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

准确预测各种化学污染物的环境生物转化速率和产物的能力不仅对化学品风险管理至关重要，而且对污染场地补救或开发绿色化学替代品也至关重要。然而，现有的预测方法不能满足这些需求，主要是因为它们没有经过与环境相关的生物转化数据的培训，而且因为模型缺乏适当的机械基础，从而无法以一致的方式预测生物转化途径和生物降解性。拟议项目的目标是开发下一代基于计算机的系统，用于准确预测不同环境相关条件下的微生物生物转化率和途径。 具体而言，项目小组将检验如何实现这一目标的两个假设。首先，建立和利用化学污染物和酶类的生物转化规则之间的联系，应允许通过挖掘酶的功能数据，完善现有的生物转化规则的底物特异性。同时，它还应通过纳入下一代测序的基因表达数据，为社区特定的生物转化预测提供技术和理论基础。其次，建立酶催化的生物转化反应的明确的机理基础的生物转化预测应提供新的预测引擎，合并到一个一致的建模框架的生物转化率和途径预测的发展，并允许考虑环境条件对生物转化率的影响，因此途径。为了实现上述研究假设，并实施环境特异性生物转化预测的改进规则和新算法，该项目将进一步开发两个必要的技术资源。首先，它将更新现有的、可化学获得的异生物质生物转化数据库和预测系统的数据库和系统架构（即，UM-BBD/PPS，http://www.umbbd.ethz.ch），以建立一个更加灵活和互动的系统，可以与其他现有的数据资源接口。其次，它将注释来自相关系统的可用生物转化途径和速率数据，即，农业土壤和活性污泥，使这一数据的可利用性，并作为基础的开发和验证的新的预测引擎的项目。拟议的项目依赖于环境化学，生物化学和化学信息学科学家与软件工程师之间的密切合作。它旨在为包括科学家、化学工业和监管当局在内的各种利益攸关方提供一个透明和方便用户的工具，支持对现代社会中的化学品风险进行基于科学和具有成本效益的评估。

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.