Introduction to Proteomics & Bottom-up Proteomics

Transcription

1 Used for MS Short Course at Tsinghua by R. Graham Cooks, Hao Chen, Zheng Ouyang, Andy Tao, Yu Xia and Lingjun Li Introduction to Proteomics & Bottom-up Proteomics W. Andy Tao Purdue University What is Proteomics? Analysis of the entire PROTEin complement expressed by a genome of a cell or tissue type (Mac Wilkins) Proteomics focuses state-related expression of proteins in biological samples Proteomics is systematic analysis and documentation Proteomics identifies and quantifies proteins, as well as determines their localization, modifications, interactions, activities, and ultimately, their functions

2 Used for MS Short Course at Tsinghua by R. Graham Cooks, Hao Chen, Zheng Ouyang, Andy Tao, Yu Xia and Lingjun Li Historical Perspective s: 2D gels & separation of hundreds of proteins at once 2. Mid 90 s: huge expansion easy-to-use high performance mass spectrometers MALDI, ESI high resolution, sensitivity, accuracy, MS/MS protein and peptide measurement and peptide fragmentation genome projects human, bacterial, plant, other animals huge searchable databases (bioinformatics) Considerable interest once transcriptome was found to be poor predictor of proteome st century: consolidation Post-translational modifications Relative and absolute quantitation Targeted analyses Biological applications It s all about separation and identification! Separation Multi-dimensional Gel-based or gel-free Identification Top-down (lectured by Dr. Yu Xia) Analytes are proteins ECD for the fragmentation of proteins Almost exclusively in FT-ICR Bottom-up Analytes are peptides (digested from proteins) CID is the most common method for fragmenting peptides In any mass spectrometer Middle-down Large peptides (5k-20k Da) Its approach is similar to top down

3 Used for MS Short Course at Tsinghua by R. Graham Cooks, Hao Chen, Zheng Ouyang, Andy Tao, Yu Xia and Lingjun Li Why bottom up to analyze peptides? They are smaller and therefore easier to ionize for MS analysis Fragment ion spectra are much easier to interpret In general it is possible to get much greater sequence coverage using a mixture of peptides that are analyzed individually Using peptide separation techniques it is possible to reduce a complex protein into simpler fractions for analysis Enzymatic digestion of proteins Sequence-specific proteases complete digestion Non-specific proteases partial digestion Chemical treatment of proteins The basic scheme for peptide generation and analysis: Protein isolation (PAGE, chromatography, etc) - optional Reduction and alkylation Enzymatic digestion or chemical cleavage Sample cleanup and/or peptide separation (RP-HPLC) Mass spec analysis

4 Used for MS Short Course at Tsinghua by R. Graham Cooks, Hao Chen, Zheng Ouyang, Andy Tao, Yu Xia and Lingjun Li Protein Identification by Mass Spectrometry Mass Fingerprint (MS) Tandem Mass Spectrometry What is it? A method for identifying an unknown protein based on measurement of the masses of peptides generated from it Requires a high mass accuracy mass spectrometer, a sequenced genome for the organism the sample is derived from, and a database search algorithm How does it work? Just like every person has a unique fingerprint pattern that can be used to identify them, every protein generates a unique set of peptides after site-specific proteolysis The collective masses of these peptides then is also unique to the parent protein and can likewise be used to identify it

5 Used for MS Short Course at Tsinghua by R. Graham Cooks, Hao Chen, Zheng Ouyang, Andy Tao, Yu Xia and Lingjun Li PMF by MALDI-TOF MS: gel separated proteins extract protein, digest with enzyme such as trypsin NH 2 NH 2 NH 2 K K R -COOH -COOH -COOH tryptic peptides MALDI-TOF analysis search masses against database m/z Mass Mapping Peptide Mass Fingerprint List of peptide masses from MS scan Sequence Database m/z Identified Protein

6 Used for MS Short Course at Tsinghua by R. Graham Cooks, Hao Chen, Zheng Ouyang, Andy Tao, Yu Xia and Lingjun Li Example Step 1: Example >sp P02666 CASB_BOVIN Beta-casein OS=Bos taurus MKVLILACLVALALARELEELNVPGEIVESLSSSEESITRINKKIEKFQSEEQQQTEDEL QDKIHPFAQTQSLVYPFPGPIPNSLPQNIPPLTQTPVVVPPFLQPEVMGVSKVKEAMAPK HKEMPFPKYPVEPFTESQSLTLTDVENLHLPLPLLQSWMHQPHQPLPPTVMFPPQSVLSL SQSKVLPVPQKAVPYPQRDMPIQAFLLYQEPVLGPVRGPFPIIV Step 2: In silico digestion w/ trypsin MK VLILACLVALALAR ELEELNVPGEIVESLSSSEESITR INKKIEK FQSEEQQQTEDEL QDK IHPFAQTQSLVYPFPGPIPNSLPQNIPPLTQTPVVVPPFLQPEVMGVSKVK EAMAPK HK EMPFPK YPVEPFTESQSLTLTDVENLHLPLPLLQSWMHQPHQPLPPTVMFPPQSVLSL SQSK VLPVPQK AVPYPQR DMPIQAFLLYQEPVLGPVR GPFPIIV Step 3: Match!!

