Maximizing Heterologous Expression of Engineered Type I Polyketide Synthases: Investigating Codon Optimization Strategies
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Type I polyketide synthases (T1PKSs) hold enormous potential as a rational production platform for the biosynthesis of specialty chemicals. However, despite great progress in this field, the heterologous expression of PKSs remains a major challenge. One of the first measures to improve heterologous gene expression can be codon optimization. Although controversial, choosing the wrong codon optimization strategy can have detrimental effects on the protein and product levels. In this study, we analyzed 11 different codon variants of an engineered T1PKS and investigated in a systematic approach their influence on heterologous expression in Corynebacterium glutamicum, Escherichia coli, and Pseudomonas putida . Our best performing codon variants exhibited a minimum 50-fold increase in PKS protein levels, which also enabled the production of an unnatural polyketide in each of these hosts. Furthermore, we developed a free online tool ( https://basebuddy.lbl.gov ) that offers transparent and highly customizable codon optimization with up-to-date codon usage tables. In this work, we not only highlight the significance of codon optimization but also establish the groundwork for the high-throughput assembly and characterization of PKS pathways in alternative hosts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".