Toward Automated N-Glycopeptide Identification in Glycoproteomics.

Advances in software-driven glycopeptide identification have facilitated N-glycoproteomics studies reporting thousands of intact N-glycopeptides, i.e., N-glycan-conjugated peptides, but the automated identification process remains to be scrutinized. Herein, we compare the site-specific glycoprofiling efficiency of the PTM-centric search engine Byonic relative to manual expert annotation utilizing typical glycoproteomics acquisition and data analysis strategies but with a single glycoprotein, the uncharacterized multiple N-glycosylated human basigin. Detailed site-specific reference glycoprofiles of purified basigin were manually established using ion-trap CID-MS/MS and high-resolution Q-Exactive Orbitrap HCD-MS/MS of tryptic N-glycopeptides and released N-glycans. The micro- and macroheterogeneous basigin N-glycosylation was site-specifically glycoprofiled using Byonic with or without a background of complex peptides using Q-Exactive Orbitrap HCD-MS/MS. The automated glycoprofiling efficiencies were assessed against the site-specific reference glycoprofiles and target/decoy proteome databases. Within the limits of this single glycoprotein analysis, the search criteria and confidence thresholds (Byonic scores) recommended by the vendor provided high glycoprofiling accuracy and coverage (both >80%) and low peptide FDRs (<1%). The data complexity, search parameters including search space (proteome/glycome size), mass tolerance and peptide modifications, and confidence thresholds affected the automated glycoprofiling efficiency and analysis time. Correct identification of ambiguous peptide modifications (methionine oxidation/carbamidomethylation) whose mass differences coincide with several monosaccharide mass differences (Fuc/Hex/HexNAc) and of ambiguous isobaric (HexNeuAc-R/FucNeuGc-R) or near-isobaric (NeuAc-R/Fuc-R) monosaccharide subcompositions remains challenging in automated glycoprofiling, arguing particular attention paid to N-glycopeptides displaying such "difficult-to-identify" features. This study provides valuable insights into the automated glycopeptide identification process, stimulating further developments in FDR-based glycoproteomics.