1 Mass Spectrometry-based Proteomics Xuehua Shen (Adapted from slides with textbook)
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Transcript of 1 Mass Spectrometry-based Proteomics Xuehua Shen (Adapted from slides with textbook)
1
Mass Spectrometry-based Proteomics
Xuehua Shen
(Adapted from slides with textbook)
2
Outline
• Motivation of proteomics
• Mass spectrometry-based proteomics
• Instrumentation of mass spectrometry
• De novo sequencing algorithm
• Database search
• Algorithms of real software (e.g., sequence tags)
3
Motivation
• Proteins are working units of the cells
– The number of found genes is much less than the number of expressed proteins
– Directly related with cell processes and diseases
>1,000,000 distinct protein forms
~30,000 human genes
DNA
Alternative
splicing
mRNA Protein
Post-translational
Modification
>100,000 RNA messages
SNP
4
Tools for Proteomics
• Edman degradation reaction
• NMR (Nuclear Magnetic Resonance)
• X-ray crystallography
• Protein array
• Mass Spectrometry
5
Mass Spectrometry-based Proteomics
• Primary sequence (sequencing, identification)
• Post-translational modification (PTM) (characterization)
• Quantitative proteomics (quantification)
• Protein-protein interaction
6
7
Components of Mass Spectrometer
• Ion source (ESI and MALDI)
• Mass analyzer (ion traps, TOF, Quadrupole, FT, etc.)
– Mass-to-charge ratio (m/z)
• Ion detector
8
Peptide and Intact Protein
• Peptide: a fragment of protein
• Some enzymes, e.g. trypsin, break protein into peptides.
• Some technology put intact protein into the mass spectrometer
9
Peptide Fragmentation
• Peptides tend to fragment along the backbone.
• Fragments can also loose neutral chemical groups like NH3 and H2O.
H...-HN-CH-CO . . . NH-CH-CO-NH-CH-CO-…OH
Ri-1 Ri Ri+1
H+
N-Terminus C-Terminus
Collision Induced Dissociation
10
Ideal Mass Spectrum
11
Real Mass Spectrum
12
N- and C-terminal Peptides
N-term
inal
pep
tides
C-te
rmin
al p
eptid
es
13
Terminal peptides and ion types
Peptide
Mass (D) 57 + 97 + 147 + 114 = 415
Peptide
Mass (D) 57 + 97 + 147 + 114 – 18 = 397
without
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N- and C-terminal Peptides
N-term
inal
pep
tides
C-te
rmin
al p
eptid
es
415
486
301
154
57
71
185
332
429
15
N- and C-terminal Peptides
N-term
inal
pep
tides
C-te
rmin
al p
eptid
es
415
486
301
154
57
71
185
332
429
16
N- and C-terminal Peptides
415
486
301
154
57
71
185
332
429
17
N- and C-terminal Peptides
415
486
301
154
57
71
185
332
429
Problem:
Reconstruct peptide from the set of masses of fragment
18
Mass Spectra
G V D L K
mass0
57 Da = ‘G’ 99 Da = ‘V’LK D V G
• The peaks in the mass spectrum:
– Prefix
– Fragments with neutral losses (-H2O, -NH3)
– Noise and missing peaks.
and Suffix Fragments.
D
H2O
19
Protein Identification with MS/MS
G V D L K
mass0
Inte
nsity
mass0
MS/MSPeptide Identification:
20
Protein Identification by Tandem Mass Spectrometry
SSeeqquueennccee
S#: 1708 RT: 54.47 AV: 1 NL: 5.27E6T: + c d Full ms2 638.00 [ 165.00 - 1925.00]
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1048.6397.1226.9
1049.6489.1
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MS/MS instrumentMS/MS instrument
De Novo interpretation•SherengaDatabase search•Sequest
21
De Novo vs. Database Search
S#: 1708 RT: 54.47 AV: 1 NL: 5.27E6T: + c d Full ms2 638.00 [ 165.00 - 1925.00]
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WR
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LP
T
L T
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De Novo
AVGELTK
Database Search
Database ofknown peptides
MDERHILNM, KLQWVCSDL, PTYWASDL, ENQIKRSACVM, TLACHGGEM, NGALPQWRT,
HLLERTKMNVV, GGPASSDA, GGLITGMQSD, MQPLMNWE,
ALKIIMNVRT, AVGELTK, HEWAILF, GHNLWAMNAC,
GVFGSVLRA, EKLNKAATYIN..
