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.

如今，水资源受到化学污染物的复杂混合物的威胁，其中许多污染物被传统的水和废水处理技术所去除。这些水污染物包括有效的药物类别，包括合成类固醇，尽管它们通过自然和人造过程转化为其他化合物，但其生物活性仍可以持续在环境中。在这个项目中，由国家科学基金会化学部的环境化学科学计划资助，爱荷华大学，华盛顿大学塔科马大学和西雅图，加利福尼亚大学圣地亚哥大学的研究人员合作团队，斯托尼·布鲁克大学开发了一个预测性的框架，以帮助改善化学风险评估。最终，该项目的结果可能会产生更安全，更可持续的供水，尤其是随着社会越来越依赖经过处理的废水以弥合供应和需求差距的扩大。这项工作的更广泛的影响包括通过使代表性不足的群体参与研究活动，将现代计算工具纳入学生学习，并通过通识教育课程的发展促进非技术观众的科学素养，从而推进本科教育。这项研究试图提高水质。该项目专注于广泛使用的非生物治疗过程，氯化和无处不在但研究研究的污染物类别，有效的合成孕激素和糖皮质激素，该项目开发了一种基于对高风险预测的高风险预测的计算和实验方法建立的高通量框架，生物活性转化产物。这种方法集成了理论计算，以使用描述符为父（部分电荷，氧化潜力）和可能的产物（热力学稳定性）物种鉴定可能的氯化产物。使用高吞吐量虚拟配体筛选根据生物活性（即风险）优先考虑潜在的产品物种。 一旦确定，高风险产物的形成和产量将在跨氯化条件的基准规模实验中评估。 高分辨率质谱检测用于检查废水和接收水。研究结果可用于预测新兴的污染物，并提供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名研究生，两个博士后和几个本科生的跨学科培训，该培训在环境化学，计算化学和生物化学的界面上提供了跨学科培训。