Protein Identification by Sequence Database Search

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Protein Identification by Sequence Database Search

• Nathan Edwards
• Department of Biochemistry and Mol. Cell.
  Biology
• Georgetown University Medical Center

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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 GLSDGEWQQV LNVWGKVEAD IAGHGQEVLI
  RLFTGHPETL EKFDKFKHLK TEAEMKASED LKKHGTVVLT
  ALGGILKKKG HHEAELKPLA QSHATKHKIP IKYLEFISDA
  IIHVLHSKHP GDFGADAQGA MTKALELFRN DIAAKYKELG FQG

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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
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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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Sample Preparation for Tandem Mass Spectrometry
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Single Stage MS
MS
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Tandem Mass Spectrometry(MS/MS)
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-
Ri1
Ri
bi1
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Peptide Fragmentation
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Peptide Fragmentation
Peptide S-G-F-L-E-E-D-E-L-K
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Peptide Fragmentation
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Peptide Fragmentation
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Peptide Fragmentation
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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
• 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

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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

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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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Sequence Database Search
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Sequence Database Search
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Sequence Database Search
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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!

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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

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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 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

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Peptide Molecular Weight
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Peptide Molecular Weight
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Peptide Molecular Weight
from Isotopes An IonSource.Com Tutorial
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Peptide Molecular Weight

• Peptide sequence WVTFISLLFLFSSAYSR
• Potential phosphorylation? S,T,Y 80 Da

• 7 Molecular Weights
• 64 Peptides

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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 Peptide Mass Fingerprint
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Mascot MS/MS Ions Search
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Mascot Sequence Query
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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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Sequence Database SearchTraps and Pitfalls

• Search options may eliminate the correct peptide
• Precursor mass tolerance too small
• Fragment m/z 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

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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

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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

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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

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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

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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!