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Record W2016795443 · doi:10.1021/sb300092n

Opportunities for Synthetic Biology in Antibiotics: Expanding Glycopeptide Chemical Diversity

2012· review· en· W2016795443 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Synthetic Biology · 2012
Typereview
Languageen
FieldMedicine
TopicMicrobial Natural Products and Biosynthesis
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsGlycopeptideSynthetic biologyAntibiotic resistanceAntibioticsBiologyDiversity (politics)Chemical biologyComputational biologyBiotechnologyDrug discoveryDrug developmentDrug resistanceAntimicrobialDrugMicrobiologyBioinformaticsGeneticsPolitical sciencePharmacology

Abstract

fetched live from OpenAlex

Synthetic biology offers a new path for the exploitation and improvement of natural products to address the growing crisis in antibiotic resistance. All antibiotics in clinical use are facing eventual obsolesce as a result of the evolution and dissemination of resistance mechanisms, yet there are few new drug leads forthcoming from the pharmaceutical sector. Natural products of microbial origin have proven over the past 70 years to be the wellspring of antimicrobial drugs. Harnessing synthetic biology thinking and strategies can provide new molecules and expand chemical diversity of known antibiotic scaffolds to provide much needed new drug leads. The glycopeptide antibiotics offer paradigmatic scaffolds suitable for such an approach. We review these strategies here using the glycopeptides as an example and demonstrate how synthetic biology can expand antibiotic chemical diversity to help address the growing resistance crisis.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.001
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 head0.158
GPT teacher head0.348
Teacher spread0.190 · 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