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1.proteomics coursework-3 dec2012-aky

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Proteomics Coursework - Part 1 of 3

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1.proteomics coursework-3 dec2012-aky

  1. 1. Course B
  2. 2. WHY BOTHER WITH 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?
  3. 3. WHY NOT MICROARRAYS? Is Proteomics the New Genomics? Jürgen Cox and Matthias Mann, Cell 130, August 10, 2007
  4. 4. ONE GENOME…MANY PROTEOMES Perhaps not… they still have a dynamic “proteome” code to break. They cannot hit a moving target
  5. 5. AN ANALYTICAL CHALLENGE Dynamic range of protein abundances is a challenge for separation sciences No equivalent of PCR for proteins-deal with µ- to nmol concentrations Alternate splice forms of a gene can make different proteins >200 Post translational modifications; cannot be deduced from a gene or mRNA Edman sequencing cannot provide the solutions !!!
  6. 6. TOOLS FOR PROTEOMICS Sequence databases DNA ESTs Protein Mass Spectrometry Ionization techniques Analyzers Software PMF MS/MS De Novo Sequencing Protein Separation Technology 2D-GE LCMS
  7. 7. MASS SPECTROMETRY The PCR for proteins ?
  8. 8. MASS SPECTROMETRY  Analytical method to measure the molecular or atomic weight of samples Slide adopted from: Dr.. Ahna Skop. Mass Spectrometry: Methods & Theory
  9. 9. SOFT IONIZATION METHODS 337 nm UV laser MALDI cyano-hydroxy cinnamic acid Gold tip needle Fluid (no salt) ESI + _ Slide adopted from: Nathan Edwards Center for Bioinformatics and Computational Biology(UMIACS)
  10. 10. MASS SPECTROMETRY PRINCIPLES Ionizer Sample + _ Mass Analyzer Detector Slide adopted from: Nathan Edwards Center for Bioinformatics and Computational Biology(UMIACS)
  11. 11. MASS SPEC EQUATION (TOF) m z 2Vt2 = m = mass of ion L = drift tube length z = charge of ion t = time of travel V = voltage L2
  12. 12. MONOISOTOPIC MASS www.matrixscience.com •Mass of the most abundant isotope of a molecule •Measured in amu or Da •Usually the lightest isotope of small molecules
  13. 13. UNDERSTANDING A SPECRUM m/z RelativeIntensity 853.2 854.3 1200.5 1201.0 +2 +1 (1200.5 × 2) – 2 = 2399.0
  14. 14. MS INSTRUMENTS A Brief Summary of the Different Types of Mass Spectrometers Used in Proteomics Methods in Molecular Biology, vol. 484: Functional Proteomics: Methods and Protocols
  15. 15. IDENTIFICATION STRATEGIES Experimental masses Theoretical Masses (database) 1. Peptide mass fingerprinting(PMF) 2. MS/MS spectral matching Experimental spectrum Theoretical spectra 3.De novo sequencing* 72.0 129.0 97.0 101.0 113.1 174.1 A E P T I R H2O *Adopted from: Brian C. Searle, Proteome Software Inc. Portland, Oregon USA 4. Spectral library search
  16. 16. Nesvizhskii. Journal of Proteomics ,2010
  17. 17. PEPTIDE MASS FINGERPRINTING A rapid way to identify proteins
  18. 18. PEPTIDE MASS FINGERPRINT The proteins from a sample are separated on 2D gels Protein of interest is digested by trypsin (or any other site specific cleavage) Ionization of peptides in a MALDI mass spectrometer m/z values detected and plotted as mass spectrum PMF database search to identify the protein
  19. 19. PROTEASE DIGESTION trypsin
  20. 20. PEPTIDE MASS FINGERPRINT m/z RelativeIntensity
