Collaborative Research: Integrated In Silico and Non-Target Analytical Framework for High Throughput Prioritization of Bioactive Transformation Products

合作研究：集成计算机和非目标分析框架，用于生物活性转化产品的高通量优先排序

• 批准号: 1608464
• 负责人: Edward Kolodziej
• 金额: $ 15.6万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1608464&HistoricalAwards=false
• 关键词: Collaborative; Research; Integrated; Silico; Non

Today, water resources are threatened by a complex mixture of chemical pollutants, many of which are poorly removed by traditional water and wastewater treatment technologies. These water pollutants include potent pharmaceutical classes including synthetic steroids, whose bioactivity can persist in the environment despite their transformation to other compounds through natural and man-made processes. In this project, funded by the Environmental Chemical Sciences Program of the Chemistry Division at the National Science Foundation, a collaborative team of researchers at the University of Iowa, University of Washington at Tacoma and Seattle, University of California at San Diego, and Stony Brook University develops a predictive framework to help improve chemical risk assessment. Ultimately, outcomes of this project may produce more safe and sustainable water supplies, particularly as society becomes more reliant on reuse of treated wastewater to bridge the widening gap in supply and demand. The broader impacts of this work include advancing undergraduate education by enabling the participation of under-represented groups in research activities, integrating modern computational tools into student learning, and promoting scientific literacy in non-technical audiences through general education coursework development. This research attempts to improve water quality. Focusing on a widely utilized abiotic treatment process, chlorination, and ubiquitous but understudied pollutant classes, potent synthetic progestins and glucocorticoids, this project develops a high-throughput framework built upon computational and experimental methods for the a priori prediction of high risk, bioactive transformation products. This approach integrates theoretical calculations to identify probable chlorination products using descriptors for both parent (partial charges, oxidation potentials) and likely product (thermodynamic stability) species. Potential product species are prioritized based on bioactivity (i.e., risk) using high throughput virtual ligand screening. Once identified, the formation and yield of high risk products are evaluated in bench-scale experiments across a range of chlorination conditions. High resolution mass spectrometric detection is used to examine wastewaters and receiving waters. Research outcomes may be used to predict emerging pollutants and provide a more holistic approach to addressing the risks posed by their bioactive products. This collaborative project provides transdisciplinary training of two graduate students, two postdocs, and several undergraduates at the interface of environmental chemistry, computational chemistry, and biochemistry.

今天，水资源受到化学污染物复杂混合物的威胁，其中许多污染物无法通过传统的水和废水处理技术去除。这些水污染物包括合成类固醇等强效药物类，其生物活性可以在环境中持续存在，尽管它们通过自然和人为过程转化为其他化合物。在这个由国家科学基金会化学部环境化学科学计划资助的项目中，来自爱荷华州大学、华盛顿大学塔科马分校和西雅图分校、加州大学圣地亚哥分校和斯托尼布鲁克大学的研究人员合作小组开发了一个预测框架，以帮助改进化学风险评估。最终，这一项目的成果可能会产生更安全和可持续的供水，特别是随着社会越来越依赖经处理的废水的再利用，以弥补供需之间日益扩大的差距。这项工作的更广泛影响包括通过使代表性不足的群体能够参与研究活动来推进本科教育，将现代计算工具纳入学生学习，并通过普通教育课程开发促进非技术受众的科学素养。这项研究旨在改善水质。专注于广泛使用的非生物处理过程，氯化，无处不在，但未充分研究的污染物类，有效的合成孕激素和糖皮质激素，该项目开发了一个高通量的框架，建立在计算和实验方法的先验预测的高风险，生物活性转化产品。这种方法集成了理论计算，以确定可能的氯化产物使用的描述符为两个母（部分电荷，氧化电位）和可能的产品（热力学稳定性）物种。基于生物活性（即，风险）。 一旦确定，高风险产品的形成和产量将在一系列氯化条件下的实验室规模实验中进行评估。 高分辨率质谱检测用于检查废水沃茨和接收沃茨。研究成果可用于预测新出现的污染物，并提供一种更全面的方法来解决其生物活性产品带来的风险。这个合作项目提供了两个研究生，两个博士后，和几个本科生在环境化学，计算化学和生物化学的接口跨学科的培训。