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Proteomics%20Technology%20and%20Protein%20Identification

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Title: Proteomics%20Technology%20and%20Protein%20Identification


1
Proteomics Technology and Protein Identification
  • Nathan Edwards
  • Center for Bioinformatics and Computational
    Biology

2
Outline
  • Proteomics
  • Mass Spectrometry
  • Protein Quantitation
  • Protein Identification
  • Computer Lab

3
Proteomics
  • Proteins are the machines that drive much of
    biology
  • Genes are merely the recipe
  • The direct characterization of a samples
    proteins en masse.
  • What proteins are present?
  • What isoform of each protein is present?
  • How much of each protein is present?

4
Systems Biology
  • Establish relationships by
  • Choosing related samples,
  • Global characterization, and
  • Comparison.

Gene / Transcript / Protein Gene / Transcript / Protein
Measurement Predetermined Unknown
Discrete (DNA) Genotyping Sequencing
Continuous Gene Expression Proteomics
5
Samples
  • Healthy / Diseased
  • Cancerous / Benign
  • Drug resistant / Drug susceptible
  • Bound / Unbound
  • Tissue specific
  • Cellular location specific
  • Mitochondria, Membrane

6
Protein Chemistry Assay Techniques
  • Gel Electrophoresis
  • Isoelectric point
  • Molecular weight
  • Liquid Chromatography
  • Hydrophobicity
  • Digestion Enzymes
  • Cut protein at motif
  • Fluorescence
  • Staining
  • Affinity capture
  • Phosphorylation
  • Protein Binding
  • Receptors
  • Complexes
  • Flow Cytometry
  • Mass Spectrometry
  • Accurate molecular weight

7
2D Gel-Electrophoresis
  • Protein separation
  • Molecular weight (Mw)
  • Isoelectric point (pI)
  • Staining
  • Birds-eye view of protein abundance

8
2D Gel-Electrophoresis
Bécamel et al., Biol. Proced. Online 2002494-104
.
9
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

10
Mass Spectrometer
  • ElectronMultiplier(EM)
  • Time-Of-Flight (TOF)
  • Quadrapole
  • Ion-Trap
  • MALDI
  • Electro-SprayIonization (ESI)

11
Mass Spectrometer(MALDI-TOF)
(b) M_at_LDITM LR by Micromass, UK
Detector (linear mode)
Reflectron
N2 Laser
Lens
Detector (reflectron mode)
Target plate with sample
12
Mass Spectrometer (MALDI-TOF)
UV (337 nm)
Microchannel plate detector
Field-free drift zone
Source
Pulse voltage
Analyte/matrix
Ed 0
Length D
Length s
Backing plate (grounded)
Extraction grid (source voltage -Vs)
Detector grid -Vs
13
Mass Spectrum
14
Mass is fundamental
15
Mass Spectrum
16
Mass Spectrum
  • Isotope Cluster
  • 12C 99
  • 13C 1

17
Peptide Mass Fingerprint
Cut out 2D-GelSpot
18
Peptide Mass Fingerprint
Trypsin Digest
19
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

20
Mass Spectrometry
  • Strengths
  • Precise molecular weight
  • Fragmentation
  • Automated
  • Weaknesses
  • Best for a few molecules at a time
  • Best for small molecules
  • Mass-to-charge ratio, not mass
  • Intensity ? Abundance

21
Proteomics Quantitation
  • 2D-Gel Electrophoresis
  • Replicate LC/MS acquisitions
  • Stable Isotope Labeling
  • Protein profiling

22
LC/MS for Peptide Abundance
23
LC/MS for Peptide Abundance
Mass Spectrometry
LC/MS 1 MS spectrum every 1-2 seconds
24
LC/MS for Peptide Abundance
25
LC/MS for Peptide Abundance
26
Stable Isotope Labeling
27
Stable Isotope Labeling
  • SILAC Lysine with 12C6 vs 13C6

28
MALDI Protein Profiling
  • Hundreds of healthy and diseased samples
  • Single MS spectrum per sample
  • Statistical datamining to find biomarkers
  • Commercialization for ovarian cancer under name
    Ovacheck

29
MALDI Protein ProfilingMale Spectra
30
MALDI Protein ProfilingFemale Spectra
31
Protein Profiling Statgram
32
MALDI Protein Profiling
33
MALDI Protein Profiling
34
Peptide Identification by MS/MS
  • Most mature proteomics workflow
  • Sample preparation
  • Instruments
  • Software
  • Compatible with quantitation by
  • Replicate LC/MS acquisitions
  • Stable isotope labeling
  • 2D-Gels (but essentially unnecessary)

35
Sample Preparation for MS/MS
36
Single Stage MS
MS
37
Tandem Mass Spectrometry(MS/MS)
Precursor selection
38
Tandem Mass Spectrometry(MS/MS)
Precursor selection collision induced
dissociation (CID)
MS/MS
39
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
40
Peptide Fragmentation
41
Peptide Fragmentation
yn-i-1
-HN-CH-CO-NH-CH-CO-NH-
CH-R
Ri
i1
R
i1
bi1
42
Peptide Fragmentation
xn-i
yn-i-1
-HN-CH-CO-NH-CH-CO-NH-
CH-R
Ri
i1
R
ai
i1
bi1
43
Peptide Fragmentation
Peptide S-G-F-L-E-E-D-E-L-K
MW 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
44
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
45
Peptide Fragmentation
1166
1020
907
778
663
534
405
292
145
88
b ions
S
K
L
E
D
E
E
L
F
G
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
46
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
47
Peptide Identification
  • Given
  • The mass of the precursor ion, and
  • The MS/MS spectrum
  • Output
  • The amino-acid sequence of the peptide

48
Peptide Identification
  • Two paradigms
  • De novo interpretation
  • Sequence database search

49
De Novo Interpretation
50
De Novo Interpretation
51
De Novo Interpretation
52
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
53
De Novo Interpretation
from Lu and Chen (2003), JCB 101
54
De Novo Interpretation
55
De Novo Interpretation
from Lu and Chen (2003), JCB 101
56
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

57
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

58
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

59
Peptide Fragmentation
K
L
E
D
E
E
L
F
G
S
100
Intensity
0
m/z
250
500
750
1000
60
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
61
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
62
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!

63
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

64
Peptide Candidate Filtering
  • gtALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDL
    GEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK

No missed cleavage sites
MK WVTFISLLFLFSSAYSR GVFR R DAHK SEVAHR FK DLGEENF
K ALVLIAFAQYLQQCPFEDHVK LVNEVTEFAK
65
Peptide Candidate Filtering
  • gtALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDL
    GEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK

One missed cleavage site
MKWVTFISLLFLFSSAYSR WVTFISLLFLFSSAYSRGVFR GVFRR RD
AHK DAHKSEVAHR SEVAHRFK FKDLGEENFK DLGEENFKALVLIAF
AQYLQQCPFEDHVK ALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK
66
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

67
Peptide Molecular Weight
i0
Same peptide,i of C13 isotope
i1
i2
i3
i4
68
Peptide Molecular Weight
i0
Same peptide,i of C13 isotope
i1
i2
i3
i4
69
Peptide Molecular Weight
from Isotopes An IonSource.Com Tutorial
70
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

71
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

72
Mascot Search Engine
73
Mascot MS/MS Ions Search
74
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

75
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

76
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

77
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

78
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

79
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!

80
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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