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Record W2079351520 · doi:10.1021/bm060210p

Synthesis of Polyvalent Inhibitors of Controlled Molecular Weight:  Structure−Activity Relationship for Inhibitors of Anthrax Toxin

2006· article· en· W2079351520 on OpenAlexaff
Kunal V. Gujraty, Amit Joshi, Arundhati Saraph, Vincent Kwok‐Man Poon, Jeremy Mogridge, Ravi S. Kane

Bibliographic record

VenueBiomacromolecules · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacillus and Francisella bacterial research
Canadian institutionsUniversity of Toronto
FundersNational Institute of Allergy and Infectious Diseases
KeywordsPotencyChemistryAnthrax toxinIn vivoLigand efficiencyLigand (biochemistry)Small moleculeStructure–activity relationshipToxinIn vitroStereochemistryCombinatorial chemistryBiochemistryReceptorRecombinant DNABiologyFusion protein

Abstract

fetched live from OpenAlex

We describe a novel method to synthesize activated polymers of controlled molecular weight and apply this method to investigate the relationship between the structure and activity of polyvalent inhibitors of anthrax toxin. In particular, we observe an initial sharp increase in potency with increasing ligand density, followed by a plateau where potency is independent of ligand density. Our simple strategy for designing polyvalent inhibitors of controlled molecular weight and ligand density will be broadly applicable for designing inhibitors for a variety of pathogens and toxins, and for elucidating structure-activity relationships in these systems. Our results also demonstrate a role for kinetics in influencing inhibitory potency in polyvalent systems. Finally, our work presents a synthetic route to polyvalent inhibitors that are more structurally defined and effective in vivo. This control over inhibitor composition will be generally useful for the optimization of inhibitor potency and pharmacokinetics, and for the eventual application of these molecules in vivo.

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 score0.003
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.008
GPT teacher head0.251
Teacher spread0.243 · 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

Citations27
Published2006
Admission routes1
Has abstractyes

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