Fungal loosenin-like proteins boost the cellulolytic enzyme conversion of pretreated wood fiber and cellulosic pulps
Bibliographic record
Abstract
Microbial expansin-related proteins, including loosenins, can disrupt cellulose networks and increase enzyme accessibility to cellulosic substrates. Herein, four loosenins from Phanerochaete carnosa (PcaLOOLs), and a PcaLOOL fused to a family 63 carbohydrate-binding module, were compared for ability to boost the cellulolytic deconstruction of steam pretreated softwood (SSW) and kraft pulps from softwood (ND-BSKP) and hardwood (ND-BHKP). Amending the Cellic® CTec-2 cellulase cocktail with PcaLOOLs increased reducing products from SSW by up to 40 %, corresponding to 28 % higher glucose yield. Amending Cellic® CTec-2 with PcaLOOLs also increased the release of glucose from ND-BSKP and ND-BHKP by 82 % and 28 %, respectively. Xylose release from ND-BSKP and ND-BHKP increased by 47 % and 57 %, respectively, highlighting the potential of PcaLOOLs to enhance hemicellulose recovery. Scanning electron microscopy and fiber image analysis revealed fibrillation and curlation of ND-BSKP after PcaLOOL treatment, consistent with increasing enzyme accessibility to targeted substrates.
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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) | 0.001 | 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".