1. Modified Peptide MS/MS Interpretation
Karl R. Clauser
Broad Institute of MIT and Harvard
Bioinformatics for Protein Identification
ASMS Fall Workshop
Baltimore, MD
November 5-6, 2009
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2. Outline Fixed, variable, mix modifications and search space
Multiple rounds of searching
Diagnostic marker ions for modifications
Data acquisition methods specific for modifications
Ambiguity in localizing phosphorylation sites
Sample handling chemistry artifacts
Resources for masses/descriptions of known modifications
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3. Fixed, Mix and Variable Modifications
Allow
2 possibilities
for an AA.
Allow both
in 1 spectrum
if more than one
location/AA.
Fixed
Redefine
the wild type as
Nearly all software package allow configuring Fixed and variable modifications. Some will allow a choice of mix, if not then one typically configures light as fixed and heavy as variable, or vice-versa.
Mix
Search in 2 cycles
Cycle 1: all KR light
Cycle 2: all KR heavy
DO NOT allow both light and heavy in 1 spectrum
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4. Variable Modifications Expand the Search Space
Fixed
Mods
only
Allow
Variable
Mods
precursor mass filter
Calculate
MH+ fixed mods only
tolerance
filter
Shift range
filter
AA composition
filter
Candidates passing precursor mass filter
Precursor
MH+ shift
Calculate MH+
Variable mod
combinations
3ST 2ST 2ST 1ST 1ST 1ST 2M 1M 2N 1N * ^Q
1M 1M 1M 1N
? ? ? ? ? ? ? ? ? ? 1 .05 AA
composition
tolerance
filter
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5. Methods of Constraining Allowed # of Modifications/Peptide
Parent mass shift range
Spectrum Mill
Max Number of mods/peptide
Sequest - all mods have same max
X!Tandem - ?
Phenyx - each mod can have different max
Max Permutations of mods/peptide
Mascot - cap on permutations/peptide
Candidate sequence contains sequence tags present in spectrum
Protein Pilot/Paragon
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6. Multiple Rounds of Searching
Round 1: search all proteins
Get high confidence peptide hits
0-1 missed cleavages
Minimal number of variable AA modifications
Round 2: limit the search to proteins identified in round 1
Semi-/un-specific cleavage
Increase the number of modifications
Allow for AA substitutions
Allow for undefined modifications
Alternate names for similar concept
X!Tandem: refinement
Mascot: Error tolerant
Spectrum Mill: search saved hits, homology mode, unassigned single mass gap
Phenyx: 2-rounds
ProteinPilot/ Paragon: thorough ID, fraglet-taglet
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7. X!Tandem - Refinement Search
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8. Mascot - Error Tolerant Search
Otherwise, submitting the search is just like submitting a standard search except that you check the Error Tolerant Checkbox
Creasy DM, and Cottrell JS. (2002) Proteomics 2,
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9. Mascot - Error Tolerant Search Result
Take a look at the match to query 218. The mass tolerance for this search was fairly wide, so the observed mass difference could correspond to either carbamidomethylation or carboxymethylation at the N-terminus. Since this sample was alkylated with iodoacetamide, we would choose carbamidomethylation as the more likely suspect, especially as this brings the error on the precursor mass into line with the general trend, whereas carboxymethylation would give an error of +0.6 Da. The assignment to carbamidomethylation is also very believable, because this is a known artefact of over-alkylation. The same modification is found for query 260. In other cases, the match may be good, but the assignment is not believable. Query 145 is listed with a substitution at F8 causing a loss of 48 Da. This seems unlikely because we have 2 other matches to the same peptide without any substitution. What else could it be? Well, notice that the other two matches are both oxidised at M7. If we suppose this peptide is also oxidised, then the mass shift becomes -64, which is a well-known loss for oxidised methionine, (loss of methanesulfenic acid). This would seem a much more likely explanation for this match. It is important to understand that the error tolerant search finds new matches by introducing mass shifts at different positions in the database sequences. The match may be very strong, but figuring out a credible assignment can require a bit of detective work.
