PepSweetener

PepSweetener visualizes theoretical peptide–glycan matches to support manual annotation of intact glycopeptides from mass spectrometry (MS) precursor masses across instruments.

Key Features:

• Theoretical glycopeptide dataset: Leverages a theoretical dataset of glycopeptides to enumerate all potential peptide–glycan combinations.
• Precursor mass matching: Matches theoretical combinations to a specified precursor ion mass within a defined mass tolerance and reports parts-per-million (ppm) deviations.
• Query modes: Provides a simple mode that searches all tryptic peptides including N-glycosites in the human proteome and an advanced mode allowing specification of proteins or peptides, glycan compositions, and experimental conditions.
• Heat-map visualization: Presents results as a heat-map of theoretical glycopeptide tiles colored by ppm deviation from the query precursor mass.
• Filtering and sorting: Supports filtering by glycan composition and sorting of candidates by mass and tolerance.
• In silico fragmentation: Generates an in silico peptide fragmentation diagram for candidate glycopeptides.

Scientific Applications:

• Intact glycopeptide annotation: Assists manual annotation of intact glycopeptides from MS precursor masses.
• Proteome-wide candidate generation: Enables searches across tryptic peptides and N-glycosites within the human proteome to generate candidate peptide–glycan pairs.
• Glycan composition refinement: Facilitates selection and refinement of peptide and glycan compositions based on precursor mass, ppm deviation, and fragmentation data.
• Application to complex samples: Has been validated on serum protein samples and immunoglobulins, demonstrating utility in complex biological matrices.

Methodology:

Enumerate theoretical peptide–glycan combinations from a theoretical dataset, match combinations to a specified precursor ion mass within a mass tolerance, compute ppm deviations and visualize as a heat-map, and provide filtering/sorting and in silico peptide fragmentation diagrams.