1 / 71

Protein Identification by Sequence Database Search

Protein Identification by Sequence Database Search. Nathan Edwards Department of Biochemistry and Mol. & Cell. Biology Georgetown University Medical Center. Peptide Mass Fingerprint. Cut out 2D-Gel Spot. Peptide Mass Fingerprint. Trypsin Digest. Peptide Mass Fingerprint. MS.

max
Télécharger la présentation

Protein Identification by Sequence Database Search

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. Protein Identification by Sequence Database Search Nathan Edwards Department of Biochemistry and Mol. & Cell. Biology Georgetown University Medical Center

  2. Peptide Mass Fingerprint Cut out 2D-GelSpot

  3. Peptide Mass Fingerprint Trypsin Digest

  4. Peptide Mass Fingerprint MS

  5. Peptide Mass Fingerprint

  6. 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

  7. Protein Sequence • Myoglobin GLSDGEWQQV LNVWGKVEAD IAGHGQEVLI RLFTGHPETL EKFDKFKHLK TEAEMKASED LKKHGTVVLT ALGGILKKKG HHEAELKPLA QSHATKHKIP IKYLEFISDA IIHVLHSKHP GDFGADAQGA MTKALELFRN DIAAKYKELG FQG

  8. Protein Sequence • Myoglobin GLSDGEWQQV LNVWGKVEAD IAGHGQEVLI RLFTGHPETL EKFDKFKHLK TEAEMKASED LKKHGTVVLT ALGGILKKKG HHEAELKPLA QSHATKHKIP IKYLEFISDA IIHVLHSKHP GDFGADAQGA MTKALELFRN DIAAKYKELG FQG

  9. Amino-Acid Masses

  10. Peptide Mass & m/z • Peptide Molecular Weight: N-terminal-mass (0.00) + Sum (AA masses) + C-terminal-mass (18.010560) • Observed Peptide m/z: (Peptide Molecular Weight + z * Proton-mass (1.007825)) / z • Monoisotopic mass values!

  11. 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

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

  13. Enzymatic Digest and Fractionation Sample Preparation for Tandem Mass Spectrometry

  14. Single Stage MS MS

  15. Tandem Mass Spectrometry(MS/MS) MS/MS

  16. 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

  17. Peptide Fragmentation

  18. yn-i bi Peptide Fragmentation yn-i-1 -HN-CH-CO-NH-CH-CO-NH- Ri+1 Ri bi+1

  19. xn-i yn-i zn-i yn-i-1 -HN-CH-CO-NH-CH-CO-NH- CH-R’ Ri i+1 R” ai bi ci i+1 bi+1 Peptide Fragmentation

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

  21. 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 Peptide Fragmentation

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

  23. S G F L E E D E L K 100 % Intensity 0 m/z 250 500 750 1000 Sequence Database Search

  24. 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 Sequence Database Search

  25. 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 Sequence Database Search

  26. Sequence Database Search • No need for complete ladders • Possible to model all known peptide fragments • Sequence permutations eliminated • All candidates have some biological relevance • Practical for high-throughput peptide identification • Correct peptide might be missing from database!

  27. Peptide Candidate Filtering • Digestion Enzyme: Trypsin • Cuts just after K or R unless followed by a P. • Basic residues (K & R) at C-terminal attract ionizing charge, leading to strong y-ions • “Average” peptide length about 10-15 amino-acids • Must allow for “missed” cleavage sites

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

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

  30. Peptide Candidate Filtering • Peptide molecular weight • Only have m/z value • Need to determine charge state • Ion selection tolerance • Mass for each amino-acid symbol? • Monoisotopic vs. Average • “Default” residual mass • Depends on sample preparation protocol • Cysteine almost always modified

  31. i=0 Same peptide,i = # of C13 isotope i=1 i=2 i=3 i=4 Peptide Molecular Weight

  32. Peptide Molecular Weight …from “Isotopes” – An IonSource.Com Tutorial

  33. Peptide Molecular Weight • Peptide sequence WVTFISLLFLFSSAYSR • Potential phosphorylation? S,T,Y + 80 Da • 7 Molecular Weights • 64 “Peptides”