7 Used for MS Short Course at Tsinghua by R. Graham Cooks, Hao Chen, Zheng Ouyang, Andy Tao, Yu Xia and Lingjun Li Online database search Mascot matrixscience.com Online database search MS-FIT prospector.ucsf.edu

8 Used for MS Short Course at Tsinghua by R. Graham Cooks, Hao Chen, Zheng Ouyang, Andy Tao, Yu Xia and Lingjun Li Protein Identification by Mass Spectrometry Mass Fingerprint Tandem Mass Spectrometry There are numerous methods out there for inducing peptide fragmentation Common methods for peptide fragmentation Post-source decay (PSD) only in MALDI Collision-induced Dissociation (CID) Electron Capture Dissociation (ECD) Electron Transfer Dissociation (ETD) CID is by far the most common and we will focus only on it, and ECD and ETD will be discussed by Dr. Xia

9 Used for MS Short Course at Tsinghua by R. Graham Cooks, Hao Chen, Zheng Ouyang, Andy Tao, Yu Xia and Lingjun Li CID Data Acquisition Peptide CID Fragmentation

10 Used for MS Short Course at Tsinghua by R. Graham Cooks, Hao Chen, Zheng Ouyang, Andy Tao, Yu Xia and Lingjun Li Fragmenting A Peptide Sequence and Tandem MS Spectrum

11 Used for MS Short Course at Tsinghua by R. Graham Cooks, Hao Chen, Zheng Ouyang, Andy Tao, Yu Xia and Lingjun Li Fragment Ions on A Tandem MS Spectrum Strategy for Protein Sequencing by Database Searching Identified protein Database Theoretical digestion (in silico); Theoretical fragmentation (in silico) theoretical theoretical theoretical theoretical theoretical theoretical theoretical theoretical theoretical theoretical Correlative database sequence search % EDACLGAJK m/z

12 Used for MS Short Course at Tsinghua by R. Graham Cooks, Hao Chen, Zheng Ouyang, Andy Tao, Yu Xia and Lingjun Li Strategy for Protein Sequencing by Database Searching Parameters for Database Searching Sequence Databases Protein sequence databases: NCBI EMBL Swissprot IPI UniPro Genomic and Expressed Sequence Tag (EST) databases: Human genome (draft completed in Oct 2004): ~25,000 Yeast genome (completed in April 1996): ~6,000 Mouse genome (completed in Dec 2004): ~25,000 Rice genome (draft completed in April 2002): >45,000

13 Used for MS Short Course at Tsinghua by R. Graham Cooks, Hao Chen, Zheng Ouyang, Andy Tao, Yu Xia and Lingjun Li Parameters for Database Searching Tandem MS Mass accuracy of precursor ion Parameters for Database Searching Tandem MS Fragmentation pattern of Tandem MS Good Fair Bad Terrible

14 Used for MS Short Course at Tsinghua by R. Graham Cooks, Hao Chen, Zheng Ouyang, Andy Tao, Yu Xia and Lingjun Li Parameters for Database Searching Search Algorithm Search engines: Sequest (Thermo) Mascot (Matrix) X!Tandem (free) SpectrumMill (Agilent) Shadforth et al, Proteomics, 2005, 5, (review) Eng et al, JASMS, 5, 976 (Sequest) Perkins et al Electrophoresis. 20:3551 (Mascot) Kapp et al, Proteomics, 5, 3475 (comparison) Parameters for Database Searching Search Algorithm Sequest TM (patented the technique to use tandem MS and database searching for sequencing) Scoring n i : the number of matched ions i m : abundances of matched ions : matched consecutive ions : immonium ions associated with amino acid residues n t : total number of predicted sequence ions Cross-correlation (Xcorr) x(t): signals from the reconstructed spectrum based on amino acid sequences y(t): signals from the reconstructed experimental spectrum : displacement value between the two signals Final value C n = C =0 C -75< <75, after normalization C n: difference between top two hits. Eng et al, JASMS, 5, 976.

