Protein Identification Using Tandem Mass Spectrometry

1
Protein Identification Using Tandem Mass
Spectrometry

• Nathan Edwards
• Center for Bioinformatics and Computational
  Biology
• University of Maryland, College Park

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
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 ions fragments

7
Tandem Mass Spectrometry(MS/MS)
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
Ri1
C-terminus
AA residuei-1
AA residuei
AA residuei1
9
Peptide Fragmentation
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-1
Ri
Ri1
C-terminus
AA residuei-1
AA residuei
AA residuei1
Ionized peptide (addition of a proton)
11
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
Ri1
Ri-1
AA residuei-1
AA residuei
AA residuei1
C-terminus
Fragmented peptide C-terminus fragment observed
12
Peptide Fragmentation
yn-i
yn-i-1
-HN-CH-CO-NH-CH-CO-NH-
CH-R
Ri
i1
R
bi
i1
bi1
13
Peptide Fragmentation
xn-i
yn-i-1
-HN-CH-CO-NH-CH-CO-NH-
CH-R
Ri
i1
R
ai
i1
bi1
14
Peptide Fragmentation
Peptide S-G-F-L-E-E-D-E-L-K
15
Peptide Fragmentation
1166
1020
907
778
663
534
405
292
145
88
b ions
K
L
E
D
E
E
L
F
G
S
147
260
389
504
633
762
875
1022
1080
1166
y ions
100
Intensity
0
m/z
250
500
750
1000
16
Peptide Fragmentation
1166
1020
907
778
663
534
405
292
145
88
b ions
K
L
E
D
E
E
L
F
G
S
147
260
389
504
633
762
875
1022
1080
1166
y ions
y6
100
y7
Intensity
y5
y2
y3
y8
y4
y9
0
m/z
250
500
750
1000
17
Peptide Fragmentation
1166
1020
907
778
663
534
405
292
145
88
b ions
K
L
E
D
E
E
L
F
G
S
147
260
389
504
633
762
875
1022
1080
1166
y ions
y6
100
y7
Intensity
y5
b3
b4
y2
y3
b5
y8
y4
b8
y9
b6
b7
b9
0
m/z
250
500
750
1000
18
Peptide Identification

• Given
• The mass of the parent ion, and
• The MS/MS spectrum
• Output
• The amino-acid sequence of the peptide

19
Peptide Identification

• Two paradigms
• De novo interpretation
• Sequence database search

20
De Novo Interpretation
100
Intensity
0
m/z
250
500
750
1000
21
De Novo Interpretation
100
Intensity
E
0
m/z
250
500
750
1000
22
De Novo Interpretation
100
Intensity
G
E
E
E
D
KL
E
E
E
D
0
m/z
250
500
750
1000
23
De Novo Interpretation
24
De Novo Interpretation
from Lu and Chen (2003), JCB 101
25
De Novo Interpretation
26
De Novo Interpretation
from Lu and Chen (2003), JCB 101
27
De Novo Interpretation

• Find good paths in spectrum graph
• Cant use same peak twice
• Forbidden pairs NP-hard
• Nested forbidden pairs Dynamic Prog.
• Simple peptide fragmentation model
• Usually many apparently good solutions
• Needs better fragmentation model
• Needs better path scoring

28
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

29
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

30
Sequence Database Search
K
L
E
D
E
E
L
F
G
S
100
Intensity
0
m/z
250
500
750
1000
31
Sequence Database Search
1166
1020
907
778
663
534
405
292
145
88
b ions
K
L
E
D
E
E
L
F
G
S
147
260
389
504
633
762
875
1022
1080
1166
y ions
100
Intensity
0
m/z
250
500
750
1000
32
Sequence Database Search
1166
1020
907
778
663
534
405
292
145
88
b ions
K
L
E
D
E
E
L
F
G
S
147
260
389
504
633
762
875
1022
1080
1166
y ions
y6
100
y7
Intensity
y5
b3
b4
y2
y3
b5
y8
y4
b8
y9
b6
b7
b9
0
m/z
250
500
750
1000
33
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!

34
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 10-15 amino-acids
• Must allow for missed cleavage sites

35
Peptide Candidate Filtering

• gtALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDL
  GEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK

No missed cleavage sites
MK WVTFISLLFLFSSAYSR GVFR R DAHK SEVAHR FK DLGEENF
K ALVLIAFAQYLQQCPFEDHVK LVNEVTEFAK
36
Peptide Candidate Filtering

• gtALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDL
  GEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK

One missed cleavage site
MKWVTFISLLFLFSSAYSR WVTFISLLFLFSSAYSRGVFR GVFRR RD
AHK DAHKSEVAHR SEVAHRFK FKDLGEENFK DLGEENFKALVLIAF
AQYLQQCPFEDHVK ALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK
37
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

38
Peptide Molecular Weight
i0
Same peptide,i of C13 isotope
i1
i2
i3
i4
39
Peptide Molecular Weight
i0
Same peptide,i of C13 isotope
i1
i2
i3
i4
40
Peptide Molecular Weight
from Isotopes An IonSource.Com Tutorial
41
Peptide Molecular Weight

• Peptide sequence WVTFISLLFLFSSAYSR
• Potential phosphorylation?
• S,T,Y 80 Da

• 7 Molecular Weights
• 64 Peptides

42
Peptide Scoring

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

43
Mascot Search Engine
44
Mascot MS/MS Ions Search
45
Mascot MS/MS Search Results
46
Mascot MS/MS Search Results
47
Mascot MS/MS Search Results
48
Mascot MS/MS Search Results
49
Mascot MS/MS Search Results
50
Mascot MS/MS Search Results
51
Mascot MS/MS Search Results
52
Mascot MS/MS Search Results
53
Mascot MS/MS Search Results
54
Mascot MS/MS Search Results
55
Sequence Database SearchTraps 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

56
Sequence Database SearchTraps 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

57
Sequence Database SearchTraps and Pitfalls

• Best available peptide isnt 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

58
Sequence Database SearchTraps 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

59
Sequence Database SearchTraps 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

60
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!

61
Further Reading

• Matrix Science (Mascot) Web Site
• www.matrixscience.com
• Seattle Proteome Center (ISB)
• www.proteomecenter.org
• Proteomic Mass Spectrometry Lab at The Scripps
  Research Institute
• fields.scripps.edu
• UCSF ProteinProspector
• prospector.ucsf.edu