Database ofknown peptides
MDERHILNM, KLQWVCSDL, PTYWASDL, ENQIKRSACVM, TLACHGGEM, NGALPQWRT,
HLLERTKMNVV, GGPASSDA, GGLITGMQSD, MQPLMNWE,
ALKIIMNVRT, AVGELTK, HEWAILF, GHNLWAMNAC,
GVFGSVLRA, EKLNKAATYIN..
Mass, Score
22
Current Status
• It is still a open problem of protein sequencing no matter whether using de novo sequencing or database search methods
• Following algorithms only deal with simplified (or ideal) spectrums
• Some algorithms combine de novo sequencing and database search
23
Pros and Cons of de novo Sequencing
• Advantage:– Gets the sequences that are not necessarily in the database.
– An additional similarity search step using these sequences may identify the related proteins in the database.
• Disadvantage:– Requires higher quality data.
– Often contains errors.
24
Outline
• Motivation of proteomics
• Mass spectrometry-based proteomics
• Instrumentation of mass spectrometry
• De novo sequencing
• Database search
• Algorithms of real software (e.g., sequence tags)
25
De novo Peptide Sequencing
S#: 1708 RT: 54.47 AV: 1 NL: 5.27E6T: + c d Full ms2 638.00 [ 165.00 - 1925.00]
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bund
ance
850.3
687.3
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851.4425.0
949.4
326.0524.9
589.2
1048.6397.1226.9
1049.6489.1
629.0
SequenceSequence
26
Peptide Sequencing Problem
Goal: Find a peptide with maximal match between an experimental and theoretical spectrum.
Input:
– S: experimental spectrum
– Δ: set of possible ion types
– m: parent mass
Output:
– P: peptide with mass m, whose theoretical spectrum matches the experimental S spectrum the best
27
Procedure of De Novo Sequencing
• Build spectrum graph
– How to create vertices (from masses)
– How to create edges (from mass differences)
• Find best path or rank paths of spectrum graph
– How to find candidate paths
– How to score paths
28
S E Q U E N C Eb
Mass/Charge (M/Z)Mass/Charge (M/Z)
From Sequence to Spectrum
29
a
Mass/Charge (M/Z)Mass/Charge (M/Z)
S E Q U E N C E
From Sequence to Spectrum(cont.)
30
S E Q U E N C E
Mass/Charge (M/Z)Mass/Charge (M/Z)
a is an ion type shift in b
From Sequence to Spectrum(cont.)
31
y
Mass/Charge (M/Z)Mass/Charge (M/Z)
E C N E U Q E S
From Sequence to Spectrum (cont.)
32
Mass/Charge (M/Z)Mass/Charge (M/Z)
Inte
nsit
yIn
tens
ity
From Sequence to Spectrum (cont.)
33
Mass/Charge (M/Z)Mass/Charge (M/Z)
Inte
nsit
yIn
tens
ity
From Sequence to Spectrum (cont.)
34
noise
Mass/Charge (M/Z)Mass/Charge (M/Z)
From Sequence to Spectrum (cont.)
35
MS/MS Spectrum
Mass/Charge (M/z)Mass/Charge (M/z)
Inte
nsit
yIn
tens
ity
36
Some Mass Differences between Peaks Correspond to Amino Acids
ss
ssss
ee
eeee
ee
ee
ee
ee
ee
qquu
uu
uu
nn
nn
nn
ee
cc
cc
cc
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Now decoding from spectrum to sequence…?
