In Vivo Assay Reveals Microbial OleA Thiolases Initiating Hydrocarbon and β-Lactone Biosynthesis

Substrate promiscuity of xenobiotic-transforming hydrolases from stream biofilms impacted by treated wastewater

Organic contaminants enter aquatic ecosystems from various sources, including wastewater treatment plant effluent. Freshwater biofilms play a major role in the removal of organic contaminants from receiving water bodies, but knowledge of the molecular mechanisms driving contaminant biotransformations in complex stream biofilm (periphyton) communities remains limited. Previously, we demonstrated that biofilms in experimental flume systems grown at higher ratios of treated wastewater (WW) to stream water displayed an increased biotransformation potential for a number of organic contaminants. We identified a positive correlation between WW percentage and biofilm biotransformation rates for the widely-used insect repellent, N,N-diethyl-meta-toluamide (DEET) and a number of other wastewater-borne contaminants with hydrolyzable moieties. Here, we conducted deep shotgun sequencing of flume biofilms and identified a positive correlation between WW percentage and metagenomic read abundances of DEET hydrolase (DH) homologs. To test the causality of this association, we constructed a targeted metagenomic library of DH homologs from flume biofilms. We screened our complete metagenomic library for activity with four different substrates, including DEET, and a subset thereof with 183 WW-related organic compounds. The majority of active hydrolases in the metagenomic library preferred aliphatic and aromatic ester substrates while, remarkably, only a single reference enzyme was capable of DEET hydrolysis. Of the 626 total enzyme-substrate combinations tested, approximately 5% were active enzyme-substrate pairs. Metagenomic DH family homologs revealed a broad substrate promiscuity spanning 22 different compounds when summed across all enzymes tested. We biochemically characterized the most promiscuous and active enzymes identified based on metagenomic analysis from uncultivated Rhodospirillaceae and Planctomycetaceae. In addition to characterizing new DH family enzymes, we exemplified a framework for linking metagenome-guided hypothesis generation with experimental validation. Overall, this study expands the scope of known enzymatic contaminant biotransformations for metagenomic hydrolases from WW-receiving stream biofilm communities.

p-Nitrophenyl esters provide new insights and applications for the thiolase enzyme OleA

The OleA enzyme is distinct amongst thiolase enzymes in binding two long (≥C8) acyl chains into structurally-opposed hydrophobic channels, denoted A and B, to carry out a non-decarboxylative Claisen condensation reaction and initiate the biosynthesis of membrane hydrocarbons and β-lactone natural products. OleA has now been identified in hundreds of diverse bacteria via bioinformatics and high-throughput screening using p-nitrophenyl alkanoate esters as surrogate substrates. In the present study, p-nitrophenyl esters were used to probe the reaction mechanism of OleA and shown to be incorporated into Claisen condensation products for the first time. p-Nitrophenyl alkanoate substrates alone were shown not to undergo Claisen condensation, but co-incubation of p-nitrophenyl esters and CoA thioesters produced mixed Claisen products. Mixed product reactions were shown to initiate via acyl group transfer from a p-nitrophenyl carrier to the enzyme active site cysteine, C143. Acyl chains esterified to p-nitrophenol were synthesized and shown to undergo Claisen condensation with an acyl-CoA substrate, showing potential to greatly expand the range of possible Claisen products. Using p-nitrophenyl 1-13C-decanoate, the Channel A bound thioester chain was shown to act as the Claisen nucleophile, representing the first direct evidence for the directionality of the Claisen reaction in any OleA enzyme. These results both provide new insights into OleA catalysis and open a path for making unnatural hydrocarbon and β-lactone natural products for biotechnological applications using cheap and easily synthesized p-nitrophenyl esters.

Ionic liquids for regulating biocatalytic process: Achievements and perspectives

Biocatalysis has found enormous applications in sorts of fields as an alternative to chemical catalysis. In the pursue of green and sustainable chemistry, ionic liquids (ILs) have been considered as promising reaction media for biocatalysis, owing to their unique characteristics, such as nonvolatility, inflammability and tunable properties as regards polarity and water miscibility behavior, compared to organic solvents. In recent years, great developments have been achieved in respects to biocatalysis in ILs, especially for preparing various chemicals. This review tends to give illustrative examples with a focus on representative chemicals production by biocatalyst in ILs and elucidate the possible mechanism in such systems. It also discusses how to regulate the catalytic efficiency from several aspects and finally provides an outlook on the opportunities to broaden biocatalysis in ILs.

Machine learning-based prediction of activity and substrate specificity for OleA enzymes in the thiolase superfamily

Enzymes in the thiolase superfamily catalyze carbon–carbon bond formation for the biosynthesis of polyhydroxyalkanoate storage molecules, membrane lipids and bioactive secondary metabolites. Natural and engineered thiolases have applications in synthetic biology for the production of high-value compounds, including personal care products and therapeutics. A fundamental understanding of thiolase substrate specificity is lacking, particularly within the OleA protein family. The ability to predict substrates from sequence would advance (meta)genome mining efforts to identify active thiolases for the production of desired metabolites. To gain a deeper understanding of substrate scope within the OleA family, we measured the activity of 73 diverse bacterial thiolases with a library of 15 p-nitrophenyl ester substrates to build a training set of 1095 unique enzyme–substrate pairs. We then used machine learning to predict thiolase substrate specificity from physicochemical and structural features. The area under the receiver operating characteristic curve was 0.89 for random forest classification of enzyme activity, and our regression model had a test set root mean square error of 0.22 (R2 = 0.75) to quantitatively predict enzyme activity levels. Substrate aromaticity, oxygen content and molecular connectivity were the strongest predictors of enzyme–substrate pairing. Key amino acid residues A173, I284, V287, T292 and I316 in the Xanthomonas campestris OleA crystal structure lining the substrate binding pockets were important for thiolase substrate specificity and are attractive targets for future protein engineering studies. The predictive framework described here is generalizable and demonstrates how machine learning can be used to quantitatively understand and predict enzyme substrate specificity.