Min-max Driving Force Analysis and In Vitro Enzyme Characterization of Proposed In Vivo Ethylenediamine Production Pathway
Bibliographic record
Abstract
Ethylenediamine is a large volume chemical used in the synthesis of advanced products across multiple industries and has multiple potential applications in sustainable chemistry. It’s currently produced from petroleum-based processes that raise environmental and health concerns. Here we report on the assessment of the thermodynamic and biochemical feasibility of a proposed enzymatic ethylenediamine bioproduction pathway that would branch from existing engineered ethylene glycol utilization pathway in Escherichia coli. The bioproduction pathway branches from glycolaldehyde, which undergoes a transamination/oxidation step followed by the other before a final transamination step. Min-max Driving Force analysis concluded that the pathway is thermodynamically feasible under certain pathway conditions. Transamination of glycolaldehyde, glyoxal, and aminoacetaldehyde by Silicibacter pomeroyi transaminase SPO3471 were observed and production of monoethanolamine and ethylenediamine were confirmed. Oxidation activity on glycolaldehyde and monoethanolamine by E. coli methylglyoxal reductase ydjG were observed, however production of glyoxal and aminoacetaldehyde could not be directly confirmed due to insufficient evidence and molecular instability respectively. Our results highlight the potential of oxidoreductases and transaminases to be used in an in vivo system for the biosynthesis of ethylenediamine.
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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 | 0.000 |
| 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) | 0.001 | 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".