protein identification by sequence database search
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Protein Identification by
Sequence Database Search
Protein Identification by
Sequence Database Search
Nathan EdwardsDepartment of Biochemistry and Mol. & Cell. BiologyGeorgetown University Medical Center
2
Outline
• Proteomics
• Mass Spectrometry
• Protein Identification• Peptide Mass Fingerprint• Tandem Mass Spectrometry
3
Proteomics
• Proteins are the machines that drive much of biology• Genes are merely the recipe
• The direct characterization of a sample’s proteins en masse. • What proteins are present?• How much of each protein is present?
4
2D Gel-Electrophoresis
• Protein separation• Molecular weight (MW)• Isoelectric point (pI)
• Staining
• Birds-eye view of protein abundance
5
2D Gel-Electrophoresis
Bécamel et al., Biol. Proced. Online 2002;4:94-104.
6
Paradigm Shift
• Traditional protein chemistry assay methods struggle to establish identity.
• Identity requires:• Specificity of measurement (Precision)
• Mass spectrometry• A reference for comparison
(Measurement → Identity)• Protein sequence databases
7
Mass Spectrometer
Ionizer
Sample
+_
Mass Analyzer Detector
• MALDI• Electro-Spray
Ionization (ESI)
• Time-Of-Flight (TOF)• Quadrapole• Ion-Trap
• ElectronMultiplier(EM)
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Mass Spectrometer (MALDI-TOF)
Source
Length = s
Field-free drift zone
Length = D
Ed = 0
Microchannel plate detector
Backing plate(grounded) Extraction grid
(source voltage -Vs)
UV (337 nm)
Detector grid -Vs
Pulse voltage
Analyte/matrix
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Mass Spectrum
10
Mass is fundamental
11
Peptide Mass Fingerprint
Cut out2D-Gel
Spot
12
Peptide Mass Fingerprint
Trypsin Digest
13
Peptide Mass Fingerprint
MS
14
Peptide Mass Fingerprint
15
Peptide Mass Fingerprint
• Trypsin: digestion enzyme• Highly specific• Cuts after K & R except if followed by P
• Protein sequence from sequence database• In silico digest• Mass computation
• For each protein sequence in turn:• Compare computer generated masses with
observed spectrum
16
Protein Sequence
• Myoglobin GLSDGEWQQV LNVWGKVEAD IAGHGQEVLI RLFTGHPETL EKFDKFKHLK TEAEMKASED LKKHGTVVLT ALGGILKKKG HHEAELKPLA QSHATKHKIP IKYLEFISDA IIHVLHSKHP GDFGADAQGA MTKALELFRN DIAAKYKELG FQG
17
Protein Sequence
• Myoglobin GLSDGEWQQV LNVWGKVEAD IAGHGQEVLI RLFTGHPETL EKFDKFKHLK TEAEMKASED LKKHGTVVLT ALGGILKKKG HHEAELKPLA QSHATKHKIP IKYLEFISDA IIHVLHSKHP GDFGADAQGA MTKALELFRN DIAAKYKELG FQG
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Amino-Acid Masses
Amino-Acid Residual MW Amino-Acid Residual MW
A Alanine 71.03712 M Methionine 131.04049
C Cysteine 103.00919 N Asparagine 114.04293
D Aspartic acid 115.02695 P Proline 97.05277
E Glutamic acid 129.04260 Q Glutamine 128.05858
F Phenylalanine 147.06842 R Arginine 156.10112
G Glycine 57.02147 S Serine 87.03203
H Histidine 137.05891 T Threonine 101.04768
I Isoleucine 113.08407 V Valine 99.06842
K Lysine 128.09497 W Tryptophan 186.07932
L Leucine 113.08407 Y Tyrosine 163.06333
19
Peptide Mass & m/z
• Peptide Molecular Weight:N-terminal-mass (0.00) + Sum (AA masses) +C-terminal-mass (18.010560)
• Observed Peptide m/z:(Peptide Molecular Weight + z * Proton-mass (1.007825)) / z
• Monoisotopic mass values!