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10. Spectrum Mill Unassigned Single Mass Gap Search
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11. Spectrum Mill - Unassigned mass gap
Wide open precursor mass filter coupled with complementary ion principle
S A M P L E R i j + Pe bi
bi * yj
yj*
0 sequence mismatches: bi , bi* , yj , yj* , Pe, Pdb match
1 sequence mismatch at A: bi* , yj match
2 sequence mismatches at A and P: yj matches
Pdb
bi + Pgap
bi *
yj* + Pgap
Pgap
Relative Abundance
Every ion in a spectrum can be thought of as a measurement of the mass of two complementary entities, both the ion and neutral (yj & yj*) resulting from fragmentation of a precursor. When comparing the experimental MS/MS spectrum of a peptide whose precursor mass is shifted from that of a candidate sequence in a database, because of a single modification, either the masses of the fragment ions or the neutrals will match (bi* & yj)while the masses of their complements (bi & yj*) will be shifted by the gap (Pdb-Pe) between the two precursor masses. The enitites containing the modification will have the shifted masses.
Mass (m/z)
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12. Spectrum Mill - Unassigned Single Mass Gap Result
b* ions
Removal of Met
Acetylation
The b*-ions (b-ions plus the precursor mass shift) contain the modification and represent the complements of the detected y-ions. The absence unmodified b-ions means that the modification is on the N-terminus.
Mass Gap # IDs Presumed Modification
-89 Da 153 Met loss + Acetylation
pyro-Glu, pyro-CamC
Oxidation
Dioxidation
+42 2 Acetylation
Overalkylation
+80 7 Phosphorylation
Number of identifications with below 5% FDR for particular mass gaps from an Agilent 6520 Q-Tof LC-MS/MS dataset collected on a HeLa cell lysate digested with trypsin and separated on the basis of peptide isoelectric point into 24 fractions by off-gel electrophoresis.
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13. Phenyx: 2 Rounds
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14. Phenyx: Effect of the parameters for one protein
1rnd,
Only 3 fixed mods
131 valid,
75% cov.
2rnd,
With all mods
And half cleaved
348 valid,
90% cov.
2rnd,
Add variable mods
205 valid,
84% cov.
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15. Phenyx: Use the Annotation in SwissProt, TrEMBL
In the Feature Tables
Sequence processing annotations
Removal of signal peptides
Removal of transit peptides
Extraction of active chains
Post-translational modifications
Sequence variants
Splicing variants
Sequence mutations
57292 variants /
20328 human proteins
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16. Phenyx: Search Annotated PTMs in SwissProt
15 unique spectra
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17. Applied Biosystems ProteinPilot™ Software Paragon™ Algorithm
Limited de novo sequencing generates Taglets
A large number of short sequence tags –‘Taglets’ – are called.
Each Taglet rated with the chance it is correct, allowing a large number to be used but more likely Taglets to have more influence. G I T
Taglets: STI, TI, AS,
YH, TIG, IT, SA, etc… I T S S A
Shilov et al Mol Cell Proteomics, 6: , (2007). H Y
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18. The Paragon™ Algorithm: Varying Search Space on a Continuum Taglets for Sequence Temperature Value (STV)
ST, TI, STI, AS, DI, DIN, SE, EQ, NA, SEQ
Sequence Tags in Order of Decreasing Certainty:
>DHE3_BOVIN (P00366) Glutamate dehydrogenase 1, mitochondrial precursor (EC ) (GDH) MYRYLGEALLLSRAGPAALGSASADSAALLGWARGQPAAAPQPGLVPPARRHYSEAAADREDDPNFFKMVEGFFDRGASIVEDKLVEDLKTRETEEQKRNRVRSILRIIKPCNHVLSLSFPIRRDDGSWEVIEGYRAQHSQHRTPCKGGIRYSTDVSVDEVKALASLMTYKCAVVDVPFGGAKAGVKINPKNYTDNELEKITRRFTMELAKKGFIGPGVDVPAPDMSTGEREMSWIADTYASTIGHYDINAHACVTGKPISQGGIHGRISATGRGVFHGIENFINEASYMSILGMTPGFGDKTFVVQGFGNVGLHSMRYLHRFGAKCITVGESDGSIWNPDGIDPKELEDFKLQHGTILGFPKAKIYEGSILEVDCDILIPAASEKQLTKSNAPRVKAKIIAEGANGPTTPEADKIFLERNIMVIPDLYLNAGGVTVSYFEWLNNLNHVSYGRLTFKYERDSNYHLLMSVQESLERKFGKHGGTIPIVPTAEFQDRISGASEKDIVHSGLAYTMERSARQIMRTAMKYNLGLDLRTAAYVNAIEKVFRVYNEAGVTFT
A segment with cold STV
The Paragon algorithm is not just another search engine. The next few slides will give you a flavor of what’s really different about this algorithm by taking a quick look at the three major innovations of the Paragon algorithm.
