Download ASMS 2013 phosphopeptide poster

Maximizing Phosphoproteome Profiling Using Mascot, PEAKS Studio, Proteome Discoverer and OMSSA Software Packages
Jayme Wiederin , Melinda Wojtkiewicz , Pawel Olszowy , Pawel Ciborowski
1
1
1,2
1
University of Nebraska Medical Center, Omaha, NE, USA; Nicolaus Copernicus University, Torun, Poland
1
2
Introduction
Abstract
Introduction
Post-translational modifications (PTM) are chemical alterations to proteins that are essential to diversity of protein functions.
One of the most commonly studied PTM is phosphorylation, a reversible PTM of proteins that plays a major role in the
regulations of many protein functions such as cell cycle, enzyme activation/deactivation and signal transduction etc.
However, quantitative analysis of phosphorylation profiling by mass spectrometry is a highly challenging task and requires
enrichment for either phosphoproteins or phosphopeptides. Enrichment at the protein level will provide better protein
identification with a risk of lower phosphorlyation identification. We chose to enrich at the peptide level to increase the
chances of identifying phosphorylated peptides.
Methods:
Trypsin digested whole cell lysates of monocytes derived macrophages- were processed using TiO2 Phosphopeptide
Enrichment and Clean-up Kit (Pierce, Inc.), then injected onto nano-LC-LTQ Orbitrap with ETD in configuration of HCD and
ETD as two microscans complementing information from MS/MS spectra. The acquisition method was created in
data-dependent mode with one precursor scan in the Orbitrap, followed by fragmentation of the 4 most abundant peaks in
both ETD detected in the LTQ, and in HCD detected in the Orbitrap. Tolerances were 10ppm for the Orbitrap precursors and
fragments and 0.8Da for the ETD fragments. The following dynamic modifications were applied: Phospho/+79.966Da (S, T,
Y), Oxidation/+15.995Da (M), Carboxymethyl/+58.005Da (C). Data were searched using Proteome Discoverer, PEAKS
Studio, OMSSA and Mascot.
Preliminary data:
In this study we are used human monocytes obtained from elutriation. These cells are widely accepted as a biological
system to test various aspects of innate immunity responses to viral and bacterial infections. Cells were lysed using
standard protocol with protease inhibitor and sodium vanadate to inhibit phosphatases. Typical lysis yields 100 µg of protein
from 1x106 cells. This whole cell lysate (WCL) is subjected to overnight tryptic digest and resulting peptides are enriched
using TiO2 spin columns. Preliminary results show that 100 µg of digested WCL passed through TiO2 columns yielded
identification of approximately 200 phosphopeptides ranging from low to very high confidence. The number of identified
phosphopeptides is dependent on the stimulation or activation of cells and the monocytes used in this study are at their
resting state with reduced metabolic activity. Optimizing the yield of phosphoproteome can be multi-fold: 1. Use more initial
cell lysate, 2. Use activated cells, 3. Use additional fragmentation of HCD, and 4. Improvement of data extraction. For the
latter, the software available for peptide identification and localization of the phosphate group (S) will have major impact on
final output. Therefore to optimize phosphoproteome profiling, we used 200 and 600 µg of initial WCL and we present here
in this study the comparison of the output of 4 search algorithms on the identification of phosphopeptides.
Novel aspect:
To maximize identification and localization of phosphate PTM by combining multiple software packages.
Materials and Methods
Samples
• Human monocyte derived macrophage (MDM) cytosolic cell lysate treated with
protease and phosphatase inhibitors.
• Control and methamphetamine treated; 200 and 600 µg (pre-digest and phosphopeptide
enrichment).
• Cleaned using ethanol precipitation.
Trypsin digest and desalting
• Samples were reduced with DTT in the presence of ammonium bicarbonate digestion buffer at
95oC for 5 min.
• Samples were allowed to cool, then alkylated with iodoacetamide at room temperature in the
dark for 20 min.
• Trypsin (0.1 µg/µl) was added to the reaction tube and incubated at 37oC for 3 h, then the
reaction was held at 30oC overnight.
