1. Protein Identification Using Tandem Mass Spectrometry
Nathan Edwards
Informatics Research
Applied Biosystems

2. Outline Proteomics context Tandem mass spectrometry
Peptide fragmentation
Peptide identification
De novo
Sequence database search
Mascot screen shots
Traps and pitfalls
Summary

3. Proteomics Context High-throughput proteomics focus
(Differential) Quantitation
How much of each protein is there?
Identification
What proteins are present?
Two established workflows
2-D Gels
LC-MS, LC-MALDI

4. Sample Preparation for Tandem Mass Spectrometry
Enzymatic Digest
and
Fractionation

5. Single Stage MS MS

6. Tandem Mass Spectrometry (MS/MS)
Acquire mass spectrum of sample
Select interesting ion by m/z value
Fragment the selected “parent” ion
Acquire mass spectrum of parent ion’s fragments

7. Tandem Mass Spectrometry (MS/MS)

8. Peptide Fragmentation
Peptides consist of amino-acids arranged in a linear backbone.
N-terminus
H…-HN-CH-CO-NH-CH-CO-NH-CH-CO-…OH
Ri-1 Ri
Ri+1
C-terminus
AA residuei-1
AA residuei
AA residuei+1

9. Peptide Fragmentation
Peptides consist of amino-acids arranged in a linear backbone.
N-terminus H+
H…-HN-CH-CO-NH-CH-CO-NH-CH-CO-…OH
Ri-1 Ri
Ri+1
C-terminus
AA residuei-1
AA residuei
AA residuei+1
Ionized peptide (addition of a proton)

10. Peptide Fragmentation
Peptides consist of amino-acids arranged in a linear backbone.
N-terminus H+
H…-HN-CH-CO NH-CH-CO-NH-CH-CO-…OH Ri
Ri+1
Ri-1
AA residuei-1
AA residuei
AA residuei+1
C-terminus
Fragmented peptide
C-terminus fragment observed

11. Peptide Fragmentation
yn-i
yn-i-1
-HN-CH-CO-NH-CH-CO-NH- Ri
CH-R’
i+1 R” bi
i+1
bi+1

12. Peptide Fragmentation
Peptide: S-G-F-L-E-E-D-E-L-K MW
ion 88 b1
S GFLEEDELK y9
1080
145 b2
SG FLEEDELK y8
1022
292 b3
SGF LEEDELK y7
875
405 b4
SGFL EEDELK y6
762
534 b5
SGFLE EDELK y5
633
663 b6
SGFLEE DELK y4
504
778 b7
SGFLEED ELK y3
389
907 b8
SGFLEEDE LK y2
260
1020 b9
SGFLEEDEL K y1
147

13. Peptide Fragmentation88
145
292
405
534
663
778
907
1020
1166
b ions S G F L E E D E L K
1166
1080
1022
875
762
633
504
389
260
147
y ions
100
% Intensity
m/z
250
500
750
1000

14. Peptide Fragmentation88
145
292
405
534
663
778
907
1020
1166
b ions S G F L E E D E L K
1166
1080
1022
875
762
633
504
389
260
147
y ions y6
100 y7
% Intensity y5 y2 y3 y4 y8 y9
m/z
250
500
750
1000

15. Peptide Fragmentation88
145
292
405
534
663
778
907
1020
1166
b ions S G F L E E D E L K
1166
1080
1022
875
762
633
504
389
260
147
y ions y6
100 y7
% Intensity y5 b3 b4 y2 y3 y4 b5 y8 b6 b8 b7 b9 y9
m/z
250
500
750
1000

16. Peptide Identification
Given:
The mass of the parent ion, and
The MS/MS spectrum
Output:
The amino-acid sequence of the peptide

17. Peptide Identification
Two paradigms:
De novo interpretation
Sequence database search

18. De Novo Interpretation
100
% Intensity
m/z
250
500
750
1000

19. De Novo Interpretation
100
% Intensity E L
m/z
250
500
750
1000

20. De Novo Interpretation
100
% Intensity
SGF L E E L F G E KL E D E D E L
m/z
250
500
750
1000

21. De Novo Interpretation
Amino-acids have duplicate masses!
Incomplete ladders create ambiguity.
Noise peaks and unmodeled fragments create ambiguity
“Best” de novo interpretation may have no biological relevance
Current algorithms cannot model many aspects of peptide fragmentation
Identifies relatively few peptides in high-throughput workflows

22. De Novo Interpretation
Amino-Acid
Residual MW A
Alanine M
Methionine C
Cysteine N
Asparagine D
Aspartic acid P
Proline E
Glutamic acid Q
Glutamine F
Phenylalanine R
Arginine G
Glycine S
Serine H
Histidine T
Threonine I
Isoleucine V
Valine K
Lysine W
Tryptophan L
Leucine Y
Tyrosine

23. Sequence Database Search
Compares peptides from a protein sequence database with spectra
Filter peptide candidates by
Parent mass
Digest motif
Score each peptide against spectrum
Generate all possible peptide fragments
Match putative fragments with peaks
Score and rank

