Sequence- and activity-based screening of microbial genomes for novel dehalogenases

Area of Science:

• Biochemistry
• Enzymology
• Environmental Microbiology

Background:

• Dehalogenases are vital enzymes for detoxifying organohalogens by breaking carbon-halogen bonds.
• Identifying dehalogenases via sequence analysis is difficult due to low sequence conservation and the presence of other enzyme types (esterases, phosphatases) within related protein families.
• Improved sequence-based methods are needed to accelerate the discovery of novel dehalogenases with enhanced or modified activities.

Purpose of the Study:

• To develop reliable sequence-based criteria for identifying genuine dehalogenases.
• To screen uncharacterized enzyme candidates from the alpha/beta hydrolase (ABH) and haloacid dehalogenase-like hydrolase (HAD) superfamilies for dehalogenase, esterase, and phosphatase activities.
• To enhance the efficiency of discovering novel dehalogenases with potential biotechnological applications.

Main Methods:

• Biochemical screening of 103 uncharacterized ABH and HAD superfamily proteins for enzymatic activity.
• Refinement of sequence-based dehalogenase selection criteria based on initial functional data.
• Application of refined criteria to a second functional screening of 24 selected protein candidates.

Main Results:

• The initial screen yielded a low success rate (7%), identifying only a few dehalogenases alongside numerous esterases and phosphatases.
• The refined sequence criteria significantly improved the success rate to 54% in the second screen, identifying novel dehalogenase activities.
• Four new L-2-haloacid dehalogenases from the HAD superfamily were discovered with the novel ability to hydrolyze fluoroacetate.

Conclusions:

• Refined sequence-based selection criteria are highly effective for identifying genuine dehalogenases.
• The study successfully discovered novel dehalogenase activities, including fluoroacetate hydrolysis by HAD superfamily enzymes, previously not attributed to this group.
• This work provides a robust framework for leveraging genomic data to accelerate the discovery of functional dehalogenases for environmental applications.