Green Fluorescent Protein-Focused Bioinformatics ... - ACS Publications

Mar 16, 2017 - This laboratory experiment explores three different protein bioinformatics tasks using freeware programs available on the National Cent...
1 downloads 0 Views 785KB Size
Laboratory Experiment pubs.acs.org/jchemeduc

Green Fluorescent Protein-Focused Bioinformatics Laboratory Experiment Suitable for Undergraduates in Biochemistry Courses Laura Rowe*,† †

Department of Chemistry, Valparaiso University, Valparaiso, Indiana 46383, United States S Supporting Information *

ABSTRACT: An introductory bioinformatics laboratory experiment focused on protein analysis has been developed that is suitable for undergraduate students in introductory biochemistry courses. The laboratory experiment is designed to be potentially used as a “stand-alone” activity in which students are introduced to basic bioinformatics tools and applications after they have covered fundamental aspects concerning the structure and function of proteins within a class. This laboratory experiment explores three different protein bioinformatics tasks using freeware programs available on the National Center for Biotechnology Information Web site. Students first determine the identity of a protein, and its close homologues, to which they have been given an amino acid sequence using the BLASTP search tool. They then perform a multiple alignment of this protein with a given selection of proteins using the COBALT program and analyze and interpret the alignment results to predict the most promising sites for mutagenesis. Lastly, students use either the embedded JSmol viewer available on the Research Collaboratory for Structural Bioinformatics Web site or the Protein Workshop program to visualize and annotate the X-ray crystal structure of the green fluorescent protein and identify secondary structural components in the protein’s tertiary structure. The suggested tasks integrate introductory background knowledge concerning the purpose of bioinformatics tools in protein research, hands-on training in the use of the relevant software, and practice in both evaluating the produced bioinformatics data and analyzing the data to predict promising experimental choices. KEYWORDS: First-Year Undergraduate/General, Upper-Division Undergraduate, Biochemistry, Laboratory Instruction, Computer-Based Learning, Inquiry-Based/Discovery Learning, Internet/Web-Based Learning, X-ray Crystallography, Computational Chemistry, Proteins/Peptides



INTRODUCTION Biochemistry research often begins with a thorough bioinformatics analysis of the macromolecules relevant to the project. Whether a study involves nucleic acids, proteins, posttranslational modifications, or metabolic pathways, the power of bioinformatics programs help to target the direction and scope of initial, and iterative, experiments.1 However, despite the proliferation of computer analysis in biochemistry research, many undergraduate biochemistry textbooks do not provide any problems or activities that actually use bioinformatics computer programs. For example, of seven standard biochemistry textbooks analyzed, only one had a chapter dedicated to bioinformatics.2 Although all analyzed textbooks discussed bioinformatics programs and tools within other chapters, only two out of seven had more than five end-of-chapter problems that required the use of any bioinformatics computer software,2a,c with another three textbooks not having a single end-of-chapter problem that used bioinformatics software or databases.2e−g For this reason, there is a significant need for a variety of bioinformatics-based problems and activities in a chemical education journal, including laboratory experiments that can be used in introductory biochemistry courses.3 Biochemistry courses often focus on the structure, functions, and reaction mechanisms of proteins more than on phylogenetic trees and nucleic acid analysis, which are domains © XXXX American Chemical Society and Division of Chemical Education, Inc.

more prevalent in molecular biology courses. To reflect this fact, chemistry education journals have published multiple protein-based bioinformatics articles in the past decade.4 A select few of these protein-based bioinformatics articles are laboratory experiments.5 However, these laboratory experiments are either over a decade old, target an audience of students enrolled in an advanced biochemistry course (such as a medicinal chemistry or homology modeling course), or present the laboratory experiment within a pre-existing lecture series, or entire course, so that a fairly specific sequence of lectures must be presented prior to undertaking the laboratory experiment. The novelty of this laboratory experiment is that it simultaneously introduces three fundamental protein-based, bioinformatics-based tools to a wide audience of students who need not have any specialized knowledge other than an introduction to amino acid and protein structure learned at the beginning of a first-semester biochemistry course, or after such a discussion in a general, organic, and biochemistry course that is commonly offered to nonscience and health science majors. Moreover, although this experiment analyzes the green Received: August 10, 2016 Revised: February 28, 2017

A

DOI: 10.1021/acs.jchemed.6b00533 J. Chem. Educ. XXXX, XXX, XXX−XXX

Journal of Chemical Education

Laboratory Experiment

Table 1. Key Learning Goals Protein Analysis

Evaluation and Prediction

BLASTP

Software Tools

Identify the protein name and function of a given amino acid sequence

COBALT

Identify and locate key information on various proteins using database information Analyze the sequence similarity between several proteins

JSmol or Protein Workshop

Visualize a protein using X-ray crystal structure coordinates in the form of PDB coordinates Identify terminus amino acids and select and label amino acid sites within the structure

Compare the similarity between amino acid sequences found in the search Determine if the sequence is statistically similar enough to guarantee identity Evaluate sequence similarity with structure and/or functional similarity Predict amino acid locations that may tolerate mutagenesis based on sequence homology Evaluate secondary structure composition of protein

fluorescent protein, it could be adapted by the end user to any protein of interest that has a published sequence and structure. Lastly, although a short lecture on bioinformatics would be helpful prior to undertaking this laboratory, the extensive introduction in the student handout (see the Supporting Information) enables this experiment to be a “stand-alone” activity to introduce students to bioinformatics via a flipped classroom or inquiry-based pedagogical approach.



