Enhanced delignification of steam-pretreated poplar by a bacterial laccase
Bibliographic record
Abstract
The recalcitrance of woody biomass, particularly its lignin component, hinders its sustainable transformation to fuels and biomaterials. Although the recent discovery of several bacterial ligninases promises the development of novel biocatalysts, these enzymes have largely been characterized using model substrates: direct evidence for their action on biomass is lacking. Herein, we report the delignification of woody biomass by a small laccase (sLac) from Amycolatopsis sp. 75iv3. Incubation of steam-pretreated poplar (SPP) with sLac enhanced the release of acid-precipitable polymeric lignin (APPL) by ~6-fold, and reduced the amount of acid-soluble lignin by ~15%. NMR spectrometry revealed that the APPL was significantly syringyl-enriched relative to the original material (~16:1 vs. ~3:1), and that sLac preferentially oxidized syringyl units and altered interunit linkage distributions. sLac's substrate preference among monoaryls was also consistent with this observation. In addition, sLac treatment reduced the molar mass of the APPL by over 50%, as determined by gel-permeation chromatography coupled with multi-angle light scattering. Finally, sLac acted synergistically with a commercial cellulase cocktail to increase glucose production from SPP ~8%. Overall, this study establishes the lignolytic activity of sLac on woody biomass and highlights the biocatalytic potential of bacterial enzymes.
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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) | 0.000 | 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 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".