Molecular Modeling the Proteins from the exo-xis Region of Lambda and Shigatoxigenic Bacteriophages
Bibliographic record
Abstract
Despite decades of intensive research on bacteriophage lambda, a relatively uncharacterized region remains between the exo and xis genes. Collectively, exo-xis region genes are expressed during the earliest stages of the lytic developmental cycle and are capable of affecting the molecular events associated with the lysogenic-lytic developmental decision. In Shiga toxin-producing E. coli (STEC) and enterohemorragic E. coli (EHEC) that are responsible for food- and water-borne outbreaks throughout the world, there are distinct differences of exo-xis region genes from their counterparts in lambda phage. Together, these differences may help EHEC-specific phage and their bacterial hosts adapt to the complex environment within the human intestine. Only one exo-xis region protein, Ea8.5, has been solved to date. Here, I have used the AlphaFold and RoseTTAFold machine learning algorithms to predict the structures of six exo-xis region proteins from lambda and STEC/EHEC phages. Together, the models suggest possible roles for exo-xis region proteins in transcription and the regulation of RNA polymerase.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".