20
Peptide Masses
1811.90 GLSDGEWQQVLNVWGK 1606.85 VEADIAGHGQEVLIR 1271.66 LFTGHPETLEK 1378.83 HGTVVLTALGGILK 1982.05 KGHHEAELKPLAQSHATK 1853.95 GHHEAELKPLAQSHATK 1884.01 YLEFISDAIIHVLHSK 1502.66 HPGDFGADAQGAMTK 748.43 ALELFR
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Peptide Mass Fingerprint
GL
SD
GE
WQ
QV
LN
VW
GK
VE
AD
IAG
HG
QE
VL
IR
LF
TG
HP
ET
LE
K
HG
TV
VL
TA
LG
GIL
K
KG
HH
EA
EL
KP
LA
QS
HA
TK
GH
HE
AE
LK
PL
AQ
SH
AT
KY
LE
FIS
DA
IIH
VL
HS
K
HP
GD
FG
AD
AQ
GA
MT
K
AL
EL
FR
22
Sample Preparation for Tandem Mass Spectrometry
Enzymatic Digestand
Fractionation
23
Single Stage MS
MS
24
Tandem Mass Spectrometry(MS/MS)
MS/MS
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Peptide Fragmentation
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
Peptides consist of amino-acids arranged in a linear backbone.
26
Peptide Fragmentation
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Peptide Fragmentation
-HN-CH-CO-NH-CH-CO-NH-
RiRi+1
bi
yn-iyn-i-1
bi+1
28
Peptide Fragmentation
-HN-CH-CO-NH-CH-CO-NH-
RiCH-R’
bi
yn-iyn-i-1
bi+1
R”
i+1
i+1ai
xn-i
ci
zn-i
29
Peptide Fragmentation
Peptide: S-G-F-L-E-E-D-E-L-KMW ion ion MW
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
30
Peptide Fragmentation
100
0250 500 750 1000
m/z
% I
nte
nsit
y
K1166
L1020
E907
D778
E663
E534
L405
F292
G145
S88 b ions
147260389504633762875102210801166 y ions
31
Peptide Fragmentation
K1166
L1020
E907
D778
E663
E534
L405
F292
G145
S88 b ions
100
0250 500 750 1000
m/z
% I
nte
nsit
y
147260389504633762875102210801166 y ions
y6
y7
y2 y3 y4
y5
y8 y9
32
Peptide Fragmentation
K1166
L1020
E907
D778
E663
E534
L405
F292
G145
S88 b ions
100
0250 500 750 1000
m/z
% I
nte
nsit
y
147260389504633762875102210801166 y ions
y6
y7
y2 y3 y4
y5
y8 y9
b3
b5 b6 b7b8 b9
b4
33
Peptide Identification
Given:• The mass of the precursor ion, and• The MS/MS spectrum
Output:• The amino-acid sequence of the
peptide
34
Peptide Identification
Two paradigms:
• De novo interpretation
• Sequence database search
35
De Novo Interpretation
100
0250 500 750 1000
m/z
% I
nte
nsit
y
36
De Novo Interpretation
100
0250 500 750 1000
m/z
% I
nte
nsit
y
E L
37
De Novo Interpretation
100
0250 500 750 1000
m/z
% I
nte
nsit
y
E L F
KL
SGF G
E DE
L E
E D E L
38
De Novo Interpretation
Amino-Acid Residual MW Amino-Acid Residual MW
A Alanine 71.03712 M Methionine 131.04049
C Cysteine 103.00919 N Asparagine 114.04293
D Aspartic acid 115.02695 P Proline 97.05277
E Glutamic acid 129.04260 Q Glutamine 128.05858
F Phenylalanine 147.06842 R Arginine 156.10112
G Glycine 57.02147 S Serine 87.03203
H Histidine 137.05891 T Threonine 101.04768
I Isoleucine 113.08407 V Valine 99.06842
K Lysine 128.09497 W Tryptophan 186.07932
L Leucine 113.08407 Y Tyrosine 163.06333
39
De Novo Interpretation
…from Lu and Chen (2003), JCB 10:1
40
De Novo Interpretation
41
De Novo Interpretation
…from Lu and Chen (2003), JCB 10:1
42
De Novo Interpretation
• Find good paths in spectrum graph• Can’t 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
43
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
44
Sequence Database Search
• Compares peptides from a protein sequence database with spectra
• Filter peptide candidates by• Precursor mass• Digest motif
• Score each peptide against spectrum• Generate all possible peptide fragments• Match putative fragments with peaks• Score and rank
45
Sequence Database Search
100
0250 500 750 1000
m/z
% I
nte
nsit
y
KLEDEELFGS
46
Sequence Database Search
100
0250 500 750 1000
m/z
% I
nte
nsit
y
K1166
L1020
E907
D778
E663
E534
L405
F292
G145
S88 b ions
147260389504633762875102210801166 y ions
47
Sequence Database Search
K1166
L1020
E907
D778
E663
E534
L405
F292
G145
S88 b ions
100
0250 500 750 1000
m/z
% I
nte
nsit
y
147260389504633762875102210801166 y ions
y6
y7
y2 y3 y4
y5
y8 y9
b3
b5 b6 b7b8 b9
b4
48
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!
