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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: 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
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-10-01 至 2021-09-30
关键词:

项目摘要

项目成果

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中文摘要
翻译
将本科生的研究经验融入实验室课程,极大地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(D2D-CURE)网络将培训和支持教师,以扩大研究参与和扩大高影响力的教育实践。该网络旨在使广泛的高等教育机构(社区学院、州立学校、文科学校以及研究型大学)能够访问这一工作流程,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,参与这个项目的学生将共同产生足够大的数据集,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,并培养进行研究的信心。这个网络既服务于培养未来的科学家,也服务于推动科学的进步。基于数据到设计课程的本科生研究经验(D2D-CURE)网络的主要目标是使全国各地的机构都可以访问经过充分开发和测试的分子建模和酶表征工作流,作为现有和新生物课程的可集成课程元素。该项目有三项主要活动:第一是协调讲习班,使来自全国各地的讲师能够在其本国机构使用D2D-CURE讲习班。第二个是扩大一个年度会议,目前专注于蛋白质建模和设计,包括一个学生发展和参与的轨道。第三是发展在线存在,允许D2D-CURE参与者作为一个社区共同致力于尖端科学问题,而不是孤立的机构和项目。尽管计算蛋白质设计被广泛采用,但仍需要重大改进,以将努力从用于丰富具有所需功能的蛋白质的工具转变为具有设计与所需功能之间的定量关系的更准确和更具预测性的计算机辅助设计工具。我们计划将重点放在酶上,因为目前存在的唯一大型数据集不是定量的,动态范围很低,并且经常将几个独立的物理测量卷积成一个单一值。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,让学生和他们的教师使用与工业相关的技术,同时生成迫切需要的蛋白质结构-功能数据集。该项目由生物科学局、生物基础设施部和教育与人力资源局联合资助。本科教育司作为他们努力应对本科生物学教育的愿景与变革:行动呼吁(http://visionandchange/finalreport/).This奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估)提出的挑战的一部分。
英文摘要
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.
期刊论文(6)
专著(0)
科研奖励(0)
会议论文
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
影响因子: --
作者: [Luong, J.A., Vater, A., Siegel, J.B.]
通讯作者: Siegel, J.B.
Collaborative Research: Enabling Scalable Redox Reactions in Biomanufacturing
  • 批准号:
    2328146
  • 项目类别:
    Standard Grant
  • 资助金额:
    $36.09万
  • 财政年份:
    2023
  • 负责人:
    Justin Siegel
  • 依托单位:
Leveraging Machine Learning to Explore the Effects of the Design2Data Course-based Undergraduate Research Experience
  • 批准号:
    2315767
  • 项目类别:
    Standard Grant
  • 资助金额:
    $39.75万
  • 财政年份:
    2023
  • 负责人:
    Justin Siegel
  • 依托单位:
RCN-UBE: Design to Data Network: expanding a faculty community of practice to broaden and diversify participation in undergraduate research
  • 批准号:
    2118138
  • 项目类别:
    Standard Grant
  • 资助金额:
    $22.13万
  • 财政年份:
    2021
  • 负责人:
    Justin Siegel
  • 依托单位:
Collaborative Research: Understanding and exploiting the structure-function link between fatty acid biosynthesis and degradation enzymes for functionalized small molecule synthesis
  • 批准号:
    1805510
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.14万
  • 财政年份:
    2018
  • 负责人:
    Justin Siegel
  • 依托单位:
国内基金
海外基金
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  • 项目类别:
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    2026
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FBXO42 经 Ube2m-Rbx1 轴促进 STAT3 介导的巨噬细胞线粒体自噬抗动脉粥样硬化的机制研究
  • 批准号:
    ZCLQN26H0201
  • 项目类别:
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  • 资助金额:
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    2026
  • 负责人:
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ROP18靶向滋养层细胞的UBE2N经代谢/表观重编程调控CD73表达致异常妊娠的分子免疫机制
  • 批准号:
    82672957
  • 项目类别:
    面上项目
  • 资助金额:
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  • 批准年份:
    2026
  • 负责人:
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