Cel9D, an Atypical 1,4-β- <scp>d</scp> -Glucan Glucohydrolase from <i>Fibrobacter succinogenes</i> : Characteristics, Catalytic Residues, and Synergistic Interactions with Other Cellulases
Bibliographic record
Abstract
The increasing demands of renewable energy have led to the critical emphasis on novel enzymes to enhance cellulose biodegradation for biomass conversion. To identify new cellulases in the ruminal bacterium Fibrobacter succinogenes, a cell extract of cellulose-grown cells was separated by ion-exchange chromatography and cellulases were located by zymogram analysis and identified by peptide mass fingerprinting. An atypical family 9 glycoside hydrolase (GH9), Cel9D, with less than 20% identity to typical GH9 cellulases, was identified. Purified recombinant Cel9D enhanced the production of reducing sugar from acid swollen cellulose (ASC) and Avicel by 1.5- to 4-fold when mixed separately with each of four other glucanases, although it had low activity on these substrates. Cel9D degraded ASC and cellodextrins with a degree of polymerization higher than 2 to glucose with no apparent endoglucanase activity, and its activity was restricted to beta-1-->4-linked glucose residues. It catalyzed the hydrolysis of cellulose by an inverting mode of reaction, releasing glucose from the nonreducing end. Unlike many GH9 cellulases, calcium ions were not required for its function. Cel9D had increased kcat/Km values for cello-oligosaccharides with higher degrees of polymerization. The kcat/Km value for cellohexaose was 2,300 times higher than that on cellobiose. This result indicates that Cel9D is a 1,4-beta-D-glucan glucohydrolase (EC 3.2.1.74) in the GH9 family. Site-directed mutagenesis of Cel9D identified Asp166 and Glu612 as the candidate catalytic residues, while Ser168, which is not present in typical GH9 cellulases, has a crucial structural role. This enzyme has an important role in crystalline cellulose digestion by releasing glucose from accessible cello-oligosaccharides.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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".