Mining Enzyme Diversity of Transcriptome Libraries through DNA Synthesis for Benzylisoquinoline Alkaloid Pathway Optimization in Yeast
Why this work is in the frame
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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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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 | 0.001 |
| 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 it