Characterization and Synergistic Interactions of <i>Fibrobacter succinogenes</i> Glycoside Hydrolases
Bibliographic record
Abstract
The objectives of this study were to characterize Fibrobacter succinogenes glycoside hydrolases from different glycoside hydrolase families and to study their synergistic interactions. The gene encoding a major endoglucanase (endoglucanase 1) of F. succinogenes S85 was identified as cel9B from the genome sequence by reference to internal amino acid sequences of the purified native enzyme. Cel9B and two other glucanases from different families, Cel5H and Cel8B, were cloned and overexpressed, and the proteins were purified and characterized. These proteins in conjunction with two predominant cellulases, Cel10A, a chloride-stimulated cellobiosidase, and Cel51A, formerly known as endoglucanase 2 (or CelF), were assayed in various combinations to assess their synergistic interactions using ball-milled cellulose. The degree of synergism ranged from 0.6 to 3.7. The two predominant endoglucanases produced by F. succinogenes, Cel9B and Cel51A, were shown to have a synergistic effect of up to 1.67. Cel10A showed little synergy in combination with Cel9B and Cel51A. Mixtures containing all the enzymes gave a higher degree of synergism than those containing two or three enzymes, which reflected the complementarity in their modes of action as well as substrate specificities.
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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 | 0.000 |
| 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 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".