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Record W2253687527 · doi:10.1093/glycob/cwv171

Diverse molecular recognition properties of blood group A binding monoclonal antibodies

2016· article· en· W2253687527 on OpenAlexfundno aff
Jeffrey C. Gildersleeve, W. Shea Wright

Bibliographic record

VenueGlycobiology · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Institutes of HealthNational Cancer InstituteCollege of Engineering, Michigan State UniversityUniversity of AlbertaMichigan State University
KeywordsGlycanMonoclonal antibodyAntibodyTrisaccharideChemistryAntigenBinding selectivityBiochemistrySerineGlycoproteinMolecular biologyBiologyImmunologyEnzyme

Abstract

fetched live from OpenAlex

Information about specificity and affinity is critical for use of carbohydrate-binding antibodies. Herein, we evaluated eight monoclonal antibodies to the blood group A (BG-A) antigen. Antibodies 87-G, 9A, HE-10, HE-24, HE-193, HE-195, T36 and Z2A were profiled on a glycan microarray to assess specificity, relative affinity and the influence of glycan density on recognition. Our studies highlight several noteworthy recognition properties. First, most antibodies bound GalNAcα1-3Gal and the BG-A trisaccharide nearly as well as larger BG-A oligosaccharides. Second, several antibodies only bound the BG-A trisaccharide when displayed on certain glycan chains. These first two points indicate that the carrier glycan chains primarily influence selectivity, rather than binding strength. Third, binding of some antibodies was highly dependent on glycan density, illustrating the importance of glycan presentation for recognition. Fourth, some antibodies recognized the tumor-associated Tn antigen, and one antibody only bound the variant composed of a GalNAc-alpha-linked to a serine residue. Collectively, these results provide new insights into the recognition properties of anti-BG-A antibodies.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.253
Teacher spread0.224 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations24
Published2016
Admission routes1
Has abstractyes

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