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Record W2774140036 · doi:10.1002/mas.21555

Mass spectrometry for protein sialoglycosylation

2017· review· en· W2774140036 on OpenAlexaff
Qi‐Wei Zhang, Zack Li, Yawei Wang, Qi Zheng, Jianjun Li

Bibliographic record

VenueMass Spectrometry Reviews · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsNational Research Council CanadaQueen's University
Fundersnot available
KeywordsChemistryGlycanGlycoproteinSialic acidNeuraminic acidGlycosylationMass spectrometryBiochemistryGlycopeptideGlycolipidAmino acidElectrospray ionizationChromatography

Abstract

fetched live from OpenAlex

Sialic acids are a family of structurally unique and negatively charged nine-carbon sugars, normally found at the terminal positions of glycan chains on glycoproteins and glycolipids. The glycosylation of proteins is a universal post-translational modification in eukaryotic species and regulates essential biological functions, in which the most common sialic acid is N-acetyl-neuraminic acid (2-keto-5-acetamido-3,5-dideoxy-D-glycero-D-galactononulopyranos-1-onic acid) (Neu5NAc). Because of the properties of sialic acids under general mass spectrometry (MS) conditions, such as instability, ionization discrimination, and mixed adducts, the use of MS in the analysis of protein sialoglycosylation is still challenging. The present review is focused on the application of MS related methodologies to the study of both N- and O-linked sialoglycans. We reviewed MS-based strategies for characterizing sialylation by analyzing intact glycoproteins, proteolytic digested glycopeptides, and released glycans. The review concludes with future perspectives in the field.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.012

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.110
GPT teacher head0.399
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations42
Published2017
Admission routes1
Has abstractyes

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