  34. Peptide Scoring • Peptide fragments vary based on • The instrument • The peptide’s amino-acid sequence • The peptide’s charge state • Etc… • Search engines model peptide fragmentation to various degrees. • Speed vs. sensitivity tradeoff • y-ions & b-ions occur most frequently • The scores have no apriority “scale”

  35. Peptide Identification • High-throughput workflows demand we analyze all spectra, all the time. • Spectra may not contain enough information to be interpreted correctly • ...cell phone call drops in and out • Spectra may contain too many irrelevant peaks • …bad static • Peptides may not match our assumptions • …its all Greek to me • “Don’t know”is an acceptable answer!

  36. Peptide Identification • Rank the best peptide identifications • Is the top ranked peptide correct?

  37. Peptide Identification • Rank the best peptide identifications • Is the top ranked peptide correct?

  38. Peptide Identification • Rank the best peptide identifications • Is the top ranked peptide correct?

  39. Peptide Identification • Incorrect peptide has best score • Correct peptide is missing? • Potential for incorrect conclusion • What score ensures no incorrect peptides? • Correct peptide has weak score • Insufficient fragmentation, poor score • Potential for weakened conclusion • What score ensures we find all correct peptides?

  40. Statistical Significance • Can’t prove particular identifications are right or wrong... • ...need to know fragmentation in advance! • A minimal standard for identification scores... • ...better than guessing. • p-value, E-value, statistical significance

  41. Random Peptide Models • "Generate" random peptides • Real looking fragment masses • No theoretical model! • Must use empirical distribution • Usually require they have the correct precursor mass • Score function can model anything we like!

  42. Random Peptide Models Fenyo & Beavis, Anal. Chem., 2003

  43. Random Peptide Models Fenyo & Beavis, Anal. Chem., 2003

  44. Random Peptide Models • Truly random peptides don’t look much like real peptides • Just use (incorrect) peptides from the sequence database! • Caveats: • Correct peptide (non-random) may be included • Homologous incorrect peptides may be included • (Incorrect) peptides are not independent

  45. Extrapolating from the Empirical Distribution • Often, the empirical shape is consistent with a theoretical model Geer et al., J. Proteome Research, 2004 Fenyo & Beavis, Anal. Chem., 2003

  46. False Positive Rate Estimation • A form of statistical significance • Search engine independent • Easy to implement • Assumes a single threshold for all spectra • Best if E-value or similar is used to compute a spectrum normalized score

  47. False Positive Rate Estimation • Each spectrum is a chance to be right, wrong, or inconclusive. • At any given threshold, how many peptide identifications are wrong? • Computed for an entire spectral dataset • Given identification criteria: • SEQUEST Xcorr, E-value, Score, etc., plus... • ...threshold • Use “decoy” sequences • random, reverse, cross-species • Identifications must be incorrect!

  48. Decoy Search Strategies • Concatenated target & decoy • “Competition” for best hit... • Masks good decoy scores due to spectral variation • Separate searches • Cleaner estimation of false hit distribution • More conservative than concatenation • Must ensure: • Decoy searches do not change target peptide scores • Single score distribution across dataset

  49. Decoy Search Strategies • Reversed Decoys • Captures redundancy of peptide sequences • Susceptible to mass-shift anomalies • Bad choice for protein-level statistics • Shuffled & Random Decoys • Multiple independent decoys can be created. • Better estimation of tail probabilities • More conservative than reversed decoys

  50. False Positive Rate Estimation: Concatenated Target & Decoy • Choose a threshold t. • Count # of (rank 1) target ids (Tt) with score ≥t. • Count # of (rank 1) decoy ids (Dt) with score ≥t. • Compute FPR = ( 2 x Dt ) / ( Tt + Dt ) Principle: • Decoy peptides equally likely as false hits at rank 1 Issues: • What to do with decoy hits? • Change in database size may affect scores

More Related