High-Performance Multidimensional Chromatography MALDI-QFTICR MS for Bottom Up Proteomics

High-Performance Multidimensional Chromatography MALDI-QFTICR MS for Bottom Up Proteomics Ansgar Brock, David M. Horn, Eric C. Peters, Christopher M. Shaw, Christer Ericson,, Qui Phung,, Art Salomon Genomics Institute of the Novartis Research Foundation, San Diego, CA Beyond Genome, San Diego, June 19th, 2003

Overview Introduction Bottom Up and Top Down approaches Off-line Proteomics Platform Scheme and Design Concepts System Automation 1D-LC Yeast Shotgun experiment Identification of a Proteins Using Accurate Mass Measurement of a Single Peptide AMT experiment on TM protein mixture

The Systems Biology Paradigm R. Aebersold & M. Mann, Mass Spectrometry based proteomics, Nature 422, 198-207 (2003)

Bottom Up and Top Down Approach to Proteomics Reduce Data and Compile Information Analyse the Fragmented and Fractionated Mixtures by MS/MS Fractionate Protein Mixture by 1Dor Multidimensional Separations Digestion of Proteins Protein Mixture Purified Protein Optional Liquid-phase Fragmentation to Produce Large Fragments Gas-phase Purification Fragment Protein / Sequence Fragments Reduce Data and Compile Information

Bottom Up and Top Down Instrumentation QIT, Q-TOF, TOF-TOF, (Q-FTICR) Key Concept: use MS/MS of partial protein to ID Instrumentation FTICR, (Q-TOF) Key Concept: use full protein to ID and characterize protein + automated implementations available - poor fragmentation of some peptides prevents characterization + comprehensive protein characterization - lack of automation, still in development stage

Off-Line Shotgun Proteomics MALDI-FTICR MS Scheme Sample Processing Code S Mix Code R Processing Reference LC/LC Sample Deposition Peak capacity multiplication, flexibility, automation Protein ID by MS accurate mass methods Remaining ID through tandem MS data MALDI FTICR Data Reduction ID MALDI-FTICR MS/MS Data Reduction DB Search Decision Differential quantitation Highest possible characterization power Iterative analysis without sample reruns

Potential Benefits of an Off-Line Approach Decoupling of LC from time scale allows more comprehensive analysis without sample reruns Optimizations of separations independent from mass spectrometer data acquisition Re-addressable, permanent record of separation allows for non- sequential and partial sample scanning in regions of interest (throughput, biomarkers) Sample storage Intelligent decisions can be made during analysis allowing results driven analysis

Benefits of High Mass Accuracy, High Resolution, and Large Dynamic range Many component to be observed in a single spectrum More confident identifications in database searching increase with mass Less time spent on data validation New schemes of sample analysis become possible

The Recognized LC-MALDI Challenge However, if MALDI-MS/MS MS/MS is to be used with peptide chromatography, the effluent of a liquid chromatography run must be deposited on a sample plate and mixed with the MALDI matrix, a process that has thus far proven difficult to automate. In general, it can be expected that the trend towards the combination of liquid chromatography with ESI- or MALDIMS/ MS (Fig. 1) will continue. R. Aebersold & M. Mann, Mass Spectrometry based proteomics Nature 422, 198-207 (2003)

1000 2000 3000 4000 m/z MALDI Signals 50:1 50:1 1000 2000 3000 4000 m/z 5 1 1000 2000 3000 4000 m/z 1:1 3 6 5:1 2 1000 2000 3000 4000 m/z 4 1000 2000 3000 4000 m/z 10:1 35:1

Technical Challenges of an MALDI Off-Line Approach Eluent collection mechanism that minimizes sample loss Develop methods for optimal sample conditioning for analysis Automation of the analysis of a large number of samples (expert systems vs. brute force approach)

Off-Line Shotgun Proteomics MALDI-FTICR MS Scheme Sample Processing Code S Mix Code R Processing Reference LC/LC Sample Deposition O MALDI FTICR HN N Data Reduction ID MALDI-FTICR MS/MS Data Reduction DB Search Decision

Four Column Deposition System 1 st LC system with autosampler, UV cell, etc. Syringe pump for matrix addition Control box, power supply, etc. Four column RP-LC set-up 2nd LC system for RP-LC Motion table with 4 MALDI targets

LC Deposition Process

LC Deposition Process

Improved MALDI Ionization of Derivatized Peptides Native Labeled with O HN N Peters, et al. Rapid Commun. Mass Spectrom., 2001, 15, 2387-2392.

