Enhancement of Synthetic Trichoderma-Based Enzyme Mixtures for Biomass Conversion with an Alternative Family 5 Glycosyl Hydrolase from Sporotrichum thermophile
Bibliographic record
Abstract
Enzymatic conversion of lignocellulosic materials to fermentable sugars is a limiting step in the production of biofuels from biomass. We show here that combining enzymes from different microbial sources is one way to identify superior enzymes. Extracts of the thermophilic fungus Sporotrichum thermophile (synonym Myceliophthora thermophila) gave synergistic release of glucose (Glc) and xylose (Xyl) from pretreated corn stover when combined with an 8-component synthetic cocktail of enzymes from Trichoderma reesei. The S. thermophile extracts were fractionated and an enhancing factor identified as endo-β1,4-glucanase (StCel5A or EG2) of subfamily 5 of Glycosyl Hydrolase family 5 (GH5_5). In multi-component optimization experiments using a standard set of enzymes and either StCel5A or the ortholog from T. reesei (TrCel5A), reactions containing StCel5A yielded more Glc and Xyl. In a five-component optimization experiment (i.e., varying four core enzymes and the source of Cel5A), the optimal proportions for TrCel5A vs. StCel5A were similar for Glc yields, but markedly different for Xyl yields. Both enzymes were active on lichenan, glucomannan, and oat β-glucan; however, StCel5A but not TrCel5A was also active on β1,4-mannan, two types of galactomannan, and β1,4-xylan. Phylogenetically, fungal enzymes in GH5_5 sorted into two clades, with StCel5A and TrCel5A belonging to different clades. Structural differences with the potential to account for the differences in performance were deduced based on the known structure of TrCel5A and a homology-based model of StCel5A, including a loop near the active site of TrCel5A and the presence of four additional Trp residues in the active cleft of StCel5A. The results indicate that superior biomass-degrading enzymes can be identified by exploring taxonomic diversity combined with assays in the context of realistic enzyme combinations and realistic substrates. Substrate range may be a key factor contributing to superior performance within GH5_5.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".