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Record W2004579769 · doi:10.1073/pnas.0804919105

In vivo supramolecular templating enhances the activity of multivalent ligands: A potential therapeutic against the <i>Escherichia coli</i> O157 AB <sub>5</sub> toxins

2008· article· en· W2004579769 on OpenAlexafffund
Pavel I. Kitov, George L. Mulvey, Thomas P. Griener, Tomasz Lipiński, Dmitry Solomon, Eugenia Paszkiewicz, Jared M. Jacobson, Joanna Sadowska, Missao Suzuki, Ken–ichi Yamamura, Glen D. Armstrong, David R. Bundle

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsAlberta Glycomics CentreUniversity of CalgaryUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAvidityEscherichia coliEffectorLigand (biochemistry)ChemistryReceptorIn vivoCell biologyBiochemistrySupramolecular chemistryScaffold proteinLinkerComputational biologyBiophysicsCombinatorial chemistryBiologySignal transduction

Abstract

fetched live from OpenAlex

We demonstrate that interactions between multimeric receptors and multivalent ligands are dramatically enhanced by recruiting a complementary templating receptor such as an endogenous multimeric protein but only when individual ligands are attached to a polymer as preorganized, covalent, heterobifunctional pairs. This effect cannot be replicated by a multivalent ligand if the same recognition elements are independently arrayed on the scaffold. Application of this principle offers an approach to create high-avidity inhibitors for multimeric receptors. Judicious selection of the ligand that engages the templating protein allows appropriate effector function to be incorporated in the polymeric construct, thereby providing an opportunity for therapeutic applications. The power of this approach is exemplified by the design of exceptionally potent Escherichia coli Shiga toxin antagonists that protect transgenic mice that constitutively express a human pentraxin, serum amyloid P component.

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)0.001
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 score0.014
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.271
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

Citations82
Published2008
Admission routes2
Has abstractyes

Explore more

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