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MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification

Why this mattered

Before MaxQuant, high-resolution LC-MS/MS proteomics was producing richer data than most analysis pipelines could reliably extract. Cox and Mann turned Orbitrap-era mass accuracy into a computational advantage: by correcting run-specific mass errors and integrating multiple peptide measurements, MaxQuant pushed peptide mass accuracy into the parts-per-billion range and increased confident identifications. The shift was not only better software convenience; it made large-scale proteomics more statistical, automated, and reproducible.

The newly important possibility was proteome-wide quantification at depth. In SILAC experiments, MaxQuant could automatically quantify hundreds of thousands of peptide signals and support robust measurement of more than 4,000 proteins in mammalian lysates, moving quantitative proteomics from selected-protein studies toward system-wide measurement. This helped make mass spectrometry a practical complement to genomics and transcriptomics: proteins, modifications, and abundance changes could be surveyed across whole biological states rather than treated as isolated biochemical readouts.

Its longer-term importance was as an enabling platform. Later MaxQuant extensions and related tools supported label-free quantification, post-translational modification analysis, larger cohort studies, and eventually newer acquisition strategies such as DIA through the same broad idea: high-quality computational inference is inseparable from modern proteomics. The paper therefore marks a point where proteomics became less limited by manual spectrum interpretation and more defined by scalable, standardized pipelines capable of turning complex mass-spectrometry data into biological measurements.

Abstract

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