1 / 65

Proteomics & Mass Spectrometry

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

terra
Download Presentation

Proteomics & Mass Spectrometry

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. Outline • Proteomics • Mass Spectrometry • Protein Identification • Peptide Mass Fingerprint • Tandem Mass Spectrometry

  3. 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?

  4. Systems Biology • Establish relationships by • Choosing related samples, • Global characterization, and • Comparison.

  5. Samples • Healthy / Diseased • Cancerous / Benign • Drug resistant / Drug susceptible • Bound / Unbound • Tissue specific • Cellular location specific • Mitochondria, Membrane

  6. Protein separation Molecular weight (MW) Isoelectric point (pI) Staining Birds-eye view of protein abundance 2D Gel-Electrophoresis

  7. 2D Gel-Electrophoresis Bécamel et al., Biol. Proced. Online 2002;4:94-104.

  8. 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

  9. Sample + _ Detector Ionizer Mass Analyzer Mass Spectrometer ElectronMultiplier(EM) Time-Of-Flight (TOF) Quadrapole Ion-Trap MALDI Electro-SprayIonization (ESI)

  10. 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

  11. Mass Spectrum

  12. Mass is fundamental

  13. Peptide Mass Fingerprint Cut out 2D-GelSpot

  14. Peptide Mass Fingerprint Trypsin Digest

  15. Peptide Mass Fingerprint MS

  16. Peptide Mass Fingerprint

  17. 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

  18. Protein Sequence • Myoglobin - Plains zebraGLSDGEWQQV LNVWGKVEAD IAGHGQEVLI RLFTGHPETL EKFDKFKHLK TEAEMKASED LKKHGTVVLT ALGGILKKKG HHEAELKPLA QSHATKHKIP IKYLEFISDA IIHVLHSKHP GDFGADAQGA MTKALELFRN DIAAKYKELG FQG

  19. Protein Sequence • Myoglobin - Plains zebraGLSDGEWQQV LNVWGKVEAD IAGHGQEVLI RLFTGHPETL EKFDKFKHLK TEAEMKASED LKKHGTVVLT ALGGILKKKG HHEAELKPLA QSHATKHKIP IKYLEFISDA IIHVLHSKHP GDFGADAQGA MTKALELFRN DIAAKYKELG FQG

  20. 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

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

  22. Mass Spectrometry • Strengths • 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

  23. Enzymatic Digest and Fractionation Sample Preparation for MS/MS

  24. Single Stage MS MS

  25. Tandem Mass Spectrometry(MS/MS) Precursor selection

  26. Tandem Mass Spectrometry(MS/MS) Precursor selection + collision induced dissociation (CID) MS/MS

  27. 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

  28. Peptide Fragmentation

  29. yn-i bi Peptide Fragmentation yn-i-1 -HN-CH-CO-NH-CH-CO-NH- CH-R’ Ri i+1 R” i+1 bi+1

  30. Peptide Fragmentation Peptide: S-G-F-L-E-E-D-E-L-K

  31. 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 0 m/z 250 500 750 1000

  32. 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 b5 y8 y4 b8 y9 b6 b7 b9 0 m/z 250 500 750 1000

  33. Peptide Identification Given: • The mass of the precursor ion, and • The MS/MS spectrum Output: • The amino-acid sequence of the peptide

  34. Peptide Identification Two paradigms: • De novo interpretation • Sequence database search

  35. 100 % Intensity 0 m/z 250 500 750 1000 De Novo Interpretation

  36. 100 % Intensity E L 0 m/z 250 500 750 1000 De Novo Interpretation

  37. 100 % Intensity SGF G E E E D E KL E E D L L L F 0 m/z 250 500 750 1000 De Novo Interpretation

  38. De Novo Interpretation

  39. De Novo Interpretation …from Lu and Chen (2003), JCB 10:1

  40. De Novo Interpretation

  41. De Novo Interpretation …from Lu and Chen (2003), JCB 10:1

  42. 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

  43. 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

  44. Peptide Fragmentation S G F L E E D E L K 100 % Intensity 0 m/z 250 500 750 1000

  45. 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 0 m/z 250 500 750 1000

  46. 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 b5 y8 y4 b8 y9 b6 b7 b9 0 m/z 250 500 750 1000

  47. 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!

  48. 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

  49. Peptide Candidate Filtering >ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK… No missed cleavage sites MK WVTFISLLFLFSSAYSR GVFR R DAHK SEVAHR FK DLGEENFK ALVLIAFAQYLQQCPFEDHVK LVNEVTEFAK …

  50. Peptide Candidate Filtering >ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK… One missed cleavage site MKWVTFISLLFLFSSAYSR WVTFISLLFLFSSAYSRGVFR GVFRR RDAHK DAHKSEVAHR SEVAHRFK FKDLGEENFK DLGEENFKALVLIAFAQYLQQCPFEDHVK ALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK …

More Related