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Mechanism of enhanced conversion of 1,2,3-trichloropropane by mutant haloalkane dehalogenase revealed by molecular modeling

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Abstract

1,2,3-Trichloropropane (TCP) is a highly toxic, recalcitrant byproduct of epichlorohydrin manufacture. Haloalkane dehalogenase (DhaA) from Rhodococcus sp. hydrolyses the carbon–halogen bond in various halogenated compounds including TCP, but with low efficiency (k cat/K = 36 s-1 M-1). A Cys176Tyr-DhaA mutant with a threefold higher catalytic efficiency for TCP dehalogenation has been previously obtained by error-prone PCR. We have used molecular simulations and quantum mechanical calculations to elucidate the molecular mechanisms involved in the improved catalysis of the mutant, and enantioselectivity of DhaA toward TCP. The Cys176Tyr mutation modifies the protein access and export routes. Substitution of the Cys residue by the bulkier Tyr narrows the upper tunnel, making the second tunnel “slot” the preferred route. TCP can adopt two major orientations in the DhaA enzyme, in one of which the halide-stabilizing residue Asn41 forms a hydrogen bond with the terminal halogen atom of the TCP molecule, while in the other it bonds with the central halogen atom. The differences in these binding patterns explain the preferential formation of the (R)- over the (S)-enantiomer of 2,3-dichloropropane-1-ol in the reaction catalyzed by the enzyme.

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Acknowledgments

We thank Dr Tjibe Bosma (Groningen, the Netherlands) for valuable discussions on the interpretation of kinetic characterizations of mutant dehalogenases. We acknowledge financial support from the Czech Ministry of Education (MO, grants MSM6198959216, LC512; JD, grant MSM0021622412). The research of JD is supported by an EMBO/HMMI grant within the Young Investigator Program. We thank Dr J. Blackwell (UK) for linguistic revisions.

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Correspondence to Michal Otyepka or Jiří Damborský.

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Banáš, P., Otyepka, M., Jeřábek, P. et al. Mechanism of enhanced conversion of 1,2,3-trichloropropane by mutant haloalkane dehalogenase revealed by molecular modeling. J Comput Aided Mol Des 20, 375–383 (2006). https://doi.org/10.1007/s10822-006-9071-1

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