protein sequencing and identification by mass spectrometry
TRANSCRIPT
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Protein Sequencing and
Identification by Mass
Spectrometry
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Masses of Amino Acid Residues
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Protein Backbone
H...-HN-CH-CO-NH-CH-CO-NH-CH-CO-…OH
Ri-1 Ri Ri+1
AA residuei-1 AA residuei AA residuei+1
N-terminus C-terminus
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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+
Prefix Fragment Suffix Fragment
Collision Induced Dissociation
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Breaking Protein into Peptides and Peptides into Fragment Ions• Proteases, e.g. trypsin, break protein into
peptides.• A Tandem Mass Spectrometer further breaks
the peptides down into fragment ions and measures the mass of each piece.
• Mass Spectrometer accelerates the fragmented ions; heavier ions accelerate slower than lighter ones.
• Mass Spectrometer measure mass/charge ratio of an ion.
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N- and C-terminal Peptides
N-term
inal
pep
tides
C-te
rmin
al p
eptid
es
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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
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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
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N- and C-terminal Peptides
415
486
301
154
57
71
185
332
429
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N- and C-terminal Peptides
415
486
301
154
57
71
185
332
429
Reconstruct peptide from the set of masses of fragment ions
(mass-spectrum)
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Peptide Fragmentation
y3
b2
y2 y1
b3a2 a3
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
b2-H2O
y3 -H2O
b3- NH3
y2 - NH3
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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
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Protein Identification with MS/MS
G V D L K
mass0
Inte
nsity
mass0
MS/MSPeptide Identification:
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Tandem Mass-Spectrometry
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Breaking Proteins into Peptides
peptides
MPSER……
GTDIMRPAKID
……
HPLCTo MS/MSMPSERGTDIMRPAKID......
protein
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Mass SpectrometryMatrix-Assisted Laser Desorption/Ionization (MALDI)
From lectures by Vineet Bafna (UCSD)
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Tandem Mass SpectrometryRT: 0.01 - 80.02
5 10 15 20 253 035 40 45 50 55 60 65 70 75 80Time (min)
0
10
20
30
40
50
60
70
80
90
100
Relati
ve Ab
undanc
e
13891991
1409 21491615 1621
14112147
161119951655
15931387
21551435 19872001 21771445 1661
19372205
1779 21352017
1313 22071307 23291105 17071095
2331
NL:1.52E8
Base Peak F: + c Full ms [ 300.00 - 2000.00]
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 2000
m/z
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Rel
ativ
e A
bund
ance
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
Scan 1708
LC
S#: 1707 RT: 54.44 AV: 1 NL: 2.41E7F: + c Full ms [ 300.00 - 2000.00]
200 400 600 800 1000 1200 1400 1600 1800 2000
m/z
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Rel
ativ
e Ab
unda
nce
638.0
801.0
638.9
1173.8872.3 1275.3
687.6944.7 1884.51742.1122.0783.3 1048.3 1413.9 1617.7
Scan 1707
MS
MS/MSIon
Source
MS-1collision
cell MS-2
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Protein Identification by Tandem Mass SpectrometrySSeeqquueennccee
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 2000
m/z
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Rel
ativ
e Ab
unda
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
MS/MS instrumentMS/MS instrument
Database search•Sequestde Novo interpretation•Sherenga
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Tandem Mass Spectrum• Tandem Mass Spectrometry (MS/MS): mainly
generates partial N- and C-terminal peptides • Spectrum consists of different ion types
because peptides can be broken in several places.
• Chemical noise often complicates the spectrum.
• Represented in 2-D: mass/charge axis vs. intensity axis
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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
90
95
100
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 of all peptides = 20n
AAAAAAAA,AAAAAAAC,AAAAAAAD,AAAAAAAE,AAAAAAAG,AAAAAAAF,AAAAAAAH,AAAAAAI,
AVGELTI, AVGELTK , AVGELTL, AVGELTM,
YYYYYYYS,YYYYYYYT,YYYYYYYV,YYYYYYYY
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
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De Novo vs. Database Search: A Paradox
• The database of all peptides is huge ≈ O(20n) .
• The database of all known peptides is much smaller ≈ O(108).
• However, de novo algorithms can be much faster, even though their search space is much larger!
• A database search scans all peptides in the database of all known peptides search space to find best one.
