Diversifying Carotenoid Biosynthetic Pathways by Directed Evolution
Bibliographic record
Abstract
Microorganisms and plants synthesize a diverse array of natural products, many of which have proven indispensable to human health and well-being. Although many thousands of these have been characterized, the space of possible natural products--those that could be made biosynthetically--remains largely unexplored. For decades, this space has largely been the domain of chemists, who have synthesized scores of natural product analogs and have found many with improved or novel functions. New natural products have also been made in recombinant organisms, via engineered biosynthetic pathways. Recently, methods inspired by natural evolution have begun to be applied to the search for new natural products. These methods force pathways to evolve in convenient laboratory organisms, where the products of new pathways can be identified and characterized in high-throughput screening programs. Carotenoid biosynthetic pathways have served as a convenient experimental system with which to demonstrate these ideas. Researchers have mixed, matched, and mutated carotenoid biosynthetic enzymes and screened libraries of these "evolved" pathways for the emergence of new carotenoid products. This has led to dozens of new pathway products not previously known to be made by the assembled enzymes. These new products include whole families of carotenoids built from backbones not found in nature. This review details the strategies and specific methods that have been employed to generate new carotenoid biosynthetic pathways in the laboratory. The potential application of laboratory evolution to other biosynthetic pathways is also discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".