Integrating Research into the Curriculum: A Low-Cost Strategy for

Chapter 8

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Integrating Research into the Curriculum: A Low-Cost Strategy for Promoting Undergraduate Research Sanchita Hati and Sudeep Bhattacharyya* Department of Chemistry, University of Wisconsin-Eau Claire, 101 Roosevelt Avenue, Eau Claire, Wisconsin 54702, United States *E-mail: (S.B.): [email protected]. E-mail: (S.H.) [email protected].

Undergraduate research provides students with hands-on training where they can apply classroom learning to solve original research problems and develop new skills. It not only enhances the problem solving and analytical skills of the students, but also promotes collaboration and teamwork among them. Additionally, it fosters an open learning environment that encourages students to respect diversity and inclusivity. Considering the invaluable benefits of engaging undergraduates in collaborative research, we have integrated authentic discovery-guided classroom projects in our chemistry curriculum. A project-based biophysical chemistry laboratory course, which is offered to the biochemistry and molecular biology majors in their senior year, is described in this chapter. For this biophysical chemistry course, the theoretical study of the relationship between structure, dynamics, and function of proteins is integrated with the discovery-based labs utilizing computer modeling and simulations. Modern computational tools are introduced and computer-based laboratory protocols including novel research projects are developed to help the students gain an in-depth understanding of the role of proteins’ dynamics in their function. The students analyze their own findings in the term papers, aiming to go beyond the standard article summary or literature review. Finally, results of these research projects are communicated in peer-reviewed journals.

© 2018 American Chemical Society Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.


Introduction One of the main goals of a liberal college education is to expand one’s intellectual horizons by understanding the diverse and complex nature of the physical world. Research opportunities in colleges offer evidence-based learning − an invaluable tool to achieve that goal. In particular, involvement in collaborative research provides students with excellent opportunities to step into the real-world situations: they get to use the classroom learning to explore active areas of research. It is evident that engagement in collaborative research generates several important educational outcomes (1). For example, it has been observed that the retention and degree completion rates are higher for the students who are engaged in collaborative research (2). Such involvement also impacts their career plans, as reflected in their improved preparedness for taking on challenges, competitiveness for future employment, and readiness for the pursuit of advanced degrees. In addition to higher grade point average (GPA) and graduate record examination (GRE) scores, undergraduate research experience is now considered a prerequisite for admission to many graduate and professional schools. Students in STEM (Science, Technology, Engineering and Math) fields often obtain research experiences through summer internships in a research lab. However, there are two main limitations of the collaborative undergraduate research conducted in traditional research lab setting (Scheme 1): i) only a few selected students get the opportunity to conduct collaborative research with a faculty mentor, as it is difficult to scale up due to limited recourses, such as limited research lab space or funding for research supplies, and ii) there is an increase in workload for faculty mentors conducting research with the undergraduate students. Most universities do not have enabling policies such as course release, reduced service obligations, or teaching credit for faculty who are involved in mentoring undergraduate students. On the other hand, incorporation of research into the curriculum provides research opportunities to many more students, including students from under-represented backgrounds or those students who do not thrive in traditional coursework. It is important to note that these groups of students are relatively hesitant to actively seek research opportunities by networking with their professors. Consequently, as has been observed on many occasions, they remain unaware of their own potential. In this scenario, faculty-driven undergraduate research opportunities could be provided to a larger group of students by expanding undergraduate research from the traditional lab setting to the classroom setting (Scheme 1). This also helps the research mentors as they can avoid overloading their work schedule. They can still pursue their research interests and provide high-impact learning opportunities to a significantly larger number of students at the same time. In order to provide research experiences to many students at a time, we decided to incorporate research into our chemistry curriculum. We started with our biophysical chemistry course (CHEM 406, 4 credits), a capstone course taken by biochemistry/molecular biology majors. It consists of one two-hour lab period per week with the class size of no more than 24 students. Earlier, the lab was only focused on protein structure and function, where the students used some common software like Rasmol (3) and Pymol (4) for molecular visualization, high-quality 120 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.


rendering, and graphic generation. Also, software like PoseView (5) was used to examine the active-site pocket of enzymes and identify the catalytically important residues. In order to introduce authentic research problems, we have redesigned the lab to focus on proteins’ structure-dynamics-function relationship as our own research is focused on protein dynamics. Several new but free online software and computational packages (vide infra) are introduced to provide students with an in-depth understanding of role of protein dynamics in their function - the missing link between structure and function.

Scheme 1. Expanding Undergraduate Research beyond Traditional Lab Setting

Learning Goals and Objectives The overarching goal is to provide the students with conceptual understanding of the course materials with the help of discovery-guided and inquiry-based research projects (Table 1). Through these projects, they are expected to develop a thorough understanding of structure-dynamics-function relationship in proteins. They also learn how to use software/computational programs for protein structure visualization, protein modeling and structure assessment, and for studying protein dynamics. In addition, students get opportunities to develop intellectual and practical skills (Table 1). The new lab experiments are designed in such a manner that the students could study structure, dynamics and functions of known protein systems through guided questions, and subsequently follow-up the investigation with an original research problem. This allowed us to provide students with hands-on and experiential learning that can’t easily be done through textbooks or in traditional lecture classes, as it requires an authentic setting where the students can work on a real scientific problem and not just solve preset exercises to obtain the known results. 121 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.

