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Record W1983227647 · doi:10.1074/jbc.m104364200

Structure of an Anti-blood Group A Fv and Improvement of Its Binding Affinity without Loss of Specificity

2002· article· en· W1983227647 on OpenAlexafffund
Roula. Thomas, Sonia I. Patenaude, C. Roger MacKenzie, Rebecca To, Tomoko Hirama, N. Martin Young, Stephen V. Evans

Bibliographic record

VenueJournal of Biological Chemistry · 2002
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsInstitute for Biological SciencesUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsGroup (periodic table)ChemistryMedicineOrganic chemistry

Abstract

fetched live from OpenAlex

The specificity of antibody recognition of the ABO blood group trisaccharide antigens has been explored by crystal structure analysis and mutation methods. The crystal structure of the Fv corresponding to the anti-blood group A antibody AC1001 has been determined to 2.2-A resolution and reveals a binding pocket that is complementary to the blood group A-trisaccharide antigen. The effect of mutating specific residues lining this pocket on binding to the A and B blood group oligosaccharide antigens was investigated through a panel of single point mutations and through a phage library of mutations in complementarity determining region H3. Both approaches gave several mutants with improved affinity for antigen. Surface plasmon resonance indicated up to 8-fold enhancement in affinity for the A-pentasaccharide with no observable binding to the blood group B antigen. This is the first example of single point mutations in a carbohydrate-binding antibody resulting in significant increases in binding affinity without loss of specificity.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.045
GPT teacher head0.298
Teacher spread0.253 · 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 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

Citations40
Published2002
Admission routes2
Has abstractyes

Explore more

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