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Can we boost N-glycopeptide identification confidence? Smart collision energy choice taking into account structure and search engine

ABSTRACT: We investigated how the structural features of N-glycopeptides and the choice of the search engine influence the optimal collision energy, delivering highest identification confidence. We carried out LC-MS/MS measurements using a series of collision energies on a large set of N-glycopeptides with both the glycan and peptide part varied, and studied the behavior of Byonic, pGlyco, and GlycoQuest scores.

INSTRUMENT(S): maXis II

ORGANISM(S): Bos Taurus (ncbitaxon:9913) Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Agnes Revesz

PROVIDER: MSV000093110 | MassIVE | Sat Oct 14 04:36:00 BST 2023

REPOSITORIES: MassIVE

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Can We Boost N-Glycopeptide Identification Confidence? Smart Collision Energy Choice Taking into Account Structure and Search Engine.

Hevér Helga H Xue Andrea A Nagy Kinga K Komka Kinga K Vékey Károly K Drahos László L Révész Ágnes Á

Journal of the American Society for Mass Spectrometry 20240129 2

High confidence and reproducibility are still challenges in bottom-up mass spectrometric N-glycopeptide identification. The collision energy used in the MS/MS measurements and the database search engine used to identify the species are perhaps the two most decisive factors. We investigated how the structural features of N-glycopeptides and the choice of the search engine influence the optimal collision energy, delivering the highest identification confidence. We carried out LC-MS/M ...[more]

PMID: 38286027

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Project description:Recent advances in software-driven glycopeptide identification in LC-MS/MS-based N-glycoproteomics have facilitated biochemical studies reporting thousands of intact N-glycopeptides, i.e. N-glycan-conjugated peptides, but the automated identification process remains to be scrutinized. Herein, we explore the efficiency of site-specific glycoprofiling using the PTM-centric search-engine Byonic relative to manual expert annotation. To allow an appropriately deep comparison, the study utilised typical glycoproteomics acquisition and data analysis strategies, but of a single glycoprotein, the uncharacterised N-glycosylated (Asn160, Asn268 and Asn302) 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 acquisition of tryptic N-glycopeptides and released N-glycans. The basigin N-glycosylation sites, which showed extensive micro- and macro-heterogeneity, were then glycoprofiled using Byonic with or without a background of complex peptides using Q-Exactive Orbitrap HCD-MS/MS data. The glycoprofiling efficiencies were assessed against the site-specific reference glycoprofiles and target and decoy proteome databases. The search criteria and confidence thresholds (Byonic scores) recommended by the vendor provided very 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 glycoprofiling efficiency and analysis time. Automated identification of peptide modifications (methionine oxidation/carbamidomethylation) that coincide with monosaccharide mass differences (Fuc/Hex/HexNAc) and accurately distinguishing isobaric (Hex1NeuAc1-R/Fuc1NeuGc1-R) or near-isobaric (NeuAc1-R/Fuc2-R) monosaccharide sub-compositions remain challenging, arguing particular attention to such “difficult-to-identify” N-glycopeptides. The presented analysis provides valuable insights into automated glycopeptide identification; knowledge that facilitates further developments in FDR-based glycoproteomics.

Project description:To unravel molecular targets involved in glycopeptide resistance, three isogenic strains of Staphylococcus aureus with different susceptibility levels to vancomycin or teicoplanin were subjected to whole-genome microarray-based transcription and quantitative proteomic profiling. Quantitative proteomics performed on membrane extracts showed exquisite inter-experimental reproducibility permitting the identification and relative quantification of >30% of the predicted S. aureus proteome. In the absence of antibiotic selection pressure, comparison of stable resistant and susceptible strains revealed 94 differentially expressed genes and 178 proteins. As expected, only partial correlation was obtained between transcriptomic and proteomic results during stationary-phase. Application of massively parallel methods identified one third of the complete proteome, a majority of which was only predicted based on genome sequencing, but never identified to date. Several over‑expressed genes represent previously reported targets, while series of genes and proteins possibly involved in the glycopeptide resistance mechanism were discovered here, including regulators, global regulator attenuator, hyper‑mutability factor or hypothetical proteins. Gene expression of these markers was confirmed in a collection of genetically unrelated strains showing altered susceptibility to glycopeptides. Our proteome and transcriptome analyses have been performed during stationary‑phase of growth on isogenic strains showing susceptibility or intermediate level of resistance against glycopeptides. Altered susceptibility had emerged spontaneously after infection with a sensitive parental strain, thus not selected in vitro. This combined analysis allows the identification of hundreds of proteins considered, so far as hypothetical protein. In addition, this study provides not only a global picture of transcription and expression adaptations during a complex antibiotic resistance mechanism but also unravels potential drug targets or markers that are constitutively expressed by resistant strains regardless of their genetic background, amenable to be used as diagnostic targets. Keywords: Molecular markers, antibiotic resistance, glycopeptides, growth-phase

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Can we boost N-glycopeptide identification confidence? Smart collision energy choice taking into account structure and search engine

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Can We Boost <i>N</i>-Glycopeptide Identification Confidence? Smart Collision Energy Choice Taking into Account Structure and Search Engine.

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