1. Yoona Kim University of California, San Diego UCSD Mass Spectrometry Journal Club 12/03/10

2. MaxQuant

4.  What is the MaxQuant?  What’s benefits from MaxQuant?  Conclusion  Critisism

5. Input : High resolution, quantitative MS data of SILAC- encoded cell populations MaxQuant Output: Identified MS/MS spectra and protein quantification graph(x=protein ration/y=intensity)

8. 4. Visualization 3. Identification and validation 2. MS/MS Ion search - Mascot 1. Feature detection and peptide quantitation

9.  2D peaks  3D peaks

10.  A bootstrap estimation over B = 150 ∵ unknown atomic composition and intensity profiles overlap

11.  Step 1: All possible pairs of isotope patterns - The correlation test >0.5 - Have equal charge, close enough in mass  Step 2 : Convolute two isotope patterns - K, R, KK, KR, RR, KKK, KKR, KRR, and RRR - Find the same atomic composition

12.  Ex. The peptide contains one K and no R - Heavy isotope labeled form,

14.  Posterior Error Probability - for calculating the false-discovery rate

16. 1. Improving peptide mass accuracy 2. High rate of identified MS/MS spectra3. Proteome-wide protein quantifiation

19.  Protein ratio = median(all SILAC peptide ratio)  P-value for detection of significant outlier ratio (significance A)

20. Significance ASignificance B

21.  MaxQuant improves ◦ Peptide identification rates ◦ Peptide mass accuracy ◦ Proteom-wide protein quantification

22.  All experimental results are based on Mascot search ◦ Mascot does not fully benefit from high-accuracy (limit 0.25Da) -> It is not working…!! (Sangtae said)

23. Go MaxQunt summer school It will be fun!!!