Engineering stable carbonic anhydrases for CO2 capture: a critical review
Bibliographic record
Abstract
Abstract In the search for green CO2-capture technology to combat global warming, bioengineering of carbonic anhydrases (CAs) is being sought for with target adaptabilities of extreme temperatures and alkaline pH conditions. The modern in silico screening of protein engineering complements the conventional in vitro high-throughput via generation of iteratively cumulating e-library of diverse beneficial mutations. As identified through various studies of randomized and rationalized mutagenesis, different features have been explored to engineer stability in CAs, including improving structural contacts in the protein quaternary architecture with disulfide bonds and salt-bridge networks, as well as enhancing the protein surface electrostatics. Advanced molecular dynamic simulation techniques and progressive training of machine learning-assisted databases are now being used to unravel wild-type CA properties and predict stable variants thereof with greater accuracy than ever before. The best fit CA achieved so forth demonstrates tolerances of up to 107°C at pH >10 with 25-fold enhancement in CO2 mass transfer. This review will provide an overview of different approaches that have been utilized for engineering CAs and will highlight potential challenges and strategies for developing CA-based CO2-capture and sequestration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".