MoFi

MoFi annotates glycoprotein mass spectra by integrating hybrid mass spectrometry (MS) data to assign glycan compositions, evaluate site-specific glycosylation, and probabilistically rank proteoforms.

Key Features:

• Integration of Hybrid MS Data: Integrates data from intact protein (top-down) and glycopeptide (bottom-up) MS to correlate information across experimental levels.
• Two-Stage Search Algorithm: Implements a two-stage search where stage one assigns compositions and PTMs to deconvoluted peaks and stage two compiles hierarchical lists of compatible glycan combinations.
• Monosaccharide and PTM Assignment: Assigns monosaccharide compositions and other post-translational modifications to each deconvoluted mass spectral peak.
• Glycan Library Validation: Validates candidate glycan combinations against a glycan library derived from glycopeptide or released glycan analysis.
• Probabilistic Ranking of Proteoforms: Ranks multiple potential glycoforms for a given mass probabilistically using relative glycan abundances.
• Site-Specific Scoring: Applies site-specific scoring so identical glycans at different protein sites can receive different scores.

Scientific Applications:

• Glycoprotein mass-spectra analysis: Applied to annotate mass spectra of glycoproteins including rituximab, ado-trastuzumab emtansine, and recombinant human erythropoietin by integrating bottom-up (glycopeptide) and top-down (intact protein) data to refine annotations.

Methodology:

Integrates hybrid MS data from intact proteins and glycopeptides; performs a two-stage search that assigns monosaccharide compositions and other PTMs to deconvoluted peaks (stage one) then compiles hierarchical lists of compatible glycan combinations (stage two); validates combinations against a glycan library derived from glycopeptide or released glycan analysis; ranks proteoforms probabilistically using relative glycan abundances and applies site-specific scoring.