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Record W4407528366 · doi:10.1128/aem.01501-24

Deciphering reductive dehalogenase specificity through targeted mutagenesis of chloroalkane reductases

2025· article· en· W4407528366 on OpenAlexafffund
Katherine J. Picott, Connor M Bowers, Elizabeth A. Edwards

Bibliographic record

VenueApplied and Environmental Microbiology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of OntarioUniversity of Wisconsin-Madison
KeywordsChemistrySaturated mutagenesisDehalogenaseActive siteStereochemistrySubstrate (aquarium)Residue (chemistry)MutagenesisProtein engineeringEnzymeBiochemistryDirected evolutionMutantBiology

Abstract

fetched live from OpenAlex

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 amino acid 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 have opposite substrate preferences. CfrA dechlorinates chloroform (CF) and 1,1,1-trichloroethane (TCA) but not 1,1-dichloroethane (DCA), while DcrA will dechlorinate 1,1-DCA but not CF or 1,1,1-TCA. 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-DCA as a substrate. When using 1,1,2-TCA as a substrate, CfrA predominately performs hydrogenolysis to 1,2-DCA, yet the introduction of the double mutant changed this preference to dihaloelimination (forming 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.IMPORTANCEReductive dehalogenases (RDases) play an integral role in the removal of chlorinated solvents from the environment. These enzymes have specificity toward different chlorinated compounds, and it is known that natural variants of highly similar RDases can have distinct activities. How specific differences in protein sequence 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 RDases. Though we are still far from predicting function from the sequence, this knowledge can give some insight into engineering RDases for new target contaminants.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.191
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2025
Admission routes2
Has abstractyes

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