Build spectrum graph
38
Peptide Fragmentation
HO NH3+
| |
R1 O R2 O R3 O R4
| || | || | || |H -- N --- C --- C --- N --- C --- C --- N --- C --- C --- N --- C -- COOH | | | | | | | H H H H H H H
y3
b2
y2 y1
b3a2 a3
b2-H2O
y3 -H2O
b3- NH3
y2 - NH3
• Different ion types (b, y, b-NH3, b-H2O)• Fragment at one site (internal ions)
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Example of Ion Type
• Δ={δ1, δ2,…, δk}
• Ion types
{b, b-NH3, b-H2O}
correspond to
Δ={0, 17, 18} *Note: In reality the δ value of ion type b is -1 but we will “hide” it for the sake of simplicity
40
Why Peptide Sequencing hard
• Two ladders of overlapping masses, could not tell whether it is b ion or y ion
• Incomplete fragmentation
• Chemical noise
• Mass accuracy of the instrument is not good enough (Q=K, G+V=156.090, R=156.101)
• Q: Is sequencing shorter or longer peptide harder?
41
Vertices of Spectrum Graph
• Vertices are generated by reverse shifts corresponding to
ion types Δ={δ1, δ2,…, δk}
• Every mass s in an MS/MS spectrum generates k vertices
V(s) = {s+δ1, s+δ2, …, s+δk}
corresponding to potential N-terminal peptides
• Vertices of the spectrum graph:
{initial vertex}V(s1) V(s2) ... V(sm) {terminal vertex}
42
Reverse Shifts
Shift in H2O+NH3
Shift in H2O
43
Edges of Spectrum Graph
• Two vertices with mass difference corresponding to
an amino acid A:
– Connect with an edge labeled by A (Directed Graph)
• Gap edges for di- and tri-peptides
– Potential sequence tag method (covered later)
44
Best Path of Spectrum Graph
• How to find candidate paths
• There are many paths, how to find the correct one?
• We need scoring to evaluate paths
45
Find Candidate Paths
• Heuristics: find a path with maximum number
of edges
• Longest path problem in DAG
• DFS (Depth First Search)
46
Path Score
• p(P,S) = probability that peptide P produces spectrum S= {s1,s2,…sq}
• Scoring = computing probabilities
47
Finding Optimal Paths in the Spectrum Graph
• For a given MS/MS spectrum S, find a peptide P’ maximizing p(P,S) over all possible peptides P:
• Peptides = paths in the spectrum graph
• P’ = the optimal path in the spectrum graph
• Some software rank paths
p(P,S)p(P',S) Pmax
48
Ratio Test Scoring for Partial Peptides
• Incorporates premiums for observed ions and penalties for missing ions.
• Example: for k=4, assume that for a partial peptide P’ we only see ions δ1,δ2,δ4.
The score is calculated as:
RRRR q
q
q
q
q
q
q
q 4321
)1(
)1(
49
Why Not Sequence De Novo?
• De novo sequencing is still not very accurate!
• Less than 30% of the peptides sequenced were completely correct!
Algorithm Amino Acid Accuracy
Whole Peptide Accuracy
Lutefisk (Taylor and Johnson, 1997). 0.566 0.189
SHERENGA (Dancik et. al., 1999). 0.690 0.289
Peaks (Ma et al., 2003). 0.673 0.246
PepNovo (Frank and Pevzner, 2005). 0.727 0.296
50
De Novo vs. Database Search
S#: 1708 RT: 54.47 AV: 1 NL: 5.27E6T: + c d Full ms2 638.00 [ 165.00 - 1925.00]
200 400 600 800 1000 1200 1400 1600 1800 2000m/z
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
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95
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Re
lative
Ab
un
da
nce
850.3
687.3
588.1
851.4425.0
949.4
326.0524.9
589.2
1048.6397.1226.9
1049.6489.1
629.0
WR
A
C
VG
E
K
DW
LP
T
L T
WR
A
C
VG
E
K
DW
LP
T
L T
De Novo
AVGELTK
Database Search
Database ofknown peptides
MDERHILNM, KLQWVCSDL, PTYWASDL, ENQIKRSACVM, TLACHGGEM, NGALPQWRT,
HLLERTKMNVV, GGPASSDA, GGLITGMQSD, MQPLMNWE,
ALKIIMNVRT, AVGELTK, HEWAILF, GHNLWAMNAC,
GVFGSVLRA, EKLNKAATYIN..
Database ofknown peptides
MDERHILNM, KLQWVCSDL, PTYWASDL, ENQIKRSACVM, TLACHGGEM, NGALPQWRT,
HLLERTKMNVV, GGPASSDA, GGLITGMQSD, MQPLMNWE,
ALKIIMNVRT, AVGELTK, HEWAILF, GHNLWAMNAC,
GVFGSVLRA, EKLNKAATYIN..
