Towards Precision Engineering of Canonical Polyketide Synthase Domains: Recent Advances and Future Prospects
Bibliographic record
Abstract
Modular polyketide synthases (mPKSs) build functionalized polymeric chains, some of which have become blockbuster therapeutics. Organized into repeating clusters (modules) of independently-folding domains, these assembly-line-like megasynthases can be engineered by introducing non-native components. However, poor introduction points and incompatible domain combinations can cause both unintended products and dramatically reduced activity. This limits the engineering and combinatorial potential of mPKSs, precluding access to further potential therapeutics. Different regions on a given mPKS domain determine how it interacts both with its substrate and with other domains. Within the assembly line, these interactions are crucial to the proper ordering of reactions and efficient polyketide construction. Achieving control over these domain functions, through precision engineering at key regions, would greatly expand our catalogue of accessible polyketide products. Canonical mPKS domains, given that they are among the most well-characterized, are excellent candidates for such fine-tuning. The current minireview summarizes recent advances in the mechanistic understanding and subsequent precision engineering of canonical mPKS domains, focusing largely on developments in the past year.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".