Table 1. Learning Goals and Objectives


Goal

Objectives

Learning biophysical chemistry through solving authentic research problem

■ Gain the conceptual understanding of the course materials ■ Visualize and classify protein structure ■ Understand the role of non-covalent interactions in protein folding ■ Study protein dynamics and their role in functions ■ Learn the use of software/computational programs for exploring protein structure and dynamics

Understanding structure-dynamicsfunction relationship in protein

■ Develop homology models and assess structural qualities of 3D models ■ Understand the types of non-covalent interactions that govern protein functions ■ Characterize backbone flexibilities and studying their impact on substrate selectivity and catalysis

Developing intellectual and practical skills

■ Develop critical and creative thinking ■ Improve written and oral communication ■ Foster inquiry and analysis-based learning ■ Develop teamwork and problem solving ■ Respect and value diversity

Plan of Activities for Fifteen Weeks Semester The 15-week lab is divided into four modules (Table 2). In the first three modules (modules I to III), the students are introduced to modern computational tools through newly designed computer-based laboratory protocols. These protocols allow them to visualize the secondary, super-secondary, and tertiary structures of proteins, analyze non-covalent interactions in protein-ligand complexes, and develop three-dimensional (3D) structural models (homology model) for new protein sequences. Additionally, the students learn to evaluate the structural qualities of 3D structures, and study the proteins’ intrinsic dynamics to understand their role in substrate binding and catalysis. In the fourth module, the students are assigned with an authentic research problem, where they apply their classroom learning and laboratory skills (acquired through modules I − III) to answer conceptual biophysical questions. All computations are carried out using dual-core personal computers using Intel® Core™ i5 processors. Preparation of Manuscript As mentioned earlier, a total of three term papers are assigned to the students, including the one for the final project in a 15-week-long semester. The students write the term papers following the format of the Journal of Biophysical Chemistry. They are encouraged to have the following sections in their term papers: abstract, introduction, methods, results (tables, figures and figure legends), discussion, conclusions, and bibliography. The instructor/faculty mentor provides written feedback to all the students to improve their writing 122 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.

and analytical skills. After successful completion of the semester, the student who scores the highest grade in the end-of-semester project (Term Paper III) is provided with an opportunity to write the manuscript. This student can enroll in an Independent Study course in the following semester, with the responsibility to compile the manuscript using the results of all other students. During the writing process, the student works closely with the instructor, discussing ideas and clarifying queries. All students who had participated in the research project are included as coauthors of the manuscript.


Table 2. The Plan for Fifteen Weeks Semester Module

Week

Activities

I

1-4

Visualization of three-dimensional structure of proteins using Visual Molecular Dynamics (VMD) package; TERM PAPER I

II

5-7

Homology modeling and structural assessment using Swiss-Model webserver; TERM PAPER II

III

8 - 10

Analysis of protein motions using Normal Mode Analysis (WEBNM@)

IV

11 - 14

Research project and oral presentation; TERM PAPER III

15

Term paper III due

Module I. Structure Analysis The main objective of this module is to visualize the three-dimensional structure of proteins and understand the relationship between the protein structure and its function. Visual Molecular Dynamics (VMD) package is used in this course, which is a powerful molecular visualization program for displaying, animating, and analyzing large biomolecular systems using 3D graphics and built-in scripting (6). Moreover, VMD is available at no cost for use in academic settings. The first day of the lab is dedicated to get familiar with the VMD program using the VMD tutorial. The VMD tutorial helps students learn graphical representations to display the tertiary structures as well as secondary structural elements and side chains of proteins. It also enables students to portray the bound substrate in the active site, and to analyze non-covalent interactions between ligand and the protein. In the following weeks, students are instructed to use the VMD program to perform in-depth study of special structural features of protein. For example, zinc-binding protein is used to study protein-DNA interactions (Figure 1), hemoglobin to examine the interaction between porphyrin ring and helix bundles, and the existence of hydrophobic pore and water channel in aquaporin to visualize how the side-chains are oriented to allow the flow of nonpolar/polar molecules through the channel. In addition, they use VMD to 123 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.


analyze geometric features (e.g. phi/psi angles, hydrogen bonding, salt-bridges, and distance between two functional sites of a protein) and local and global fluctuations using simulated trajectory conformations (e.g. conformational changes, movement of loops and domains, etc.). Students use different "Drawing Methods" for graphical representation of the secondary, super-secondary and tertiary structures of protein and the bound small molecules. For example, in the case of zinc finger protein (PDB code: 1A1F), students display the protein and double stranded DNA and analyze the structural differences between these two biomolecules. Specifically, they visualize the location of side chains in protein alpha helix versus DNA double helix. They also look at the surface and charge compatibilities between these two biomolecules to analyze the non-covalent forces that favor DNA-protein interactions (Figure 1). Similarly, in the case of hemoglobin, students visualize the four subunits of hemoglobin, assessing the orientation of helix bundles and the site of each porphyrin ring. They also analyze residues surrounding the porphyrin ring by creating a small subset by selecting only those residues, which are within 5 - 10 Å of porphyrin ring. Through this exercise, students get familiarity with the 3D structure of hemoglobin and attributes of amino acids surrounding the porphyrin ring. In module I, students also analyze additional protein systems to have better understanding of the interplay of protein structure and function. This also includes conformational changes upon substrate binding [example: GTP- and GDP-bound Ras protein (PDB code: 1PLL and 121P, respectively); Ras protein acts as a molecular switch to control cellular process and undergoes a conformational change when bound GTP is hydrolyzed to GDP] and structural classification of proteins [visualizing different families of proteins (Helical, Sheet, and Mixed Helical/Sheet Proteins)]. Each year, the students are also challenged with newly reported protein structures.

