MoFi: A Software Tool for Annotating Glycoprotein Mass Spectra by Integrating Hybrid Data from the Intact Protein and Glycopeptide Level

Hybrid
mass spectrometry (MS) is an emerging technique for characterizing
glycoproteins, which typically display pronounced microheterogeneity.
Since hybrid MS combines information from different experimental levels,
it crucially depends on computational methods. Here, we describe a
novel software tool, MoFi, which integrates hybrid MS data to assign
glycans and other post-translational modifications (PTMs) in deconvoluted
mass spectra of intact proteins. Its two-stage search algorithm first
assigns monosaccharide/PTM compositions to each peak and then compiles
a hierarchical list of glycan combinations compatible with these compositions.
Importantly, the program only includes those combinations which are
supported by a glycan library as derived from glycopeptide or released
glycan analysis. By applying MoFi to mass spectra of rituximab, ado-trastuzumab
emtansine, and recombinant human erythropoietin, we demonstrate how
integration of bottom-up data may be used to refine information collected
at the intact protein level. Accordingly, our software reveals that
a single mass frequently can be explained by a considerable number
of glycoforms. Yet, it simultaneously ranks proteoforms according
to their probability, based on a score which is calculated from relative
glycan abundances. Notably, glycoforms that comprise identical glycans
may nevertheless differ in score if those glycans occupy different
sites. Hence, MoFi exposes different layers of complexity that are
present in the annotation of a glycoprotein mass spectrum.