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.
