Abstract

Developing student creativity and ability to develop a testable hypothesis represents a significant challenge in most laboratory courses. This lesson demonstrates how students use facets of molecular evolution and bioinformatics approaches involving protein sequence alignments (Clustal Omega, Uniprot) and 3D structure visualization (Pymol, JMol, Chimera), along with an analysis of pertinent background literature, to construct a novel hypothesis and develop a research proposal to explore their hypothesis. We have used this approach in a variety of institutional contexts (community college, research intensive university and primarily undergraduate institutions, PUIs ) as the first component in a protein-centric course-embedded undergraduate research experience (CURE) sequence. Built around the enzyme malate dehydrogenase, the sequence illustrates a variety of foundational concepts from the learning framework for Biochemistry and Molecular Biology. The lesson has three specific learning goals: i) find, use and present relevant primary literature, protein sequences, structures, and analyses resulting from the use of bioinformatics tools, ii) understand the various roles that non-covalent interactions may play in the structure and function of an enzyme. and iii) create/develop a testable and falsifiable hypothesis and propose appropriate experiments to interrogate the hypothesis. For each learning goal, we have developed specific assessment rubrics. Depending on the needs of the course, this approach builds to an in-class student presentation and/or a written research proposal. The module can be extended over several lecture and lab periods. Furthermore, the module lends itself to additional assessments including oral presentation, research proposal writing and the validated pre-post Experimental Design Ability Test (EDAT). Although presented in the context of course-based research on malate dehydrogenase, the approach and materials presented are readily adaptable to any protein of interest.

Primary image: Mind map of the hypothesis development.

Citation

Callahan KP, Mans T, Zhang J, Bell E, Bell J. 2021. Using bioinformatics and molecular visualization to develop student hypotheses in a malate dehydrogenase oriented CURE. CourseSource. https://doi.org/10.24918/cs.2021.43

Society Learning Goals

Biochemistry and Molecular Biology

• Energy is required and transformed in biological systems
• How do enzymes catalyze biological reactions?
• Macromolecular Structure Determines Function and Regulation
• What factors determine structure?
• How are structure and function related?
• What is the role of noncovalent intermolecular interactions?
• Discovery requires objective measurement, quantitative analysis and clear communication
• What is the scientific process?
• What skills are needed to access, comprehend and communicate science?

Bioinformatics

• Computation in the life sciences
• What is the role of computation in hypothesis-driven discovery processes within the life sciences?
• Protein - Information in Action [PROTEOMICS]
• Where are data about the proteome found (e.g., amino acid sequence and structure) and how are they stored and accessed?
• How can bioinformatics tools be employed to examine protein structure and function?

Lesson Learning Goals

When students have completed this lesson, they should be able to: i) Find, use and present relevant primary literature, protein sequences and structures, and bioinformatics tools. ii) Understand the various roles that non-covalent interactions may play in the structure and function of an enzyme. iii) Create/develop a testable and falsifiable hypothesis and propose appropriate experiments to interrogate the hypothesis. Our three major learning goals encompass several of the society learning goals (italicized) of both the Bioinformatics and Biochemistry and Molecular Biology frameworks, and associated learning objectives (indented) as detailed below: Bioinformatics: What is the role of computation in hypothesis driven discovery processes within the life sciences?

Explain the necessity for computation in life sciences research. Explain the role of wet-bench techniques in verifying computational results in life science research. Compare and contrast computer-based research with wet-lab research. Read a scientific article and evaluate how bioinformatics methods were employed by the authors to explore a particular hypothesis. Given a scientific question, develop a hypothesis and define computational approaches that could be used to explore the hypothesis. Where are data about the proteome found (e.g., amino acid sequence and structure) and how are they stored and accessed?

Describe the types of metadata that accompany sequence, structure, and function data to make for useful biological interpretation (e.g., biological source, accession number, UniProt number, journal articles, etc.). How can bioinformatics tools be employed to examine protein structure and function?

Query a dataset with a specific protein sequence to learn about potential functions (e.g., Pfam, CDD, SwissProt, UniProt, etc.). View and interpret the structure output from Protein Data Bank (e.g., Cn3D, Jmol, etc.). Propose potential functions for a given protein structure. Biochemistry and Molecular Biology: How do enzymes catalyze biological reactions?

Identify the factors contributing to the activation energy of a reaction. Use kinetic parameters to compare enzymes. Interpret the physical meaning of various kinetic parameters and describe the underlaying assumptions and conditions on which different parameters depend. What factors determine structure?

