OleB from Bacterial Hydrocarbon Biosynthesis Is a ß-Lactone Decarboxylase That Shares Key Features with Haloalkane Dehalogenases.

OleB is an α/ß-hydrolase found in bacteria that biosynthesize long-chain olefinic hydrocarbons, but its function has remained obscure. We report that OleB from the Gram-negative bacterium Xanthomonas campestris performs an unprecedented ß-lactone decarboxylation reaction, to complete cis-olefin biosynthesis. OleB reactions monitored by 1H nuclear magnetic resonance spectroscopy revealed a selectivity for decarboxylating cis-ß-lactones and no discernible activity with trans-ß-lactones, consistent with the known configuration of pathway intermediates. Protein sequence analyses showed OleB proteins were most related to haloalkane dehalogenases (HLDs) and retained the canonical Asp-His-Asp catalytic triad of HLDs. Unexpectedly, it was determined that an understudied subfamily, denoted as HLD-III, is comprised mostly of OleB proteins encoded within oleABCD gene clusters, suggesting a misannotation. OleB from X. campestris showed very low dehalogenase activity only against haloalkane substrates with long alkyl chains. A haloalkane substrate mimic alkylated wild-type X. campestris OleB but not OleBD114A, implicating this residue as the active site nucleophile as in HLDs. A sequence-divergent OleB, found as part of a natural OleBC fusion and classified as an HLD-III, from the Gram-positive bacterium Micrococcus luteus was demonstrated to have the same activity, stereochemical preference, and dependence on the proposed Asp nucleophile. H218O studies with M. luteus OleBC suggested that the canonical alkyl-enzyme intermediate of HLDs is hydrolyzed differently by OleB enzymes, as 18O is not incorporated into the nucleophilic aspartic acid. This work defines a previously unrecognized reaction in nature, functionally identifies some HLD-III enzymes as ß-lactone decarboxylases, and posits an enzymatic mechanism of ß-lactone decarboxylation.

Predicting biodegradation products and pathways: a hybrid knowledge- and machine learning-based approach.

MOTIVATION: Current methods for the prediction of biodegradation products and pathways of organic environmental pollutants either do not take into account domain knowledge or do not provide probability estimates. In this article, we propose a hybrid knowledge- and machine learning-based approach to overcome these limitations in the context of the University of Minnesota Pathway Prediction System (UM-PPS). The proposed solution performs relative reasoning in a machine learning framework, and obtains one probability estimate for each biotransformation rule of the system. As the application of a rule then depends on a threshold for the probability estimate, the trade-off between recall (sensitivity) and precision (selectivity) can be addressed and leveraged in practice. RESULTS: Results from leave-one-out cross-validation show that a recall and precision of approximately 0.8 can be achieved for a subset of 13 transformation rules. Therefore, it is possible to optimize precision without compromising recall. We are currently integrating the results into an experimental version of the UM-PPS server. AVAILABILITY: The program is freely available on the web at http://wwwkramer.in.tum.de/research/applications/biodegradation/data. CONTACT: kramer@in.tum.de.

Data-driven extraction of relative reasoning rules to limit combinatorial explosion in biodegradation pathway prediction.

MOTIVATION: The University of Minnesota Pathway Prediction System (UM-PPS) is a rule-based expert system to predict plausible biodegradation pathways for organic compounds. However, iterative application of these rules to generate biodegradation pathways leads to combinatorial explosion. We use data from known biotransformation pathways to rationally determine biotransformation priorities (relative reasoning rules) to limit this explosion. RESULTS: A total of 112 relative reasoning rules were identified and implemented. In one prediction step, i.e. as per one generation predicted, the use of relative reasoning decreases the predicted biotransformations by over 25% for 50 compounds used to generate the rules and by about 15% for an external validation set of 47 xenobiotics, including pesticides, biocides and pharmaceuticals. The percentage of correctly predicted, experimentally known products remains at 75% when relative reasoning is used. The set of relative reasoning rules identified, therefore, effectively reduces the number of predicted transformation products without compromising the quality of the predictions. AVAILABILITY: The UM-PPS server is freely available on the web to all users at the time of submission of this manuscript and will be available following publication at http://umbbd.msi.umn.edu/predict/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.