Proteomics

1
Proteomics Mass Spectrometry

• Nathan Edwards
• Center for Bioinformatics and Computational
  Biology

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 samples
  proteins en masse.
• What proteins are present?
• How much of each protein is present?

4
Systems Biology

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

5
Samples

• Healthy / Diseased
• Cancerous / Benign
• Drug resistant / Drug susceptible
• Bound / Unbound
• Tissue specific
• Cellular location specific
• Mitochondria, Membrane

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2D Gel-Electrophoresis

• Protein separation
• Molecular weight (MW)
• Isoelectric point (pI)
• Staining
• Birds-eye view of protein abundance

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

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Mass Spectrometer

• ElectronMultiplier(EM)

• Time-Of-Flight (TOF)
• Quadrapole
• Ion-Trap

• MALDI
• Electro-SprayIonization (ESI)

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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
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Mass Spectrum
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Mass is fundamental
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Peptide Mass Fingerprint
Cut out 2D-GelSpot
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Peptide Mass Fingerprint
Trypsin Digest
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Peptide Mass Fingerprint
MS
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Peptide Mass Fingerprint
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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

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Protein Sequence

• Myoglobin - Plains zebraGLSDGEWQQV LNVWGKVEAD
  IAGHGQEVLI RLFTGHPETL EKFDKFKHLK TEAEMKASED
  LKKHGTVVLT ALGGILKKKG HHEAELKPLA QSHATKHKIP
  IKYLEFISDA IIHVLHSKHP GDFGADAQGA MTKALELFRN
  DIAAKYKELG FQG

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Protein Sequence

• Myoglobin - Plains zebraGLSDGEWQQV LNVWGKVEAD
  IAGHGQEVLI RLFTGHPETL EKFDKFKHLK TEAEMKASED
  LKKHGTVVLT ALGGILKKKG HHEAELKPLA QSHATKHKIP
  IKYLEFISDA IIHVLHSKHP GDFGADAQGA MTKALELFRN
  DIAAKYKELG FQG

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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
YLEFISDAIIHVLHSK
GHHEAELKPLAQSHATK
GLSDGEWQQVLNVWGK
HPGDFGADAQGAMTK
VEADIAGHGQEVLIR
HGTVVLTALGGILK
KGHHEAELKPLAQSHATK
ALELFR
LFTGHPETLEK
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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

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Sample Preparation for MS/MS
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Single Stage MS
MS
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Tandem Mass Spectrometry(MS/MS)
Precursor selection
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Tandem Mass Spectrometry(MS/MS)
Precursor selection collision induced
dissociation (CID)
MS/MS
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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
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Peptide Fragmentation
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Peptide Fragmentation
yn-i-1
-HN-CH-CO-NH-CH-CO-NH-
CH-R
Ri
i1
R
i1
bi1
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Peptide Fragmentation
Peptide S-G-F-L-E-E-D-E-L-K
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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
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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
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Peptide Identification

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

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Peptide Identification

• Two paradigms
• De novo interpretation
• Sequence database search

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De Novo Interpretation
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De Novo Interpretation
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De Novo Interpretation
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De Novo Interpretation
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De Novo Interpretation
from Lu and Chen (2003), JCB 101
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De Novo Interpretation
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De Novo Interpretation
from Lu and Chen (2003), JCB 101
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De Novo Interpretation

• Find good paths in spectrum graph
• Cant use same peak twice
• Simple peptide fragmentation model
• Usually many apparently good solutions
• Amino-acids have duplicate masses!
• Best de novo interpretation may have no
  biological relevance
• Identifies relatively few peptides in
  high-throughput workflows

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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

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Peptide Fragmentation
K
L
E
D
E
E
L
F
G
S
100
Intensity
0
m/z
250
500
750
1000
45
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
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
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Sequence Database Search

• Sequence fills in gaps in the spectrum
• All candidates have biological relevance
• Practical for high-throughput peptide
  identification
• Correct peptide might be missing from database!

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Peptide Candidate Filtering

• Digestion Enzyme Trypsin
• Cuts just after K or R unless followed by a P.
• Must allow for missed cleavage sites
• Average peptide length about 10-15 amino-acids

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Peptide Candidate Filtering

• gtALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDL
  GEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK

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

• gtALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDL
  GEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK

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

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Mascot Search Engine
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Mascot MS/MS Ions Search
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Mascot MS/MS Search Results
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Mascot MS/MS Search Results
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Mascot MS/MS Search Results
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Mascot MS/MS Search Results
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Mascot MS/MS Search Results
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Mascot MS/MS Search Results
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Mascot MS/MS Search Results
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Mascot MS/MS Search Results
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Mascot MS/MS Search Results
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Mascot MS/MS Search Results
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Summary

• Protein identification by mass spectrometry is a
  key element of proteomics and systems biology.
• Mass spectrometry sequence databases represent
  a huge leap for protein (bio-)chemistry.
• Sample prep, instruments and algorithms still
  maturing, much work to be done.

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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