Compare and contrast the primary, secondary, tertiary and quaternary structure of proteins. Use bioinformatics and computational approaches to compare primary sequences and identify the impact of conservation and/or evolutionary change on structure and function. How are structure and function related? What is the role of noncovalent intermolecular interactions?

Predict the biological and chemical effects of mutation on the affinity of binding and design appropriate experiments to test the predictions. What is the Scientific Process?

When presented with an observation, develop a testable and falsifiable hypothesis, identify appropriate experimental observations and controllable variables. What skills are needed to access, comprehend and communicate science?

Identify, locate and use the primary literature. Use databases and bioinformatics tools. Use visual and verbal tools to explain concepts and data.

Lesson Learning Objectives

For each of our learning goals, we have specific learning objectives with associated rubrics for assessment (Supporting File S1. Using Bioinformatics – Specific Learning Objectives With Associated Rubrics). As we adjusted the lesson for different levels of students and different amounts of total time spent, instructors worked to meet the three general learning goals in ways that fit their courses. The specific objectives listed below are a comprehensive list, from which instructors could select the ones which seem appropriate to the course they teach. Learning Goal 1: Understand the various roles that non-covalent interactions may play in the structure and function of an enzyme. Students will be able to:

1. Compare and contrast the physical basis for coulombic (ionic) interactions and hydrophobic interactions.
2. Outline the types of non-covalent interactions you would expect to stabilize secondary structure in a protein.
3. Outline the types of non-covalent interactions you would expect to be involved in maintaining a functional tertiary structure in a protein.
4. Describe types of non-covalent interactions that might be found across a subunit interface and the functions of the interactions.
5. Predict types of non-covalent interactions that would be involved in substrate-enzyme binding and compare their relative strengths.
6. Describe how non-covalent interactions in a protein-substrate complex might promote catalysis.
7. Predict what types of mutations at nearby residues might alter the pKa of a protonatable group on a protein.

Learning Goal 2: Find, use and present relevant primary literature, protein sequences and structures, and analyses obtained using bioinformatics tools. Students will be able to:

1. Find and use appropriate literature to illustrate the generalizable (big picture) aspects of the work.
2. Use appropriate literature to document specific background to the enzyme.
3. Use appropriate databases such as Uniprot to obtain sequence information.
4. Utilize Clustal Omega and interpret the resultant data.
5. Use the Protein Data Bank to obtain 3D coordinates for a protein.
6. Use Pymol or other visualization tools to illustrate key features of the protein and their hypothesis.
7. Use appropriate bioinformatics tools to design primers for mutagenesis.

Learning Goal 3: Create/develop and present a testable and falsifiable hypothesis and propose appropriate experiments to interrogate the hypothesis. Students will be able to:

1. Describe how the work fits into the field/fills a gap in knowledge.
2. Clearly state their hypothesis and the requisite background information that led to the hypothesis.
3. Clearly indicate the testable and falsifiable predictions the hypothesis makes.
4. Briefly outline the types of experiments that will be used to interrogate the hypothesis.

Introduction

The “Vision & Change in Undergraduate Biology Education: A Call for Action” initiative and its following report (1), building on prior publications over the past 15-20 years (2-5) recommends 1) integration of core concepts and skills throughout the curriculum, 2) a focus on student-centered learning environments, and 3) validated high impact practices, such as research experiences, as integral components of biology education for all students. Research experiences have major effects on persistence in science (6-9) and positive outcomes in conceptual understanding and skills development, essential for effective workforce development (10-16). The Council on Undergraduate Research (CUR) defines undergraduate research as inquiry or investigation conducted by undergraduates that makes original intellectual or creative contributions to the discipline (16). Such work is a high impact practice that provides robust learning for students, increases retention, enhances student learning though mentorship by faculty and develops a deeper critical thinking ability, as well as intellectual independence. Realization that authentic research was important for student development was evident in the Boyer Report (17), where smaller schools that provide research mentoring disproportionately produce more graduate school students than research intensive institutions where resources and time allocated to research training are not as focused on undergraduates.

