Exploring the promoting mechanisms of bovine serum albumin, lignosulfonate, and polyethylene glycol for lignocellulose saccharification from perspective of molecular interactions with cellulase
Bibliographic record
Abstract
Bovine serum albumin (BSA), polyethylene glycol (PEG) and lignosulfonate (LS) have been extensively employed as synergistic agents in lignocellulose saccharification. Nevertheless, the promoting mechanisms have not been fully understood and there are a number of controversial opinions existed. All attention has been paid to the interactions between respective additive and substrate. However, rarely attention has been paid to the interactions between additives and enzymes (cellulase from Trichoderma reesei in this investigation). This interaction is actually more important since cellulase interacts with the additives before it contacts with substrate. Therein, Quartz crystal microbalance with dissipation monitoring (QCM-D), surface plasma resonance (SPR) and small angel X-ray scattering (SAXS) were incorporated to study the interaction between enzyme and additives. The results showed synergistic agents have different interaction modes with cellulase. BSA and LS can form complexes with cellulase and the formed complexes prevent them from nonproductive binding by residue lignin; what’s more, the cellulase-BSA complexes improve the hydrolytic capability of pristine enzyme whereas cellulase-LS complexes reduce. PEG prevents the unproductive binding of cellulase to the residual lignin by forming a thin layer that actually acts as a steric hindrance to the residual lignin. This investigation helps us to understand the sophisticated interactions among the components in the complicated enzymatic system, especially the interactions between enzymes and synergistic agents. It will be helpful in the design and utilization of synergistic additives in the lignocellulose biorefinery process as well.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".