The first major innovation is the use of a new kind of sequence tag algorithm. For the search of a single spectrum, we call many small tags. We don’t make black and white decisions about what tags are correct or not – we give them quality ratings. We don’t use thresholds on tag evidence – the degree of implication of all segments are rated on a continuum. We capture this as a quantity we call Sequence Temperature Value, which essentially tells you how likely we are to find the right answer in that sequence segment. This lets us search harder in places that are more likely to produce the right answer and not so hard in less likely places. Now all we need is a way to determine what features should be included where on that spectrum.
A segment with warmer STV
The segment with the hottest STV in this protein
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19. Controlling Search Space with the Paragon™ Algorithm
Using feature probabilities avoids include/exclude decisions and simplistic rules.
When combined with STVs, search space is dynamic by spectrum and even segment of the database.
1.0
Probability of Feature
MMTS on C
Dehydration of E,D
Oxidized M
Deamidation on N,Q
Pyroglutamic acid of E
iTRAQ on K, N-term
iTRAQ on Y
Try only most likely mods for ‘cold’ segments
Try only more likely mods for ‘warm’ segments
Try all mods for ‘hot’ segments in the database
With the Paragon algorithm, it doesn’t work like that at all. Instead, features are given probabilities. This allows use to make very specific decisions about when to consider a feature like a modification. (walk through animation series about which mods are considered with cold, warm, and hot Sequence Temperature Values).
…same idea with digestion, etc. Thus, for example, you never have to decide to define Trypsin to allow cleavage of K-P or not – it simply has a lower probability.
Same concept also used with digestion specificity, mass tolerances, etc.
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20. Pause for Questions
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21. Diagnostic Marker Ions for Modifications (Immonium ions and Neutral Losses from Precursor)
Mass Modification
P-98 H3PO4 phospho Ser, Thr
216, P-80 phospho Tyr
P-64 SOCH4 oxidized Met
P-43 carbamylated N-term
204, P-203 N-Acetylglucosamine (GlcNAc)
Phospho
Ser
Dehydroalanine
m/z 98
Phosphoric Acid
CID
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22. Data Acquisition Methods Specific for Modifications
ETD - Electron transfer dissociation
ECD - Electron capture dissociation
MS3 - ion trap
Multi-stage activation - ion trap
Precursor ion scan - triple quadrupole, Q-Tof
Neutral-loss scan - triple quadrupole
Review: Boersema, P; Mohammed, S; and Heck, A.
Phosphopeptide fragmentation and analysis by mass spectrometry.
