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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Open-source workflow design and management software to interrogate duckweed growth conditions and stress responses
Duckweeds, a family of floating aquatic plants, are ideal model plants for laboratory experiments because they are small, easy to cultivate, and reproduce quickly. Duckweed cultivation, for the purposes of scientific research, requires that lineages are maintained as continuous populations of asexually propagating fronds, so research teams need to develop optimized cultivation conditions and coordinate maintenance tasks for duckweed stocks. Additionally, computational image analysis is proving to be a powerful duckweed research tool, but researchers lack software tools to assist with data collection and storage in a way that can feed into scripted data analysis. We set out to support these processes using a laboratory management software called Aquarium, an open-source application developed to manage laboratory inventory and plan experiments. We developed a suite of duckweed cultivation and experimentation operation types in Aquarium, which we then integrated with novel data analysis scripts. We then demonstrated the efficacy of our system with a series of image-based growth assays, and explored how our framework could be used to develop optimized cultivation protocols. We discuss the unexpected advantages and the limitations of this approach, suggesting areas for future software tool development. In its current state, our approach helps to bridge the gap between laboratory implementation and data analytical software for duckweed biologists and builds a foundation for future development of end-to-end computational tools in plant science.
more » « less- NSF-PAR ID:
- 10452406
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Plant Methods
- Volume:
- 19
- Issue:
- 1
- ISSN:
- 1746-4811
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Contact richard.wilton@jhu.edu
Supplementary information Appendices referenced in this article are available at Bioinformatics online.
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