Abstract
Rapidly improving methods for glycoproteomics have enabled increasingly large-scale analyses of complex glycopeptide samples, but annotating the resulting mass spectrometry data with high confidence remains a major bottleneck. We recently introduced a fast and sensitive glycoproteomics search method in our MSFragger search engine, which reports glycopeptides as a combination of a peptide sequence and the mass of the attached glycan. In samples with complex glycosylation patterns, converting this mass to a specific glycan composition is not straightforward; however, as many glycans have similar or identical masses. Here, we have developed a new method for determining the glycan composition of N-linked glycopeptides fragmented by collisional or hybrid activation that uses multiple sources of information from the spectrum, including observed glycan B-type (oxonium) and Y-type ions and mass and precursor monoisotopic selection errors to discriminate between possible glycan candidates. Combined with false discovery rate estimation for the glycan assignment, we show that this method is capable of specifically and sensitively identifying glycans in complex glycopeptide analyses and effectively controls the rate of false glycan assignments. The new method has been incorporated into the PTM-Shepherd modification analysis tool to work directly with the MSFragger glyco search in the FragPipe graphical user interface, providing a complete computational pipeline for annotation of N-glycopeptide spectra with false discovery rate control of both peptide and glycan components that is both sensitive and robust against false identifications.
Highlights
Glycosylation is one of the most common post-translational modifications of proteins, involved in a vast array of biological processes and implicated in numerous diseases [1,2,3,4]
We have developed a sensitive and robust method for determining the composition of the glycan component of N-glycopeptides from tandem mass spectrometry (MS) data by combining information from multiple types of fragment ions with mass and isotope errors to distinguish between candidate compositions
Journal Pre-proof demonstrate that false discovery rate (FDR) control of the resulting glycan matches performs as expected even in analyses of complex glycan lists and in the presence of entrapment peptide sequences and glycan compositions
Summary
Glycosylation is one of the most common post-translational modifications of proteins, involved in a vast array of biological processes and implicated in numerous diseases [1,2,3,4]. Some search tools provide additional capabilities that can assist in controlling the false discovery rate (FDR) of modified peptides, such as the use of the extended mass model of PeptideProphet [21] to model distinct probabilities for modifications with different masses used with MSFragger [17], or distinguishing between rare and common modifications in Byonic [12] These tools and many others have increasingly been applied to large scale glycoproteomics analyses [14, 18, 22,23,24,25] utilizing peptide-focused FDR methods, often in conjunction with a second empirical filtering or manual validation step
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