Figure 1. DNA-Protein interactions in zinc-finger protein (PDB code: 1A1F). Reproduced with permission from reference (14). Copyright © 2016 John Wiley & Sons, Inc. (see color insert) At the end of module I, a new protein is assigned to each student for their TERM PAPER I, where the student describes the structure-function relationship through proper display of the assigned protein’s structure and identification of 124 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.

structural features that governs its function. Taken together, the first module is meant for the students to develop familiarity with the VMD program and have an in-depth understanding of proteins’ structure and how they regulate their functions.


Module II. Homology Modeling As knowledge of a protein structure is key to understand its function, the focus of the first module is on protein structure visualization to engender deep insight into the relationship between protein structure and function. However, for many proteins the experimental structures have remained unknown. Therefore, the second module is designed to teach the students how to develop 3D models of proteins for which the experimental structures are unavailable. In this module, students use homology modeling to develop 3D structural model for a new protein sequence using template protein(s), which is/are homologous protein(s) with known 3D structure(s). The homology modeling lab starts with an introductory lecture on the importance of homology modeling, the criteria for safe homology modeling, and the steps involved in generating the 3D model structures. In this module, students are introduced to many online servers: Clustal Omega (7) and Basic Local Alignment Search Tool (BLAST) (8) for sequence alignment of proteins, Swiss-Model (9) for generating 3D model of new protein sequence, DALI (10) for proteins structure alignment, PROCHECK and ANOLEA for structure assessment (bond angel, bond distance, dihedral angles) and bad contacts, respectively (9). As the first step of homology modeling is to identify the template protein(s), the students first conduct BLAST search (8) to identify a suitable template protein(s) for the assigned target protein. PSI-BLAST (Position-Specific Iterative Basic Local Alignment Search Tool) is used to search for template proteins with known experimental structure by restricting the search only to the PDB database. Pair-wise sequence alignments between target and template proteins are conducted to obtain the sequence identity and sequence similarity. Potentially suitable templates are chosen based on BLAST score (< 0.001) and reasonable sequence identity (> 30%) (11, 12). To understand why 30 % sequence identity is reasonable for a homologous protein to consider as a good template, students are assigned a task, in which they compare structure and sequence identity for different pairs of proteins. Generally, the overall folding of protein pairs that exhibit variable degrees of sequence identity is compared. Through this exercise students learn about proteins’ structural modularity and robustness. They get the idea that a high degree of structural homology could exists between proteins (of length 200 amino acids or greater) even when the sequence identity is low (30 %) (11, 13). This exercise helps students to understand the criteria of safe homology and that 3D structural models could be generated for a target protein even if the sequence identity between template and target proteins is not significantly high. The homology modeling is performed using the fully-automated homology modeling server known as SWISS-MODEL (14, 15). The input files for homology modeling are the sequence alignment file of target and template proteins in FASTA format, which can be generated using the Clustal Omega (7), and the PDB codes 125 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.


for the template proteins. The internal routines like ANOLEA and PROCHECK of the SWISS-MODEL server are used for structural assessment of the 3D models (14). ANOLEA (Atomic Non-Local Environment Assessment) performs energy calculations to evaluate the "Non-Local Environment" of each heavy atom in the protein molecule. The input for this analysis is a PDB file containing one or more protein chains. The output is an energy profile, which gives an energy value for each amino acid of the protein; High Energy Zones (HEZs) in the profile represents errors in the protein structure. PROCHECK evaluates the stereochemical quality of a protein structure and identifies disfavored phi/psi angles, unusual bond lengths and bond angles, steric clashes, very large RMSD among templates, etc. Once they understand the criterion of template selection, students perform the homology modeling to generate the 3D model structure of a protein of unknown structure. For example, the crystal structure of Escherichia coli (Ec) prolyl-tRNA synthetase (ProRS) is not available yet, so the homology model of this protein was developed in one year. Ec ProRS catalyzes the covalent attachment of proline to the 3´-end of the tRNAPro, an essential reaction in protein biosynthesis. Ec ProRS is a multi-domain enzyme. Therefore, the students got the opportunity to study not only the secondary and super-secondary structural elements but they also visualized the different domains and their folding. The structures of other bacterial ProRSs are known. As a first step, the amino acid sequence of the Ec ProRS was gathered from NCBI (National Center for Biotechnology Information) and then PSI-BLAST was performed to identify template protein(s). The PSI-BLAST search was restricted to PDB database while searching the template proteins. Students then developed the 3D model structure of the Ec ProRS (target protein) using SWISS-MODEL. Through this process, they found Enterococcus faecalis (Ef) ProRS as a potential target with which Ec ProRS bear significantly high sequence identity (65%). The 3D model structure of Ec ProRS obtained from SWISS-MODEL was found to possess identical folding with very similar secondary and supersecondary structural elements compared to the template protein (Figure 2).

Figure 2. The template, Ef ProRS (left) and the target protein, Ec ProRS (right). Adapted with permission from reference (14). Copyright © 2016 John Wiley & Sons, Inc. (see color insert) 126 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.


