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

Outline Proteomics Mass Spectrometry Protein Quantitation Protein Identification Computer Lab

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?

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

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

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

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

2D Gel-Electrophoresis Bécamel et al., Biol. Proced. Online 2002;4:94-104.

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

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)

Mass Spectrometer (MALDI-TOF) (b) M@LDITM 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

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

Mass Spectrum

Mass is fundamental

Mass Spectrum

Mass Spectrum Isotope Cluster 12C ≈ 99% 13C ≈ 1%

Peptide Mass Fingerprint Cut out 2D-Gel Spot

Peptide Mass Fingerprint Trypsin Digest

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

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

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

LC/MS for Peptide Abundance Enzymatic Digest and Fractionation

LC/MS for Peptide Abundance Liquid Chromatography Mass Spectrometry LC/MS: 1 MS spectrum every 1-2 seconds

LC/MS for Peptide Abundance

LC/MS for Peptide Abundance

Stable Isotope Labeling

Stable Isotope Labeling SILAC: Lysine with 12C6 vs 13C6

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”

MALDI Protein Profiling Male Spectra

MALDI Protein Profiling Female Spectra

Protein Profiling Statgram

MALDI Protein Profiling

MALDI Protein Profiling

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)

Sample Preparation for MS/MS Enzymatic Digest and Fractionation

Single Stage MS MS

Tandem Mass Spectrometry (MS/MS) Precursor selection

Tandem Mass Spectrometry (MS/MS) Precursor selection + collision induced dissociation (CID) MS/MS

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

Peptide Fragmentation

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

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

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

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

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

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

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

Peptide Identification Two paradigms: De novo interpretation Sequence database search

De Novo Interpretation 100 250 500 750 1000 m/z % Intensity

De Novo Interpretation 100 250 500 750 1000 m/z % Intensity E L

De Novo Interpretation 100 250 500 750 1000 m/z % Intensity E L F KL SGF G D

De Novo Interpretation 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 Y Tyrosine 163.06333

De Novo Interpretation …from Lu and Chen (2003), JCB 10:1

De Novo Interpretation

De Novo Interpretation …from Lu and Chen (2003), JCB 10:1

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

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

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

Peptide Fragmentation S G F L E E D E L K 100 % Intensity m/z 250 500 750 1000

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

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

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!

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

Peptide Candidate Filtering >ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK… No missed cleavage sites MK WVTFISLLFLFSSAYSR GVFR R DAHK SEVAHR FK DLGEENFK ALVLIAFAQYLQQCPFEDHVK LVNEVTEFAK …

Peptide Candidate Filtering >ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK… One missed cleavage site MKWVTFISLLFLFSSAYSR WVTFISLLFLFSSAYSRGVFR GVFRR RDAHK DAHKSEVAHR SEVAHRFK FKDLGEENFK DLGEENFKALVLIAFAQYLQQCPFEDHVK ALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK …

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

Peptide Molecular Weight Same peptide, i = # of C13 isotope i=1 i=2 i=3 i=4

Peptide Molecular Weight Same peptide, i = # of C13 isotope i=1 i=2 i=3 i=4

Peptide Molecular Weight …from “Isotopes” – An IonSource.Com Tutorial

Peptide Molecular Weight Peptide sequence WVTFISLLFLFSSAYSR Potential phosphorylation? S,T,Y + 80 Da WVTFISLLFLFSSAYSR 2018.06 2098.06 2178.06 … 2418.06 7 Molecular Weights 64 “Peptides”

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

Mascot Search Engine

Mascot MS/MS Ions Search

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

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

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

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

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

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!

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