Inst.), ATCC
Hepatoma-derived growth factor is
elevated in primary melanoma
HDGF
Melanocyte
0.2
0.15
0.1
0.05
WM115 (VGP)
0
C D E F
Mcyte
RGP
G H I
J K L M N
VGP
MET
1205Lu
WM239A
Lu451
WM1232
WM852
HS294T
A375
WM1617
WM164
WM793
Metastatic
WM115
WM278
WM39
WM75
VGP
WM1552C
WM1789
WM35
SBCL2
RGP
WM3208
78T
78B
71T
71B
Melanocyte
A B
HDGF
Bernard, K., Litman, E., Fitzpatrick, J.L., Shellman, Y.G., Argast, G., Polvinen, K.,
Everett, A.D., Fukasawa, K, Norris, D.A., Ahn, N.G., Resing, K.A. (2003) Functional
proteomic analysis of melanoma progression. Cancer Research. 63: 6716-6725.
Anti-HDGF immunoreactivity in melanoma biopsies
Advanced malignant
melanoma (Clark’s IV)
anti-HDGF (Vector Red)
anti-S100 (DAB), anti-HDGF (VR)
Early stage Lentigo
melanoma (Clark’s II)
Benign nevus
(Non-transformed)
PMA induced
megakaryocyte
differentiation
Inhibitory interactions between Raf1 and Rac1 pathways
Red - increased
Blue - decreased
Proteins responsive to active Rho GTPases
Rac1-V12
* Stathmin
Fatty acid binding protein 5
tRNA-Trp synthetase
* Cofilin, Destrin
Calponin
Annexin 5
ATP synthase
NAD isocitrate deH2
Adenylsuccinate lyase
BC015471
BC001493
Unk 3, Unk 41
Cdc42-V12
BTF homologue
Calcium regulatedheat stable protein
Unk 38
Bip
Cytoglobin
Tropomyosin
Tubulin cofactor
Phosphatidylinositol
-transfer protein
Protein tyrosine phosphatase-1B
AL137515
BC001763
RhoA-V14
Red: up-regulated proteins
Blue: down-regulated proteins
2D-Western blots reveal regulated covalent modifications
Trp - tRNA synthetase
PTP-1B
Phosphatidylinositol
transfer protein b
FABP5
CMV
PTP-1B
Rac
Cdc42
RhoA
A function for regulation of PTP1B by RhoA
No reports showing regulation of PTP-1B by RhoA
However, these two molecules share the same target: p130Cas
PTP1B binds and
dephosphorylates p130Cas
RhoA promotes p130Cas Model:
Rho inhibits
phosphorylation
p130Cas dephos’n
by PTP1B
Anti-pY blot
RhoA
PTP-1B
p130 blot
PTP1B
Liu et.al (1996) JBC
P
p130Cas
Tsuji et.al (2002) JCB
RhoA inactivates PTP-1B. PTP1B modification
correlates with p130Cas phosphorylation
IP’d PTP1B
cas
PTP1B sp. act.
p130
PTP1B-2D Westerns:
phosphorylation
PTP1B/p130 diverge from Rho-induced stress fiber formation
F-actin
PTP1B
2DWestern
RhoA
PTP1B
P?
Rho K
P
P
P
p130Cas
FAK
Migration
MLC
Paxillin
P
Focal adhesion
complex
Stress Fiber
Proteomics studies with 2D gels provide
information about potential markers and
pathway behavior.
On the other hand, . . .
