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Proteomics Technology and Protein Identification
Nathan Edwards Center for Bioinformatics and Computational Biology
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Outline Proteomics Mass Spectrometry Protein Quantitation
Protein Identification Computer Lab
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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? What isoform of each protein is present? How much of each protein is present?
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Gene / Transcript / Protein
Systems Biology Establish relationships by Choosing related samples, Global characterization, and Comparison. Gene / Transcript / Protein Measurement Predetermined Unknown Discrete (DNA) Genotyping Sequencing Continuous Gene Expression Proteomics
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Samples Healthy / Diseased Cancerous / Benign
Drug resistant / Drug susceptible Bound / Unbound Tissue specific Cellular location specific Mitochondria, Membrane
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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
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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 2002;4:
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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 Ionizer Sample Mass Analyzer Detector MALDI
+ _ Mass Analyzer Detector MALDI Electro-Spray Ionization (ESI) Time-Of-Flight (TOF) Quadrapole Ion-Trap Electron Multiplier (EM)
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Mass Spectrometer (MALDI-TOF)
(b) LR by Micromass, UK Energy transfer from matrix to sample Matrix Sample Laser Ionization Schematic of the MALDI process (a) Detector (linear mode) Reflectron N2 Laser Lens Detector (reflectron mode) Target plate with sample
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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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Mass Spectrum
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Mass Spectrum Isotope Cluster 12C ≈ 99% 13C ≈ 1%
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Peptide Mass Fingerprint
Cut out 2D-Gel Spot
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Peptide Mass Fingerprint
Trypsin Digest
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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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Mass Spectrometry Strengths Weaknesses 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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Proteomics Quantitation
2D-Gel Electrophoresis Replicate LC/MS acquisitions Stable Isotope Labeling Protein profiling
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LC/MS for Peptide Abundance
Enzymatic Digest and Fractionation
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LC/MS for Peptide Abundance
Liquid Chromatography Mass Spectrometry LC/MS: 1 MS spectrum every 1-2 seconds
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LC/MS for Peptide Abundance
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LC/MS for Peptide Abundance
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Stable Isotope Labeling
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Stable Isotope Labeling
SILAC: Lysine with 12C6 vs 13C6
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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”
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MALDI Protein Profiling Male Spectra
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MALDI Protein Profiling Female Spectra
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Protein Profiling Statgram
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MALDI Protein Profiling
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MALDI Protein Profiling
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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)
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Sample Preparation for MS/MS
Enzymatic Digest and Fractionation
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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 Ri+1 C-terminus AA residuei-1 AA residuei AA residuei+1
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Peptide Fragmentation
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Peptide Fragmentation
bi yn-i yn-i-1 -HN-CH-CO-NH-CH-CO-NH- Ri CH-R’ i+1 R” i+1 bi+1
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Peptide Fragmentation
xn-i bi yn-i ci zn-i yn-i-1 -HN-CH-CO-NH-CH-CO-NH- Ri CH-R’ i+1 ai R” i+1 bi+1
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Peptide Fragmentation
Peptide: S-G-F-L-E-E-D-E-L-K MW ion 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
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Peptide Fragmentation
88 145 292 405 534 663 778 907 1020 1166 b ions S G F L E E D E L K 1166 1080 1022 875 762 633 504 389 260 147 y ions 100 % Intensity m/z 250 500 750 1000
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Peptide Fragmentation
88 145 292 405 534 663 778 907 1020 1166 b ions S G F L E E D E L K 1166 1080 1022 875 762 633 504 389 260 147 y ions y6 100 y7 % Intensity y5 y2 y3 y4 y8 y9 m/z 250 500 750 1000
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Peptide Fragmentation
88 145 292 405 534 663 778 907 1020 1166 b ions S G F L E E D E L K 1166 1080 1022 875 762 633 504 389 260 147 y ions y6 100 y7 % Intensity y5 b3 b4 y2 y3 y4 b5 y8 b6 b8 b7 b9 y9 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
100 250 500 750 1000 m/z % Intensity
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De Novo Interpretation
100 250 500 750 1000 m/z % Intensity E L
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De Novo Interpretation
100 250 500 750 1000 m/z % Intensity E L F KL SGF G D
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De Novo Interpretation
Amino-Acid Residual MW A Alanine M Methionine C Cysteine N Asparagine D Aspartic acid P Proline E Glutamic acid Q Glutamine F Phenylalanine R Arginine G Glycine S Serine H Histidine T Threonine I Isoleucine V Valine K Lysine W Tryptophan L Leucine Y Tyrosine
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De Novo Interpretation
…from Lu and Chen (2003), JCB 10:1
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De Novo Interpretation
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De Novo Interpretation
…from Lu and Chen (2003), JCB 10:1
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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
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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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Peptide Fragmentation
S G F L E E D E L K 100 % Intensity m/z 250 500 750 1000
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Peptide Fragmentation
88 145 292 405 534 663 778 907 1020 1166 b ions S G F L E E D E L K 1166 1080 1022 875 762 633 504 389 260 147 y ions 100 % Intensity m/z 250 500 750 1000
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Peptide Fragmentation
88 145 292 405 534 663 778 907 1020 1166 b ions S G F L E E D E L K 1166 1080 1022 875 762 633 504 389 260 147 y ions y6 100 y7 % Intensity y5 b3 b4 y2 y3 y4 b5 y8 b6 b8 b7 b9 y9 m/z 250 500 750 1000
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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 amino-acids Must allow for “missed” cleavage sites
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Peptide Candidate Filtering
>ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK… No missed cleavage sites MK WVTFISLLFLFSSAYSR GVFR R DAHK SEVAHR FK DLGEENFK ALVLIAFAQYLQQCPFEDHVK LVNEVTEFAK …
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Peptide Candidate Filtering
>ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK… One missed cleavage site MKWVTFISLLFLFSSAYSR WVTFISLLFLFSSAYSRGVFR GVFRR RDAHK DAHKSEVAHR SEVAHRFK FKDLGEENFK DLGEENFKALVLIAFAQYLQQCPFEDHVK 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
Same peptide, i = # of C13 isotope i=1 i=2 i=3 i=4
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Peptide Molecular Weight
Same peptide, i = # of C13 isotope i=1 i=2 i=3 i=4
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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 WVTFISLLFLFSSAYSR … 7 Molecular Weights 64 “Peptides”
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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
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Mascot Search Engine
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Mascot MS/MS Ions Search
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Sequence Database Search Traps 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
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Sequence Database Search Traps 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 Search Traps 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? 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 Search Traps 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 Search Traps 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!
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Further Reading Matrix Science (Mascot) Web Site
Seattle Proteome Center (ISB) Proteomic Mass Spectrometry Lab at The Scripps Research Institute fields.scripps.edu UCSF ProteinProspector prospector.ucsf.edu
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