• De novo eliminates the need to scan database of all peptides by modeling the problem as a graph search.
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De novo Peptide Sequencing
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 2000
m/z
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Rel
ativ
e A
bund
ance
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
SequenceSequence
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Theoretical Spectrum
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Theoretical Spectrum (cont’d)
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Theoretical Spectrum (cont’d)
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Building Spectrum Graph• How to create vertices (from masses)
• How to create edges (from mass differences)
• How to score paths
• How to find best path
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S E Q U E N C E
b
Mass/Charge (M/Z)Mass/Charge (M/Z)
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a
Mass/Charge (M/Z)Mass/Charge (M/Z)
S E Q U E N C E
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S E Q U E N C E
Mass/Charge (M/Z)Mass/Charge (M/Z)
a is an ion type shift in b
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y
Mass/Charge (M/Z)Mass/Charge (M/Z)
E C N E U Q E S
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Mass/Charge (M/Z)Mass/Charge (M/Z)
Inte
nsit
yIn
tens
ity
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Mass/Charge (M/Z)Mass/Charge (M/Z)
Inte
nsit
yIn
tens
ity
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noise
Mass/Charge (M/Z)Mass/Charge (M/Z)
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MS/MS Spectrum
Mass/Charge (M/z)Mass/Charge (M/z)
Inte
nsit
yIn
tens
ity
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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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Ion Types• Some masses correspond to fragment
ions, others are just random noise
• Knowing ion types Δ={δ1, δ2,…, δk} lets us
distinguish fragment ions from noise
• We can learn ion types δi and their
probabilities qi by analyzing a large test
sample of annotated spectra.
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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
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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
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Peptide Sequencing ProblemGoal: 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
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Vertices of Spectrum Graph• Masses of potential N-terminal peptides
• Vertices are generated by reverse shifts corresponding to ion types
Δ={δ1, δ2,…, δk}
• Every N-terminal peptide can generate up to k ions
m-δ1, m-δ2, …, m-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}
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Reverse Shifts
Shift in H2O+NH3
Shift in H2O
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Edges of Spectrum Graph
• Two vertices with mass difference
corresponding to an amino acid A:
• Connect with an edge labeled by A
• Gap edges for di- and tri-peptides
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Paths
• Path in the labeled graph spell out amino acid sequences
• There are many paths, how to find the correct one?
• We need scoring to evaluate paths
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Path Score• p(P,S) = probability that peptide P produces
spectrum S= {s1,s2,…sq}
• p(P, s) = the probability that peptide P generates a peak s
• Scoring = computing probabilities
• p(P,S) = πsєS p(P, s)
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• For a position t that represents ion type dj :
qj, if peak is generated at t
p(P,st) =
1-qj , otherwise
Peak Score
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Peak Score (cont’d)
• For a position t that is not associated with an ion type:
qR , if peak is generated at t
pR(P,st) =
1-qR , otherwise
• qR = the probability of a noisy peak that does not correspond to any ion type
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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
p(P,S)p(P',S) Pmax
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Ions and Probabilities• Tandem mass spectrometry is characterized
by a set of ion types {δ1,δ2,..,δk} and their probabilities {q1,...,qk}
• δi-ions of a partial peptide are produced independently with probabilities qi
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Ions and Probabilities
• A peptide has all k peaks with probability
• and no peaks with probability
• A peptide also produces a ``random noise'' with uniform probability qR in any position.
k
iiq
1
k
iiq
1
)1(
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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(
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Scoring Peptides
• T- set of all positions.
• Ti={t δ1,, t δ2,..., ,t δk,}- set of positions that represent ions of partial peptides Pi.
• A peak at position tδj is generated with probability qj.
• R=T- U Ti - set of positions that are not associated with any partial peptides (noise).
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Probabilistic Model
• For a position t δj Ti the probability p(t, P,S) that peptide P produces a peak at position t.
• Similarly, for tR, the probability that P produces a random noise peak at t is:
otherwise1
position tat generated ispeak a if),,( j
j
j
q
qSPtP
otherwise1
position tat generated ispeak a if)(
R
RR q
qtP
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Probabilistic Score
• For a peptide P with n amino acids, the score for the whole peptides is expressed by the following ratio test:
n
i
k
j iR
i
R j
j
tp
SPtp
Sp
SPp
1 1 )(
),,(
)(
),(
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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
90
95
100
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..