The quality of the model structure of Ec ProRS was assessed through ANOLEA and PROCHECK. The ANOLEA plots identified only a few high energy regions for the 3D model structure of Ec ProRS suggesting existence of local unfavorable interactions, which were absent in the template protein, Ef ProRS (13). Analysis of Ramachandran plots revealed ~86 % of residues fell into the most-favored regions, ~10% in the additionally allowed regions and 0.6% fell into generously allowed regions (13). Only a few residues in the model structures fell into the restricted regions. Students also noted that few residues possessed distorted main-chain bond lengths and angles. These small deviations could be fixed through further energy minimization. Overall, the 3D model structures were satisfactory, and students concluded that Ef ProRS was a good template for generating the homology model structure of Ec ProRS. At the end of the second module, students are assigned the second term paper on homology modeling (Term Paper II). For this term paper, each student is assigned a protein sequence with unknown structure. They apply the knowledge gained in this module to develop the 3D model structure of the assigned protein, assess their stereochemical qualities, and finally write the term paper.

Module III. Normal Mode Analysis As a sequel to the 3D structural analysis and homology modeling covered in modules I and II, respectively, module III is introduced to provide an understanding of protein dynamics and their significance in protein functions. Proteins are intrinsically dynamic in nature and undergo transition between different conformational substates (16). Protein motions, on various timescales, are believed to play important roles in substrate recognition and catalysis. Protein dynamics help enzymes to achieve enormous rate enhancement (17–21); the intrinsic flexibility of a protein is either responsible for presenting an active site conformation conducive to catalysis or directly influencing the bond breaking and bond forming processes during catalysis. To provide in-depth insight into the relationship between protein dynamics and function, all-atom and coarse-grained simulations are widely used. As all-atom molecular dynamics simulations are computationally more expensive, we decided to use coarse-grained normal mode analysis for our biophysical labs. Normal mode analysis characterizes all possible vibrations that a protein can undergo around its equilibrium conformational state. The low-frequency vibrations typically correspond to collective motions, while the higher frequency modes represent local deformations (22, 23). The coarse-grained NMA is a powerful tool to identify biologically relevant conformational dynamics from protein structure with no limit in time-scale or system size. Prior to the first NMA lab, students receive an introductory lecture on the theory and applications of NMA. A user-friendly web-based tool, WEBnm@, is used to study protein dynamics (24). The WEBnm@ provides three main pieces of information regarding backbone Cα atoms dynamics: i) the backbone flexibility; ii) the direction and magnitude of protein displacement, and iii) the correlated and anticorrelated motion between residue pairs and various protein segments. 127 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.


Students use WEBnm@ server to analyze the low-frequency modes (vibrational motions) of an allosteric enzyme, adenylate kinase (PDB code: 4AKE). For this lab, the default settings of WEBnm@ are used to perform the following analyses – "Atomic Displacement Analysis" to identify the flexible region of the protein, "Correlation Matrix Analysis" to identify the protein segments engaged in correlated/anticorrelated motions, and finally, "Mode Visualization" and "Vector Field Analysis" to obtain the direction of collective motions of the protein. The normalized squared atomic fluctuation for each Cα atom in a protein is obtained from the "Atomic Displacement Analysis". Peaks in an atomic fluctuation profile correspond to flexible regions of proteins. The correlated or anticorrelated motions between residue pairs of distant structural elements are determined from the dynamic cross-correlation matrix (DCCM). In a DCCM, the correlation coefficient values range from −1 to +1; a negative correlation value indicates anticorrelated motion, whereas a positive value identifies correlated patterns of dynamics between two Cα atoms. As the low-frequency motions are biologically more relevant, directions of protein motions in the first 3 lowest-frequency modes are obtained from the NMA study and displayed and analyzed using VMD following the instruction provided in the WEBnm@ site. Once students are familiar with the single NMA to analyze the low-frequency vibrational modes, which requires only the PDB file of the protein, they perform comparative NMA. Comparative NMA is useful for comparing the dynamics of two or more different proteins or the same protein from different species to understand the conservation of intrinsic dynamics across species. The comparative analysis is performed using an aligned FASTA sequence file and corresponding PDB coordinate files of proteins to be compared. For comparative analyses of the dynamic features, the Mustang program (25), which employs the structure-based sequence alignment algorithm to generate multiple sequence alignments of proteins is used. From the comparative analysis, normalized squared atomic fluctuation profiles and the dynamic similarity (measured as root mean squared inner product (RMSIP) and Bhattacharyya coefficient (BC) (26) are obtained. RMSIP measures the similarity of atomic fluctuations between proteins by comparing their normal modes, whereas BC compares the covariance matrices obtained from the normal modes (27–29). Values of BC and RMSIP vary between 0 to 1 and represent the amount of overlap between the collective dynamics of two or more proteins; a value of 1 represents maximum overlap. As mentioned earlier, a simple protein system, Ec adenylate kinase, is used first for NMA. This enzyme catalyzes the Mg-dependent nucleotide phosphoryl exchange reaction ATP + AMP ⇋ 2ADP. It comprises of three domains (Figure 3) − the LID domain, residues 118–160; the NMP (nucleoside monophosphate) domain, residues 30–67; and the central CORE domain, residues 1–29, 68–117, and 161–214. Adenylate kinase undergoes a large conformational change upon substrate binding which favors its catalytic function. The LID and the NMP domains are intrinsically very dynamic in nature and undergo an "open" to "closed" conformational change upon substrate binding (Figure 3A); in the "closed" conformation, substrates are situated in a catalytically favorable environment. For this exercise, students first visualize the crystal structure of the enzyme to identify the secondary and super-secondary structural elements 128 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.


present in this protein. Students then study the intrinsic dynamics of this protein by performing NMA with the open conformation of this protein (4AKE.pdb). They first analyze the normalized atomic fluctuation profile (Figure 3B); peaks in an atomic fluctuation profile correspond to the relatively more flexible regions of a protein. Figure 3 revealed two distinct flexible regions; residues 125-155 of the LID domain are the most flexible part of the protein. Similarly, residues 30-60 (NMP domain) are also observed to be very flexible, whereas the CORE domain is quite rigid. The flexibilities of these two domains are experimentally observed and found to be essential for substrate binding and catalysis by this enzyme.

