Friday, December 17, 2010

MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies

MaxQuant enables high peptide identification rates, individualized 
p.p.b.-range mass accuracies and proteome-wide protein quantification
Jurgen Cox & Matthias Mann, Nature Biotechnology


* What is MaxQuant?
===================
high-res quantitative MS data SILAC => MS/MS ID and prot quantification
SILAC = stable amino acid isotope-labeled 
   labeled with light vs. heavy isotope => pairs of heavy and light isotope 
    patterns  => ratio heavy/light

pipeline:
1. feature detection and peptide quantitation
  1a. peak detection: 
    + 2D (centroid) and 3D (peak alignment)
    + bootstrap estimation
  1b. SILAC pair detection
    + for all pairs of isotope patterns: 
      - correlation test > 0.5, equal charge, close enough mass
      - equal charge, close enough mass
      comments: 0.5 too low? lots of false positive? rely on mass too much?
    + convolute 2 isotope patterns
      - with possible KR combinations (K, R, KK, KR, RR, KKK, and more)
      - find same atomic composition
    comments: how to treat those that did not fully incorporate heavy isotope?
  1c. quantitation (discussed)
    used SILAC intensity ratio: heavy/light. one peak, total? 
                                 monoisotopic? most intense peak?

2. MS/MS ion search -- Mascot

3. ID and validation
  3a. posterior error probability for calculating FDR
    + probability that ID is false knowing Mascot score and peptide length
      comments: 
        normalize Mascot score by peptide length?
        what is the training data? don't need one, use decoy
        why didn't they do FDR like everyone else? by normalizing by length, 
          get more IDs
          how do they justify it?
    + peptide score distributions: decoy vs target hit
      after length normalization: score distributions target vs decoy 
       more separated
      comments:
        proportions of target hits vs decoy hits are off
        why does length normalization make such a difference? 
          Mascot gives higher score to longer peptides.
  
4. visualization


* What are the benefits of MaxQuant?
====================================
1. improving peptide mass accuracy
  after re-calibration: p.p.m. much lower
2. high rate of identified MS/MS spectra
  not many ID for low mass but many IDs at high mass
  comments: doesn't look like 70% ID
3. proteome-wide protein quantification
  + protein ratio = median(all SILAC peptide ratio)
    comments:
      1 peptide or both peptides in pair IDed?
        both peptides in pair must ID as same peptide
      what if MS/MS for only one of the pair?
      if 2 peptides IDed but not paired earlier, can pair after (10% extra)
      how helpful is it to use tight tolerance? did not say
  + P-value for detection of significant outlier ratio (significance A)
    A. as above
    B. bin proteins by intensities
    significance A better than significance B


* Conclusion
============
MaxQuant improves:
 peptide ID rate
 mass accuracy
 protein-wide quantitation


* Criticism
===========
All experimental results are based on Mascot search 
  Mascot does not fully benefit high-accuracy data (0.25 Da)
comments: LTQ so it's fine
Speaker: Yoona Kim
Scribe: Jocelyne Bruand
Slides: here

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