• Following digestion, samples were dried in speed vac and reconstituted in 0.1% TFA.
• Samples were cleaned using HPLC column Phenomenex Jupiter 4u Proteo 90A; 50x4.60mm
4micron.
Phosphopeptide enrichment and clean-up
• Sample was enriched for phosphopeptides using Pierce TiO2 Phosphopeptide Enrichment and
Clean-up kit following standard manufacturer’s protocol. Briefly, the spin column is activated
and equilibrated with 2 buffers, Buffer A containing 0.4% TFA and 80% ACN, and Buffer B
containing 70% Buffer A with 25% lactic acid. After binding of sample and a series of washes,
phosphopeptides were eluted using 2 other buffers, one containing 1.5% NH4OH and the other
with 5% pyrrolidine.
• Phosphopeptides were acidified in 2.5% TFA before processing through Pierce Graphite Spin
Columns to clean-up the sample before mass spec analysis. The graphite columns were
washed with 1 M NH4OH and activated with 100% ACN. Sample was bound to the graphite for
10 min with periodic vortexing. The column was washed with 1% TFA and sample was eluted
using 0.1% formic acid in 50% ACN.
• Phosphopeptides were dried in speed-vac.
LC and MS/MS acquisition
• Phosphopeptides were resuspended in 0.1% formic acid for mass spec analyis.
• The samples were injected onto nano-LC-LTQ Orbitrap with ETD in configuration of HCD and
ETD as two microscans complementing information from MS/MS spectra.
• Acquisition method was greated in data-dependent mode with one precursor scan in Orbitrap,
followed by fragmentation of the 4 most abundant peaks in both ETD (detected in LTQ) and in
HCD (as detected in Orbitrap).
Software
• Phosphopeptide analysis performed using 4 softwares/algorithms: Proteome Discoverer v1.2
(Thermo Scientific), PEAKS Studio 6 (Bioinformatics Solutions, Inc.), Protein Pilot v4.5 (AB
Sciex), and MASCOT.
Table 2. Comparison of the alignment of identified phosphopeptides using four different software.
Protein phosphorylation is one of the most studied post-translational modifications (PTM) as it is fundamental in the regulation of many cellular functions such as signal transduction, enzyme activation/deactivation, protein function, cellular signaling and protein-complex formation. ETD and HCD analysis of phosphopeptides helped in lowering the
challenges in mass spectrometry determination of phosphate groups. Nevertheless, for successful phosphoproteomics, samples need to be enriched and TiO2 is becoming
widely used for this purpose. The next challenging point is determining which software or algorithm is appropriate for comprehensive data analysis. Numerous algorithms
and software packages have been developed, including Mascot, Open Mass Spectrometry Search Algorithm, Scaffold PTM, and Phosphopeptide FDR Estimator (PNNL). In
this study, we compare four software packages/algorithms: Proteome Discoverer v1.2 (Thermo Scientific), Protein Pilot v4.5 (AB Sciex), PEAKS Studio 6 (Bioinformatics Solutions, Inc.) and MASCOT.