24. Sequence Database Search88
145
292
405
534
663
778
907
1020
1166
b ions S G F L E E D E L K
1166
1080
1022
875
762
633
504
389
260
147
y ions
100
% Intensity
m/z
250
500
750
1000

25. Sequence Database Search88
145
292
405
534
663
778
907
1020
1166
b ions S G F L E E D E L K
1166
1080
1022
875
762
633
504
389
260
147
y ions y6
100 y7
% Intensity y5 b3 b4 y2 y3 y4 b5 y8 b6 b8 b7 b9 y9
m/z
250
500
750
1000

26. Sequence Database Search
No need for complete ladders
Possible to model all known peptide fragments
Sequence permutations eliminated
All candidates have some biological relevance
Practical for high-throughput peptide identification
Correct peptide might be missing from database!

27. Peptide Candidate Filtering
Digestion Enzyme: Trypsin
Cuts just after K or R unless followed by a P.
Basic residues (K & R) at C-terminal attract ionizing charge, leading to strong y-ions
“Average” peptide length about amino-acids
Must allow for “missed” cleavage sites

28. Peptide Candidate Filtering
>ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK…
No missed cleavage sites
MKWVTFISLLFLFSSAYSRGVFR R
DAHK
SEVAHR FK
DLGEENFK
ALVLIAFAQYLQQCPFEDHVK
LVNEVTEFAK …

29. Peptide Candidate Filtering
>ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK…
One missed cleavage site
MKWVTFISLLFLFSSAYSRGVFRR
RDAHK
DAHKSEVAHR
SEVAHRFK
FKDLGEENFK
DLGEENFKALVLIAFAQYLQQCPFEDHVK
ALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK …

30. Peptide Candidate Filtering
Peptide molecular weight
Only have m/z value
Need to determine charge state
Ion selection tolerance
Mass for each amino-acid symbol?
Monoisotopic vs. Average
“Default” residual mass
Depends on sample preparation protocol
Cysteine almost always modified

31. Peptide Molecular Weight
Same peptide, i = # of C13 isotope
i=1
i=2
i=3
i=4

32. Peptide Molecular Weight
Same peptide, i = # of C13 isotope
i=1
i=2
i=3
i=4

33. Peptide Molecular Weight
…from “Isotopes” – An IonSource.Com Tutorial

34. Peptide Scoring Peptide fragments vary based on
The instrument
The peptide’s amino-acid sequence
The peptide’s charge state
Etc…
Search engines model peptide fragmentation to various degrees.
Speed vs. sensitivity tradeoff
y-ions & b-ions occur most frequently

35. Mascot Search Engine

36. Mascot MS/MS Ions Search

37. Mascot MS/MS Search Results

38. Mascot MS/MS Search Results

39. Mascot MS/MS Search Results

40. Mascot MS/MS Search Results

41. Mascot MS/MS Search Results

42. Mascot MS/MS Search Results

43. Mascot MS/MS Search Results

44. Mascot MS/MS Search Results

45. Mascot MS/MS Search Results

46. Mascot MS/MS Search Results

47. Sequence Database Search Traps and Pitfalls
Search options may eliminate the correct peptide
Parent mass tolerance too small
Fragment m/z tolerance too small
Incorrect parent ion charge state
Non-tryptic or semi-tryptic peptide
Incorrect or unexpected modification
Sequence database too conservative
Unreliable taxonomy annotation

48. Sequence Database Search Traps and Pitfalls
Search options can cause infinite search times
Variable modifications increase search times exponentially
Non-tryptic search increases search time by two orders of magnitude
Large sequence databases contain many irrelevant peptide candidates

49. Sequence Database Search Traps and Pitfalls
Best available peptide isn’t necessarily correct!
Score statistics (e-values) are essential!
What is the chance a peptide could score this well by chance alone?
The wrong peptide can look correct if the right peptide is missing!
Need scores (or e-values) that are invariant to spectrum quality and peptide properties

50. Sequence Database Search Traps and Pitfalls
Search engines often make incorrect assumptions about sample prep
Proteins with lots of identified peptides are not more likely to be present
Peptide identifications do not represent independent observations
All proteins are not equally interesting to report

51. Sequence Database Search Traps and Pitfalls
Good spectral processing can make a big difference
Poorly calibrated spectra require large m/z tolerances
Poorly baselined spectra make small peaks hard to believe
Poorly de-isotoped spectra have extra peaks and misleading charge state assignments

52. Summary
Protein identification from tandem mass spectra is a key proteomics technology.
Protein identifications should be treated with healthy skepticism.
Look at all the evidence!
Spectra remain unidentified for a variety of reasons.
Lots of open algorithmic problems!

53. Further Reading Matrix Science (Mascot) Web Site
Seattle Proteome Center (ISB)
Proteomic Mass Spectrometry Lab at The Scripps Research Institute
fields.scripps.edu
UCSF ProteinProspector
prospector.ucsf.edu