8oe2
Structure of hyperstable haloalkane dehalogenase variant DhaA223
Structural highlights
FunctionDHAA_PSEPV Catalyzes hydrolytic cleavage of carbon-halogen bonds in halogenated aliphatic compounds, leading to the formation of the corresponding primary alcohols, halide ions and protons. Has a broad substrate specificity, as it is able to dehalogenate mono- and di- chlorinated and brominated alkanes (up to at least C10), and the two isomers of 1,3-dichloropropene to 3-chloroallyl alcohol; the highest activity was found with 1,2-dibromoethane, while no activity was observed with the analog 1,2-dichloroethane.[1] Publication Abstract from PubMedThermostability is an essential requirement for the use of enzymes in the bioindustry. Here, we compare different protein stabilization strategies using a challenging target, a stable haloalkane dehalogenase DhaA115. We observe better performance of automated stabilization platforms FireProt and PROSS in designing multiple-point mutations over the introduction of disulfide bonds and strengthening the intra- and the inter-domain contacts by in silico saturation mutagenesis. We reveal that the performance of automated stabilization platforms was still compromised due to the introduction of some destabilizing mutations. Notably, we show that their prediction accuracy can be improved by applying manual curation or machine learning for the removal of potentially destabilizing mutations, yielding highly stable haloalkane dehalogenases with enhanced catalytic properties. A comparison of crystallographic structures revealed that current stabilization rounds were not accompanied by large backbone re-arrangements previously observed during the engineering stability of DhaA115. Stabilization was achieved by improving local contacts including protein-water interactions. Our study provides guidance for further improvement of automated structure-based computational tools for protein stabilization. Advancing Enzyme's Stability and Catalytic Efficiency through Synergy of Force-Field Calculations, Evolutionary Analysis, and Machine Learning.,Kunka A, Marques SM, Havlasek M, Vasina M, Velatova N, Cengelova L, Kovar D, Damborsky J, Marek M, Bednar D, Prokop Z ACS Catal. 2023 Sep 11;13(19):12506-12518. doi: 10.1021/acscatal.3c02575. , eCollection 2023 Oct 6. PMID:37822856[2] From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine. References
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