Labeling of Myoglobin with Cyclic O-Methylisourea Analog O HN N X X X X X = H, D m = 4; 1 Label GLSDGEWQQVLNVWGK m = 8; 2 Labels GHHEAELKPLAQSHATK m m = 12; 3 Labels LFTGHPETLEKFDKFK Peters, et al. Rapid Commun. Mass Spectrom., 2001, 15, 2387-2392.

MALDI-QFTICR System

Automated MALDI FT-ICR MS

Robust Automation 5:1 50:1 50:1 1000 2000 3000 4000 m/z 1000 2000 3000 4000 m/z 5 1 1000 2000 3000 4000 m/z 1:1 3 2 6 35:1 1000 2000 3000 4000 m/z 4 1000 2000 3000 4000 m/z 10:1 1000 2000 3000 4000 m/z 1000 2000 3000 4000 m/z

BSA Peptide Map Summary Mass Peptide FTMS 1 2 3 4 5 6 3510 S286-K316 X X X X X X 2540 Q94-K114 X X X X 2491 G21-K41 X X X X 2457 D295-K316 X X X X X X 2246 E243-K261 X X X 2112 V240-R256 X 2044 R144-K159 X X X X 2018 L115-K131 X X X 1906 L505-K520 X X X X X X 1900 N99-K114 X X X X 1887 H145-K159 X 1879 R484-K499 X X X X X X X 1746 Y160-K173 X X X X 1723 M445-R458 X 1672 Q94-K106 X X X X 1638 K413-R427 X X X X X X 1575 L115-K127 X X X X 1566 D323-R335 X X X X X X X 1553 D363-K375 X X X X X X X 1531 L274-K285 X X X X 1501 E351-K362 X X X X X 1478 L397-R409 X X X X X X X 1462 T52-K64 X X X X X 1442 Y262-K273 X X X X 1438 R336-R347 X X X 1418 S65-K76 X X X X X X X 1398 T545-K556 X X X X X X X 1304 H378-K388 X X X X 1282 H337-R347 X 1248 F11-K20 X X X X X X 1162 L42-K51 X X X X X X 1141 K524-K533 X X 1137 C475-R483 X X X X 1106 E564-K573 X X X X 1067 Q389-K396 X X X 1000 A209-R217 X Isotopic clusters 142 77 26 15 53 86 10

Warning! Performance claims of MS manufacturer s are made mostly for very idealistic conditions that really hardly ever apply in proteomic analyses of even comparatively simple mixtures A typical analysis and automation require tradeoffs between sensitivity, dynamic range, mass accuracy, and speed.

RP-LC/MS of 200 fmol BSA Digest

Chromatographic Resolution

Automated LC/MALDI QFT-ICR MS/MS 1) Perform LC/MALDI FT-ICR MS and return a mass list 2) Select peptides based on criteria such as ion intensity, expression ratio, etc. for tandem MS 3) Submit selection list to customized software to perform automated LC/MALDI QFT-ICR MS/MS for peptides in list 4) Reduce data and submit to Mascot for protein identification

