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Utilization of computational techniques and tools to introduce or reinforce knowledge of biochemistry and protein structure–function relationships

As computational biology and chemistry labs have the potential for training large number of researchers, our research lab has aimed to develop a streamlined, structured, and scalable approach to working with undergraduate research (UR) students. The scalable approaches and techniques presented utilize a module-based learning method and have been easily adapted to experimental labs since COVID-19 began, allowing for researchers of all levels to continue progression of their research projects. This structure and scale have continued to evolve, providing a solid foundation in the wake of COVID-19 and providing avenues to further refine and improve our pedagogical approach to UR in computational biochemistry. One pedagogical improvement was the route to engage in UR in our group and the formalization of learning objectives and deliverables. In order to scale numbers of students engaged and to ensure equal training and foundation for all students, online modules were created that train students in research and data literacy, introduce basic biochemistry topics, and utilize common computational techniques (molecular visualization, sequence alignments, homology modeling, molecular docking, and molecular dynamics simulations) in an applied, deliverable-based environment. Our research lab works with students from every academic level (first year to senior), from a multitude of educational backgrounds and majors, thus objectively all students would require the same base knowledge regardless of starting point. Modules were designed to be accessible to multiple disciplines (e.g., not lab specific) and focus on building technique, skill, and biochemical knowledge before students dive into individual research projects (Figure 1). Protein structure–function relationships are highlighted throughout the module content given the research lab focus, and further emphasize this important tenant of biochemistry and molecular biology education in a hands-on learning environment. As observed during COVID-19, the structure and module content can be easily readapted and repurposed for other undergraduate-level biochemistry courses and for other research labs seeking ways to keep their students engaged and complete the Spring 2020 semester. Computational tools can be used remotely, are often open source, and if framed with quality learning objectives, can be applied in many educational environments. While our original intent in using Canvas-based modules to deploy UR training was primarily initiated to manage a large number of students ( 20/semester, 5 new students/semester), we quickly observed the benefits for student learning, for instructor/PI documentation, and for adaptability over time. This was specifically observed with two of our modules—Molecular Visualization for Exploring Protein Structures and Molecular Docking for Exploring Protein-Small Molecule Interactions. Our modules served multiple purposes, in that they trained students to do high-quality, individual research projects in our lab, while also reinforcing and accelerating the students' biochemical knowledge retention. We explore protein structure–function relationships using PyMOL and UCSF Chimera to visualize structural features of a protein, emphasizing the importance of amino acid characteristics, and molecular docking to evaluate spatial Received: 6 July 2020 Revised: 17 August 2020 Accepted: 9 September 2020