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Record W2277580266 · doi:10.1385/1-59259-301-1:267

Using Bio-Panning of FLITRX Peptide Libraries Displayed on E. coli Cell Surface to Study Protein-Protein Interactions

2003· article· en· W2277580266 on OpenAlexaff
Zhijian Lu, Edward R. LaVallie, John McCoy

Bibliographic record

VenueHumana Press eBooks · 2003
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsWomen's Health Research Institute
Fundersnot available
KeywordsProteomePanning (audio)Phage displayThioredoxinFlagellinOrganismComputational biologyBiologyPeptideGenomeBiochemistryGeneGenetics

Abstract

fetched live from OpenAlex

The completion of the human genome project has ushered in a new era of life science (1,2) in which the new challenge is to understand functions of the entire collection of the gene products, or the proteome. One important feature of biological research in this post-genomics era is the emphasis on understanding how individual components of a proteome interact with one another temporally and spatially to constitute a living organism. Over the past decade, researchers have developed various methods designed to study protein-protein interactions including displaying proteins and peptides on live microorganisms, the most well-known example being the display of random peptide libraries on filamentous phage (3,4). Many people (including the authors) have explored the use of E. coli as an alternative organism for protein and peptide display (5). Based on our expertise and experience with both flagellin (6) and thioredoxin (7), we developed FLITRX technology, a unique system that displays conformation-constrained random peptides on the bacterial surface as functional fusions between flagellin and thioredoxin (8).

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)0.000
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 score0.034
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

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 head0.154
GPT teacher head0.374
Teacher spread0.220 · 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

Citations22
Published2003
Admission routes1
Has abstractyes

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