Studies in course-based research (18-24) show that, independent of the type of institution, authentic research experience enhances knowledge gains. It is also important to recognize that while reform to include course-embedded undergraduate research experiences (CUREs) in many laboratory courses has been impressive, there remain several important unanswered questions. Assessment has primarily focused on motivation and retention of students (all very positive) but assessment tools linked to student learning outcomes are limited. Further, Brownell and Kloser warn, "despite published articles on CUREs, the impact of these CUREs are still in question" (25). There have been a number of publications discussing the key elements of a CURE as developed by Lopatto, Dolan, Kloser and others (26-30). The NSF-funded CURE network (CUREnet) described the key elements of a CURE (19,31). These elements include 1) the use of scientific practices, 2) discovery, 3) broadly relevant or important work, 4) collaboration, and 5) iteration. Lopatto and others describe seven components of authentic research (11,12): novel questions, student-generated questions, development of a hypothesis, experimental design, data collection, data analysis and presentation or publication of the research. Key parameters for such an experience include minimized role of instructor, an unknown scientific outcome, a project of student design where students are responsible for the design and do most of the work.

The Malate Dehydrogenase CUREs Community (MCC) consists of a diverse community of STEM disciplinary faculty members from institutions that vary across a number of dimensions including type (two-year versus four-year), enrollment size, selectivity, and student population (low-income, first generation). Students enrolled in the CURE use facets of bioinformatics, 3D structure visualization and pertinent background literature to construct a novel hypothesis about the role of a specific amino acid in the activity of malate dehydrogenase. Additional information and resources can be found at the Malate Dehydrogenase CUREs Community (MCC).

In the current lesson we have built on previously published work using a CURE based on malate dehydrogenase (32-34) and incorporated a number of foundational concepts and skills (35-37) that are taken from the ASBMB approved Biochemistry and Molecular Biology Learning Framework (38).

In many CURE type activities, the emphasis is on data collection and analysis. Here we place the emphasis on hypothesis development and proposal construction, areas that are frequently difficult to incorporate into a CURE or coursework in a meaningful way (39-42). We have also placed a focus on collaboration and teamwork since both contribute to the high impact nature of the lesson (43-45). Many CUREs use molecular biology techniques and answer other types of questions; here we place emphasis on protein structure-function relationships, and in particular the role that non-covalent interactions (a gateway concept in biochemistry) (46) play in enzyme function.

We have chosen to focus on malate dehydrogenase (MDH) because the genetic, species, and organismal diversity of MDH make it an ideal protein to explore foundational concepts of protein structure and function. The interaction between MDH and its substrate allow students to explore hydrogen bonding, steric effects, charge-charge interactions, and the hydrophobic effect. However, the approach, materials, and rubrics that we have developed are broadly applicable to almost any protein of interest and can be easily tailored to a faculty person’s given research interests.

Intended Audience

This lesson is intended for a broad audience and has been used with a variety of students ranging from incoming first year students in a primarily undergraduate institution (PUI), first and second year community college students, and junior/senior biochemistry majors in both PUI and research intensive university settings. It has also been used with third and fourth year non-science majors.

Required Learning Time

The lesson has been implemented in a variety of formats using up to a total of eight teaching sessions (for example four lecture periods plus four laboratory sessions). Students have multiple homework assignments associated with the lesson and are expected to spend about 1-2 hours out of class working on the homework assignments and preparing for class; written proposals and presentations will typically require more than 2 hours to prepare.

Prerequisite Student Knowledge

The lesson requires a basic knowledge of the central dogma, an introductory knowledge of the levels of protein structure (primary, secondary, tertiary & quaternary) and function (catalytic mechanism and enzyme kinetics) and an understanding of various types of non-covalent interactions.

Prerequisite Teacher Knowledge

Instructors should have a basic understanding of protein structure and function, basic enzymology, and bioinformatics, as well as fundamental knowledge of evolution and molecular biology.

Scientific Teaching Themes

Active Learning

The various components of the module were developed with student-centered learning as their focus. Student work is framed by a discussion of “biological literacy” as defined by the Vision and Change Final Report (1), focusing on how science is done in a real-world context and how the practice of science has changed. Students are provided with background reading and assignments for out of class work, allowing them to connect the concepts and learning objectives of the module with previous knowledge and skills from other courses. Many lesson periods include think-pair-share and other student-centered activities. Students are encouraged to discuss their ideas with one another as well as with the faculty involved. Students work in groups to learn background information and new skills, and these groups become research teams as they develop their hypotheses.