51
Outline
• Motivation of proteomics
• Mass spectrometry-based proteomics
• Instrumentation: Mass Spectrometry
• De novo sequencing algorithm
• Database search
• Algorithms of real software (e.g., sequence tags)
52
Peptide Identification Problem
Goal: Find a peptide from the database with maximal match between an experimental and theoretical spectrum.
Input:
– S: experimental spectrum
– database of peptides
– Δ: set of possible ion types
– m: parent mass
Output:
– A peptide of mass m from the database whose theoretical spectrum matches the experimental S spectrum the best
53
Match between Spectra and the Shared Peak Count
• The match between two spectra is the number of masses
(peaks) they share (Shared Peak Count or SPC)
• In practice mass-spectrometrists use the weighted SPC
that reflects intensities of the peaks
• Match between experimental and theoretical spectra is
defined similarly
54
MS/MS Database Search
Database search in mass-spectrometry has been successful in identification of already known proteins.
Experimental spectrum can be compared with theoretical spectra of database peptides to find the best fit.
SEQUEST (Yates et al., 1995)
But reliable algorithms for identification of peptides is a much more difficult problem.
Q: Why can a peptide be not identical to a sequence in the database
55
Deficiency of the Shared Peaks Count
Shared peaks count (SPC): intuitive measure of spectral similarity.
Problem: SPC diminishes very quickly as the number of mutations increases.
Only a small portion of correlations between the spectra of mutated peptides is captured by SPC.
56
SPC Diminishes Quickly
S(PRTEIN) = {98, 133, 246, 254, 355, 375, 476, 484, 597, 632}
S(PRTEYN) = {98, 133, 254, 296, 355, 425, 484, 526, 647, 682}
S(PGTEYN) = {98, 133, 155, 256, 296, 385, 425, 526, 548, 583}
no mutations
SPC=10
1 mutation
SPC=5
2 mutations
SPC=2
57
Post-Translational ModificationsProteins are involved in cellular signaling and
metabolic regulation.
They are subject to a large number of biological modifications.
Almost all protein sequences are post-translationally modified and 200 types of modifications of amino acid residues are known.
58
Examples of Post-Translational Modification
Post-translational modifications increase the number of “letters” in amino acid alphabet and lead to a combinatorial explosion in both database search and de novo approaches.
59
Search for Modified Peptides: Virtual Database Approach
Yates et al.,1995: an exhaustive search in a virtual database of all modified peptides.
Exhaustive search leads to a large combinatorial problem, even for a small set of modifications types.
Problem (Yates et al.,1995). Extend the virtual database approach to a large set of modifications.
60
Modified Peptide Identification Problem
Goal: Find a modified peptide from the database with maximal match between an experimental and theoretical spectrum.
Input:
– S: experimental spectrum
– database of peptides
– Δ: set of possible ion types
– m: parent mass
– Parameter k (# of mutations/modifications)
Output:
– A peptide of mass m that is at most k mutations/modifications apart from a database peptide and whose theoretical spectrum matches the experimental S spectrum the best
61
Spectrum Alignment
• See 8.14 and 8.15 in the text book for one algorithm
• Complicated for real spectrums
62
Outline
• Motivation of proteomics
• Mass spectrometry-based proteomics
• Instrumentation: Mass Spectrometry
• De novo sequencing algorithm
• Database search
• Algorithms of real software (e.g., sequence tags)
63
Combining de novo and Database Search in Mass-Spectrometry
• So far de novo and database search were presented as two separate techniques
• Database search is rather slow: many labs generate more than 100,000 spectra per day. SEQUEST takes approximately 1 minute to compare a single spectrum against SWISS-PROT (54Mb) on a desktop.
• It will take SEQUEST more than 2 months to analyze the MS/MS data produced in a single day.
• Q: Can slow database search be combined with fast de novo analysis?
64
What Can be Done with De Novo?
• Given an MS/MS spectrum:
– Can de novo predict the entire peptide sequence?
– Can de novo predict a set of partial sequences, that with high probability, contains at least one correct tag?
