Responding to the need to teach remotely due to COVID-19, we used readily available computational approaches (and developed associated tutorials (https://mdh-cures-community.squarespace.com/virtual-cures-and-ures)) to teach virtual Course-Based Undergraduate Research Experience (CURE) laboratories that fulfil generally accepted main components of CUREs or Undergraduate Research Experiences (UREs): Scientific Background, Hypothesis Development, Proposal, Experiments, Teamwork, Data Analysis, Conclusions, and Presentation1. We then developed and taught remotely, in three phases, protein-centric CURE activities that are adaptable to virtually any protein, emphasizing contributions of noncovalent interactions to structure, binding and catalysis (an ASBMB learning framework2 foundational concept).
The courses had five learning goals (unchanged in the virtual format),focused on i) use of primary literature and bioinformatics, ii) the roles of non-covalent interactions, iii) keeping accurate laboratory notebooks, iv) hypothesis development and research proposal writing, and, v) presenting the project and drawing evidence based conclusions
The first phase, Developing a Research Proposal, contains three modules, and develops hallmarks of a good student-developed hypothesis using available literature (PubMed3) and preliminary observations obtained using bioinformatics, Module 1: Using Primary Literature and Data Bases (Protein Data Base4, Blast5 and Clustal Omega6), Module 2: Molecular Visualization (PyMol7 and Chimera8), culminating in a research proposal (Module 3). Provided rubrics guide student expectations. In the second phase, Preparing the Proteins, students prepared necessary proteins and mutants using Module 4: Creating and Validating Models, which leads users through creating mutants with PyMol, homology modeling with Phyre29 or Missense10, energy minimization using RefineD11 or ModRefiner12, and structure validation using MolProbity13.
In the third phase, Computational Experimental Approaches to Explore the Questions developed from the Hypothesis, students selected appropriate tools to perform their experiments, chosen from computational techniques suitable for a CURE laboratory class taught remotely. Questions, paired with computational approaches were selected from Modules 5: Exploring Titratable Groups in a Protein using H++14, 6: Exploring Small Molecule Ligand Binding (with SwissDock15), 7: Exploring Protein-Protein Interaction (with HawkDock16), 8: Detecting and Exploring Potential Binding Sites on a Protein (with POCASA17 and SwissDock), and 9: Structure-Activity Relationships of Ligand Binding & Drug Design (with SwissDock, Open Eye18 or the Molecular Operating Environment (MOE)19).
All involve freely available computational approaches on publicly accessible web-based servers around the world (with the exception of MOE). Original literature/Journal club activities on approaches helped students suggest tie-ins to wet lab experiments they could conduct in the future to complement their computational approaches.
This approach allowed us to continue using high impact CURE teaching, without changing our course learning goals. Quantitative data (including replicates) was collected and analyzed during regular class periods. Students developed evidence-based conclusions and related them to their research questions and hypotheses. Projects culminated in a presentation where faculty feedback was facilitated with the Virtual Presentation platform from QUBES20
These computational approaches are readily adaptable for topics accessible for first to senior year classes and individual research projects (UREs). We used them in both partial and full semester CUREs in various institutional settings. We believe this format can benefit faculty and students from a wide variety of teaching institutions under conditions where remote teaching is necessary.
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Integration of materials visualization with a materials database in a Materials Science and Engineering freshman course
Over the past years, our team has taken a concerted effort to integrate computational modules into courses across the undergraduate curriculum, in order to equip students with computational skills in a variety of contexts that span the field of Materials Science and Engineering. This effort has proven sustainable during the recent period of online transition of many courses, illustrating one of the benefits of computational modules. The most recent addition to our set of modules included a visualization component that was incorporated into our introductory freshman course for the first time in Fall 2019. Students can perform this module either using local computing labs, access those resources remotely, or can use their own computers. In the Fall of 2020, we modified this module and expanded it towards the utilization of a materials database to teach students how to search for materials with specific properties. The results were then interfaced with the previously existing visualization module to connect the structure and symmetry of materials with their properties and to compare them with experimental results. We implement a more detailed survey to learn to what extent students gained the capability of using databases for future research and education. We will also use these responses to further develop and improve our existing modules.
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- Award ID(s):
- 1846206
- NSF-PAR ID:
- 10317377
- Date Published:
- Journal Name:
- 2021 ASEE Virtual Annual Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Most undergraduates studying biochemistry and molecular biology get their broadest exposure to wet-lab techniques in protein and nucleic acid chemistry (and, increasingly, computer/visualization) in their upper-level laboratory courses. These tend to be juniors and seniors with well-defined career goals. Some of these students will have already have a research background in a traditional one-to-one (or one-to-few) research mentoring setting, for example a summer research program. This approach has proved effective at increasing student learning and persistence in the sciences. At the same time, extended full-time PI-directed research is limited in the number of students served, and can even present a barrier. To broaden the impact of teaching through research, many practitioners have adopted a CURE, or Course-based Undergraduate Research Experience, approach.This presentation reports on “BASIL” (Biochemical Authentic Scientific Inquiry Laboratory), a team of faculty who have worked to bring computational and wet-lab protein science to the biochemistry teaching lab. Together, we have developed a protein biochemistry CURE to determine enzymatic function of proteins of unknown activity. This work leverages the results of the Protein Structure Initiative, a fifteen-year NIH-funded effort which concluded in 2015 with the publication and distribution of more than 5000 previously uncharacterized proteins. The great majority of these are “orphans,” with high quality structures and pre-cloned expression plasmids available, but no research on their enzymatic function or role in native organisms. The BASIL consortium of undergraduate biochemistry faculty and students seeks to identify functional properties of a subset of these uncharacterized proteins, seeking to unify structure and function relationships. Currently, implementable modules are available for faculty who wish to adopt them, and expected student results will be presented.Support or Funding InformationSupported by NSF IUSE 1709278This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18260/1-2--38269
