Biocatalytic <i>in Vitro</i> and <i>in Vivo</i> FMN Prenylation and (De)carboxylase Activation
Bibliographic record
Abstract
Reversible UbiD-like (de)carboxylases represent a large family of mostly uncharacterized enzymes, which require the recently discovered prenylated FMN (prFMN) cofactor for activity. Functional characterization of novel UbiDs is hampered by a lack of robust protocols for prFMN generation and UbiD activation. Here, we report two systems for in vitro and in vivo FMN prenylation and UbiD activation under aerobic conditions. The in vitro one-pot prFMN cascade includes five enzymes: FMN prenyltransferase (UbiX), prenol kinase, polyphosphate kinase, formate dehydrogenase, and FMN reductase, which use prenol, polyphosphate, formate, ATP, NAD +, and FMN as substrates and cofactors. Under aerobic conditions, this cascade produced prFMN from FMN with over 98% conversion and activated purified ferulic acid decarboxylase Fdc1 from Aspergillus niger and protocatechuic acid decarboxylase ENC0058 from Enterobacter cloaceae . The in vivo system for FMN prenylation and UbiD activation is based on the coexpression of Fdc1 and UbiX in Escherichia coli cells under aerobic conditions in the presence of prenol. The in vitro and in vivo FMN prenylation cascades will facilitate functional characterization of novel UbiDs and their applications.
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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".