A Covering Set of Tags
- No! (accuracy is less than 30%).
- Yes!
65
Peptide Sequence Tags
• A Peptide Sequence Tag is short substring of a peptide.
Example: G V D L KG V D V D L
D L K
Tags:
66
Filtration with Peptide Sequence Tags
• Peptide sequence tags can be used as filters in database searches.
• The Filtration: Consider only database peptides that contain the tag (in its correct relative mass location).
• First suggested by Mann and Wilm (1994).
• Similar concepts also used by:– GutenTag - Tabb et. al. 2003.
– MultiTag - Sunayev et. al. 2003.
– OpenSea - Searle et. al. 2004.
67
Why Filter Database Candidates?
• Effective filtration can greatly speed-up the process, enabling expensive searches involving post-translational modifications.
• Goal: generate a small set of covering tags and use them to filter the database peptides.
68
Summary
• Protein sequencing
• Mass spectrum
• De novo search and database search
• Difficulty of protein sequencing
69
The End
70
Quality Measure of Mass Spectrometer
• Sensitivity
• Mass accuracy
• Resolution
• Dynamic range
71
Exhaustive Search for Modified Peptides
• YFDSTDYNMAK
• 25=32 possibilities, with 2 types of modifications!
Phosphorylation?
Oxidation?
• For each peptide, generate all modifications.
• Score each modification.
72
Peptide Identification Problem: Challenge
Very similar peptides may have very different spectra!
Goal: Define a notion of spectral similarity that correlates well with the sequence similarity.
If peptides are a few mutations/modifications apart, the spectral similarity between their spectra should be high.
73
Why Filtration ?
Scoring
Protein Query
Sequence Alignment – Smith Waterman Algorithm
Sequence matches
Protein Sequences
Filtration
Filtered Sequences
Sequence Alignment – BLAST
Database
actgcgctagctacggatagctgatccagatcgatgccataggtagctgatccatgctagcttagacataaagcttgaatcgatcgggtaacccatagctagctcgatcgacttagacttcgattcgatcgaattcgatctgatctgaatatattaggtccgatgctagctgtggtagtgatgtaaga
• BLAST filters out very few correct matches and is almost as accurate as Smith – Waterman algorithm.
Database
actgcgctagctacggatagctgatccagatcgatgccataggtagctgatccatgctagcttagacataaagcttgaatcgatcgggtaacccatagctagctcgatcgacttagacttcgattcgatcgaattcgatctgatctgaatatattaggtccgatgctagctgtggtagtgatgtaaga
74
Filtration and MS/MS
Scoring
MS/MS spectrum
Peptide Sequencing – SEQUEST / Mascot
Sequence matches
Peptide Sequences
Filtration
Peptide Sequences
Database
MDERHILNMKLQWVCSDLPTYWASDLENQIKRSACVMTLACHGGEMNGALPQWRTHLLERTYKMNVVGGPASSDALITGMQSDPILLVCATRGHEWAILFGHNLWACVNMLETAIKLEGVF
GSVLRAEKLNKAAPETYIN..
Database
MDERHILNMKLQWVCSDLPTYWASDLENQIKRSACVMTLACHGGEMNGALPQWRTHLLERTYKMNVVGGPASSDALITGMQSDPILLVCATRGHEWAILFGHNLWACVNMLETAIKLEGVF
GSVLRAEKLNKAAPETYIN..
75
Filtration in MS/MS Sequencing
• Filtration in MS/MS is more difficult than in BLAST.
• Early approaches using Peptide Sequence Tags were not able to substitute the complete database search.
• Current filtration approaches are mostly used to generate additional identifications rather than replace the database search.
• Can we design a filtration based search that can replace the database search, and is orders of magnitude faster?
76
Asking the Old Question Again: Why Not Sequence De Novo?
• De novo sequencing is still not very accurate!
Algorithm Amino Acid Accuracy
Whole Peptide Accuracy
Lutefisk (Taylor and Johnson, 1997). 0.566 0.189
SHERENGA (Dancik et. al., 1999). 0.690 0.289
Peaks (Ma et al., 2003). 0.673 0.246
PepNovo (Frank and Pevzner, 2005). 0.727 0.296