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Record W2088941833 · doi:10.1021/ja0258529

Optimization of Tether Length in Nonglycosidically Linked Bivalent Ligands That Target Sites 2 and 1 of a <i>Shiga</i>-like Toxin

2003· article· en· W2088941833 on OpenAlexaff
Pavel I. Kitov, H. Shimizu, S. W. Homans, David R. Bundle

Bibliographic record

VenueJournal of the American Chemical Society · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsChemistryBivalent (engine)Intramolecular forceDimerStereochemistryAffinitiesCrystallographyBinding siteIntermolecular forceTrisaccharideMoleculeMetalBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

A series of bivalent ligands for a Shiga-like toxin have been synthesized, their experimentally determined inhibitory activities were compared with a simplified thermodynamic model, and computer simulations were used to predict the optimal tether length in bivalent ligands. The design of the inhibitors exploits the proximity of the C-2' hydroxyl groups of two P(k)-trisaccharides when bound to two different, neighboring carbohydrate recognizing binding sites located on the surface of Shiga-like toxin. NMR studies of the complex between the toxin and bivalent ligands show that site 2 and site 1 of a single B subunit are simultaneously occupied by a tethered P(k)-trisaccharide dimer. A simplified thermodynamic treatment provides the intrinsic affinities and binding energies for the intermolecular and intramolecular association events and permits the deconvolution of the contributions to the relative binding energies for the set of bivalent ligands. Conformational analysis based on MD simulations for bivalent galabioside dimers containing different tethers demonstrated that the calculated local concentrations of the pendant ligand at the second binding site correlate with the experimentally determined relative affinity values of the respective bivalent ligands, thereby providing a predictive method to optimize tether length.

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 score0.016
Threshold uncertainty score0.260

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 head0.012
GPT teacher head0.259
Teacher spread0.247 · 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

Citations93
Published2003
Admission routes1
Has abstractyes

Explore more

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