MaxQuant Enables High Peptide Identification Rates, Individualized p.p.b.-range Mass Accuracies and Proteome-Wide Protein Quantification

Nat Biotechnol. 2008 Dec;26(12):1367-72. doi: 10.1038/nbt.1511. Epub 2008 Nov 30.

Abstract

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.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Computational Biology / methods*
  • Databases, Protein
  • HeLa Cells
  • Humans
  • Mass Spectrometry / methods*
  • Peptides / analysis*
  • Peptides / chemistry
  • Proteins / analysis*
  • Proteins / chemistry
  • Proteome / analysis
  • Proteome / metabolism
  • Proteomics / methods*

Substances

  • Peptides
  • Proteins
  • Proteome