Representing and Comparing Site-Specific Glycan Abundance Distributions of Glycoproteins
Concepcion Remoroza, Meghan Burke Harris, Yi Liu, Yuri Mirokhin, Dmitrii V. Tchekhovskoi, Xiaoyu (Sara) Yang, Stephen E. Stein
A method for representing and comparing distributions of N-linked glycans located at specific sites in proteins is presented. The representation takes the form of a simple mass spectrum for a given peptide sequence, with each peak corresponding to a different glycopeptide. The mass (in place of m/z) of each peak is that of the glycan mass, and its abundance corresponds to its relative abundance in the electrospray MS1 spectrum. This provides a facile means of representing all identifiable glycopeptides arising from a single protein 'sequon' on a specific sequence, thereby enabling the comparison and searching of these distributions as routinely done for mass spectra. Likewise, these Reference Glycopeptide Abundance Distribution Spectra (GADS) can be stored in searchable libraries. This enables the facile discovery of all library GADS similar to a query GADS in a traditional 'hit list', ranked by similarity to the query. A set of such libraries created from available data is provided along with an adapted version of the widely used NISTMS library-search software. Users may create their own GADS and build their own libraries using an extension of the current format, allowing N-glycan annotation for each glycopeptide 'peak'. Since GADS contain only MS1 abundances and identifications, they are equally suitable for expressing CID/HCD and ETD determinations of glycopeptide identity. Comparisons of GADS for N-glycosylated sites on several proteins, especially the SARS-CoV-2 Spike protein, demonstrate the potential reproducibility of GADS and their utility for comparing site-specific distributions.
, Burke Harris, M.
, Liu, Y.
, Mirokhin, Y.
, Tchekhovskoi, D.
, Yang, X.
and Stein, S.
Representing and Comparing Site-Specific Glycan Abundance Distributions of Glycoproteins, ACS Journal of Proteome Research, [online], https://doi.org/10.1021/acs.jproteome.1c00442 , https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932512
(Accessed December 9, 2022)