J. Mass. Spectrom. 2009, 44, 861–878.
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23. Multi-stage Activation in an Ion Trap
Single fill
Single isolation
Multi Activation
Single Mass Analysis
Multi fill
Multi isolation
Multi Activation
Multi Mass Analysis
Figure 1. Schematic illustration of CAD-based methods for phosphopeptide ion dissociation: (A) full-scan mass spectrum containing a doubly
charged, doubly phosphorylated peptide; (B) MS/MS spectrum containing typical fragment ions following conventional phosphopeptide ion
dissociation of precursor ion by CAD (note the majority of fragment ion signal is represented by loss of two phosphoric acid residues, denoted
by P); (C) MS3 spectrum following isolation and activation of the most abundant neutral loss product ion (dashed lines indicate fragment ions
produced as a result of neutral loss activation); note that fragment ions generated from original MS/MS event are not retained; (D) Pseudo MSn
spectrum, a composite containing fragment ions generated from both initial MS/MS event (solid lines) and subsequent activations of the neutral
loss product ions (dashed lines).
Schroeder, MJ, Schabanowitz, J, Schwartz, JC, Hunt, DF and Coon JJ. Anal. Chem. 2004, 76,
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24. Single vs. Multi-stage Activation MS/MS in an Ion Trap
(K)L/G/V|S|V/s|P S R(A)
Single
Activation
Multi-stage
Activation
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25. Time Considerations for Different Acquisition Strategies
Figure 9. Time window of ESI-MS approaches in phosphoproteomics. Depicted here are approximations of ion accumulation and reaction and scan
times. In reality, these times depend on the amount of ions injected. (∗ Current generation Q-TOFs).
Boersema, P; Mohammed, S; and Heck, A: J. Mass. Spectrom. 2009, 44, 861–878.
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26. O-GlcNAcylation
Addition of a single sugar residue: N-Acetylglucosamine (GlcNAc) to serine or threonine residues of nuclear and cytoplasmic proteins.
Present in all multi-cellular organisms
Different from ‘conventional’ glycosylation:
Inside the cell
Transient modification
Enzymes responsible for addition and removal of modification
i.e. analogous to phosphorylation
O-GlcNAc modification and phosphorylation interact / affect each other
Modification is involved in cellular response to nutritional and other stresses
Clear links to Diabetes and Alzheimer Disease and elevated in cancer.
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27. Side-chain Fragmentation Yields Diagnostic Neutral Losses
Phospho
Ser
Dehydroalanine
m/z 98
Phosphoric Acid
CID
GlcNAcylated
Ser
Unmodified
m/z 204
GlcNAc
oxonium Ion
CID
In CID, O-GlcNAc bond is more labile than peptide backbone, so neutral-loss of sugar occurs prior to peptide fragmentation.
Site assignment often not possible since an unmodified residue remains following neutral-loss of the sugar (so multi-stage activation is ineffective).
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28. CID/ETD MS/MS of Same Doubly GlcNAcylated Peptide
GLAGPTtVPAtKASLLR - Protein bassoon
Mass difference between z10-z11 identifies one site as residue T2941.
Mass difference between c10-c11 identifies other site as residue T2945.
GlcNAc
MH33+ -GlcNAc
MH22+ -GlcNAc
MH22+ -2GlcNAc
MH+ -2GlcNAc
CID
m/z
c11 z6 z5 z4 z3 z2
z10
c10 z8
c13
c14
z12
z11
c16
ETD
m/z
The CID MS/MS spectrum of this peptide shows major fragment ions are all neutral losses from sugar residues. Clearly a doubly O-GlcNAc-modified peptide. However, one can not confidently identify the peptide, nor localize the modification sites. However, the ETD MS/MS spectrum enables both peptide identification and modification site localization in a database search with Protein Prospector.
Chalkley, R. J. et al. Proc Natl Acad Sci USA (2009) 106, 22,
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29. Phospho Site Ambiguity – S/T
L A G G Q/T/S Q|P T T|P L\T s/P Q R
Site-localizing ion
L A G G Q/T/S Q|P T T|P L\t S/P Q R
The same spectrum is shown here labeled with two possible locations of the phosphorylated residue (either on Thr-14 or Ser-15). The presence of the b14 ion at ion represents fragmentation between an unmodified Thr and a phospho-Serine and enables unambiguous assignment of the site of modification.