Figure 3. A) The "open" (PDB code: 4AKE) and "closed" (PDB code: 1AKE) conformations of the adenylate kinase. The substrate induced conformational changes from "open" to "closed" are apparent from the "blue" (substrate-unbound) and "green" (substrate-bound) color representation of the LID domain and the NMP domain. B) Atomic fluctuation of each Cα atom in adenylate kinase. The x-axis represents amino acid numbers, while the y-axis represents the normalized displacement corresponding to each amino acid. Peaks in an atomic fluctuation profile correspond to flexible regions of proteins. Adapted with permission from reference (14). Copyright © 2016 John Wiley & Sons, Inc. (see color insert) The WEBnm@ analysis also enabled students to visualize the direction of the domain motions and have some understanding of the amplitude of their displacement from the equilibrium position (Figure 4). As it is evident from Figure 4A and B, the movements of the LID and NMP domains are in opposite direction. The extent of movement of the LID domain from its original position is greater than the NMP domain as is apparent from the magnitude of the vectors representing the direction of protein motions. The anticorrelated motion between these two domains is also observed by analyzing the DCCM. The blue region shown by rectangle box in Figure 4C indicates the anticorrelated motion between the two domains. Through this exercise, students for the first time get an opportunity to visualize the dynamical nature of proteins. They also understand how movements of different structural elements of proteins are important for their functions such as substrate binding and positioning reactants into proper orientation for effective catalysis. 129 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.


Figure 4. A) The three domains of the adenylate kinase (PDB code: 4AKE), B) the direction of movements of the three domains in mode 9, and C) DCCM plot showing correlated/anticorrelated motions between Cα atoms of adenylate kinase. Both axes represent the amino acid residue index. Each cell in the DCCM shows the correlation between the motions of two amino acid residues (Cα atoms) on a range from -1 (anticorrelated, blue) to 1 (correlated, red). The rectangle box indicates the anticorrelated motion between residues 120-150 and residues 1-80. Adapted with permission from reference (14). Copyright © 2016 John Wiley & Sons, Inc. (see color insert)

Module IV. End-of-Semester Research Projects Once students gain experience in protein visualization, homology modeling, and studying protein dynamics through the exercises in modules I-III, they are assigned an original research problem for their third term paper. In this section, we are describing some of the end-of-semester projects that were conducted by biophysical chemistry students in three successive years, 2012-2014. The results of these projects were published in peer-reviewed journals (30–32).

Project 1. Comparison of the Intrinsic Dynamics of Aminoacyl-tRNA Synthetases This project was conducted by a class of 20 students over five weeks. AARSs are an important family of enzymes that play a key role in protein biosynthesis. They catalyze the ligation of an amino acid to its cognate tRNA molecule (33). They are modular enzymes and most of them have two main domains - the catalytic domain, where the adenylate formation and aminoacylation of tRNA take place and the anticodon binding (ACB) domain recognizes and binds the correct tRNA. Some AARSs also have evolved to have additional domains (insertion and/or extension). These domains assist AARSs in substrate recognition, catalysis, and/or editing (hydrolyzing) misacylated tRNAs. There are 20 common AARSs, which are divided into two broad classes of 10 each - class I and class II. Traditional classification of AARSs is based on protein sequence and structure similarities (34). 130 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.


The big question for the research project was - are these two classes of AARSs display distinct patterns of motions? As protein dynamics is an intrinsic property, it was hypothesized that AARSs could be classified based on their intrinsic dynamics. Students investigated if AARSs of a given class exhibit similar patterns of motions, which are distinctively different from the dynamic patterns of enzymes of another class. A thorough comparison of the intrinsic dynamics of class I and II enzymes was conducted by 20 students. They characterized the functional relevance of the collective motions in these enzymes. The tasks given to the students were as follows: i) learn about AARSs family, ii) collect sequence and structure of the assigned AARS from three different species, iii) develop structural model for the assigned AARS if the structure is not known for all the three species, iv) perform structural characterization and assessment, v) analyze the intrinsic dynamics of the assigned AARS, vi) compile and share their results (oral presentation), vii) participate in classroom discussions to explore if AARSs could be classified based on their intrinsic dynamics, and viii) write the term paper. Students first analyzed the structural difference between the two distinct classes of AARSs by visualizing the class I Thermus thermophilus leucyl-tRNA synthetase (Tt LeuRS) and Class II Ef ProRS structures using VMD (13). They observed that there is a distinct difference in the folding of the catalytic core of class I and II enzymes. The catalytic core of class I synthetases has the Rossmann fold, consisting of a central five-stranded parallel-β sheet connected by α helices. On the other hand, the catalytic domain of class II synthetases is composed of six-stranded antiparallel β-sheet flanked by loops and α helices. As there were 20 students working on this project, with 20 different AARSs, each student performed the NMA of an AARS. For this study the Ec enzymes were considered. Homology models were first generated for the three AARSs (ProRS, SerRS and TrpRS) for which the crystal structure of Ec were not available. The intrinsic dynamic patterns were compared by analyzing the DCCM for each AARS. A clear distinction between class I and II enzymes was made based on the collective dynamics of the ACB domain with respect to the catalytic domain. For class II enzymes, the ACB domain motion is predominantly anticorrelated with respect to the catalytic domain. On the other hand, the ACB domain of class I enzymes exhibits a mix of correlation and anticorrelation motions with respect to the catalytic domain. These differences in dynamic patterns between class I and II enzymes are expected as the mode of interactions of class I enzymes with tRNA differ significantly from that of class II enzymes. Also, it was observed that the insertion domain of both class I and II enzymes predominantly exhibits anticorrelated motion with the catalytic domain. Students explained that the very existence of the anticorrelated motion between insertion (editing) and catalytic domains allows the 3′-end of tRNA molecules to access the catalytic domain for aminoacylation. Additionally, anticorrelated movement between these two domains favors the translocation of the misacylated-tRNA from the central catalytic domain to the insertion (editing) domain during the editing process (30). Taken together, the students used their knowledge of homology modeling and protein dynamics to study a real scientific problem. Their work demonstrated that AARSs can be grouped into two distinct classes based on their intrinsic dynamics. 131 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.

