Can we boost N-glycopeptide identification confidence? Smart collision energy choice taking into account structure and search engine
Ontology highlight
ABSTRACT:
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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Project description:Analyses of N- and O-glycopeptide using pGlyco3 as well as Byonic, MSFragger and MetaMorpheus.
N-glycopeptides and O-Man glycopeptides of fission yeast and budding yeast are also analyzed using pGlyco3.
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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.
2016-08-17 | PXD004243 | Pride
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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Project description:We present the adaptability of Mascot search engine for automated identification of intact glycopeptide mass spectra. The steps involved in adopting Mascot for intact glycopeptide analysis include: i) assigning unique one letter codes for monosaccharides, ii) linearizing glycan sequences and iii) preparing custom glycoprotein databases. Stepped normalized collision energy (NCE) for HCD mostly provided both the peptide and glycan information in a single MS2 spectrum. Using standard glycoproteins, we showed that Mascot can be adopted for automated annotation of both N- and O-linked glycopeptides. In a large scale validation study, a total of 257 glycoproteins containing 970 unique glycosylation sites and 3447 non-redundant N-linked glycopeptide variants were identified in serum samples. This represent a single tool that collectively allows the i) elucidation of N- and O-linked glycopeptide spectra, ii) matching glycopeptides to known protein sequences, and iii) high-throughput, batch wise analysis of large scale glycoproteomics data sets.
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Project description:Protein glycosylation, one of the most heterogeneous post-translational modifications, can play a major role in cellular signal transduction and disease progression. Traditional mass spectrometry (MS)-based large-scale glycoprotein sequencing studies heavily rely on identifying enzymatically released glycans and their original peptide backbone separately, as there is no efficient fragmentation method to produce unbiased glycan and peptide product ions simultaneously in a single spectrum, and that can be conveniently applied to high throughput glycoproteome characterization, especially for N-glycopeptides, which can have much more branched glycan side chains than relatively less complex O-linked glycans. In this study, a redefined electron-transfer/higher-energy collision dissociation (EThcD) fragmentation scheme is applied to incorporate both glycan and peptide fragments in one single spectrum, enabling complete information to be gathered and great microheterogeneity details to be revealed. Fetuin was first utilized to prove the applicability with 19 glycopeptides and corresponding five glycosylation sites identified. Subsequent experiments tested its utility for human plasma N-glycoproteins. Large-scale studies explored N-glycoproteomics in rat carotid arteries over the course of restenosis progression to investigate the potential role of glycosylation. The integrated fragmentation scheme provides a powerful tool for the analysis of intact N-glycopeptides and N-glycoproteomics. We also anticipate this approach can be readily applied to large-scale O-glycoproteome characterization. Graphical Abstract ?.
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Project description:Bottom-up proteomics relies on identification of peptides from tandem mass spectra, usually via matching against sequence databases. Confidence in a peptide-spectrum match can be characterized by a score value given by the database search engines, and it depends on the information content and the quality of the spectrum. The latter are influenced by experimental parameters, of which the collision energy is the most important one in the case of collision-induced dissociation. We examined how the identification score of the Byonic and Andromeda (MaxQuant) engines varies with collision energy for more than a thousand individual peptides from a HeLa tryptic digest on a QTof instrument. We thereby extended our earlier study on Mascot scores and corroborated its findings on the potential bimodal nature of this energy dependence. Optimal energies as a function of m/z show comparable linear trends for the three engines. On the basis of peptide-level results, we designed methods with one or two liquid chromatography-tandem mass spectrometry (LC-MS/MS) runs and various collision energy settings and assessed their practical performance in peptide and protein identification from the HeLa standard sample. A 10-40% gain in various measures, such as the number of identified proteins or sequence coverage, was obtained over the factory default settings. Best performing methods differ for the three engines, suggesting that the experimental parameters should be fine-tuned to the choice of the engine. We also recommend a simple approach and provide reference data to ease the transfer of the optimized methods to other mass spectrometers relevant for proteomics. We demonstrate the utility of this approach on an Orbitrap instrument. Data sets can be accessed via the MassIVE repository (MSV000086379).
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Project description:Anti-sense non-coding transcripts, genes-within-genes, and convergent gene pairs are prevalent among eukaryotes. The existence of such transcription units raises the question of what happens when RNA polymerase II (RNAPII) molecules collide head-to-head. Here we use a combination of biochemical and genetic approaches in yeast to show that polymerases transcribing opposite DNA strands cannot bypass each other. RNAPII stops, but does not dissociate upon head-to-head collision in vitro, suggesting that opposing polymerases represent insurmountable obstacles for each other. Head-to-head collision in vivo results in RNAPII stopping as well, and removal of collided RNAPII from the DNA template can be achieved via ubiquitylation-directed proteolysis. Indeed, in cells lacking efficient RNAPII poly-ubiquitylation, the half-life of collided polymerases increases, so that these can be detected between convergent genes by ChIP-Seq. These results provide new insight into fundamental mechanisms of gene traffic control, and point to an unexplored effect of anti-sense transcription on gene regulation via polymerase collision. Total RNA was extracted from WT or Elongin C deletion mutant (elc1M-bM-^HM-^F) cells and strand-specific RNA-Seq was performed. Three biological replicates were performed for WT and elc1M-bM-^HM-^F.
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