Deciphering Reductive Dehalogenase Specificity Through Targeted Mutagenesis of Chloroalkane Reductases
Bibliographic record
Abstract
Abstract Reductive dehalogenases (RDases) are essential in the anaerobic degradation of various organohalide contaminants. This family of enzymes has broad sequence diversity, but high structural conservation. There have been few studies assessing how RDase peptide sequences affect their substrate selectivity. Here we focus on two chloroalkane RDases, CfrA and DcrA, which have 95% protein sequence identity but have diverged to hold distinct substrate preferences. CfrA will dechlorinate chloroform and 1,1,1-trichloroethane, whilst DcrA will dechlorinate 1,1-dichloroethane. We mutated several residues in the active site of CfrA to investigate a change in substrate preference and to identify which wild-type residues contribute the most to substrate specialization. We determined that no individual residue solely dictates substrate discrimination, but both Y80W and F125W mutations were needed to force CfrA to prefer 1,1-dichloroethane as a substrate. This double mutation also altered the transformation pathway of 1,1,2-trichloroethane from hydrogenolysis (forms 1,2-dichloroethane) to dihaloelimination (forms vinyl chloride). We use predictive protein models and substrate docking to predict what interactions are made between the enzyme and substrate to aid in selection. The residues of significance identified in this study are consistent with those identified from chloroethene RDases, suggesting residue locations with a particularly high impact on activity. Importance Reductive dehalogenases play an integral role in the removal of chlorinated solvents from the environment. These enzymes have specificity towards different chlorinated compounds, and it is known that small natural changes in their peptide sequence can change their activity drastically. How these specific sequence variations influence activity is largely unknown. In this study, we demonstrate that mutating a few residues within the active site of CfrA—a chloroform and trichloroethane-specific dehalogenase—changes its substrate preference to dichloroethane. We determine that only two mutations are needed to disrupt the native activity, underscoring the nuances in substrate-structure relationships in reductive dehalogenases. Though we are still far from predicting function from the sequence, this knowledge can give some insight into engineering reductive dehalogenases for new target contaminants.
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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 | 0.001 |
| 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".