Proteomics & Mass Spectrometry Nathan Edwards Center for Bioinformatics and Computational Biology

Outline Proteomics Mass Spectrometry Protein Identification Peptide Mass Fingerprint Tandem Mass Spectrometry

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? 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

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) 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

Peptide Mass Fingerprint Cut out 2D-Gel Spot

Peptide Mass Fingerprint Trypsin Digest

Peptide Mass Fingerprint MS

Peptide Mass Fingerprint

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

Protein Sequence Myoglobin - Plains zebra GLSDGEWQQV LNVWGKVEAD IAGHGQEVLI RLFTGHPETL EKFDKFKHLK TEAEMKASED LKKHGTVVLT ALGGILKKKG HHEAELKPLA QSHATKHKIP IKYLEFISDA IIHVLHSKHP GDFGADAQGA MTKALELFRN DIAAKYKELG FQG

Protein Sequence Myoglobin - Plains zebra GLSDGEWQQV LNVWGKVEAD IAGHGQEVLI RLFTGHPETL EKFDKFKHLK TEAEMKASED LKKHGTVVLT ALGGILKKKG HHEAELKPLA QSHATKHKIP IKYLEFISDA IIHVLHSKHP GDFGADAQGA MTKALELFRN DIAAKYKELG FQG

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

Peptide Mass Fingerprint YLEFISDAIIHVLHSK GLSDGEWQQVLNVWGK GHHEAELKPLAQSHATK HGTVVLTALGGILK HPGDFGADAQGAMTK VEADIAGHGQEVLIR KGHHEAELKPLAQSHATK ALELFR LFTGHPETLEK

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

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 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 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 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

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 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!

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

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 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

Mascot MS/MS Search Results

Mascot MS/MS Search Results

Mascot MS/MS Search Results

Mascot MS/MS Search Results

Mascot MS/MS Search Results

Mascot MS/MS Search Results

Mascot MS/MS Search Results

Mascot MS/MS Search Results

Mascot MS/MS Search Results

Mascot MS/MS Search Results

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.

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