It was observed that the intrinsic dynamics based classification is similar to the traditional classification based on sequence-structure homology (30). Although traditional classification of AARSs based on sequence and structure has been useful, the study of AARSs dynamics have provided better insight into the catalytic processes (tRNA binding and release) of AARSs. This work resulted into a peer-reviewed publication (30).


Project 2. Comparison of Intrinsic Dynamics of Cytochrome P450 Proteins To Understand if the Difference in Atomic Fluctuations Is Related to Substrate Specificity Cytochrome P450 (CYP) enzymes catalyze the monooxygenation reaction:

These heme containing-enzymes receive electrons from NADH/NADPH via electron transfer proteins. CYP proteins are classified into six broad classes based on their electron transfer partner − bacterial, CYB5R/cyb5, FMN/Fd, microsomal, mitochondrial, and P450 only (35). These proteins possess very similar structure but catalyze a wide-range of structurally diverse substrates of endogenous and exogenous origin. So, the research problem we studied was − Is there any correlation between atomic fluctuations and substrate recognition? As protein dynamics play an important role in molecular recognition and catalytic activity, students hypothesized that there should be some correlation between differences in intrinsic dynamics of CYP proteins and the substrate specificity. They performed NMA of five classes of CYP proteins to characterize their intrinsic dynamics; one CYP family of enzymes, FMN/Fd class, was not included because of the lack of X-ray crystal structures. This project was conducted by a class of 17 students. Therefore, each group of 3 or 4 students was assigned one class of CYP proteins to study for their term paper III. Three proteins, each from three different species, were studied for each CYP class. The CYP enzymes share a common tertiary fold with < 25% sequence identity. Students first performed the qualitative analysis of structural and dynamic similarities. The conservation of the overall tertiary structure of CYP proteins was observed on visualizing proteins from different CYP classes using VMD (31). Pair-wise protein structure comparison is performed using the Dali Server (10). Students then performed the NMA analysis and their study revealed some important dynamic features of these proteins. The DCCM obtained from the NMA analysis of individual proteins revealed strikingly similar patterns of correlated/anticorrelated motions among all of the CYP proteins studied. Patterns of the correlated motions between residues surrounding the heme cofactor are very similar across CYP family. However, a scrutiny of DCCM of five families of CYP proteins revealed some noticeable differences in correlated motions between residues in the heme and substrate binding pocket. Also, the atomic displacement fluctuation profiles of different class of CYP proteins were compared and a perceptible difference in the flexibility of Cα atoms of structural elements surrounding the heme cofactor and substrate binding pocket was noted 132 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.


(Figure 5). These differences in residue fluctuations are believed to be crucial for substrate selectivity in these enzymes. Students concluded that the local dynamical differences between different classes of CYP proteins allow these enzymes to catalyze reaction involving structurally diverse substrates. In addition to the qualitative analyses, students also conducted the quantitative analysis of sequence, structure and dynamic similarities in CYP proteins. Structural similarity was obtained from the pairwise structural comparison of CYP proteins (10). Significant structural similarities were observed between CYP proteins; the root-mean-square-deviation (RMSD) varies from 2.8 – 3.6 Å. Dynamic similarities between different classes of CYP proteins were obtained by computing the RMSIP and BC, the quantitative measures of dynamic similarity between proteins. The comparative study revealed that CYP enzymes share a strong dynamic similarity (RMSIP > 55% and BC > 80%) despite the low sequence identity (< 25%) and sequence similarity (< 50%) across the CYP superfamily. The CYP enzymes are mainly monooxygenases, and the presence of high dynamical similarities among these enzymes suggests their similar catalytic function, which depends upon the heme cofactor and the electron transfer protein partner. However, the local dynamical differences between different classes of CYP proteins explain how these enzymes can catalyze reactions involving wide-varieties of substrates with different size and chemical properties. In this project, undergraduate students dealt with a fundamental question regarding the role of protein dynamics in substrate recognition. While teaching the thermodynamics and kinetics of ligand-protein interactions, we usually emphasized on the non-covalent interactions and structural requirements for substrate binding. Through this study, students obtained first-hand exposure to the role of protein dynamics in substrate binding. They learned that differences in local fluctuations modulate substrate specificity in this very important superfamily of these enzymes. This work also resulted into a peer-reviewed publication (31).