Results
Experimental design
In the presented study, we used four datasets acquired on a nano-LC LTQ Orbitrap with ETD in configuration of
HCD and ETD as outlined in the experimental design in Figure 1. Samples used in this study were two different
amounts (200 and 600 µg pre-digest and phosphopeptide enrichment) of cell lysate from monocyte derived
macrophages (MDM), control and treated with methamphetamine. These datasets were analyzed using
Proteome Discoverer v1.2 (Thermo Scientific), Protein Pilot v4.5 (AB Sciex), PEAKS Studio 6 (Bioinformatics
Solutions, Inc.), and MASCOT. Datasets were not searched through OMSSA due to budgetary constraints at
NCBI (see OMSSA announcement online). For all searches, the same raw data sets were used and the search
parameters for each software were as follows:
Proteome Discoverer, PEAKS, MASCOT
1. False discovery rate (FDR): 0.05
2. Precursor (MS) mass tolerance: 10 ppm
3. Fragment (MS/MS) mass tolerance: 0.8 Da
4. Maximum missed cleavage sites: 2
5. Dynamic modifications: Phospho/+79.966 Da (S, T, Y); Oxidation/+15.995 Da (M);
Carboxymethy/+58.005 Da (C)
6. Database: Swissprot
1. Cytosolic fractions
from MDM
2. Add protease and
phosphatase inhibitor
followed by EtOH
precipitation
proteases
phosphatases
3. Trypsin digest
Protein Pilot
1. FDR: 0.05
2. Thorough ID search
3. Database: Swissprot
4. Biological modifications
5. Phosphorylation emphasis
4. Phosphopeptide
enrichment
+
graphite clean-up
Phosphopeptide
Phosphopeptide
5. MS/MS analysis
using LTQ Orbitrap
with ETD/HCD
Table 1 summarizes the database search outputs from each software. Unique phosphopeptides were
determined using amino acid sequence and site of phosphorylation. High confidence was defined at 95%
confidence with FDR of 0.05. Although the number of identified phosphopeptides is lower than expected, this
can partially be explained since the cells used in this study, MDMs, are at a metabolic minimum; they are not
dividing or activated. The macrophage is a cell that is waiting to be activated by external stimuli, therefore,
unless the cell is exposed to activating factors, we expect that many (if not majority) of the signaling pathways
based on phosphorylation or de-phosphorylation are at the lowest activity. The effect of methamphetamine on
MDM function is a subject of concurrent but separate investigation, however, it cannot be excluded that this illicit
drug suppresses MDMs metabolism. It is important to note, that tripling the amount of material for enrichment
only lead to an approximate 1.5 fold increase in phosphopeptides, thereby supporting the notion that the overall
size of the phosphoproteome is small, explaining the less than expected number of identified phosphopeptides.
Regardless of the overall level of phosphopeptides, the comparison of the phosphopeptides identified using
each software remains valid. Generally, the number of phosphopeptides is greater in control cells, compared to
cells treated with Meth. Interestingly, PEAKS provided us with the largest number of high confidence
non-phosphorylated peptides (Table 1).
Besides analyzing total number of phosphopeptides, an equally important question is whether each software
detected the same, partially the same or a unique set of phosphopeptides. The results in Table 2 summarize the
comparison of the alignment of identified phosphopeptides with the different software, using the representative
dataset from 600 µg (pre-digest and phosphopeptide enrichment) of control sample. It is evident based on Table
2 that these software provide data that is compatible, but not exclusive of each other. We performed the same
analysis with cells exposed to Meth and we also see the same trends of compatibility, not exclusiveness,
however with a less number of phosphopeptides overall compared to control cells.
6. Data analysis using
Proteome Discoverer,
Protein Pilot, MASCOT,
and PEAKS Studio
Figure 1. Experimental design workflow.
We are much more interested in what the output for database searches means to provide insights into function
of biological process being under investigation rather than mechanisms of algorithms underlying software
packages.
