Using Bio-Panning of FLITRX Peptide Libraries Displayed on E. coli Cell Surface to Study Protein-Protein Interactions
Bibliographic record
Abstract
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 ). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".