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Record W1570027065 · doi:10.1042/ba20030100

Recombinant antimicrobial peptides efficiently produced using novel cloning and purification processes

2004· article· en· W1570027065 on OpenAlexaff
Luba Metlitskaia, Jennifer E. Cabralda, Dinar Suleman, Cynthia Kerry, Jacquelyn Brinkman, Daniel Bartfeld, M. Marta Guarna

Bibliographic record

VenueBiotechnology and Applied Biochemistry · 2004
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsVancouver Biotech (Canada)
Fundersnot available
KeywordsEscherichia coliAntimicrobialAntimicrobial peptidesRecombinant DNAPeptideInclusion bodiesBiochemistryFusion proteinBiologyAmphiphileChemistryMicrobiology

Abstract

fetched live from OpenAlex

Endogenous antimicrobial peptides are ubiquitous components of animal and plant host defences. These peptides, usually cationic and amphipathic, kill target cells rapidly and are efficacious against antibiotic-resistant and clinically relevant pathogens. A practical challenge in the development of cationic peptides as therapeutics is to meet the production requirements for large quantities of highly purified drug substance at competitive costs. While chemical peptide synthesis can be used to manufacture cationic peptides, we have developed cost-effective methods for recombinant production by expressing fusion proteins comprised of multiple copies of the peptides. The fusion proteins accumulate in Escherichia coli inclusion bodies and constitute over 50% of the total cellular proteins. Active antimicrobial peptides are released by chemical reagents and purified by chromatography, combining both standard and novel approaches. Challenges of industrial-scale manufacturing of therapeutics were considered in the development of this process.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.212
Teacher spread0.201 · 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

Citations20
Published2004
Admission routes1
Has abstractyes

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