  21. 21. PMF DATABASE SEARCH 450.2201 609.3667 698.3100 1007.5391 1199.4916 2098.9909 PEAKLIST >gi|2924450|emb|CAA17750.1| PROBABLE FATTY-ACID-CoA LIGASE FADD18 (FRAGMENT) (FATTY-ACID-CoA SYNTHETASE) (FATTY-ACID-CoA SYNTHASE) [Mycobacterium tuberculosis H37Rv] MAASLSENLSCHSSNMCRLSGNAATNLERPGEEPPGDRCTRRQAVRPARTLAKKGNIPVGYYKDEKKTAETFRTINGVRYAIPGD YAQVEEDGTVTMLGRGSVSINSGGEKVYPEEVEAALKGHPDVFDALVVGVPDPRY GQQVAAVVQARPGCRPSLAELDSFVRSEIAGYKVPRSLWFVDEVKRSPAGKPDYRWAKEQTEARPADDVH AGHVTSGS >gi|15610649|ref|NP_218030.1| fatty-acid-CoA ligase [Mycobacterium tuberculosis H37Rv] MAASLSENLSCHSSNMCRLSGNAATNLERPGEEPPGDRCTRRQAVRPARTLAKKGNIPVGYYKDEKKTAE TFRTINGVRYAIPGDYAQVEEDGTVTMLGRGSVSINSGGEKVYPEEVEAALKGHPDVFDALVVGVPDPRY GQQVAAVVQARPGCRPSLAELDSFVRSEIAGYKVPRSLWFVDEVKRSPAGKPDYRWAKEQTEARPADDVH AGHVTSGS Protein FASTA database 450.2017 (P21234) 609.2667 (P12345) 664.3300 (P89212) 1007.4251 (P12345) 1114.4416 (P89212) 1183.5266 (P12345) 1300.5116 (P21234) 1407.6462 (P21234) 1526.6211 (P89212) 1593.7101 (P89212) 1740.7501 (P21234) 2098.8909 (P12345) in silico digestion OUTPUT: 2 Unknown masses 1 hit on P21234 3 hits on P12345 RESULT: protein is P12345
  22. 22. 22 MODIFICATIONS  Fixed modifications: will be present on any occurrence of the affected amino acid.Eg.+57@C  Variable modifications: may be present on some or all positions of the affected amino acid. Eg.+16@M Slide adopted from: Nathan Edwards Center for Bioinformatics and Computational Biology(UMIACS)
  23. 23. TANDEM MASS SPECTROMETRY Peptide Sequencing by two stage MS
  24. 24. PRECURSOR SELECTION m/z RelativeIntensity MS1 Tandem MS or MS/MS or MS2 Unfragmented parent/precursor ion
  25. 25. COLLISION INDUCED DISSOCIATION CID in presence of inert gas
  26. 26. 26 FRAGMENTATION PEPTIDE MW ion ion MW 98 b1 P EPTIDE y6 703 227 b2 PE PTIDE y5 574 324 b3 PEP TIDE y4 477 425 b4 PEPT IDE y3 376 538 b5 PEPTI DE y2 263 653 b6 PEPTID E y2 148
  27. 27. SHOTGUN PROTEOMICS & DATABASE SEARCH The pros and cons of peptide-centric proteomics. Mark W. Duncan, Ruedi Aebersold, Richard M. Caprioli Nature Biotechnology, Vol. 28, No. 7. (01 July 2010), pp. 659-664
  28. 28. DATABASE SEARCH ALGORITHMS  SEQUEST  Mascot  X!Tandem  OMSSA  ProbID  Phenyx  Myrimatch  MassWiz
  29. 29. DE NOVO SEQUENCING Sequencing a peptide from scratch
  30. 30. 30 DE NOVO INTERPRETATION 100 0 250 500 750 1000 m/z %Intensity Slide adopted from: Nathan Edwards Center for Bioinformatics and Computational Biology(UMIACS)
  31. 31. 31 DE NOVO INTERPRETATION 100 0 250 500 750 1000 m/z %Intensity E L Slide adopted from: Nathan Edwards Center for Bioinformatics and Computational Biology(UMIACS)
  32. 32. 32 DE NOVO INTERPRETATION 100 0 250 500 750 1000 m/z %Intensity E L F KL SGF G E D E L E E D E L Slide adopted from: Nathan Edwards Center for Bioinformatics and Computational Biology(UMIACS)
  33. 33. 33 SUMMARY  Proteomics is large-scale study (qualitative and quantitative) study of proteins by mass spec.  Mass spectrometry + sequence databases represent a huge leap for protein (bio-)chemistry.  ProteinSeparation - 2DGE and HPLC  Ionization - MALDI and ESI  Identification - PMF, MS/MS and de novo sequencing  Sample prep, instruments and algorithms still maturing, much work to be done.
  34. 34. NEXT…  Significance Assessment of database matches  False Discovery rate  Protein Inference

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