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30. Reliability of LC/MS/MS Phosphoproteomic Literature
Citation Approach Instrument #sites #ambiguous Scores Site Supplem.
sites Shown Ambiq Labeled
Shown Spectra
Ballif, BA,…Gygi, SP 1DGel LCQ Deca XP yes yes no
2004 MCP, 3, digest, SCX
LC/MS/MS
Rush, J, … Comb, MJ digest lysate LCQ Deca XP yes no no
2005, Nat Biotech, 23, pTyr Ab
LC/MS/MS
Collins, MO, …Grant, SGN protein IMAC Q-Tof Ultima no yes no
2005, J Biol Chem, 280, peptide IMAC
LC/MS/MS
Gruhler, A, … Jensen, ON digest lysate LTQ-FT yes no no
2005 MCP, 4, SCX, IMAC
LC/MS/MS
“Resulting sequences were inspected manually …. When the exact site of phosphorylation could not be assigned for a given phosphopeptide, it was tabulated as ambiguous.”
“All identified phosphopeptides were manually validated, and localization of phosphorylated residues within the individual peptide sequences were manually assigned…”
“All spectra supporting the final list of assigned peptides used to build the tables shown here were reviewed by at least three people to establish their credibility.”
“Assignment of phosphorylation sites was verified manually with the aid of PEAK Studio (Bioinformatics Solutions) software.”
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31. MCP draft Guideline for publishing PTM data
III. POST-TRANSLATIONAL MODIFICATIONS
Studies focusing on posttranslational modifications require specialized methodology and documentation to assign the presence and the site(s) of modification. No current MS data analysis software is infallible in the automatic assignment of modification sites in peptides, and these analyses are particularly error prone when multiple possible sites within a peptide are being utilized. For these reasons, additional documentation supporting assignment of PTMs is required. In addition to the tabular presentation(s) of the data described in guideline II:
The site(s) of modification within each peptide sequence must be clearly presented.
An indication of the certainty of localization for each PTM: The manner in which the modification was located (by computation or manually) and a description of the software used, if any.
A justification for any localization score threshold employed.
Ambiguous assignments: Peptides containing ambiguous PTM site localizations must be listed in a separate table from those with unambiguous site localizations. In cases where there are multiple modification sites and at least one is ambiguous, then these peptides should be listed with the ambiguous assignments. Ambiguous assignments must be clearly labeled as such.
Examples of ambiguities include:
Modified peptides in which one or more modification sites are ambiguous.
Instances where the peptide sequence is repeated in the same protein so the specific modification site cannot be assigned.
Instances in which the same peptide is repeated in multiple proteins, e.g. paralogs and splice variants (See also Section IV).
Isobaric modifications (e.g., acetylation vs. trimethylation, phosphorylation vs. sulfonation etc), where the possibilities may not be distinguished. Examples of methods able to distinguish between these include mass spectrometric approaches such as accurate mass determination, observation of signature fragment ions (e.g. m/z 79 vs. m/z 80 in negative ion mode for assignment of phosphorylation over sulfation), or biological or chemical strategies.
Annotated, mass labeled spectra: Spectra for ALL modified peptides must be either submitted to a public repository or accompany the manuscript as described in guideline II.