Project 3. Comparisons of the Intrinsic Dynamics of Enzymes Involved in Metabolic Pathways and Develop a Dynamic-Based Tool for the Functional Identification of Proteins This project was assigned to a class of 14 students. For their project, they studied the intrinsic dynamics of enzymes involved in primary metabolic pathways. The main objective of this project was to investigate if the dynamic information could be used for the functional characterization of unknown proteins. The coarse-grained NMA was performed to examine the intrinsic dynamic patterns of 24 different metabolic enzymes. Enzymes from six species were chosen for this NMA study. If six X-ray crystal structures were not available for any enzymes, homology models were generated and used for dynamic analysis. By comparing DCCMs it was observed that each metabolic enzyme exhibits unique patterns of motions, which are conserved across multiple species and functionally relevant. Analysis of DCCMs revealed that they are visibly identical for a given enzyme family but significantly different from DCCMs of other protein families (32). Students were also successful in functional identification 133 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.


of six unknown proteins by matching the DCCM of six unknown proteins to the DCCM of a set of known proteins (32). Their work demonstrated that the DCCMs of proteins could be used for functional classification of proteins as well as correct identification of unknown proteins based on their intrinsic mobility patterns. This work resulted into presentations at national meetings and a peer-reviewed publication (32). Similar projects are being designed to provide hand-on experiences with modern computational tools while investigating authentic research problems. These enriched, experiential, and collaborative learning experiences are extremely valuable for students going to graduate school or entering the workforce.

Figure 5. The fluctuations of Cα atoms of different CYP proteins. The various helices are designated by letter A to L. Reproduced with permission from reference (32). Copyright © 2015 The Protein Society. 134 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.


Discussion The Boyer Report (36) on “Reinventing Undergraduate Education”, published by the Carnegie Foundation in the late 1990s, had stressed the importance of a “research-based education.” It was pointed out that a “learning-as-inquiry” model of education has better learning outcomes than the traditional lecture, which encourages students to be passive learners and suppresses student-centered enquiry. To develop transferable skills of critical thinking, reasoning, problem-solving, communication, initiative, and teamwork, students should be provided with active learning opportunities. One of the best ways to provide active learning opportunities is to engage students in original and meaningful research projects. This chapter describes our efforts to provide authentic research experiences to many students by integrating research into the chemistry curriculum. The primary objective of the biophysical chemistry course is to explore protein functions at the molecular level while studying the principles of thermodynamics and kinetics. Therefore, the interplay of protein structure, dynamics, and function has been the focus of the lab component of this course. We redesigned the lab course in such a way that the students get to learn the course materials by solving original research problems. New computational experiments and protocols were developed as a part of the biophysical lab by integrating structural and dynamic features of proteins to understand their functions. Each year, a new project was introduced where students performed computational experiments to probe a hypothesis. In particular, the students were guided to explore hypothesis-driven new biophysical concepts such as dynamics as a tool for classification of proteins, functional identification of new proteins based on dynamics, and the role of dynamics in substrate selection by enzymes. Recently, new projects on drug design have been introduced. Students are pursuing docking and molecular simulations to explore if inhibitors based on unique protein dynamics could be used to target the desired enzymes. The four modules that are described in this chapter were designed to provide an in-depth understanding of the role of protein dynamics in its function and to impart hands-on experience in modern computational tools. Module I was dedicated to protein structures, where the focus was to characterize the four different levels of protein structure and understand the role of non-covalent interactions in protein-ligand complexes. The VMD program was introduced for visualization and analysis of protein structures. Through guided questions, the students received a thorough understanding of the role of protein structure and types of non-covalent interactions that govern protein functions. In module II, homology modeling was introduced, where the students learnt how to develop 3D model structure of a protein of unknown structure from its sequence. In addition, they also learnt how to assess the quality of the homology model structure using different software, available online. Finally, in module III, the use of NMA enabled them to characterize the backbone flexibilities as well as understand how these fluctuations impact substrate selectivity and catalysis. They visualized and analyzed different low-frequency motions and their role in substrate selectivity and catalysis. After completion of the three modules, students were challenged 135 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.


with an original research problem, where they used the lab skills acquired through modules I-III to solve the assigned problem. An important result of our endeavor was that we were able to provide an authentic research experience to many more students, who otherwise would not have had such an opportunity through traditional research lab setting. During the period 2012 to 2014, only 22 out of 51 students enrolled in CHEM. 406, had the opportunity to conduct research in traditional lab setting. The rest, i.e. 29 more students (57% of the total enrollment), could obtain research experience exclusively through this course (in the classroom setting). Apart from making the collaborative research experience accessible to a much larger group of students, some definitive learning outcomes of the redesigned course were also noted: a) through these lab experiments and assignments, students were able to gain molecular-level understanding of interplay of protein structure, dynamics, and function; b) the discovery-guided end-of-semester research project enabled students to apply the classroom learning and lab skills to investigate new scientific problems; c) incorporation of three term papers (Table 2) helped students to improve their communication skills, specifically writing skills. Students’ performances were assessed based on their ability to i) relate their project topic with the contemporary advances in protein dynamics as supported by literature, ii) analyze their data and relate that to the core questions of the assigned project, iii) provide a rational explanation of their findings, and finally iv) prepare a complete report in a professional manner. The assessment results from 2012–2014 demonstrated a very satisfactory performance by our students. The relative number of students, who obtained higher overall course grades (≥ 85%) were consistent; thirteen out of twenty students in 2012, thirteen out of seventeen students in 2013, and nine out of fourteen students in 2014. The high grades indicated a good understanding of the core biophysical concepts among a significantly high number of the students. Similarly, assessment of students’ performance in their final term paper was also quite satisfactory; fifteen out of twenty students in 2012, seventeen out of seventeen students in 2013, and twelve out of fourteen students in 2014 scored ≥ 85% in the final term paper. Furthermore, their ability to communicate scientific ideas was also evaluated through final oral presentation and a significantly high number of students scored ≥ 90%: sixteen out of twenty students in 2012, ten out of seventeen students in 2013, and eleven out of fourteen students in 2014. These numbers reflect that a significant majority of the students developed strong intellectual and practical skills besides conceptual understanding of the course materials. Finally, the three peer-reviewed publications (Table 3), which emerged out of this process, also provide a clear evidence of the students’ in-depth understanding of structure-function-dynamics relationship in proteins. Overall, we were successful in providing a research environment in the classroom setting, where students participated and collaborated in challenging research problems, shared their data, prepared reports in professional manner, and finally, got credited as a co-author in a research paper (Table 3). Through this collaborative research work, students also got opportunities to interact with diverse group of students, which is expected to promote their ability to respect and value diversity. This model of fostering high-impact learning experience is 136 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.

