Aiping Lu. Key Laboratory of System Biology Chinese Academic Society

Transcription

1 Aiping Lu Key Laboratory of System Biology Chinese Academic Society

2 Proteome and Proteomics PROTEin complement expressed by genome Marc Wilkins Electrophoresis (7): proteomics is the principle to study the proteome

3 Proteomics in broad sense Two-Dimentional Electrophoresis large-scale two-hybrid assays Protein Microarrays Proteomics in normal sense high-throughput protein production and crystallization Mass spectrometry based proteomics

4 Workflow of MS-based proteomics Protein seperation digestion Peptide seperation Protein lysate Peptide MS and MS/MS acquisition Bioinformatics analysis MS and MS/MS acquisition Top-down Database search Bottom-up

5 Studying targets Tissues Body fluid Cell lines Cell organelles

6 Sample preparation is critical for proteomic analysis Protein extraction Soluble proteins Membrane proteins Recovery Protein digestion Trypsin Lys-C Suitable for MS analysis Protein/peptide separation IEF Multiple dimensional HILIC SCX SAX RP-HPLC SDS-PAGE + RP-HPLC Dynamic range Sensitivity

7 Mass spectrometry is the basis of proteomics Ion Source Ion from solution to gas Mass Analyzer Acquire mass-to-charge value Detector Electron multiplier orbitrap Quadrupole Electrospray Ionization Continuous dynode electron multiplier Time-of-Flight FT-ICR:Fourier transform ion cyclotron resonance Matrix-Assisted Laser Desorption/Ionization Microchannel plate detectors Linear quadrupole ion trap

8 Pros and Cons of Different Analyzer Accuracy Resolution Sensitivity Dynamic Range FT-ICR 1 ppm High Low High Orbitrap 5 ppm High Low High TOF 50 ppm Medium Medium Medium Quadrupole 500 ppm Low High Low Ion trap 500 ppm Low High Low Hybrid instrument

9 Tandem mass spectrometer Tandem in space Collision Cell Ion Source Mass Analyzer C C Mass Analyzer Detector e.g. QQQ, Q-TOF, TOF-TOF Tandem in time Ion Source Mass Analyzer Detector e.g. ion trap, LTQ, FT-ICR, orbitrap

10 Peptide fragmentation and nomenclature Low energy collision High energy collision

11 MS data interpretation Isotopic Cluster Mass Resolution Dynamic Range m/z FWHH Mass Accuracy FWHH:full width at the half-height Signal-to-noise ratio

12 MS data - an example Precursor ion mass Precursor ion charge state Sample information Total Ion Current / Base Peak Current Retention time Fragment ion m/z Fragment ion intensity MS MS/MS Database search

13 Search Engine Mascot Sequest In short, SEQUEST performs automated peptide/protein sequencing via database searching of MS/MS spectra User friendly interpretation Straightforward search

14 Search mechanism Real Identical mass within MMD Theoretical m/z m/z Compare and scoring SEQUEST MASCOT X!tandem ProbID m/z NCBI Swiss-Prot IPI Ensembl Theoretical digestion SFATFDIMAETLK DILSVDYTDIMK Theoretical fragment ladders identification

15 Significant identification Mascot Probability based scoring Compute the probability that the observed match between the experimental data and mass values calculated from a candidate peptide sequence is a random event. The correct match has a very low probability. Ion score = -10Log 10 (P), P = p/n, (p : prosibility threshold, N: database entries of the precursor ions ) Sequest a two-tiered scoring scheme 1. the preliminary score (Sp), is an empirically derived score that restricts the number of sequences analyzed in the correlation analysis. 2. XCorr is independent of database size and reflects the quality of the match between spectrum and sequence, whereas ΔCn is database dependent and reflects the quality of the match relative to near misses. 3. normalize XCorr for spectral noise and peptide size target /Reversed FDR: NT NT + NR FPR: NT NT + 2 X NR NT: Number of peptide in target database NR: Number of peptide in reversed database

16 Qualitative and Quantitative Proteomics Qualitative proteomics Quantitative proteomics Protein identifications PTM analysis Biomarker Protein dynamics Protein interaction partners

17 Quantitative approaches 15 N, SILAC ICAT, itraq Bantscheff M. et al. Anal Bioanal Chem (2007) 389:

18 Labeling reagents SILAC: 2 H 4 -Lys, 13 C 6 15 N 2 -Lys, 13 C 6 -Arg, 13 C 6 15 N 4 -Arg 2 H 3 -Leu Mann M. MCP 2002 ICAT: Aebersold R. Nat. Biotechnol itraq: Pappin D.J. MCP, 2004

19 SILAC stable isotopic labeling of amino acid in cell culture

20 How to analyze long list of protein?

21 Bioinformatics for qualitative proteomics Chanchal Kumar & Matthias Mann. FEBS Letters 583 (2009)

22 Bioinformatics for quantitative proteomics Chanchal Kumar & Matthias Mann. FEBS Letters 583 (2009)

23 Useful bioinformatics tools Qualitative analysis: BINGO Quantitative analysis: CLUSTER & PCA Biological analysis: KEGG

24 Tool Bingo Qualitative analysis of overrepresented groups Entrez Gene Biological Process Cellular Component Molecular Function Control database

25 Result display of Bingo

26 Tool Cluster

27 Result display of cluster

28 Different samples Tool PCA Principal component analysis involves a mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components Different proteins Intensity Matrix

29 Tool KEGG Kyoto Encyclopedia of Genes and Genomes

30 Pathway display of KEGG

31 Proteomics is not the end of a story Immunohistochemistry candidates Fluorescent Knock-out animal model ChIP

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