Modulating the pH-activity profile of cellulase A from Cellulomonas fimi by replacement of surface residues
Bibliographic record
Abstract
One industrial process for the production of cellulosic ethanol and or value-added products involves exposing the cellulose content of plant materials by steam explosion in the presence of strong acid, followed by its neutralization and subsequent digestion with a cocktail of cellulolytic enzymes. These enzymes typically have activity optima at slightly acidic or neutral pH and so generating enzymes that are more active and tolerant in more acidic conditions would help to reduce associated costs. Here, we describe the engineering of cellulase A from Cellulomonas fimi as a model to replace residues that were identified as potentially influencing the pH-activity profile of the enzyme based on sequence alignments and analysis of the known three-dimensional structures of other CAZy family 6 glycoside hydrolases with the aim to lower its pH optimum. Twelve specific residues and a sequence of eight were identified and a total of 30 mutant enzymes were generated. In addition to being replaced with natural amino acids, some of the identified residues were substituted with cysteine and subsequently oxidized to cysteinesulfinate. Of the four single amino acid replacements that produced enhancements of activity at acidic pH, three involved the removal of charged groups from the surface of the enzyme. The generation of double mutations provided mixed results but the combination of Glu407 → Ala and Tyr321 → Phe replacements had an additive effect on the enhancement, reaching a total activity that was 162% of the wild-type level. This study thus illustrated the utility of altering the surface charge properties of the family 6 glycoside hydrolases to enhance activity at low pH and thereby an avenue for further protein engineering.
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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".