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32. Phosphosite Localization Scoring
Figure 3 Resolving ambiguity in phosphorylation site localization. (a) Peptides containing multiple serine, threonine and/or tyrosine residues should be evaluated for precise site assignment. This phosphopeptide is from Zinc finger protein 638. (b) General scheme for calculating a probability-based ion matching score (Peptide Score) for each potential phosphorylation site. The tandem mass (MS/MS) spectrum for the phosphopeptide from panel a is shown. The spectrum was separated into 100 m/z windows where the top N most-intense peaks per window were matched to predicted b- and y-type ions for each possibility. This was repeated using from 1 to 10 ions in each window (6 is shown). The cumulative binomial probability P was calculated using the number of trials (all b- and y-type ions) and the number of successes (matched ions) for each possibility and plotted as -10 log (P) vs peak depth (peaks per 100 m/z). The peptide corresponding to the red line matched more ions at every peak depth than any other possibility. The actual ambiguity score (Ascore) for this peptide is calculated using only site-determining ions as shown in Figure 3c using information from this plot. (c) The Ascore is a probability-based metric that measures the likelihood that a difference in site-determining ions between two site positions was matched by random chance. In this example, only six b- or y-type ions could potentially differentiate the two phosphorylation sites. A peak depth of six was determined from Figure 3b as the earliest maximal difference in the number of matched ions. The cumulative binomial probability was applied as in Figure 3b but using only the site-determining subset of ions. An Ascore of would represent a probability of less than 1 in 200,000 of matching a difference of at least 5 ions in 6 trials by random chance. Any of these 5 ions, if not due to chance, can differentiate between the two potential sites.
Supports Sequest results only, Linux only
Beausoleil SA, Villen J, Gerber SA, Rush J, Gygi SP (2006) Nat Biotechnol 24:1285–1292.
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33. Phosphosite Localization Scoring
P = (k!/[n!(n-k)!] [pk] [(1-p) (n-k) ])
= (k!/[n!(n-k)!] [0.04k] [(0.96) (n-k) ])
PTM score = -10 x log (P)
p: use the 4 most intense fragment ions per 100 m/z units
n: total num possible b/y ions in the observed mass range for all possible combinations of PO4 sites in a peptide
k: number of peaks matching n
Figure S6. Derivation of Phosphorylation Site Probabilities from the PTM Score
(A) The matrix represents amino acid positions for a doubly phosphorylated peptide, with red positions indicating candidate phosphorylation sites. The second position is phosphorylated in each of the top four PTM scores but the second phosphorylation site could be located at position 4,5,6 or 7.
(B) The table shows a specific example of this situation (phosphopeptide of Eps8). The five top scoring possibilities for phosphorylation have PTM scores from 30.4 to Corresponding inverted probabilities add up to , which is set equal to one. P-site is the proportional probability for each possibility and is assigned to the two phosphorylation sites in each case. Next, probabilities are summed up for each candidate site and sites with less than 0.25 are discarded. In the example, site 2 would be a class I site (p>0.75). Sites 4 and 7 are class III sites unless they also match a known kinase motif, in which case they are class II sites.
Olsen, J. V.; Blagoev, B.; Gnad, F.; Macek, B.; Kumar, C.; Mortensen, P.; Mann, M. Cell (2006), 127 (3), 635–48.
Olsen, J.V., and Mann, M. Proc. Natl. Acad. Sci. USA. (2004) 101, 13417–13422.
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34. True Probability or Just Effective Scores?
Peak selection assumptions
All regions of spectrum equally likely
multiply charged fragements below precursor
some m/z values not possible dipeptide AA combinations
Tall and short peak intensities equally diagnostic
Fragment ion type assumptions
All ion types equally probable
Neutral losses ignored, y-H3P04, y-H2O
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35. Spectral Matching if Modified & Unmodified Peptides Present
FIG. 1. Identification of a novel modification on a peptide belonging to human saliva PRP. A, 9-min integrated survey scan showing two ions separated by Da. B, CAD spectrum of the lowest mass ion in the survey scan identified as peptide GPPQQGGHQQ from PRP. The inset shows the mass deviation of the fragment masses for this identification. C, CAD spectrum of the Da peptide. Note the similarity between this spectrum and the one depicted in B. Full sequence cleavage is achieved, and no fragment mass deviates more than 6 mDa.
ModifiComb - Savitski, MM; Nielsen, ML; and Zubarev, RA. Mol Cell Proteomics 5, 935–948, 2006.
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36. Software Tools Specialized for Identifying Modifications and Localizing Sites
Ascore
Beausoleil SA, Villen J, Gerber SA, Rush J, Gygi SP (2006) Nat Biotechnol. 24, 1285–1292.