easily implementable as one can provide research experience to many students without overloading the work schedule. Although a thorough study to evaluate the long-term impact of this model on students’ performance is required, the present assessment of three years is noteworthy and could serve as inspiration to the “research in classroom” movement.


Table 3. End-of-Semester Projects and Their Outcomes Year

Biophysical concepts/hypothesis

Number of students

Contributions to Biophysical Community

2012

Intrinsic dynamics as a tool for AARS classification

20

The Protein Journal (2014)

2013

Intrinsic dynamics to probe substrate selectivity in Cytochrome P450

17

Protein Science (2015)

2014

Intrinsic dynamics to identify unknown protein families

14

Cogent Biology (2017)

Students’ Feedback Analysis of students’ responses, collected from the course evaluation, showed that they enjoyed the research-based biophysical chemistry lab. These responses were collected from an anonymous survey conducted at the end of each semester. In general, students found computational lab experiments highly exciting and extremely useful. They were fascinated by the scope of VMD and WEBnm@ in viewing protein structures and depicting their collective motions, respectively. As it happens with any research, students experienced both joy and frustrations, while carrying out their research projects. Some students felt overwhelmed in the beginning of the computational lab. In particular, a small number of students felt uncomfortable with the VMD program in the beginning, but slowly, through collaboration with other students, they managed to learn the VMD commands and enjoy the overall process of investigating a real scientific problem. Some typical students’ comments about the lab include: “VMD and WEBnm@ are cool. It was neat to see the structure and movements of protein molecules, which helped me to have better understanding of how proteins work.”; “The lab is honestly very well done. I feel like I have a better understanding of databases and software that are utilized in research and industry.”; “All the interactive labs on VMD, SWISS-MODELING, and NMA are very beneficial. I feel like I have a 137 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.


good command of those programs now.” Regarding term papers, the students had mixed feelings. Although most of the students liked the end-of-semester project and took ownership of the assigned research project, some students expressed frustrations regarding the time it took them to prepare the term papers. Some of the remarks related to term paper include: “The partitioning of topics in the lab is a good directive. Using papers to assess student’s understanding not only are a good indicator of the student’s knowledge but also develops scientific writing skills.” “The term papers are extremely helpful in understanding the direct applications of biophysical chemistry, which may go missed by some students. It’s just not the best use of time to always meet, especially when the papers do take time to write.” Overall, the students’ feedback was positive. The group of students who matriculated to doctoral programs or joined the work force especially appreciated the experience and reported that their research-based computational lab experiences had well-prepared them for their current job.

Conclusions Our work shows that the high-impact leaning experiences could be provided to many students by integrating research into the curriculum. Through these discovery-guided research projects, the students received intensive but satisfying lab experiences. Moreover, they gained an in-depth understanding of protein structure-dynamics-function relationships through these innovative lab experiments. They also got opportunities to be involved and contribute significantly in innovative research projects, which provided them with unique and valuable learning experiences. The process of solving research problems enabled students to learn to think critically. They also got opportunities to disseminate their research findings in the form of oral presentations as well as research papers, subsequently shared to the academic community. They got credited as a co-author on these published research papers. This is critically important as graduate and professional schools often expect the students to conduct research during their undergraduate careers. Our work also demonstrates that important findings can be made in the classroom through collaboration with undergraduates. Moreover, with the decline in funding and increase in faculty workload, the integration of research into the curriculum is a cost-effective strategy to provide high impact learning experiences to our students. We also observed that the hours spent by two students during an academic year [30 weeks (30 weeks x 2 students x 6 hours per week) + 3 weeks of Winterim (3 weeks x 2 students x 40 hours per week) = 600 hours] to complete a project like those described over here is comparable to the hours spent by fifteen students during one semester of biophysical class [15 weeks x 20 students x 2 hours per week = 450 hours). Overall, we are satisfied with the results of our efforts to integrate research into the biophysical chemistry lab curriculum, which not only provided high-impact learning opportunity to many more students but also resulted into important new findings.

138 Gourley and Jones; Best Practices for Supporting and Expanding Undergraduate Research in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.

Acknowledgments We gratefully acknowledge the computational support from Learning and Technology Services of the University of Wisconsin-Eau Claire. The authors would like to thank the department of chemistry and the Office of Research and Sponsored Programs, University of Wisconsin-Eau Claire, for providing the financial support. The authors would also like to thank Ms. Clorice R. Reinhardt and Mr. Ajay Rai for careful reading of the manuscript and providing helpful comments and suggestions. Finally, the authors would like to thank the reviewer for the insightful and constructive comments.


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