Weak Lignin-Binding Enzymes
Bibliographic record
Abstract
Economic barriers preventing commercialization of lignocellulose-toethanol bioconversion processes include the high cost of hydrolytic enzymes. One strategy for cost reduction is to improve the specific activities of cellulases by genetic engineering. However, screening for improved activity typically uses “ideal” cellulosic substrates, and results are not necessarily applicable to more realistic substrates such as pretreated hardwoods and softwoods. For lignocellulosic substrates, nonproductive binding and inactivation of enzymes by the lignin component appear to be important factors limiting catalytic efficiency. A better understanding of these factors could allow engineering of cellulases with improved activity based on reduced enzyme-lignin interaction (“weak lignin-binding cellulases”). To prove this concept, we have shown that naturally occurring cellulases with similar catalytic activity on a model cellulosic substrate can differ significantly in their affinities for lignin. Moreover, although cellulose-binding domains (CBDs) are hydrophobic and probably participate in lignin binding, we show that cellulases lacking CBDs also have a high affinity for lignin, indicating the presence of lignin-binding sites on the catalytic domain.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.015 |
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".