Results: MSPlus and Isoform Resolver
Number
peptides
Unique
peptides
Sample 1: 16 SCX  LC/MS/MS (2,117 files)
MSPlus
856 (40%)
434
Sequest only
680 (32%)
351
Mascot only
702 (33%)
377
Proteins
243
209
219
Sample 2: 11 SCX  LC/MS/MS x 10 gpf (47,598 files)
MSPlus
8,190 (17%)
4,541
1,757
Sequest only
5,804 (12%)
3,385
1,433
Mascot only
5,173 (11%)
3,057
1,320
Sample 3: 7 gel filtration fx 14 SCX  LC/MS/MS x 6 gpf (602,520 files)
MSPlus
85,267 (18%)
20,675
5,130
Sequest only
64,194 (13%)
15,217
4,120
Mascot only
63,431 (13%)
16,006
3,971
Resing et al. Analytical Chemistry (in press)
Overall Analytical Plan
cells
K562
+/-PMA
+/-U0126
extract
Soluble
Soluble
In
or
high
Membrane
Salt
bound
Wash in
0.42M NaCl,
PBS &
50mM NaF,
snapfreeze 25 mM b-glyceroIn N2
phosphate,
DTT,
EGTA, EDTA
pH7.5
Sonicate
ultracentrifuge
Fractionate
fractionate
Proteins
proteins
gel filtration
or
Ion
exchange
proteolysis
SCX
LC/MS
Digest
with
trypsin
Reduce &
alkylate
WithPMA
iodo- induced
acetamide
megakaryocyte
differentiation
108 K562 cells produce
15-20 mg protein (Bradford)
data
analysis
13 gel filtration fractions (1,3,5,7,9,10,12,13)
14 SCX fractions:6 Gas Phase Fractions
300-1718
300-678
1390-1718
670-918
910-1038
1150-1398
1030-1158
1390-1718
1092 LC/MS runs
623,568 total
MS sequencing files
46,616 peptides
14,775 unique peptides
1150-1398
1030-1158
910-1032
670-918
300-678
Continue panning on these—how
many more can we identify?
Distribution of XCORR scores for
correctly vs incorrectly identified peptides
0.25
number of peptides
Total
0.2
Incorrect
Correct
0.15
0.1
XCorr
1.05
0.05
XCorr
3.4
0
0
2
4
6
XCORR scores
8
10
How many MS/MS files are assignable?
Peptides from standard proteins:
46% validated assignments were above threshold
54% validated assignments were below threshold
After Sequest, 523 of 2,117 DTAs (23%) were
above XCorr threshold [XCorr > 3.0 (+1), 3.23(+2),
3.34(+3)]
In Sample 1, 1,137 of 2,117 MS/MS files (54%) are
expected to be assignable
Repeated analysis improves sampling of
peptides with scores above threshold
Peptide assignments increase by >2x after 5 repeats
Unique peptide sequences identified in three repeats of the same sample
indicate there are two classes of MS/MS: those that are almost always
identified, and those that are identified in a stochastic manner (coin toss model).
HTT
33
24
HTH
27
HHT
87
19
HHH
TTH
THH
30
TTT
~25
26
THT
Repeated analysis improves sampling of
peptides with scores above threshold
Peptide assignments increase by >2x after 5 repeats
Variable scoring between Sequest and Mascot
Scores indicated when the same sequence was
assigned by both programs
+1 ions
5
4
XCorr
+2 ions
8
+3 ions
8
6
6
4
4
2
2
3
2
1
0
0
0
20
40
60
80
0
0
50
100
150
0
50
100
Mowse
~7.5% DTA files that failed Sequest (XCorr) were
validated by high Mascot scores (Mowse)
150
Peptide elution from SCX is mostly dependent on # of basic residues
Misassignments caused by “distraction”
Database
First Sequest
XCorr RSP
First Mascot
Mowse
Restricted (644)
IPI (~48,000)
IPI, no protease
AIGTEPDSDVLSEIMHSFAK
AIGTEPDSDVLSEIMHSFAK
AIGTEPDSDVLSEIMHSFAK
1.98 1
1.98 1
1.98 422
AIGTEPDSDVLSEIMHSFAK
39.5
AIGTEPDSDVLSEIMHSFAK
39.5
TTIGAAGLPGRDGLPGPPGPPGPP 40.0
Restricted (644)
IPI (~48,000)
IPI, no protease
EGLELPEDEEEK
EGLELPEDEEEK
EGIELLLNEGSEL
2.00
2.00
2.23
EGLELPEDEEEK
EGLELPEDEEEK
EGLELPEDEEEK
50.4
50.4
50.4
Restricted (644)
IPI (~48,000)
IPI, no protease
GDAMIMEETGK
YPILFLTQGK
AVYVEMLQIL
0.74 1