Control 200 µg
Control 600 µg
Meth 200 µg
Meth 600 µg
AEEDEILNRsPR
DTGEATLtVDGPPR
EAAAQEAGADTPGKGEPPAPKsPPK
EAAAQEAGADtPGKGEPPAPKSPPK
EGEEPTVYsDEEEPKDESAR
EGEEPTVySDEEEPKDESAR
EGEEPtVYSDEEEPKDESAR
EGQPSPADEKGNDsDGEGESDDPEK
FAsDDEHDEHDENGATGPVK
GAGDGsDEEVDGKADGAEAKPAE
KGAGDGsDEEVDGKADGAEAKPAE
KPGSLFAALmAtATSSLR
KPGSLFAALmATAtSSLR
KPGSLFAALmATATsSLR
KPGSLFAALmATATSsLR
KPSGINGEAsKSQEMVHLVNK
KPSGINGEASKsQEMVHLVNK
KVEEEQEADEEDVsEEEAESK
KVEEEQEADEEDVSEEEAEsK
Total phosphopeptide spectra
Unique non-phosphorylated peptides
Proteome Protein
Proteome Protein
Proteome Protein
Discoverer Pilot PEAKS Mascot Discoverer Pilot PEAKS Mascot Discoverer Pilot PEAKS Mascot
26
12
26
42
41
16
32
80
2
1
81
4
40
21
36
53
59
26
40
80
1
1
8
2
19
12
25
36
25
15
31
64
1
2
30
5
30
18
41
54
41
22
46
96
1
0
10
1
Protein Pilot
PEAKS
AEEDEILNRsPR
AEEDEILNRsPR
DSFSNLSNSKsTSTPYtAPGGPPPNVGG
PISANSEQIGRLR
EAAAQEAGADTPGKGEPPAPKsPPK
DYGVFIQFPsGLSGLAPK
EAAAQEAGADTPGKGEPPAPKsPPK
EGQPsPADEKGNDSDGEGESDDPEKK
EGQPSPADEKGNDsDGEGESDDPEKK EGQPSPADEKGNDsDGEGESDDPEK
EKtPsPKEEDEEPEsPPEKK
EKtPsPKEEDEEPESPPEKK
EKtPSPKEEDEEPEsPPEKK
EKTPsPKEEDEEPEsPPEKK
EKTPSPKEEDEEPEsPPEKK
ESEDKPEIEDVGsDEEEEKK
ESEDKPEIEDVGsDEEEEKKDGDK
FAsDDEHDEHDENGATGPVK
FAsDDEHDEHDENGATGPVK
FSGTLCISLVPAsTPPLDTPSHSPSPPTA
QTtTAGRsKR
GAGDGsDEEVDGKADGAEAKPAE
GAGDGsDEEVDGKADGAEAKPAE
KAEQGsEEEGEGEEEEEEGGESK
KGAGDGsDEEVDGKADGAEAKPAE
HSTPSNSSNPSGPPsPNSPHR
KAEQGsEEEGEGEEEEEEGGESK
KGAGDGsDEEVDGKADGAEAKPAE
KPSGINGEASKsQEMVHLVNK
KVEEEQEADEEDVsEEEAESK
KVEEEQEADEEDVsEEEAESK
LKsEDGVEGDLGETQSR
LGSGStSIsHLPtGTTSPTK
LKsEDGVEGDLGETQSR
LLKPGEEPSEyTDEEDTKDHNKQD
LLKPGEEPSEYtDEEDTKDHNKQD
LLKPGEEPSEYTDEEDtKDHNKQD
LPIWGIGcNPCVGDDVTTLLtR
LLKPGEEPSEyTDEEDTKDHNKQD
LLKPGEEPSEYtDEEDTKDHNKQD
LPSGSGAAsPTGSAVDIR
LPSGSGAAsPTGSAVDIR
NRNsNVIPYDYNR
RPsQEQSASASSGQPQAPLNR
NRNsNVIPYDYNR
NRNsNVIPYDYNR
QKsDAEEDGGTVSQEEEDR
QKsDAEEDGGTVSQEEEDRKPK
QKsDAEEDGGTVsQEEEDRKPK
QKsDAEEDGGTVSQEEEDRKPK
RLISWVLsLPADITQVLtsGCTHYK
RPsQEQSASASSGQPQAPLNR
SGPKPFSAPKPQtSPSPK
s GPKPFSAPKPQTSPSPK
SGPKPFsAPKPQTSPSPK
SGPKPFSAPKPQTsPSPK
SGPKPFSAPKPQTSPsPK
SGPKPFSAPKPQTsPSPK
SHsPSSPDPDTPSPVGDSR
SKAPGsPLSSEGAAGEGVR
SKAPGSPLsSEGAAGEGVR
SKAPGSPLSsEGAAGEGVR
sPEGEQEDRPGLHAYEK
sPEGEQEDRPGLHAYEK
SVTKVNAALLsQAtNLK
TPSPKEEDEEPEsPPEKK
Table 1. Summary of high confidence (FDR 0.05) unique phosphopeptides, total phosphopeptide spectra and unique non-phosphorylated peptides identified using
four different software.