49
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
50
Peptide Candidate Filtering>ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK…
No missed cleavage sitesMKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK…
51
Peptide Candidate Filtering>ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK…
One missed cleavage siteMKWVTFISLLFLFSSAYSRWVTFISLLFLFSSAYSRGVFRGVFRRRDAHKDAHKSEVAHRSEVAHRFKFKDLGEENFKDLGEENFKALVLIAFAQYLQQCPFEDHVKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK…
52
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
53
Peptide Molecular Weight
Same peptide,i = # of C13 isotope
i=0
i=1
i=2
i=3i=4
54
Peptide Molecular Weight
Same peptide,i = # of C13 isotope
i=0
i=1
i=2
i=3i=4
55
Peptide Molecular Weight
…from “Isotopes” – An IonSource.Com Tutorial
56
Peptide Molecular Weight
• Peptide sequence WVTFISLLFLFSSAYSR• Potential phosphorylation? S,T,Y + 80 Da
WVTFISLLFLFSSAYSR 2018.06
WVTFISLLFLFSSAYSR 2098.06
WVTFISLLFLFSSAYSR 2098.06
WVTFISLLFLFSSAYSR 2098.06
WVTFISLLFLFSSAYSR 2098.06
WVTFISLLFLFSSAYSR 2098.06
WVTFISLLFLFSSAYSR 2098.06
WVTFISLLFLFSSAYSR 2178.06
WVTFISLLFLFSSAYSR 2178.06
… …
WVTFISLLFLFSSAYSR 2418.06
- 7 Molecular Weights- 64 “Peptides”
57
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
58
Peptide Identification
• High-throughput workflows demand we analyze all spectra, all the time.
• Spectra may not contain enough information to be interpreted correctly• …fading in and out on a cell phone
• Spectra may contain too many peaks• …static or background noise
• Peptides may not match our assumptions• …its all Greek to me
• “Don’t know” is an acceptable answer!
59
Peptide Identification
• Rank the best peptide identifications
• Is the top ranked peptide correct?
60
Peptide Identification
• Rank the best peptide identifications
• Is the top ranked peptide correct?
61
Peptide Identification
• Rank the best peptide identifications
• Is the top ranked peptide correct?
62
Peptide Identification
• Incorrect peptide has best score• Correct peptide is missing?• Potential for incorrect conclusion• What score ensures no incorrect
peptides?• Correct peptide has weak score
• Insufficient fragmentation, poor score• Potential for weakened conclusion• What score ensures we find all correct
peptides?
63
Statistical Significance
• Can’t prove particular identifications are right or wrong...• ...need to know fragmentation in advance!
• A minimal standard for identification scores...• ...better than guessing.• p-value, E-value, statistical significance
64
Mascot MS/MS Ions Search
65
Mascot MS/MS Search Results
66
Mascot MS/MS Search Results
67
Mascot MS/MS Search Results
68
Mascot MS/MS Search Results
69
Mascot MS/MS Search Results
70
Mascot MS/MS Search Results
71
Mascot MS/MS Search Results
72
Sequence Database SearchTraps and Pitfalls
Search options may eliminate the correct peptide• Precursor mass tolerance too small• Incorrect precursor ion charge state• Non-tryptic or semi-tryptic peptide• Incorrect or unexpected modification• Sequence database too conservative• Unreliable taxonomy annotation
73
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
74
Sequence Database SearchTraps 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?
• Incorrect instrument settings or fragment tolerance can render scores non-specific.
• 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
75
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
76
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
77
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.
78
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
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