1.11 1
1.34 12
GDAMIMEETGK
GDAMIMEETGK
GIMAIEMVEGE
41.4
41.4
43.9
Restricted (644)
IPI (~48,000)
IPI, no protease
DLSLEEIQK
DLSLEEIQK
NSQVKELKQ
1.15 1
1.64 11
1.53 243
DLSLEEIQK
IDCEAPLKK
ALASQSAGITGV
25.2
27.7
31.5
1
1
2
Correct sequence assignments are replaced by
incorrect assignments as database size increases
Accuracy of the MSPlus approach:
Expt 1:
Manual analysis of 540 peptide assignments from Sample 1
(half accepted and half rejected, by panning method)
3.4% false positive assignments
8.0% false negative assignments
Expt 2:
Searching the randomized database:
Sample 1: Soluble extract  16 SCX fr  LC/MS/MS
4.0% false positives
Sample 2: Soluble extract  11 SCX fr  10 gpf on LC/MS/MS
3.2% false positives
Expt 3:
Reproducibility in protein assignments between different analyses
A protein database entry in FASTA format
>IPI:IPI00027488.1 …. Alpha enolase (2-phospho-D-glycerate hydrolyase)
BLink, Links
MSILKIHAREIFDSRGNPTVEVDLFTSKGLFRAAVPSGASTGIYEALELR
DNDKTRYMGKGVSKAVEHINKTIAPALVSKKLNVTEQEKIDKLMIEMDG
TENKSKFGANAILGVSLAVCKAGAVEKGVPLYRHIADLAGNSEVILPVP
AFNVINGGSHAGNKLAMQEFMILPVGAANFREAMRIGAEVYHNLKNVI
KEKYGKDATNVGDEGGFAPNILENKEGLELLKTAIGKAGYTDKVVIGM
DVAASEFFRSGKYDLDFKSPDDPSRYISPDQLADLYKSFIKDYPVVSIE
DPFDQDDWGAWQKFTASAGIQVVGDDLTVTNPKRIAKAVNEKSCNCL
LLKVNQIGSVTESLQACKLAQANGWGVMVSHRSGETEDTFIADLVVGL
CTGQIKTGAPCRSERLAKYNQLLRIEEELGSKAKFAGRNFRNPLAK
140/(953+140) = 12.8% of hits with same peptide sequence
have different protein database ID reference numbers.
Some examples showing how Turbosequest
and Mascot handle redundancy differently.
Sequest
Mascot
ALAAAGYDVEK
ALAAAGYDVEK
ALAAAGYDVEK
IPI00020958
IPI00020958
IPI00020958
IPI00020958
IPI00022360
IPI00028535
TIGGGDDSFNTFFSETGAGK
TIGGGDDSFNTFFSETGAGK
TIGGGDDSFNTFFSETGAGK
IPI00022360
IPI00022360
IPI00022360
IPI00028535
IPI00028535
IPI00028535
Why does the software pick the L form over the I form? Both engines do this.
TLTLVDTGIGMTK
TLTLVDTGIGMTK
IPI00024739
IPI00024739
IPI00024739
IPI00027749
TLTIVDTGIGMTK
TLTIVDTGIGMTK
IPI00047217
IPI00047217
IPI00047217
IPI00013921
Isoform Resolver
“Peptide-centric” database:
catalogues each unique peptide in the IPI database
reports proteins redundantly associated with each peptide
(1)
(2)
(3)
(4)
(5)
IPI00023860
IPI00017763
IPI00180912
IPI00184769
IPI00185366
Protein
Isoform
(1)
(1)
(1)
(1)
(1)
(1), (5)
(1), (5)
(1), (2), (3), (4)
(2), (3)
(2), (3)
(2), (3)
(2), (3)
(2), (3), (4)
(4)
(45,374 Da)
(42,823 Da)
(44,630 Da)
(39,223 Da)
(29,538 Da)
NUCLEOSOME ASSEMBLY PROTEIN 1- LIKE 1
NUCLEOSOME ASSEMBLY PROTEIN 1- LIKE 4
SIMILAR TO NAP1
SIMILAR TO NAP1
SIMILAR TO NAP1
Peptide sequence
EQSELDQDLDDVEEVEEEETGEETK
KYAVLYQPLFDK
LDGLVETPTGYIESLPR
YAVLYQPLFDK
YAVLYQPLFDKR
GIPEFWLTVFK
NVDLLSDMVQEHDEPILK
FYEEVHDLER
KYAALYQPLFDK
LDNVPHTPSSYIETLPK
QVPNESFFNFFNPLK
YAALYQPLFDK
GIPEFWFTIFR
AAATAEEPNPK
Highest
Number
Xcorr Mowse observed
5.0
4.0
5.7
4.1
2.7
2.9
6.4
3.8
4.7
4.9
3.4
2.6
3.0
2.9
88
74
113
58
51
61
109
49
73
66
77
64
60
56
4
8
5
3
3
4
3
18
6
4
5
3
3
1
Removed 24% of protein IDs from Sequest assigned list
Accuracy of the MSPlus approach:
Expt 1:
Manual analysis of 540 peptide assignments from Sample 1
(half accepted and half rejected, by panning method)
3.4% false positive assignments
8.0% false negative assignments
Expt 2:
Searching the randomized database:
Sample 1: Soluble extract  16 SCX fr  LC/MS/MS
4.0% false positives
Sample 2: Soluble extract  11 SCX fr  10 gpf on LC/MS/MS
3.2% false positives
Expt 3:
