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: 1609501
• 负责人: Ruben Abagyan
• 金额: $ 18万
• 依托单位: University of California-San Diego
• 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 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1609501&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，水资源受到复杂的化学污染物混合物的威胁，其中许多污染物无法通过传统的水和废水处理技术去除。这些水污染物包括强效药物类别，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，但其生物活性可以在环境中持续存在。在该项目中，由美国国家科学基金会化学部环境化学科学项目资助，爱荷华大学、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石溪大学的研究人员合作团队开发了一个预测框架，以帮助改进化学品风险评估。最终，该项目的成果可能会产生更安全和可持续的供水，特别是当社会越来越依赖经过处理的废水的再利用来缩小日益扩大的供需差距时。这项工作的更广泛影响包括通过让代表性不足的群体参与研究活动来推进本科教育，将现代计算工具融入学生学习，以及通过通识教育课程开发提高非技术受众的科学素养。这项研究试图改善水质。该项目重点关注广泛使用的非生物处理工艺、氯化以及普遍存在但尚未充分研究的污染物类别、强效合成孕激素和糖皮质激素，开发了一个基于计算和实验方法的高通量框架，用于先验预测高风险、生物活性转化产物。该方法整合了理论计算，使用母体（部分电荷、氧化电位）和可能的产物（热力学稳定性）物种的描述符来识别可能的氯化产物。使用高通量虚拟配体筛选根据生物活性（即风险）对潜在产品种类进行优先级排序。 一旦确定，高风险产物的形成和产量将在一系列氯化条件下的小规模实验中进行评估。 高分辨率质谱检测用于检查废水和受纳水体。研究成果可用于预测新出现的污染物，并提供更全面的方法来解决其生物活性产品带来的风险。该合作项目为两名研究生、两名博士后和几名本科生提供环境化学、计算化学和生物化学领域的跨学科培训。