RCN-UBE: Data-to-Design Course-based Undergraduate Research Experience ? protein modeling and characterization to enhance student learning and improve computational protein design

RCN-UBE：基于数据到设计课程的本科研究经验？

• 批准号: 1827246
• 负责人: Justin Siegel
• 金额: $ 29.38万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1827246&HistoricalAwards=false
• 关键词: RCN; UBE; Data; Design; Course

Incorporating undergraduate research experiences into laboratory courses vastly increases the reach and accessibility of these opportunities. The Data-to-Design Course-based Undergraduate Research Experience (D2D-CURE) network will train and support faculty to broaden research participation and expand high-impact educational practices. The network is designed to make this workflow accessible to a wide array of higher education institutions (community colleges, state schools, liberal arts schools, as well as research universities), as a high-impact module for existing and new biochemistry and bioengineering courses. Collectively and over time students who engage in this project will be generating datasets large enough to begin utilizing machine learning tools to improve protein design algorithms, while learning translatable skills, the process of science, and developing confidence in doing research. This network serves to both train future scientist and to advance the progress of science.The Data-to-Design Course-based Undergraduate Research Experience (D2D-CURE) network's primary goal is to make a well-developed and tested molecular modeling and enzyme characterization workflow accessible to institutions nation-wide as an integratable curricular element for existing and new biology courses. The project has three major activities: The first is the coordination of workshops that will enable instructors from across the country to employ the D2D-CURE workshop at their home institutions. The second is the expansion of an annual conference, currently focused on protein modeling and design, to include a student development and engagement track. Third is the development of an online presence allowing the D2D-CURE participants to work in concert on cutting-edge scientific problems as a community as opposed to isolated institutions and programs. Despite widespread adoption of computational protein design, significant improvements are still needed to move efforts from being a tool used to enrich proteins with the desired function into a more accurate and predictive computer-aided design tool with a quantitative relationship between design and desired function. We plan to focus on enzymes since the only large data sets that currently exist are not quantitative, have a low dynamic range, and often convolve several independent physical measurements into a single value. To generate the data sets needed to develop a new generation of computational protein design software for enzymes, we are proposing a Research Coordination Network to engage students and their instructors in using industrially relevant techniques while generating acutely needed protein structure-function datasets.This project is being jointly funded by the Directorate for Biological Sciences, Division of Biological Infrastructure, and the Directorate for Education and Human Resources, Division of Undergraduate Education as part of their efforts to address the challenges posed in Vision and Change in Undergraduate Biology Education: A Call to Action (http://visionandchange/finalreport/).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

将本科生的研究经验纳入实验室课程，大大增加了这些机会的覆盖面和可及性。基于数据到设计课程的本科生研究体验（D2 D-CURE）网络将培训和支持教师扩大研究参与和扩大高影响力的教育实践。该网络的目的是使这一工作流程可用于各种高等教育机构（社区学院，州立学校，文科学校以及研究型大学），作为现有和新的生物化学和生物工程课程的高影响力模块。随着时间的推移，参与该项目的学生将共同生成足够大的数据集，以便开始利用机器学习工具来改进蛋白质设计算法，同时学习可翻译的技能，科学过程，并培养做研究的信心。该网络既能培养未来的科学家，又能推动科学的进步。基于数据到设计课程的本科生研究体验（D2 D-CURE）网络的主要目标是为全国的机构提供一个完善的、经过测试的分子建模和酶表征工作流程，作为现有和新的生物学课程的一个可整合的课程元素。该项目有三项主要活动：第一是协调讲习班，使来自全国各地的教员能够在其本国机构使用D2 D-CURE讲习班。第二个是扩大年度会议，目前专注于蛋白质建模和设计，包括学生发展和参与轨道。第三是发展在线存在，使D2 D-CURE参与者能够作为一个社区，而不是孤立的机构和项目，共同致力于尖端科学问题。尽管计算蛋白质设计被广泛采用，但仍需要进行重大改进，以将努力从用于富集具有所需功能的蛋白质的工具转变为更准确和预测性的计算机辅助设计工具，其在设计和所需功能之间具有定量关系。我们计划专注于酶，因为目前存在的唯一大型数据集不是定量的，动态范围较低，并且通常将几个独立的物理测量值卷积为一个值。为了生成开发新一代酶的计算蛋白质设计软件所需的数据集，我们提议建立一个研究协调网络，让学生和教师使用工业相关技术，同时生成急需的蛋白质结构-功能数据集。该项目由生物科学理事会、生物基础设施司、和教育和人力资源理事会，本科教育部，作为他们努力解决的一部分，在本科生物学教育的愿景和变化所带来的挑战：行动呼吁（http：//visionandchange/finalreport/）。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Development of a Broadly Accessible, Computationally Guided Biochemistry Course-Based Undergraduate Research Experience

开发广泛可获取的、计算指导的生物化学课程本科研究经验

• DOI: 10.1021/acs.jchemed.0c01073
• 发表时间: 2021
• 期刊: Journal of Chemical Education
• 影响因子: 3
• 作者: Vater, Ashley;Mayoral, Jaime;Nunez-Castilla, Janelle;Labonte, Jason W.;Briggs, Laura A.;Gray, Jeffrey J.;Makarevitch, Irina;Rumjahn, Sharif M.;Siegel, Justin B.
• 通讯作者: Siegel, Justin B.

Design to Data for mutants of β-glucosidase B from Paenibacillus polymyxa: I45K, A357S, I20A, I20V, and I20E

多粘类芽孢杆菌β-葡萄糖苷酶 B 突变体的数据设计：I45K、A357S、I20A、I20V 和 I20E

• DOI: 10.1101/2020.10.07.330233
• 发表时间: 2020
• 期刊: bioRxiv
• 作者: Luong, J.A.;Vater, A.;Siegel, J.B.
• 通讯作者: Siegel, J.B.