Reproducibility in protein assignments between different analyses
Sample 1
73
170
16
241
Repeat of sample 1
Reproducibility
studies support
accuracy of panning
approach
89
Analyze 11 SCX fractions
of sample 1 with gas
phase fractionation
1514
Prefractionate proteins by gel filtration,
then 16 SCX and gas phase fractionation
9
234
4787
Functional categories of represented proteins
Hypothetical
proteins (17%)
Misc.
(15%)
Metabolism
(11%)
DNA replication, repair
(5%)
Transcription
(6%)
Translation
and RNA
metabolism
(14%)
Cytoskeletal
Intracellular
(6%)
signaling (9%)
Transport,
trafficking
Protein folding, Cell-cell communication (3%)
(6%)
Degradation (7%)
Protein kinases: Cdk 2,5,6,7,9, MAPK, MAPKK, MAPKKK, PKA, PKC, Abl,
A-Raf, Plk, CSK (~200,000 – 1,000,000 copies/cell)
Cyclins: A2, B1, E1, K, T1 (~20,000 copies/cell)
Txn factors: Sp1, ERF, ATF1,5, CREB1, CCAAT, EKLF (~10,000 copies/cell)
~20,000 proteins in a
human proteome
~5000 proteins
by shotgun
cytoskeleton
~1800 proteins
on 2D gels
109 copies/cell = mM
metabolism
108 copies/cell = 0.1 mM
107 copies/cell = 10 µM
ribosomes
kinases
cyclins
106 copies/cell = µM
105 copies/cell = 0.1 µM
transcription factors
104 copies/cell = 10 nM
103 copies/cell = nM
102 or less copies/cell = 0.1 nM
Functional changes based on coverage
MEK/ERK dependent megakaryocyte diff’n in K562 cells
Peptide representation
Control
PMA PMA/U0126
Affymetrix (n=2)
Control
PMA
PMA/U0126
direct quantification of parent ion intensities
1. From parent ion masses of identified MSMS spectra, scan
MS raw files for intensities
2. Fit peak to Gausian, identify resolved peaks
3. Map all MSMS onto peaks, eliminate peaks that are complex
4. If desired, group charge forms, pull together information from
different SCX fractions or from gel filtrations fractions
MS/MS taken here
Correlation in intensities between peptides common to two runs with 5%
vs 20% loading of the same sample. The left shows correlations on a
linear scale; the right shows the same data on a log2 scale.
Fraction 14
Fraction 8
Fraction 10
Fraction 14
Fraction 16
Fraction 14
Fraction 14
Fraction 8
Fraction 10
Fraction 12
Tubulin a1 – Comparison of gel filtration fractions
Fraction 16
Fraction 16
Comparison of recoveries of peptides from different SCX fractions
when chromatographed on different resins (J4 vs XP)
008
009
J4’s
XP’s
J4’s
61%
XP’s
112
141 111
229
012
XP’s
XP’s
36 98
XP’s
126
72%
XP’s
40%
67%
018
J4’s
34 51
201
27%
66%
017
J4’s
68%
J4’s
255
18%
016
26%
37%
014
XP’s
29 133
267
148
40 69
44%
J4’s
65%
1748
XP’s
013
J4’s
29%
J4’s
82 64
57%
44%
57 141
010
XP’s
J4’s
13 23
98
70%
36%
52
69%
Hydrogen exchange mass spectrometry:
Regulated conformational mobilities
in protein kinases
Conformational changes in ERK2 upon phosphorylation
(Canagarajah et al, 1997)
Activation
Lip
Unphosphorylated ERK2
Phosphorylated ERK2
Overlay
Rate of phosphoryl transfer increases 60,000x in ERK2
(Prowse et al. 2001, JBC)
Red: increased HX
Green: decreased HX
Yellow: both increase/decrease
Inconsistencies between hydrogen exchange and structure
suggest altered backbone flexibility in the hinge region
Total hydrogen exchange shows conserved patterns
between p38 MAPK and ERK2
p38
ERK2
% deuterium incorporation in 5 h:
> 75%
51-75%
25-50%
< 25%
Effects of phosphorylation on hydrogen exchange
are not conserved between related protein kinases
Could small
molecule
inhibitors be
developed
that target
regions of
regulated
flexibility?
