MHCquant

MHCquant processes LC-MS/MS immunopeptidomics mass spectrometry raw data to identify and quantify HLA-presented peptides for neoepitope discovery in tumor immunotherapy.

Key Features:

• Automated Data Processing: An automated computational pipeline processes liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) data for immunopeptidomics analyses.
• High Sensitivity and Accuracy: Demonstrates higher sensitivity than established methods by identifying increased numbers of unique peptides while maintaining comparable rates of predicted MHC binders.
• Reproducibility and Portability: Employs container-based virtualization to enable execution on clusters and cloud environments, ensuring reproducible results across platforms.
• KNIME Integration: Integrates with the KNIME workbench for mining and handling large-scale immunopeptidomics datasets.
• Quantitative Annotation: Produces annotated lists of (neo-)epitopes with associated relative quantification information.

Scientific Applications:

• Neoepitope discovery for tumor immunotherapy: Identification of neoepitopes from cancer tissue samples by analyzing immunoaffinity-purified HLA-presented peptides via LC-MS/MS and providing annotated, relatively quantified peptide lists for vaccine development.
• Reanalysis of immunopeptidomics datasets: Reprocessing of LC-MS/MS datasets to detect neoepitopes not identified by previously applied methods.

Methodology:

Integrates tailor-made pipeline components with existing open-source software into a coherent workflow and processes LC-MS/MS data to produce false discovery rate-controlled lists of epitopes.