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Reporting Protein Identifications from MS/MS Results. Brian C. Searle Proteome Software Inc. Portland, Oregon USA Brian.Searle@ProteomeSoftware.com. Creative Commons Attribution. Outline. Assigning Proteins from Peptide IDs Correcting for One-Hit-Wonders Protein False Discovery Rates?
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Reporting Protein Identifications from MS/MS Results Brian C. Searle Proteome Software Inc. Portland, Oregon USA Brian.Searle@ProteomeSoftware.com Creative Commons Attribution
Outline • Assigning Proteins from Peptide IDs • Correcting for One-Hit-Wonders • Protein False Discovery Rates? • Correcting for Shared Peptides • Publication Standards
Outline • Assigning Proteins from Peptide IDs • Correcting for One-Hit-Wonders • Protein False Discovery Rates? • Correcting for Shared Peptides • Publication Standards
Just to Review: F possibly correct R clearly wrong Elias JE, Gygi SP. Nat Methods. 2007 Mar;4(3):207-14.
AEPTIR Protein IDVCIVLLQHK NTGDR
85% AEPTIR ??% 65% Protein IDVCIVLLQHK 25% NTGDR
FDRs for Whole Datasetsvs Individual Peptides • Cumulative FDRs only estimate the validity of a data set • Probabilities (or instantaneous FDRs) estimate the validity of a peptide of interest
One Possible Approach • Instantaneous False Discovery Rate • PeptideProphet (TPP, Scaffold) • Percolator • Spectral Energies • RAId De Novo Many Others:
Just to Review: 4 to 5 3 to 4 2 to 3 1 to 2 0 to 1 -1 to 0 -2 to -1
Histogram of Decoy Matches “2x Decoy” # of Matches “Correct” Ion Score – Identity Score
Histogram of Decoy Matches “2x Decoy” # of Matches “Correct” Ion Score – Identity Score
Curve Fit Distributions “2x Decoy” # of Matches “Correct” Ion Score – Identity Score Choi H, Ghosh D, Nesvizhskii AI. J Proteome Res. 2008 Jan;7(1):286-92.
Instantaneous FDR Method “2x Decoy” # of Matches “Correct” Ion Score – Identity Score Choi H, Ghosh D, Nesvizhskii AI. J Proteome Res. 2008 Jan;7(1):286-92.
AEPTIR 85% ??% Protein 65% IDVCIVLLQHK 25% NTGDR
AEPTIR (15%) (??%) Protein (35%) IDVCIVLLQHK (75%) NTGDR Feng J, Naiman DQ, Cooper B. Anal Chem. 2007 May 15;79(10):3901-11.
AEPTIR (15%) (4%) Protein (35%) IDVCIVLLQHK (75%) NTGDR 0.15 * 0.35 * 0.75 = 0.04 Feng J, Naiman DQ, Cooper B. Anal Chem. 2007 May 15;79(10):3901-11.
AEPTIR 85% 96% Protein 65% IDVCIVLLQHK 25% NTGDR 0.15 * 0.35 * 0.75 = 0.04 Feng J, Naiman DQ, Cooper B. Anal Chem. 2007 May 15;79(10):3901-11.
Peptide 1 Peptide 2 Peptide 3 Peptide 4 Peptide 5 Peptide 6 Peptide 7 Peptide 8 Peptide 9 Peptide 10 80% Peptides
Peptide 1 Correct Protein A Peptide 2 Peptide 3 Correct Protein B Peptide 4 Peptide 5 Peptide 6 Peptide 7 Peptide 8 Peptide 9 Peptide 10 80% Peptides
Peptide 1 Correct Protein A Peptide 2 Peptide 3 Correct Protein B Peptide 4 Peptide 5 Incorrect Protein C Peptide 6 Peptide 7 Incorrect Protein D Peptide 8 Peptide 9 Peptide 10 80% Peptides 50% Proteins
Outline • Assigning Proteins from Peptide IDs • Correcting for One-Hit-Wonders • Protein False Discovery Rates? • Correcting for Shared Peptides • Publication Standards
Actual Probability Computed Probability Nesvizhskii, A. I.; Keller, A. et al Anal. Chem.75, 4646-4658
UNDER estimation Actual Probability OVER estimation Computed Probability Nesvizhskii, A. I.; Keller, A. et al Anal. Chem.75, 4646-4658
UNDER estimation Actual Probability OVER estimation Computed Probability Nesvizhskii, A. I.; Keller, A. et al Anal. Chem.75, 4646-4658
What if we could scoreone-hit-wonderness? Nesvizhskii, A. I.; Keller, A. et al Anal. Chem.75, 4646-4658
Combining different peptides • Quantify as a score: If different peptides agree: Good! If peptides are one-hit-wonders: Bad! Nesvizhskii, A. I.; Keller, A. et al Anal. Chem.75, 4646-4658
Combining different peptides • Quantify as a score: If different peptides agree: Good! If peptides are one-hit-wonders: Bad! • Peptide agreement score: Nesvizhskii, A. I.; Keller, A. et al Anal. Chem.75, 4646-4658
Combining different peptides • Quantify as a score: If different peptides agree: Good! If peptides are one-hit-wonders: Bad! • Peptide agreement score: NSP score for peptide (k) is the sum of other agreeing peptides (not k) Nesvizhskii, A. I.; Keller, A. et al Anal. Chem.75, 4646-4658
Protein Prophet Distributions One-hit Wonders Multi-hit Proteins
Protein Prophet Distributions multi-hit proteins (increase prob) in between (keep same) one hit wonders (decrease prob)
UNDER estimation Actual Probability OVER estimation Computed Probability Nesvizhskii, A. I.; Keller, A. et al Anal. Chem.75, 4646-4658
with NSP Actual Probability without NSP Computed Probability Nesvizhskii, A. I.; Keller, A. et al Anal. Chem.75, 4646-4658
Option 1:Throw Out One-Hit-Wonders Advantages: Easy, works! Disadvantages: Loss of sensitivity!
Option 2: Use Multiple Filters Filter 2 - Peptide Mode Filter 1 - Protein Mode • 1 peptide/protein • high spectrum threshold • ≥2 peptides/protein • moderate spectrum threshold
Option 2: Use Multiple Filters Advantages: More sensitive! Disadvantages: Pretty arbitrary!
Option 3: • Assigning Proteins from Peptide IDs • Correcting for One-Hit-Wonders • Protein False Discovery Rates? • Correcting for Shared Peptides • Publication Standards
Protein FDRs only accurate with >100 Proteins Uncertainty in Protein FDR 1% Error In FDR Estimation Number of Confidently IDed Proteins
Histogram of Decoy PROTEIN Matches “2x Decoy” # Protein Identifications “Correct” Protein Score