Assessment

Many formative assessments were used in this lesson, although these varied as appropriate for the different course levels (from introductory to capstone) and with the amount of time that the students were expected to spend on the lesson. For example, introductory students used an instructional worksheet with explanations of the structure of a scientific paper along with prompts to find specific information from assigned papers. Senior capstone students were asked to search the literature and make a mind map of the information they gleaned from the papers they found. Likewise, though all students used rendering software to examine protein structure, introductory and non-major students were given a very specific set of questions and prompts to answer as they learned how to use the program, while senior students were given more general guidelines of what type of pictures they might wish to generate, to fit the broader range of scientific questions their hypothesis might address. See the Supporting Files for details of five different deployments of the lesson (Supporting Files S2. Using Bioinformatics – PUI capstone cCURE materials, S3. Using Bioinformatics – PUI junior/senior cCURE materials, S4. Using Bioinformatics – R1 junior/senior mCURE materials, S5. Using Bioinformatics – CC intro level mCURE materials, and S6. Using Bioinformatics – CC intro level cCURE materials).

In all variants of the lesson, a summative assessment was given as a culminating assignment, in which students wrote and/or orally presented a hypothesis statement and prediction. In a modular setting (in which a shorter time of the semester was used for the CURE), students were guided to write a paragraph with their hypothesis and explanatory information or assigned to deliver a short oral presentation of their hypothesis and reasoning. In complete CURE (full semester) settings, students wrote formal scientific proposals. Opportunities for peer review, instructor feedback, and revision were given so that students could improve their work.

Inclusive Teaching

We have been pleased to see this lesson successfully incorporated in a variety of institution types and at all levels, including courses for non-majors. We see this flexibility as part of its strength, as instructors can see many different depths and lengths that the lesson can reach. The resources used in the various parts of the lesson are all freely available through the web and students learn to access and assess these materials while creating their own knowledge and understanding, thereby promoting ownership of the hypotheses they develop, all important aspects of inclusive teaching (47). Papers selected for class discussion are chosen to highlight a diversity of ideas and author backgrounds, demonstrating how diversity in its many forms contributes to better problem solving (48). To further illustrate and model a scientific community, the authors of the discussion paper frequently attend (virtually) the hypothesis presentations of the other authors’ students, leading to a robust discussion that enhances the students’ sense of ownership in their project. In several iterations of the project, students have collaborated across institutions, so that they may brainstorm with each other, and mentor and learn from students at other levels of study. In some cases, project ideas were “handed off:” hypotheses developed by one group of students were tested by other groups, allowing for shared resources and time, as well as ideas.

Lesson Plan

We have implemented this lesson in a variety of settings, ranging from a PUI upper-level capstone course to a community college lab associated with an introductory course (course descriptions and syllabi are listed in part A of each of the following: Supporting Files S2. Using Bioinformatics – PUI capstone cCURE materials, S3. Using Bioinformatics – PUI junior/senior cCURE materials, S4. Using Bioinformatics – R1 junior/senior mCURE materials, S5. Using Bioinformatics – CC intro level mCURE materials, and S6. Using Bioinformatics – CC intro level cCURE materials.). Our Malate Dehydrogenase CURE Community has differentiated courses in which the complete semester is spent in CURE-related activity as cCUREs (c for complete) and those in which CURE-related activity takes a shorter part of the semester as mCUREs (m for modular). We have used between two and eight class meetings for the lesson. Table 1 concisely lays out the varied settings and time allowances of five different deployments of the lesson. Below, we explain our general approach, with references to the collections of notes, assignments, and rubrics from our varied course formats all shared in the supporting materials.

Table 1. Course schedule and structure of the course across all courses and institution types. Please note cCUREs (c for complete) denotes courses in which the complete semester is spent in CURE-related activity; mCURE (m for modular) denotes courses in which CURE-related activity accounts for only a portion of the semester.

Foundational Knowledge and Introduction to the Project

The overall organization of the lesson is illustrated in Figure 1 and summarized in Table 2. We introduce or review the foundational knowledge and project plan with slideshow- or whiteboard-supplemented lectures (part B in each of the following Supporting Files: S2. Using Bioinformatics – PUI capstone cCURE materials, S3. Using Bioinformatics – PUI junior/senior cCURE materials, S4. Using Bioinformatics – R1 junior/senior mCURE materials, S5. Using Bioinformatics – CC intro level mCURE materials, and S6. Using Bioinformatics – CC intro level cCURE materials; some of these refer to materials in Supporting File S7. Using Bioinformatics – Hypothesis Development Module.). The detail and depth of the presentations vary, depending on the level and prior knowledge of students. Foundational knowledge for our project includes protein structure and noncovalent interactions, function and action of enzymes, and how the evidence of evolutionary conservation and change may be reflected in biomolecules. These concepts are illustrated when possible with specifics of malate dehydrogenase structure and function, so that students are prepared for future investigation of this particular enzyme. Teams of 2-4 students are formed. The project plan is laid out, including the basic schedule and expectations, the philosophy behind using a CURE, plans for outside collaboration which will occur during the project, and the rationale and process for various pedagogical tools, such as think-pair-share and mind-mapping.