RP-LC/MS 10µg Yeast Cytosolic Protein Digest

Automated LC/MALDI QFT-ICR MS/MS Yeast 10 µg, 1D RPLC-MALDI MALDI-FTMS/MS: 799 attempts (SNR >30), 226 peptide ID s, 111 proteins Ribosomal (40 total): Rpl10[5], Rpl11b[1], Rpl13ap[2], Rpl14bp[1], Rpl16ap[1], Rpl16bp[1], Rpl17ap[6], Rpl19ap[1], Rpl20ap[1], Rpl26bp[4], Rpl27bp[2], Rpl2ap[1], Rpl30p[1], Rpl4ap[1], Rpl5p[1], Rpl9ap[1], Rpm1p[1], Rpp0p[5], Rpp2bp[1], Rps10bp[1], Rps11bp[2], Rps12p[1], Rps13p[2], Rps14ap[1], Rps16bp[2], Rps17bp[1], Rps19bp[2], Rps1ap[1], Rps21bp[1], Rps24ap[1], Rps25ap[2], Rps29bp[1], Rps3p[2], Rps4bp[1], Rps5p[1], Rps6bp[1], Rps7ap[1], Rps7bp[1], Rps8ap[6], Rps9bp[1], Rsp14ap[1] Others (71 total): Others (71 total): Act1p[1], Adh1p[5], Adk1p[1], Ahp1p[1], Arg1p[1], Asc1p[2], Asn2p[1], Bmh2p[1], Cat8p[1], Cdc19p[10], Cof1p[1], Cph1p[2], Cys3p[1], Cys4p[1], Dal7p[1], Ded1p[1], Ded81p[1], Efb1p[1], Eft2p[5], Eno2p[9], Fas2p[2], Fba1p[5], Gdh3p[1], Glk1p[1], Gnd1p[1], Gpm1p[3], Gsp1p[1], Hom2p[1], Hsc82p[4], Hxk1p[2], Hyp2p[1], Leu1p[1], Lys1p[1], Lys9p[2], Met17p[2], Met6p[2], Msd1p[1], Oye2p[1], Pab1p[1], Pdc1p[11], Pdr13p[1], Pfk1p[3], Pgi1p[1], Pgk1p[11], Rnr4p[1], Sah1p[1], Ssa1p[1], Ssa2p[7], Ssb1p[1], Ssb2p[1], Sse1p[1], Tdh1p[3], Tdh3p[8], Tef2p[2], Tef4p[2], Tfp1p[1], Thr4p[1], Tif2p[1], Tkl1p[1], Tpi1p[4], Trp5p[1], Tsa1p[1], Uba1p[1], Ugp1p[2], Ura2p[1], Vma2p[1], Yef3p[3], Ykl056cp[1], Zuo1p[2]

LC/MALDI QFT-ICR MS/MS Results 799 attempts (S/N >30), 226 peptide ID s, 111 proteins For 799 submissions Mascot returned 257 possible assignments of which 31 were rejected in validation Unambiguous identification in the majority of cases despite Mascot scores below 95% confidence threshold (30-40), only 5 peptides with scores below 6 were rejected in validation 182 out of 226 identifications were assignable to only one peptide in the whole yeast proteome High mass accuracy data allows data validation to be performed in i less than 30 minutes and removes the data validation bottleneck without the need for additional score analysis software.

Automated LC/MALDI QFT-ICR MS/MS Parent ion mass: 1907.949 Identified by Mascot: O-acetylhomoserine-O-acetylserine acetylserine sulfhydralase Sequence: HGSQLFGLEVPGYVYSR No other suggested candidates by Mascot! Score: 48 y 1+ 8 Mass accuracy for fragments: y 1+ 5 y 1+ 6 y 1+ 7 b 1+ 10 y 1+ 11 y 1+ 12 y 1+ 13 y 1+ 14 -H 2 O 800 1000 1200 m/z 1400 1600

Auto MS/MS of Yeast (spot 157; mass 1753) Significant hits: gi 6322468 glyceraldehyde 3-phosphate dehydrogenase; Tdh2p Mass 37607 Top scoring peptide matches to query 1. Score greater than 11 indicates identity Score Delta Hit Protein Peptide 25.7 0.00 1 gi 6322468 LVSWYDNEYGYSTR RMS error of fragments 2ppm (Search Limits: MI ±10ppm; Frag. ±20mmu) 1 MVRVAINGFG RIGRLVMRIA LQRKNVEVVA LNDPFISNDY SAYMFKYDST 51 HGRYAGEVSH DDKHIIVDGH KIATFQERDP ANLPWASLNI DIAIDSTGVF 101 KELDTAQKHI DAGAKKVVIT APSSTAPMFV MGVNEEKYTS DLKIVSNASC 151 TTNCLAPLAK VINDAFGIEE GLMTTVHSMT ATQKTVDGPS HKDWRGGRTA 201 SGNIIPSSTG AAKAVGKVLP ELQGKLTGMA FRVPTVDVSV VDLTVKLNKE 251 TTYDEIKKVV KAAAEGKLKG VLGYTEDAVV SSDFLGDSNS SIFDAAAGIQ 301 LSPKFVKLVS WYDNEYGYST RVVDLVEHVA KA y6 746.35 y7 875.39 y8 989.43 MI 1752.78 400 600 800 1000 1200 1400 1600 1800 2000