MaxQuant
Cox J, Mann M.(2008) Nat Biotechnol. 26,
Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, Mann M. (2006) Cell. 127, 635–48.
Inspect, MS-Alignment, PTMFinder
Tanner S, Payne S, Dasari S, Shen Z, Wilmarth PA, David L, Loomis WF, Briggs SP, Bafna V. (2008) J Proteome Res. 7, 170–181.
Payne S, Yau M, Smolka MB, Tanner S, Zhou H, Bafna V. (2008) J Proteome Res. 7, 3373–3381.
Tsur D, Tanner S, Zandi E, Bafna V, Pevzner P. (2005) Nat Biotechnol. 23, 1562–1567.
Tanner S, Shu H, Frank A, Wang LC,Zandi E, Mumby M, Pevzner P, Bafna V. (2005) Anal Chem. 77,
PhosphoScore
Ruttenberg BE, Pisitkun T, Knepper MA, Hoffert JD. (2008) J Proteome Res. 7,
Debunker
Lu B, Ruse C, Xu T, Park SK, Yates J 3rd. (2007) Anal Chem. 79,
SloMo - ETD/ECD
Bailey CM, Sweet SM, Cunningham DL, Zeller M, Heath JK, Cooper HJ. (2009) J Proteome Res. 8,
ModifiComb
Savitski MM, Nielsen ML, Zubarev RA. (2006) Mol Cell Proteomics. 5, 935–48.
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37. Pause for Questions
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38. Expect Woes & Nuisances
Sample Handling Chemistry
Carbamylation +43 nterm, Lys urea in digest buffer
Deamidation N -> D sample in acid
pyroGlutamic acid -17 nterm Q sample in acid
pyroCarbamidomethyl Cys -17 nterm C sample in acid
Oxidized Met +16 M gels
Cys alkylation reagent +x n-term, W side reaction
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39. Stinkers (b-NH3) & Pyroglutamic Acid
-17 Da
Q to q
(R)Q L/Q/L/A|Q/E/A|A Q\K(R)
P(m/z)-NH3
(R)q L/Q|L|A|Q|E|A|A\Q\K(R)
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40. Deamidation of Asn +1Da Asn –NH + O = Asp ionsource.com

41. Deamidation G S/E/S|G|I|F|T|n\T K G S/E/S|G|I|F|T|D\T K
18.35
96.9%
+0.007 Da
G S/E/S|G|I|F|T|D\T K
G S/E S\G\I\F\T\N/T K
6.62
43.4%
+0.986 Da
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42. Carbamylation from Urea in Digest Buffer +43Da
CNHO +43Da
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43. Carbamylated N-term I/G/E|G/T/y/G V|V|Y\K unmodified +43 b ions N-term
P(m/z)-CNHO
+43
b ions
N-term
Carbamylated
P(m/z)-CNHO-H2O
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44. Unimod Resource for Masses of Modifications
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45. Delta Mass Resource for Masses of Modifications
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46. RESID Resource for Masses of Modifications
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47. Broad Institute of MIT and Harvard
Acknowledgements
Broad Institute of MIT and Harvard
Steven Carr
Philipp Mertins
Pierre-Alain Binz GeneBio Phenyx
Robert Chalkley University of California San Francisco O-GlcNAc
John Cottrell Matrix Science Mascot
Chris Miller Agilent Technologies Spectrum Mill
Sean Seymour Applied Biosystems Protein Pilot, Paragon
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48. iPRG-2010: Proteome Informatics Research Group Study - Phosphopeptide Identification
In this study, an LC-MS/MS dataset from a lysate digested with trypsin and enriched for phosphopeptides using strong cation exchange fractionation followed by immobilized metal affinity chromatography (SCX/IMAC) will be provided.
Participants are asked to return a list of identified peptides and localized phosphorylation sites
Requests to participate must be submitted by to prior to Monday, November 30, Please include the words “iPRG Study 2010 request” in the subject line and provide contact name and affiliation in the body of the message.
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