phospho-p38 vs p38
ppERK vs ERK
Activation of MKK1 leads to enhanced hydrogen
exchange in the N terminal lobe
MKK1 (WT vs DN4/S218E/S222D)
PD184352 binds the region in MKK1
with greatest increase in flexibility.
Mutations block binding, and
enhance catalytic activity
Delaney, Printen, Chen, Fauman
and Dudley (2002) MCB
Protein interactions regulate MAPK signaling
Activators:
Scaffolds/Insulators:
MAP kinase kinases
MEKK1
TAB1
Ste5, JIPs, MP1,
AKAPs, KSR,
b-arrestin
Phosphatases:
MKPs
PTPs
ERKs, JNKs
p38 MAPKs,
& orthologs
Localization:
nucleoporins
PEA15,
kinetochores
centrosomes
microtubules
Allosteric:
MKPs
topoisomerase IIa
Ste12
Substrates: Protein kinases, transcription factors
RNA binding proteins, proteases, cytoskeletal regulators, ion channels,
lipases, metabolic enzymes (specificity P-X-S/T-P)
DEJL motif
human Elk1
DEF motif
PP
P
PP
PP
PP
ETS DBD
1
428
PGKGRKPRDLELPL
310
Lee, T., et al. (2004) Molecular Cell
323
TLSPIAPRSPAKLSFQFPSS
383
389
400
MKK3b peptide binding to p38 MAPK –changes in HX
b7- b8
aD-aE
Elk1-DEJL peptide binding to ppERK2 –changes in HX
b7- b8
aD-aE
Elk1-DEF peptide binding to ppERK2 – changes in HX
Overlap between solvent protected and hydrophobic regions
D147
pT183
pY185
Structure of Canagarajah et al, 1997
Overlap between solvent protected and hydrophobic regions
D147
pT183
pY185
M197
Yellow = hydrophobic residues
Modeling Elk1-DEF peptide (PRpSPAKLSFQFP) into ppERK2
(-2)
D147
Y191
P
W190
R
(+1)
pY185
A187
S P V186
F
Y231
pY185
L232
Y261
F
P
M197
L198
L262
ERK2 mutants disrupt GST-Elk binding, phosphorylation
GST-Elk1 pulldowns
pY185
Y231
M197
L232
L198
L235
Y261
L262
Conformational changes in ERK2 upon phosphorylation
Activation
Lip
Unphosphorylated ERK2
(Canagarajah et al, 1997)
Phosphorylated ERK2
Obstruction of the DEF binding site in ERK2
L182
pY185
Y231
Y185
F181
M197
Y231
L232
L232
L182
F181
L198
Y261
ppERK2
Y261
ERK2
Lab members:
Functional proteomics
by 2D-PAGE:
Karine Bernard
Yukihito Kabuyama
Tim Lewis
Betsy Litman
Beth Roberts
Rebecca Schweppe
Kinase dynamics:
Michelle Emrick
Andy Hoofnagle
Thomas Lee
Shotgun Proteomics:
Lauren Aveline
Claire Haydon
Karen Jonscher
Kevin Pierce
Will Old
Steve Russell
Tom Cheung
Alex Mendoza
Karen Meyer-Arendt
Joel Sevinsky
Collaborators:
Natalie Ahn
Lynn Chen
UCHSC, Denver
David Norris
James Fitzpatrick
UTSW, Dallas
Betsy Goldsmith