MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.
Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
Cox
Jürgen
J
Department for Proteomics and Signal Transduction, Max-Planck Institute for Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany. cox@biochem.mpg.de
Mann
Matthias
M
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006367:HeLa Cells; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D010455:Peptides / Q000032:analysis* / Q000737:chemistry; D011506:Proteins / Q000032:analysis* / Q000737:chemistry; D020543:Proteome / Q000032:analysis / Q000378:metabolism; D040901:Proteomics / Q000379:methods*
D010455:Peptides; D011506:Proteins; D020543:Proteome