Optimizing Protein Production in Therapeutic Phages against a Bacterial Pathogen, Mycobacterium abscessus
Bibliographic record
Abstract
Therapeutic phages against pathogenic bacteria should kill the bacteria efficiently before the latter evolve resistance against the phages. While many factors contribute to phage efficiency in killing bacteria, such as phage attachment to host, delivery of phage genome into the host, phage mechanisms against host defense, phage biosynthesis rate, and phage life cycle, this paper focuses only on the optimization of phage mRNA for efficient translation. Phage mRNA may not be adapted to its host translation machinery for three reasons: (1) mutation disrupting adaptation, (2) a recent host switch leaving no time for adaptation, and (3) multiple hosts with different translation machineries so that adaptation to one host implies suboptimal adaptation to another host. It is therefore important to optimize phage mRNAs in therapeutic phages. Theoretical and practical principles based on many experiments were developed and applied to phages engineered against a drug-resistant Mycobacterium abscessus that infected a young cystic fibrosis patient. I provide a detailed genomic evaluation of the three therapeutic phages with respect to translation initiation, elongation, and termination, by making use of both experimental results and highly expressed genes in the host. For optimizing phage genes against M. abscessus, the start codon should be AUG. The DtoStart distance from base-pairing between the Shine-Dalgarno (SD) sequence and the anti-SD sequence should be 14–16. The stop codon should be UAA. If UAG or UGA is used as a stop codon, they should be followed by nucleotide U. Start codon, SD, or stop codon should not be embedded in a secondary structure that may obscure the signals and interfere with their decoding. The optimization framework should be generally applicable to developing therapeutic phages against bacterial pathogens.
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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) | 0.000 | 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".