2D-LC MS of Yeast Cytosolic Protein Digest Fraction 24: 10836 masses Fraction 26: 7713 masses Fraction 34: 2486 masses SCX UV Trace 20 25 30 35 40 45 50 55 minutes

SCX of Yeast Cytosolic Protein Digest Fraction # masses Fraction # masses Fraction # masses 23 9447 31 4588 39 2794 24 10836 32 6451 40 2237 25 7264 33 2394 41 2123 26 7713 34 2486 42 1783 27 5080 35 2628 43 993 28 5435 36 2203 44 499 29 3658 37 2328 45 471 30 6335 38 5042 46 677 Total # of masses: ~76000

Assignment of Peptides Using Accurate Mass Introduced for use in FT-ICR MS in Conrads et al. 1 High throughput No extra tandem MS experiments required Improved dynamic range Can identify peptides that are not intense enough to perform tandem MS 1 Conrads, T. P., et al, Anal. Chem., 72, 3349-3354 (2000).

Assignment of Peptides Using Accurate Mass Thermotoga maritima - 1852 sequences % Thermotoga proteome coverage 100.0% 80.0% 60.0% 40.0% 20.0% 0.0% 1 5 10 50 Mass only # K known Mass Accuracy (PPM)

Assignment of Peptides Using Accurate Mass Thermotoga maritima - 1852 sequences 131792 tryptic peptides peptide % Thermotoga coverage 100.0% 80.0% 60.0% 40.0% 20.0% 0.0% ~50 identifiable peptides/protein Mass only # K known 1 5 10 50 PPM Error

Assignment of Peptides Using Accurate Mass Human - 36307 sequences % human proteome coverage 100.0% 80.0% 60.0% 40.0% 20.0% 0.0% 253934 identifiable tryptic peptides 40695 identifiable tryptic peptides 1 5 10 50 PPM Error Mass only # K known

Identification of Proteins from Thermotoga maritima by Accurate Mass 1) Combine 85 previously identified TM proteins 2) Digest the TM protein mixture 3) Split the TM digest into two aliquots and label with D4 and H4 labels 4) Combine the labeled materials and perform LC separation 5) Analyze by LC/MS only of MALDI FTICR 6) Assign uniquely identifiable peptides based on mass (5 ppm) ) and number of lysine

Identification of Proteins from Thermotoga maritima by Accurate Mass 8 1 2 2 3 2 1 2 2 4 1 2 2 3 1 2 2 1 1 2 2 7 3 2 2 1 1 2 2 1 1 4 4 4 7 3 4 1 2 1 1 1 3 1 2 5 2 1 7 1 2 1 3 3 2 1 3 4 2 2 2 1 4 1 4 Accurate mass + #K identified 65 of the proteins This is approximately the same number of identifications that resulted from an analysis by data dependent LC/MS/MS on a QTOF

Conclusions Multidimensional LC MALDI FT-ICR MS has been demonstrated for a complex proteome Protein Identification by accurate mass and number of lysine constraints possible for smaller organism Protein profiling by LC MALDI FTICR takes full advantage of the benefits of an off-line approach to capitalize optimally from the high mass accuracy capabilities of FTICR The technology seems to be uniquely suited for intelligent data driven proteomic bootstrapping approaches

Acknowledgments GNF Larry Brill Heath Klock Scott Lesley Matrix Science John Cottrell