Table 2. Lesson Plan Timeline across all courses and institution types.

Malate Dehydrogenase Background in Survey of Primary Literature

We may begin with a review quiz or mini lecture to remind students of key points from the foundational knowledge portion of the lesson. As we are preparing students to make unique and authentic contributions to a field that is unfamiliar to them, we teach them that scientists become familiar with a new area of research by reviewing the existing literature in the field. We introduce primary literature, define it, and discuss scientific peer review and publishing norms. We demonstrate how to find scientific literature in general and papers about malate dehydrogenase in particular, and may provide sample papers for the students highlighting approaches or structures of focus during the project. We outline the structure of scientific papers and present approaches for dissecting and analyzing them, through discussion and/or a guided worksheet focusing on a pre-chosen paper. Students are assigned to find and read one to ten papers, and summarize in a form appropriate for their capability. Assignments are in part C in each of the following Supporting Files: S2. Using Bioinformatics – PUI capstone cCURE materials, S3. Using Bioinformatics – PUI junior/senior cCURE materials, S4. Using Bioinformatics – R1 junior/senior mCURE materials, S5. Using Bioinformatics – CC intro level mCURE materials, and S6. Using Bioinformatics – CC intro level cCURE materials.

Bioinformatics, Structural Visualization, and Alignments

We introduce students to databases and bioinformatics tools which can be used to collect information, explore, and develop hypotheses about malate dehydrogenase. These include RCSB Protein Data Bank and UniProt, and molecular rendering and visualization tools such as PyMol, Jmol, and Chimera. We show how multiple sequence alignments (Clustal Omega) can be generated and used to inform our hypotheses about structure and function of the protein. Students are guided via worksheets, pre-recorded videos available on the internet, and instructor coaching when needed. Students complete one or more assignments to summarize data and structures that can inform their hypotheses. The varied programs used in our differing settings are illustrated in Table 3. Assignments are in part D in each of the following Supporting Files: S2. Using Bioinformatics – PUI capstone cCURE materials, S3. Using Bioinformatics – PUI junior/senior cCURE materials, S4. Using Bioinformatics – R1 junior/senior mCURE materials, S5. Using Bioinformatics – CC intro level mCURE materials, and S6. Using Bioinformatics – CC intro level cCURE materials.

Table 3. Comparison of bioinformatic and molecular visualization components across all courses and institution types.

Hypothesis Development, Predictions and Experimental Design

In the closing phase of the lesson, the students are guided to develop a hypothesis. Student teams are prompted to synthesize the information they have gathered about malate dehydrogenase from the literature, from the databases, molecular visualizations, and sequence alignments. We remind them that hypotheses make testable predictions; to facilitate the connection to experimentation, at this point or earlier we discuss or practice biochemical assays and consider the sort of data gathered from these. Teams discuss their hypothesis and predictions informally with their partner(s) and instructor, and in some cases, present these more formally before the other teams, guest faculty, or students from collaborating classes. Through the discussions and questions that come out during presentations, the teams refine their hypotheses. After chances for reflection and revision, students are asked to write out their hypothesis with supporting evidence, in appropriate formats such a formal proposal in a capstone course, or a simple paragraph for introductory students participating in an abbreviated version of the project. The varied approaches we used for hypothesis development in different settings are laid out in Table 4. Assignments are in part E in each of the following Supporting Files: S2. Using Bioinformatics – PUI capstone cCURE materials, S3. Using Bioinformatics – PUI junior/senior cCURE materials, S4. Using Bioinformatics – R1 junior/senior mCURE materials, S5. Using Bioinformatics – CC intro level mCURE materials, and S6. Using Bioinformatics – CC intro level cCURE materials.

Table 4. Comparison of hypothesis development, prediction and experimental design components across all courses and institution types.

Teaching Discussion

The lesson plans presented here were developed as part of the Malate Dehydrogenase CURE Community (MCC). MCC CUREs were instituted with two goals: 1) to provide high impact teaching practices for students, and 2) to create new knowledge on structure function relationships in malate dehydrogenase that can be submitted for publication in peer-reviewed journals.

