Short-Chain Flavor Ester Synthesis in Organic Media by an E. coli Whole-Cell Biocatalyst Expressing a Newly Characterized Heterologous Lipase
Bibliographic record
Abstract
Short-chain aliphatic esters are small volatile molecules that produce fruity and pleasant aromas and flavors. Most of these esters are artificially produced or extracted from natural sources at high cost. It is, however, possible to 'naturally' produce these molecules using biocatalysts such as lipases and esterases. A gene coding for a newly uncovered lipase was isolated from a previous metagenomic study and cloned into E. coli BL21 (DE3) for overexpression using the pET16b plasmid. Using this recombinant strain as a whole-cell biocatalyst, short chain esters were efficiently synthesized by transesterification and esterification reactions in organic media. The recombinant lipase (LipIAF5-2) showed good affinity toward glyceryl trioctanoate and the highest conversion yields were obtained for the transesterification of glyceryl triacetate with methanol. Using a simple cetyl-trimethylammonium bromide pretreatment increased the synthetic activity by a six-fold factor and the whole-cell biocatalyst showed the highest activity at 40°C with a relatively high water content of 10% (w/w). The whole-cell biocatalyst showed excellent tolerance to alcohol and short-chain fatty acid denaturation. Substrate affinity was equally effective with all primary alcohols tested as acyl acceptors, with a slight preference for methanol. The best transesterification conversion of 50 mmol glyceryl triacetate into isoamyl acetate (banana fragrance) provided near 100% yield after 24 hours using 10% biocatalyst loading (w/w) in a fluidized bed reactor, allowing recycling of the biocatalyst up to five times. These results show promising potential for an industrial approach aimed at the biosynthesis of short-chain esters, namely for natural flavor and fragrance production in micro-aqueous media.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".