The development and use of an ELISA-based method to follow the distribution of cellulase monocomponents during the hydrolysis of pretreated corn stover
Bibliographic record
Abstract
BACKGROUND: It is widely recognised that fast, effective hydrolysis of pretreated lignocellulosic substrates requires the synergistic action of multiple types of hydrolytic and some non-hydrolytic proteins. However, due to the complexity of the enzyme mixture, the enzymes interaction with and interference from the substrate and a lack of specific methods to follow the distribution of individual enzymes during hydrolysis, most of enzyme-substrate interaction studies have used purified enzymes and pure cellulose model substrates. As the enzymes present in a typical "cellulase mixture" need to work cooperatively to achieve effective hydrolysis, the action of one enzyme is likely to influence the behaviour of others. The action of the enzymes will be further influenced by the nature of the lignocellulosic substrate. Therefore, it would be beneficial if a method could be developed that allowed us to follow some of the individual enzymes present in a cellulase mixture during hydrolysis of more commercially realistic biomass substrates. RESULTS: A high throughput immunoassay that could quantitatively and specifically follow individual cellulase enzymes during hydrolysis was developed. Using monoclonal and polyclonal antibodies (MAb and PAb, respectively), a double-antibody sandwich enzyme-linked immunosorbent assay (ELISA) was developed to specifically quantify cellulase enzymes from Trichoderma reesei: cellobiohydrolase I (Cel7A), cellobiohydrolase II (Cel6A), and endoglucanase I (Cel7B). The interference from substrate materials present in lignocellulosic supernatants could be minimized by dilution. CONCLUSION: A double-antibody sandwich ELISA was able to detect and quantify individual enzymes when present in cellulase mixtures. The assay was sensitive over a range of relatively low enzyme concentration (0 - 1 μg/ml), provided the enzymes were first pH adjusted and heat treated to increase their antigenicity. The immunoassay was employed to quantitatively monitor the adsorption of cellulase monocomponents, Cel7A, Cel6A, and Cel7B, that were present in both Celluclast and Accellerase 1000, during the hydrolysis of steam-pretreated corn stover (SPCS). All three enzymes exhibited different individual adsorption profiles. The specific and quantitative adsorption profiles observed with the ELISA method were in agreement with earlier work where more labour intensive enzyme assay techniques were used.
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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".