While all MCC CUREs involve students in each stage of the scientific process (Scientific Background, Hypothesis Development, Proposal, Experiments/Teamwork to test hypothesis, Data Analysis and Conclusions, and Presentation), one of the distinguishing features of MCC CUREs is the emphasis on student-generated hypotheses. As outlined in Figure 1 students use foundational knowledge, primary literature, bioinformatics and structural analysis to develop their hypothesis. Using this information, all student-generated hypotheses should lead to a proposal containing the following elements:

• Big Picture- why is this important to science, to society
• Specific Hypothesis
• Predictions arising from the hypothesis
• Proposed experiments to test the predictions, specifically what types of information do you need to falsify/support the hypothesis

Finally, as outlined in Figure 1, this process should culminate in some type of presentation of the hypothesis and proposed research.

As MCC resources grow, we have available a number of mutants that have validated sequences, and in many cases pre-existing data that has been validated by subsequent experiments after the culmination of the course. These mutants and their validated data can be made available for use in MCC CURE Classes.

We propose that this information be available to students as they develop their own hypotheses in an MCC CURE Class. For example, they will be able to see what mutants have been made previously and any validated data on those mutants. The database of this information does NOT contain any information as to what hypotheses were developed with respect to the mutation, nor are any suggestions as to what hypotheses were being tested given to students.

There are several ways this information can be used in a class:

1. Students could be given a selection of these mutants with the validated background information and asked to develop a hypothesis as to what function they think the wildtype residue played in the protein, and what they think the mutation will do to the structure-function relationships of the enzyme and devise and conduct experiments to explore their hypothesis.
2. Students could be given a “big picture” question. For example, what governs folding of malate dehydrogenase? As part of the accessible background information about MDH, the list of validated mutants and associated effects would be provided to the students. They would use this information in much the same way as they would use published information and would develop their own hypothesis. At the instructor’s discretion, they could be restricted to using one of the pre-existing mutations.

The goal of this module is to guide students to develop a testable hypothesis about malate dehydrogenase. Two major challenges were encountered by the students taking this course: 1) they only have superficial understanding of malate dehydrogenase and 2) are not familiar with bioinformatic and structural visualization software. To overcome the first challenge, as simple as it may sound, it is necessary to talk about the various aspects of malate dehydrogenase (structure, reaction mechanism, substrate binding, etc.) every single class. It is important that the students feel as though they are becoming experts on MDH. The visualization of the structure of MDH and the specific roles amino acid side chains play in its functionality in conjunction with the evolutionary conservation of these amino acids is critical to the student’s ability to formulate their hypothesis. To faculty with limited experience with structural visualization tools, we would direct you to the instruction videos in section D in each of the following Supporting Files: S2. Using Bioinformatics – PUI capstone cCURE materials, S3. Using Bioinformatics – PUI junior/senior cCURE materials, S4. Using Bioinformatics – R1 junior/senior mCURE materials, S5. Using Bioinformatics – CC intro level mCURE materials, and S6. Using Bioinformatics – CC intro level cCURE materials.

Finally, one aspect of the lesson that we have all found extremely effective is the impact of sitting in on one another’s student presentations of their hypothesis. We have used Skype or Zoom to virtually sit in on presentations, and students are energized by having someone other than their own instructor involved. This aspect has in some cases led to student collaborations between institutions. It seems clear that student ownership of their project is enhanced by these interactions. Through the Malate Dehydrogenase CUREs Community we are in the process of assessing this aspect in detail as part of our current IUSE grant (49).

SUPPORTING MATERIALS

• Supporting File S1. Using Bioinformatics – Specific Learning Objectives with Associated Rubrics
• Supporting File S2. Using Bioinformatics – PUI capstone cCURE materials 
• Supporting File S3. Using Bioinformatics – PUI junior/senior cCURE materials 
• Supporting File S4. Using Bioinformatics – R1 junior/senior mCURE materials
• Supporting File S5. Using Bioinformatics – CC intro level mCURE materials
• Supporting File S6. Using Bioinformatics – CC intro level cCURE materials
• Supporting File S7. Using Bioinformatics – Hypothesis Development Module

Acknowledgments

The materials presented here have in large part been developed as part of an NSF funded IUSE grant: NSF-1726932 EHR-IUSE “MDH CUREs Community, a Protein Centered Approach to CUREs,” $598,666, 9/1/2017 to 8/20/2020, Principal Investigator Ellis Bell, Co Investigators Jessica Bell and Joseph Provost.

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