MHCpLogics

MHCpLogics analyzes immunopeptidome datasets using machine learning to visualize and cluster peptide-binding sequences presented by human leukocyte antigens (HLAs) encoded by the major histocompatibility complex (MHC), enabling deconvolution of allotype-specific peptide motifs.

Key Features:

• Visualization of clustered peptides: Visualization of clustered peptide sequences to inspect sequence patterns across multiple immunopeptidome datasets.
• Machine learning motif mining: Machine learning algorithms mine peptide-binding sequence motifs to identify allotype-specific and sub-peptidome motifs.
• Cluster analysis for deconvolution: Cluster analysis deconvolutes immunopeptidome datasets to segregate peptides by binding specificity and allotype contribution.
• Data export: Export of clustered peptide sequence lists for downstream analysis.

Scientific Applications:

• Immunopeptidome deconvolution: Deconvolution of complex immunopeptidomes into allotype-specific contributions.
• HLA allotype motif characterization: Characterization of HLA allotype-specific peptide binding motifs to dissect binding specificities.
• Vaccine design: Inform vaccine design by identifying peptide motifs relevant to antigen presentation across HLA allotypes.
• Personalized medicine: Support personalized medicine approaches by resolving HLA-restricted peptide presentation relevant to immune response variability.

Methodology:

Unsupervised data visualization, cluster analysis, and machine learning-based motif mining applied to mono-allelic and multi-allelic immunopeptidomics datasets to deconvolute allotype-specific peptide motifs.