15 Used for MS Short Course at Tsinghua by R. Graham Cooks, Hao Chen, Zheng Ouyang, Andy Tao, Yu Xia and Lingjun Li Parameters for Database Searching Search Algorithm Mascot TM Scoring: MOWSE program & probability based scoring MOWSE: MOlecular Weight SEarch. Bleasby Pappin DJC, Hojrup P, and Bleasby AJ (1993) Rapid identification of proteins by peptidemass fingerprinting. Curr. Biol. 3: Scoring based on peptide frequency distribution from non redundant Database. Takes into account relative abundance of peptides in the database when calculating scores. Protein size is compensated for. Probability-based scoring The probability that the observed match between experimental data and a protein sequence is a random event is approximately calculated for each protein in the sequence database. Probability model details not published. Mascot score S = 10Log(P), where P is the probability that the observed match is a random event Comparison Between MS/MS Search Algorithms Heuristic Algorithm Sequest Spectrum Mill X! Tandem Probabilistic Algorithm MASCOT PeptideProphet (rescoring algorithm) Kapp et al, Proteomics, 5, 3475.

16 Used for MS Short Course at Tsinghua by R. Graham Cooks, Hao Chen, Zheng Ouyang, Andy Tao, Yu Xia and Lingjun Li Identification of Post-translational Modifications Static Simply change mass of any residue (or N- or C-terminus). Adds no additional computer processing time. Preprocessed or pre-calculated peptide masses no longer valid Variable/Differential Specified residues need to be considered as un-modified or modified in all combinations. Search complexity increases (a lot). Search time increases dramatically. Preprocessed or pre-calculated peptide masses no longer directly valid Separation Why do we need separations? Concentrate Multidimensional selectivity Eliminate interfering substances To be compatible with MS instruments (time frame; ion suppression; trapping capacity)

17 Separation Methods Coupling to MS Separation Interface MS 2D PAGE (off line) IEF (mainly off line) LC Electrophoresis Ion Trap Quadrupole TOF FTICR Sector 2D PAGE-MS

18 Limitations of 2D PAGE 2D-PAGE still is a powerful separation technique but has several disadvantages: Restricted to proteins < 10 6 and > 10 4 Da MW Cannot detect proteins expressed at low levels Typically limited to 600~800 separate spots Gel to gel reproducibility is poor Quantitation is poor, ± 50% or worse Dynamic range is limited, < 10X Analysis is not directly coupled to separation Analysis of membrane proteins is poor Time-consuming process Limitations of 2D PAGE 2D-PAGE still is a powerful separation technique but has several disadvantages: Restricted to proteins < 10 6 and > 10 4 Da MW Cannot detect proteins expressed at low levels Typically limited to 600~800 separate spots Gel to gel reproducibility is poor Quantitation is poor, ± 50% or worse Dynamic range is limited, < 10X Analysis is not directly coupled to separation Analysis of membrane proteins is poor Time-consuming process

19 LC Coupled to ESI Decrease column size, increase sensitivity Size 2.1 cm Sensitivity cm 4.4 fold 4.6 mm 21 fold 1.0 mm 50 m 441 fold 176,400 fold Reverse-phase LC LC Coupled to ESI Typical system (micro-column) for proteomics research: Diameter: μm x cm Packing material: 3-5 μm C18 silica Flow rate: nl/min Spray needle: <30 μm Mobile phase: Solvent A- 0.1% Formic acid (or 0.5% acetic acid) B-acetonitrile with 0.1% formic acid (or 0.5% acetic acid) Injection system: typically 1-10μl (direct injection or autosampler injection) Gradient: most peptides elute at B 10-40%

20 Packed C 18 Tip for μlc-ms μm fused silica capillary packed with C18 LC-MS/MS Data-dependant Acquisition Ion chromatogram MS min MS min min MS min MS min

21 Factors Limiting Efficiency of LC-MS/MS Experiments Multiple peptide per protein redundancy (avg. 50) Specific precursors selected repeatedly (in spite of dynamic exclusion) Only a fraction of sequencing attempts is successful Only a fraction of successful sequencing attempts identify differentially regulated proteins There is a conflict between sensitivity (small column) and sample capacity (larger column). Factors Limiting Efficiency of LC-MS/MS Experiments Average peak width is 10-30s for a single peptide on RPLC. For 60 min gradient, average No. of peptides at any moment is 200 peptides for a 500 protein mixture. Proteins in cells range in the concentration of over 7 orders of magnitude. Low abundant peptides are usually overwhelmed by high abundant ones. Limited MS data acquisition speed; Ion surrpresion; Trapping capacity.

