Mining Enzyme Diversity of Transcriptome Libraries through DNA Synthesis for Benzylisoquinoline Alkaloid Pathway Optimization in Yeast
Bibliographic record
Abstract
The ever-increasing quantity of data deposited to GenBank is a valuable resource for mining new enzyme activities. Falling costs of DNA synthesis enables metabolic engineers to take advantage of this resource for identifying superior or novel enzymes for pathway optimization. Previously, we reported synthesis of the benzylisoquinoline alkaloid dihydrosanguinarine in yeast from norlaudanosoline at a molar conversion of 1.5%. Molar conversion could be improved by reduction of the side-product N-methylcheilanthifoline, a key bottleneck in dihydrosanguinarine biosynthesis. Two pathway enzymes, an N-methyltransferase and a cytochrome P450 of the CYP719A subfamily, were implicated in the synthesis of the side-product. Here, we conducted an extensive screen to identify enzyme homologues whose coexpression reduces side-product synthesis. Phylogenetic trees were generated from multiple sources of sequence data to identify a library of candidate enzymes that were purchased codon-optimized and precloned into expression vectors designed to facilitate high-throughput analysis of gene expression as well as activity assay. Simple in vivo assays were sufficient to guide the selection of superior enzyme homologues that ablated the synthesis of the side-product, and improved molar conversion of norlaudanosoline to dihydrosanguinarine to 10%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".