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Record W2019472718 · doi:10.1021/ja031698u

Glycodendriproteins:  A Synthetic Glycoprotein Mimic Enzyme with Branched Sugar-Display Potently Inhibits Bacterial Aggregation

2004· article· en· W2019472718 on OpenAlexaff
Phillip M. Rendle, Andreas Seger, João Rodrigues, Neil J. Oldham, R. Bott, J. Bryan Jones, M. M. Cowan, Benjamin G. Davis

Bibliographic record

VenueJournal of the American Chemical Society · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiochemical and Structural Characterization
Canadian institutionsUniversity of Toronto
FundersBiotechnology and Biological Sciences Research Council
KeywordsBacterial adhesinChemistryBacteriaVirulenceGlycoproteinEnzymeSmall moleculeBiochemistryVirulence factorMicrobiologyPathogenic bacteriaBiologyGene

Abstract

fetched live from OpenAlex

The continuing ability of bacteria to resist current antibiotic treatments highlights the need for alternative strategies for inhibiting their pathogenicity. Bacterial attachment is a major factor in infectivity and virulence. This key binding phase of bacteria to any potential host is mediated by adhesin proteins and so these present an attractive therapeutic target for antiinfective blocking strategies. However, the natural ligands to adhesins are large, typically complex molecules that are difficult to mimic with small molecules. We describe here a method that creates precise synthetic mimics of glycoproteins that are designed to bind adhesins. By using protein-degrading enzymes as the basis for these mimics we have created large-molecule protein ligands that inhibit aggregation of pathogenic bacteria at levels greater than a million-fold higher than small-molecule inhibitors of adhesins.

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 score0.000
Threshold uncertainty score0.002

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)0.0000.000

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 head0.004
GPT teacher head0.203
Teacher spread0.199 · 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

Citations91
Published2004
Admission routes1
Has abstractyes

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