22 Orthogonal Separation Methods 1 st dimension X 2 nd dimension 1D PAGE IEF Affinity chromatography Electrophoresis Ion exchange Size exclusion Specific targeting (e.g., chemical derivation) RPLC??? MudPIT: Multi-dimensional Protein Identification Technology Two columns in one IEX resin RP-resin Principle: Two different column packing materials in same capillary 2D chromatography in a single operation Increase sample capacity? Washburn et al Nat Biotechnol 2001

23 Two-dimensional Orthogonal HPLC Separation Buffer A B C D MudPIT Cycle 1) load sample 2) wash 3) salt step (stepwise increase conc.) 4) wash 5) RP gradient 6) re-equilibration 7) go to step (3) SCX RP Buffers A: 5% Acetonitrile/0.02%HFBA B: 80% Acetonitrile/0.02%HFBA Sample Mass Spectrometer C: 250 mm NH 4 Ac/0.02%HFBA D: 500 mm NH4Ac/0.02%HFBA

24 Bottom-up Proteomics II Covered topics Quantitative proteomics Post-translational modifications Protein complex

25 Why quantify proteins? What do you want to learn from a quantitative proteomics experiment? Why quantify proteins? DNA Post-translational modifications mrna X Degradation Functional Protein Biological functions The correlation between mrna levels and protein expression levels is low (correl coef < 0.4 overall)

26 How to quantify proteins? There is a poor correlation between the amount of a peptide present and the MS and MS/MS signal intensities MALDI: ionization hot spots Different ionization efficiency for different peptides Variable ion transmission Competition for charges Point of precursor ion selection in chromatographic peak Accurate Quantitation Using Isotope Dilution Sample 1 Sample 2 (Reference) Incorporate Stable heavy (H) Isotope Incorporate Stable regular (L) Isotope Combine Samples Analyze by Mass Spectrometer H/L analytes are chemically identical identical specific signal in MS Ratio of H/L signals indicates ratio of analytes

27 Stable Isotope Labeling Strategies Metabolic stable isotope oto top pe labeling eliing g Isotope tagging by chemical reaction Stable isotope incorporation via enzyme reaction y Label Digest Digest Intensity Intensity Intensity Digest m/z m/z m/z Stable Isotope Labeling Strategies Metabolic stable isotope oto top pe labeling eliing g Digest Prototypical applications: Zhou et al, RCMS (2002) Mann et al, Mol Cell Prot (2003) Veenstra eta l JASMS (2000) 15N-enriched media (ammonium sulfate-15n for Intensity yeast culture) acid (Lys-13C, Arg-13C) for mammalian cell lines Amino m/z

28 Stable Isotope Tagging by Metabolic Labeling Strengths No chemical reactions Potentially all peptides labeled Simple labeling protocols Quantitative labeling - no side reactions Weaknesses Compatible with selected species, samples only No inherent sample enrichment Labeling potentially perturbs biological system Label potentially metabolized Mass difference between sequence identical peptides can vary Stable Isotope Tagging by Metabolic Labeling SILAC: Stable isotopic labeling with amino acids in cell culture Lys- 13 C Arg- 13 C

29 Stable Isotope Labeling Strategies Stable isotope incorporation via enzyme reaction Prototypical applications: Stewart et al, Rapid Comm in Mass Spec (2001) Reynolds KJ et al, J Prot Res (2002) Schevchenko A et al, Rapid Comm in Mass Spec (1997) Schnolzer M Electrophoresis (1996) Digest Intensity m/z Stable Isotope Incorporation via Enzyme Reaction Enzyme in H 2 O or H 2 18 O Strengths General Compatible with any source of protein Constant mass shift Weaknesses Minimal mass difference Side reactions

30 Stable Isotope Labeling Strategies Isotope tagging by chemical reaction Label Prototypical application: Isotope coded affinity tags (Gygi et al, 1999; Zhou et al, 2002) Intensity Digest m/z Isotope Coded Affinity Tags (ICAT) ICAT Reagents: Heavy reagent: d8-icat (X =deuterium) Light reagent: d0-icat (X =hydrogen) Affinity group Labeled linker Gygi et al Nat Biotech, 1999 Reactive group

