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Record W2037004316 · doi:10.1073/pnas.0509870103

Polyvalent inhibitors of anthrax toxin that target host receptors

2006· article· en· W2037004316 on OpenAlexaff
Saleem Basha, Prakash Rai, Vincent Kwok‐Man Poon, Arundhati Saraph, Kunal V. Gujraty, Mandy Y. Go, Skanda Sadacharan, Mia Frost, Jeremy Mogridge, Ravi S. Kane

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacillus and Francisella bacterial research
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersNational Institute of Allergy and Infectious Diseases
KeywordsAnthrax toxinReceptorToxinBiologyMicrobiologyAntimicrobialPhage displayBacillus anthracisPathogenIn vivoPeptideComputational biologyBacteriaRecombinant DNABiochemistryFusion proteinBiotechnologyGene

Abstract

fetched live from OpenAlex

Resistance of pathogens to antimicrobial therapeutics has become a widespread problem. Resistance can emerge naturally, but it can also be engineered intentionally, which is an important consideration in designing therapeutics for bioterrorism agents. Blocking host receptors used by pathogens represents a powerful strategy to overcome this problem, because extensive alterations to the pathogen may be required to enable it to switch to a new receptor that can still support pathogenesis. Here, we demonstrate a facile method for producing potent receptor-directed antitoxins. We used phage display to identify a peptide that binds both anthrax-toxin receptors and attached this peptide to a synthetic scaffold. Polyvalency increased the potency of these peptides by >50,000-fold in vitro and enabled the neutralization of anthrax toxin in vivo. This work demonstrates a receptor-directed anthrax-toxin inhibitor and represents a promising strategy to combat a variety of viral and bacterial diseases.

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 score0.023
Threshold uncertainty score0.302

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 science0.0000.000
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 head0.025
GPT teacher head0.288
Teacher spread0.263 · 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

Citations77
Published2006
Admission routes1
Has abstractyes

Explore more

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