MHCquant2

MHCquant2 leverages OpenMS tools and peptide property predictors to identify and quantify human leukocyte antigen (HLA)-presented peptides from immunopeptidomics mass spectrometry data for tumor antigen discovery.

Key Features:

• Peptide Property Prediction: Integrates DeepLC and MS2PIP predictors to improve peptide identification sensitivity.
• Cross-Platform Compatibility: Supports HLA peptide identification and quantification across various mass spectrometry platforms.
• Identification Improvement: Increases peptide identifications up to 27% with significant expansion of low-abundant peptides compared to state-of-the-art pipelines.
• Benign Tissue Repository Construction: Builds a comprehensive benign tissue repository comprising re-analyzed data from available benign immunopeptidomes and a novel benignMHCquant2 dataset.
• Neoepitope Detection: Enables identification and quantification of mutation-derived low-abundant neoepitopes.

Scientific Applications:

• Tumor-Associated Antigen Refinement: Refines tumor-associated antigens using the benign repository and MHCquant2 pipeline.
• Tumor-Exclusive Peptide Antigen Discovery: Detects novel, high-frequent tumor-exclusive peptide antigens for multiple tumor entities.
• Neoepitope Identification: Identifies and quantifies mutation-derived low-abundant neoepitopes for personalized immunotherapy design.
• Off-the-Shelf Immunotherapy Development: Paves the way for implementation of off-the-shelf and personalized immunotherapy design through refined tumor antigen discovery.

Methodology:

MHCquant2 is implemented as a Nextflow-based open-source pipeline that leverages OpenMS tools and peptide property predictors (DeepLC, MS2PIP) for HLA peptide identification and quantification.