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Record W3041196067 · doi:10.1039/d0ob01116a

Structural advances of Siglecs: insight into synthetic glycan ligands for immunomodulation

2020· review· en· W3041196067 on OpenAlexafffund
Levon D. Movsisyan, Matthew S. Macauley

Bibliographic record

VenueOrganic & Biomolecular Chemistry · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsAlberta Hospital EdmontonUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsGlycanSialic acidComputational biologyChemistryNeuroscienceCell biologyBiochemistryBiologyGlycoprotein

Abstract

fetched live from OpenAlex

Sialic acid-binding immunoglobulin-like lectins (Siglecs) are transmembrane proteins of the immunoglobulin (Ig) superfamily predominantly expressed on the cells of our immune system. Siglecs recognize sialic acid via their terminal V-set domain. In mammals, sialic acid-terminated glycolipids and glycoproteins are the ligands of Siglecs, and the monomeric affinity of Siglecs for their sialic acid-containing ligands is weak. Significant efforts have been devoted toward the development of chemically modified sialoside ligands to target Siglecs with higher affinity and selectivity. In this review we discuss natural and synthetic sialoside ligands for each human Siglec, emphasizing the ligand binding determinants uncovered from recent advances in protein structural information. Potential therapeutic applications of these ligands are also discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.526
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.306
Teacher spread0.292 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations45
Published2020
Admission routes2
Has abstractyes

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