Observing and modeling BMCC degradation by commercial cellulase cocktails with fluorescently labeled <i>Trichoderma reseii</i> Cel7A through confocal microscopy
Bibliographic record
Abstract
Understanding the depolymerization mechanisms of cellulosic substrates by cellulase cocktails is a critical step towards optimizing the production of monosaccharides from biomass. The Spezyme CP cellulase cocktail combined with the Novo 188 β-glucosidase blend was used to depolymerize bacterial microcrystalline cellulose (BMCC), which was immobilized on a glass surface. The enzyme mixture was supplemented with a small fraction of fluorescently labeled Trichoderma reseii Cel7A, which served as a reporter to track cellulase binding onto the physical structure of the cellulosic substrate. Both micro-scale imaging and bulk experiments were conducted. All reported experiments were conducted at 50 °C, the optimal temperature for maximum hydrolytic activity of the enzyme cocktail. BMCC structure was observed throughout degradation by labeling it with a fluorescent dye. This method allowed us to measure the binding of cellulases in situ and follow the temporal morphological changes of cellulose during its depolymerization by a commercial cellulase mixture. Three kinetic models were developed and fitted to fluorescence intensity data obtained through confocal microscopy: irreversible and reversible binding models, and an instantaneous binding model. The models were successfully used to predict the soluble sugar concentrations that were liberated from BMCC in bulk experiments. Comparing binding and kinetic parameters from models with different assumptions to previously reported constants in the literature led us to conclude that exposing new binding sites is an important rate-limiting step in the hydrolysis of crystalline cellulose.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".