Next-generation environmental biotransformation pathway prediction system - NGE-PPS

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The ability to predict rates and products of environmental biotransformation for a broad variety of chemical contaminants accurately is essential not only for chemical risk management but also in the context of contaminated site remediation or the development of green chemical alternatives. Existing prediction methods, however, fall short of fulfilling these needs, mostly because they were not trained on environmentally relevant biotransformation data and because the models lack a proper mechanistic underpinning, preventing the prediction of biotransformation pathways and biodegradability in a consistent manner. The goal of the proposed project is to develop a next-generation, computer-based system for the accurate prediction of microbial biotransformation rates and pathways under different, environmentally relevant conditions. Specifically, the project team will test two hypotheses on how this can be achieved. First, establishing and exploiting links between biotransformation rules for chemical contaminants and enzyme classes should allow refining the substrate specificity of existing biotransformation rules through mining of enzyme functional data. At the same time, it should also provide the technical and theoretical basis for community-specific biotransformation prediction through inclusion of gene expression data from next-generation sequencing. Second, establishing enzyme-catalyzed biotransformation reactions as the explicit mechanistic basis of biotransformation prediction should afford the development of novel prediction engines that merge biotransformation rate and pathway prediction into a consistent modeling framework, and that allow considering the influence of environmental conditions on biotransformation rates, and hence pathways. To work on the above research hypotheses and implement the refined rules and novel algorithms for environment-specific biotransformation prediction, the project will further develop two essential technical resources. First, it will renew the database and system architecture of an existing, publically available xenobiotics biotransformation database and prediction system (i.e., UM-BBD/PPS, http://www.umbbd.ethz.ch) towards a more flexible and interactive system that can be interfaced with other existing data resources. And, second, it will annotate available biotransformation pathway and rate data from relevant systems, i.e., agricultural soils and activated sludge, make this data publically available and serve as a basis for development and validation of novel prediction engines within the project. The proposed project relies on the intense collaboration between scientists from environmental chemistry, biochemistry, and chemoinformatics and software engineers. It seeks to provide diverse stakeholders, including scientists, chemical industry and regulatory authorities, with a transparent and user-friendly tool that supports a science-based and cost-effective assessment of chemical risk in modern society.