Post-Search Validation and Curation of Site-Resolved N-Glycoproteomics Data

Abstract

Given the significant role of glycosylation in modulating protein structure and activity, glycoproteomics is gaining increased interest from the broad scientific community. Large-scale site-resolved N-glycoproteomics relies on automated MS²-based searches, but candidate glycopeptide assignments can remain ambiguous when isomeric or isobaric glycan structures, adducts, chemical modifications, in-source fragments, or incomplete MS² evidence support more than one plausible interpretation. Here, we systematically categorize common challenges and misassignments in glycoproteomics and present a post-search validation workflow using Skyline software to identify and correct these. The workflow matches search-engine-derived candidate assignments to LC–MS/MS evidence for correct precursor monoisotope assignment, retention time behavior, and glycosite context. The workflow is demonstrated with Byonic-derived glycopeptide candidate lists and converts automated search results into curated, verifiable, site-resolved N-glycopeptide features for downstream quantification and reporting. We applied the workflow to data from 52 human serum samples, and reviewed 3,071 candidate N-glycopeptide IDs. From these, 1,722 MS² candidate IDs were refuted as inconsistent with chromatographic and/or precursor-level evidence. Curation added 320 glycopeptide features, comprising 152 MS¹-supported composition-level assignments and 168 additional LC-resolved isomer features, yielding a final curated feature set of 1,436 N-glycopeptides across the serum N-glycoproteome. Together, these results show that reviewing the raw LC-MS/MS data associated with search-engine results improves both the accuracy and comprehensiveness of detectable N-glycopeptides, supporting more transparent and reliable reporting. The curated dataset provides a resource for future method development, benchmarking and machine–learning efforts directed at automated glycopeptide validation.