31 Stable Isotope Tagging by Chemical Reaction Strengths Compatible with any protein source Selective tagging reduces sample complexity Different specificities can be designed into reagent Constant mass difference Potentially multiplexed Weaknesses Cys-specific reagents miss cysteine-free proteins Chemical reactions required Each specificity requires different reagent Tag might interfere with MS/MS Potential for side reactions, incomplete reactions Potential chromatographic isotope effect mixture 1 (light) 100 quantification light heavy labeled cysteines mixture 2 (heavy) combine and proteolyze Fractionations & affinity enrichment Fractionations currently used Sub-cellular fractionation Immunoprecipitation Ion-exchange Reversed-phase HPLC Isoelectric focusing MS analysis 100 Identification (MS/MS) m/z NH 2 -EACDPLR-COOH m/z

32 Human myeloid Leukemia (HL-60) cells well characterized in vitro model for cell differentiation +/- 12-phorbol-13-myristate acetate (PMA) induces morphological changes cells become more adherent expect to see changes in cell-surface protein profile Han, et al (2001) Nat Biotech 19:

33 HL-60 microsomal fraction biotinylated Cys residues combined samples (d0 & d8) tryptic digest Extensive fractionation: 1. Cation exchange (SCX) 2. Affinity chrom (avidin) 3. Capillary reverse phase gradient pressure 214 nm 280 nm * ICAT labeled peptides MS 2 (12 peptides total) Circled MS1 peaks shown on next slide LC retention time differs by 4 s peptide sequencing by MS/MS CD45 identified - transmembrane Tyr phosphatase ATC2 identified - calcium pump calculate ratio of light:heavy from MS1: CD45 = 1:0.7 and ATC2 = 1:1.2

34 d0:d8 ratio = 1:0.77 +/ unchanged abundance: ribosomal proteins, cytoskeletal proteins, metabolic enzymes, cellsurface receptors, channel proteins changed abundance: membrane associated signal transduction proteins ex. farnesyldiphosphate farnesyl transferase (20-fold reduction) n range: 2-36 d 0 :d 8 range: down-regulated w/ PMA treatment up-regulated w/ PMA treatment

35 491 proteins identified from microsomal fraction of HL-60 cells one SCX fraction 100 min 1025 proteins total analysis time = 50 h need 2 peptides from same protein to confirm

36 Consequence: Identification of many un-interesting proteins Quantitative Analysis of Androgen-regulated microsomal proteins from LNCaP prostate epithelia Log d0/d8 Number of proteins identified Up to ~90% of identified proteins show un-changed abundance mixture 1 (light) mixture 2 (heavy) Isotope tag Combine and digestion Affinity purification Fractionation on MALDI sample plate for MS ignore peptides with unchanged abundance m/z Identify differential expressed peptides for MS/MS m/z

37 Quantitative Analysis Based on Tandem Mass Spectrometry itraq TM (Applied Biosystems): Ross et al MCP, 3(12), 1154 Quantitative Analysis Based on Tandem Mass Spectrometry TPHPALTEAK + 114/115/116/117 (1:1:1:1) MS MS/MS Ross et al MCP, 3(12), 1154

38 Label-free Quantitation 1. Peak detection; 2. Peak matching; 3. Peak normalization; 4. Area/ height measurement; 5. Statistical evaluation. 1. # of spectra correlates with protein abundance; 2. Normalization; 3. Statistical evaluation. Protein Post-Translational Modifications (PTMs) Histone modification:

39 Case Studies with Phosphoproteomics Protein Highly dynamic kinase phosphatase P Low abundance Low ionization efficiency Protein Poor fragmentation A typical workflow for phosphoproteomics P P P P MS analysis Labeling Fractionation Separation Quantitation Sampling Digestion Enrichment Identification Annotation

40 Enrichment of phosphopeptides Metal oxide: TiO2; ZrO2 Immobilized metal ion affinity chromatography (IMAC): Fe(III); Ga(III) PolyMAC: polymer-based metal ion affinity capture Mixture of peptides P PolyMAC Ti Solid-phase beads Ti Ti Identification of Post-translational Modifications Static Simply change mass of any residue (or N- or C-terminus). Adds no additional computer processing time. Preprocessed or pre-calculated peptide masses no longer valid Variable/Differential Specified residues need to be considered as un-modified or modified in all combinations. Search complexity increases (a lot). Search time increases dramatically. Preprocessed or pre-calculated peptide masses no longer directly valid

41 Identification of protein-protein interactions Proteolysis and Separation μlc-ms & MS/MS control Immunopurified sample Immunopurified control Proteolysis and Separation μlc-ms & MS/MS sample Advantage: One of the most sensitive and univeral methods to identify interacting partners Disadvantage: Difficult to remove contaminants