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Record W2109971510 · doi:10.1002/cbic.200800550

The Influence of Ligand Valency on Aggregation Mechanisms for Inhibiting Bacterial Toxins

2008· article· en· W2109971510 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemBioChem · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEscherichia coli research studies
Canadian institutionsInstitute for Biological Sciences
FundersRoyal SocietyWellcome TrustBiotechnology and Biological Sciences Research CouncilUniversity of St AndrewsAstraZeneca
KeywordsDivalentChemistryValencyProtein aggregationLigand (biochemistry)Dynamic light scatteringBiophysicsEscherichia coliBiochemistryStereochemistryReceptorOrganic chemistryBiologyNanotechnology

Abstract

fetched live from OpenAlex

Divalent and tetravalent analogues of ganglioside GM1 are potent inhibitors of cholera toxin and Escherichia coli heat-labile toxin. However, they show little increase in inherent affinity when compared to the corresponding monovalent carbohydrate ligand. Analytical ultracentrifugation and dynamic light scattering have been used to demonstrate that the multivalent inhibitors induce protein aggregation and the formation of space-filling networks. This aggregation process appears to arise when using ligands that do not match the valency of the protein receptor. While it is generally accepted that multivalency is an effective strategy for increasing the activity of inhibitors, here we show that the valency of the inhibitor also has a dramatic effect on the kinetics of aggregation and the stability of intermediate protein complexes. Structural studies employing atomic force microscopy have revealed that a divalent inhibitor induces head-to-head dimerization of the protein toxin en route to higher aggregates.

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.

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)0.001
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 score0.002
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.015
GPT teacher head0.262
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