Evaluation of substrate specificity and catalytic promiscuity of <i>Bacillus albus</i> cellulase: an insight into <i>in silico</i> proteomic study aiming at enhanced production of renewable energy
Bibliographic record
Abstract
Cellulases are enzymes that aid in the hydrolysis of cellulosic fibers and have a wide range of industrial uses. In the present in silico study, sequence alignment between cellulases from different Bacillus species revealed that most of the residues are conserved in those aligned enzymes. Three dimensional structures of cellulase enzymes from 23 different Bacillus species have been predicted and based on the alignment between the modeled structures, those enzymes have been categorized into 7 different groups according to the homology in their conformational folds. There are two structural contents in Gr-I cellulase namely β1-α2 and β3-α5 loops which varies greatly according to their static position. Molecular docking study between the B. albus cellulase and its various cellulosic substrates including xylanoglucan oligosaccharides revealed that residues viz. Phe154, Tyr258, Tyr282, Tyr285, and Tyr376 of B. albus cellulase are significantly involved in formation stacking interaction during enzyme-substrate binding. Residue interaction network and binding energy analysis for the B. albus cellulase with different cellulosic substrates depicted the strong affinity of XylGlc3 substrate with the receptor enzyme. Molecular interaction and molecular dynamics simulation studies exhibited structural stability of enzyme-substrate complexes which are greatly influenced by the presence of catalytic promiscuity in their substrate binding sites. Screening of B. albus in carboxymethylcellulose (CMC) and xylan supplemented agar media revealed the capability of the bacterium in degrading both cellulose and xylan. Overall, the study demonstrated B. albus cellulase as an effective biocatalyst candidate with the potential role of catalytic promiscuity for possible applications in biofuel industries.Communicated by Ramaswamy H. Sarma
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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) | 0.000 | 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 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".