MaxQuant

MaxQuant analyzes high-resolution mass spectrometry data to identify and quantify peptides and proteins for quantitative proteomics and post-translational modification studies.

Key Features:

• Automated data analysis: Processes large-scale LC-MS/MS shotgun proteomics datasets to perform peptide identification and quantification automatically.
• Advanced visualization: Visualizes LC-MS/MS mass- and retention time-dependent signals, supports label-free quantification monitoring across multiple runs, and provides 3D representations of data.
• MS/MS annotation: Provides expert annotation of MS/MS spectra to support peptide identification.
• Large dataset handling: Optimized for processing very large volumes of raw LC-MS data, including experiments with many samples and pre-fractionation.
• Mass accuracy correction: Achieves parts-per-billion mass accuracy by integrating multiple mass measurements and correcting linear and nonlinear mass offsets, increasing identified fragmentation spectra (reported up to 73% for SILAC peptide pairs).
• Statistical quantification: Enables statistically robust identification and quantification of thousands of proteins (over 4,000 per mammalian cell lysate experiment) and handles several hundred thousand peptides.
• Precursor mass filtering (MaxQuant.Live): Applies precursor mass filtering to increase selectivity and identification rates of post-translational modifications such as SUMO and ubiquitin and to exclude unmodified peptides without requiring spectral libraries.

Scientific Applications:

• Quantitative proteomics (SILAC): Identification and quantification of proteins and SILAC peptide pairs in high-resolution LC-MS/MS datasets.
• Post-translational modification analysis (SUMOylation and ubiquitination): Enhanced detection and identification of SUMO- and ubiquitin-modified peptides using precursor mass filtering.
• Large-scale proteome profiling: Comparative analysis of complex biological systems and large sample cohorts to study protein expression, modification, and interaction networks.

Methodology:

Integrated algorithms use correlation analysis and graph theory to detect peaks, isotope clusters, and SILAC peptide pairs as three-dimensional objects in m/z, elution time, and signal intensity space; integrate multiple mass measurements and correct linear and nonlinear mass offsets; and apply precursor mass filtering (MaxQuant.Live) to increase PTM selectivity.