Enzyme cocktail development for the conversion of pretreated wood biomass
Bibliographic record
Abstract
A steady rise in global consumption of fossil-based energy has led to a surge in prices of petroleum-derived fuels, chemicals and materials in recent years. The abundantly available lignocellulosic biomass is a renewable, potential substitute for petroleum that can be biotransformed into biofuels, chemicals and materials. Commercial lignocellulose-degrading enzyme cocktails used in this biotransformation still require improvement. In this thesis, I report the biochemical characterization of 34 plant cell wall degrading proteins from Aspergillus niger expressed in one or more of four host systems (Aspergillus niger, Pichia pastoris, Escherichia coli and Nicotiana benthamiana), in order to identify industrially relevant differences in their catalytic function. The results show that N. benthamiana is as effective a production host as A. niger itself and the recombinant proteins produced in the four host systems show similar biochemical properties. I also report the development of a medium- to high-throughput-adaptable screening method for evaluating the hydrolytic capability of lignocellulolytic enzymes at biomass loadings greater than 15% dry w/v. The results show that the method is suitable for the screening of cell wall degrading enzymes with superior properties and for evaluating biomass hydrolysability. I also report the identification of biomass-liquefying enzymes from the secretome of thermophilic saprotroph, Myceliophthora thermophila grown on various pretreated wood biomass types. Results reveal an arsenal of 47 secreted proteins which concertedly liquefy pretreated wood biomass at 15% dry solids and boost glucose release by a commercial cellulase system. A highly-expressed GH7 cellobiohydrolase, MtCBH7 was found to liquefy 15% black spruce kraft pulp, acting optimally at 55 °C. The overall finding are: (i) N. benthamiana holds potential as a production host for eukaryotic biomass-degrading enzymes; (ii) a screening method for evaluating the hydrolytic potential of enzymes on natural biomass has been developed; and (iii) MtCBH7 is a promising enzyme candidate for application in biorefineries where enhanced liquefaction of lignocellulose is required.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".