Unique phosphopeptides
Proteome Discoverer
VAAAAGsGPSPPGsPGHDR
VAAAAGSGPsPPGsPGHDR
VEEEQEADEEDVsEEEAESK
TDSREDEIsPPPPNPVVK
Mascot
AEEDEILNRsPR
EAAAQEAGADTPGKGEPPAPKsPPK
EAAAQEAGADtPGKGEPPAPKSPPK
EGEEPTVYsDEEEPKDESAR
EGEEPTVySDEEEPKDESAR
EGEEPtVYSDEEEPKDESAR
EGQPSPADEKGNDsDGEGESDDPEK
EGQPSPADEKGNDSDGEGEsDDPEKK
ESEDKPEIEDVGsDEEEEKKDGDK
FAsDDEHDEHDENGATGPVK
GAGDGsDEEVDGKADGAEAKPAE
GsVSDEEmmELR
GSVsDEEmmELR
KAEQGsEEEGEGEEEEEEGGESK
KGAGDGsDEEVDGKADGAEAKPAE
KPSGLNGEAsKSQEmVHLVNK
KPSGLNGEASKsQEmVHLVNK
KVEEEQEADEEDVsEEEAESK
KVEEEQEADEEDVSEEEAEsK
LKsEDGVEGDLGETQSR
LLKPGEEPsEYTDEEDTKDHNKQD
LLKPGEEPSEyTDEEDTKDHNKQD
LLKPGEEPSEYtDEEDTKDHNKQD
LLKPGEEPSEYTDEEDtKDHNKQD
LPsGSGAASPTGSAVDIR
LPSGsGAASPTGSAVDIR
LPSGSGAAsPTGSAVDIR
LPSGSGAASPTGsAVDIR
LPSGSGAASPtGSAVDIR
mGPSGGEGmEPERRDsQDGSSYR
mGPSGGEGmEPERRDSQDGsSYR
mGPSGGEGmEPERRDSQDGSsYR
mHEGDEGPGHHHKPGLGEGtP
NRNsNVIPYDYNR
QPSDECtLLsNK
RPsQEQSASASSGQPQAPLNR
SGPKPFSAPKPQTsPSPK
SHsPSSPDPDTPSPVGDSR
RPsQEQSASASSGQPQAPLNR
RPSQEQsASASSGQPQAPLNR
RPSQEQSAsASSGQPQAPLNR
SGPKPFSAPKPQtSPSPK
SGPKPFsAPKPQTSPSPK
SGPKPFSAPKPQTsPSPK
SGPKPFSAPKPQTSPsPK
sHSPSSPDPDTPSPVGDSR
SHsPSSPDPDTPSPVGDSR
SHSPsSPDPDTPSPVGDSR
SHSPSsPDPDTPSPVGDSR
sPEGEQEDRPGLHAYEK
SSDsPPRPQPAFK
TDSREDEIsPPPPNPVVK
TPSPKEEDEEPEsPPEKK
VAAAAGsGPSPPGsPGHDR
VAAAAGSGPsPPGsPGHDR
VEEEQEADEEDVsEEEAESK
tDSREDEISPPPPNPVVK
TDsREDEISPPPPNPVVK
TDSREDEIsPPPPNPVVK
TPSPKEEDEEPEsPPEKK
VAAAAGsGPsPPGSPGHDR
VAAAAGsGPSPPGsPGHDR
VAAAAGSGPsPPGsPGHDR
VEEEQEADEEDVsEEEAESK
Conclusions and Discussion
1. The low number of high confidence unique and total phosphopeptides identified is expected since these
samples were from resting macrophages, therefore not as metabolically active as stimulated cells.
2. The low number of high confidence non-phosphorylated peptides suggests that phosphopeptide enrichment
was specific (exception noted for PEAKS Studio).
3. The software packages compared in this study are compatible but not exclusive.
4. We predict that the feature of compatibility is independent of the size of phosphoproteome.
5. It is our recommendation to use multiple search software to maximize data output.
Acknowledgements
This work was supported, in part, by National Institute of Health Grants 5 P30MH06ZZ61, 5R01DA030962, and 5 P20RR016469 and Nebraska Research Initiative.