Tailoring the substrate specificity of secondary alcohol dehydrogenase
Bibliographic record
Abstract
Our research with the thermophilic secondary-alcohol dehydrogenase (SADH) from Thermoanaerobacter ethanolicus has provided novel information regarding the physical basis of enzyme substrate specificity and stereospecificity. We demonstrated that oxidation of secondary alcohols catalyzed by T. ethanolicus SADH exhibits temperature-dependent enantiospecificity. In other studies, we found that the structure of co-factor analogs also significantly affects the stereochemistry of the SADH reaction. More recently, we demonstrated that pH can also have a modest effect on SADH enantiospecificity. Organic solvents have also been shown by others to affect the stereochemistry of SADH reactions. We designed and prepared S39T and C295A mutant forms of SADH by site-directed mutagenesis, and we evaluated the effects of the mutations by analysis of the temperature dependence of the enantiomeric ratio (E) for simple chiral alcohols such as 2-butanol. This procedure allows for the determination of the differential Eyring parameters (ΔΔH and ΔΔS) for the reaction. We demonstrated that this technique is a sensitive method for analysis of the effects of mutation on enzyme stereospecificity. S39T and C295A SADH exhibit significant changes in substrate specificity and stereospecificity consistent with the changes in the volume of the alkyl-binding pockets. Thus, it is possible to alter the substrate specificity and stereospecificity of alcohol dehydrogenase by changing either the reaction medium or the protein structure.Key words: alcohol dehydrogenase, substrate specificity, stereospecificity, temperature dependence, site-directed mutagenesis.
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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) | 0.001 | 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".