The first part of this laboratory experiment is searching for the identity, and close homologues, of the GFP based on its known amino acid sequence that is given in the laboratory experiment by using a BLASTP search.10 Basic local alignment search tools (BLAST) are algorithms that are used to compare sequence information (such as amino acid sequence) to sequences that have been deposited into a library or database. BLASTP compares a given amino acid sequence to deposited amino acid sequences, and this tool enables one to either identify a given amino acid sequence as an already known protein or to determine its sequence similarity to previously discovered proteins and peptides. An analysis of such similarity often suggests potential functional roles to explore if the function of proteins with similar sequences has already been revealed. Second, students align several different proteins (including the GFP from Part 1) with published amino acid sequences using a multiple alignment tool, COBALT.4f,10,11 Constraintbased multiple alignment tool (COBALT) is an algorithm used to align multiple protein sequences using pairwise constraints to produce progressive multiple alignments.6c The results of multiple alignments are a critical first step in homology modeling, the process of predicting the unknown threedimensional structure of a protein using sequence-similar proteins that do have resolved structures. Additionally, multiple alignments are useful in predicting regions and amino acids that may benefit, or at least tolerate, mutagenic strategies of protein engineering. This task (identifying conserved amino acid locations among proteins that have a similar sequence and function) is emphasized to the students during the postlaboratory questions portion of this experiment. Lastly, students visualize and annotate the three-dimensional structure of GFP using the Protein Data Bank (PDB) coordinates and either an embedded JSmol viewer or the Protein Workshop program12 to produce an annotated threedimensional image of GFP (Figure 1). These programs are free and user-friendly three-dimensional display and annotation programs in which one can manipulate the view and display of a protein (ribbon, surface, etc.), select and annotate specific amino acids and structures, and color according to hydrophobicity, secondary structure, and the like. Students use these programs to visualize and manipulate the GFP from Part 1 and 2, locate and annotate specific amino acids, recognize key secondary structural features within the overall tertiary structure of the protein, and analyze the structure-to-function relationship of GFP.

EXPERIMENT

Software Employed

All software and programs are freeware available on the National Center for Biotechnology Information (NCBI) or Research Collaboratory for Structural Bioinformatics (RCSB) Web sites. The programs used were: BLASTP, COBALT, a JSmol viewer, and Protein Workshop.6 Protein Workshop is not Macintosh compatible. Pedagogical Goals of the Laboratory

Bioinformatics can be defined as “the development of computational methods for studying the structure, function, and evolution of genes, proteins, and whole genomes”,7 and as such encompasses an enormous arsenal of analysis tools.1,4j,8 This computer-based laboratory experiment focuses on three common protein-related bioinformatics tasks: the use of two amino acid sequence tools and one protein structure-based activity. The learning goals for each of these three tasks are outlined in Table 1, and pedagogical goals and assessment are outlined in Table 2. A discussion of the pedagogical goals and their assessment is also included in the Results and Discussion section. Overview of the Experiment

This laboratory experiment not only introduces the fundamentals of protein-focused bioinformatics to undergraduates, but also leads them through three protein-based bioinformatics tasks: an amino acid homologue search, a multiple alignment of several known proteins, and protein visualization and analysis. This sequence of experimental tasks herein was selected due to this work flow (homologue searching → multiple alignment → structure visualization) mimicking the order in which bioinformatics tools are often used when one begins researching the structure and function of an individual protein.5e Although this laboratory experiment could be adapted to any number of different proteins that have published sequence and structural data (perhaps related to the instructor’s research interests), herein a selection of green fluorescent protein (GFP) derivatives were chosen due to their ubiquity in biochemistry and cell biology and recent publicity.9 B

DOI: 10.1021/acs.jchemed.6b00533 J. Chem. Educ. XXXX, XXX, XXX−XXX

4

3

2

Laboratory Experiment a These specific goals were assessed in the introductory biochemistry lecture course. bAssessment occurred during an examination administered about one month after the completed the laboratory experiment. Students passed the assessment if they were able to correctly and completely perform the task described in the assessment during their examination. cTotal N = 47; percentage calculated by (number of students passing)/(number of students assessed).

85 (40/47) A PDB ID code of a protein and were asked to produce a three-dimensional image of the protein using either JSmol or Protein Workshop. Additionally, students had to exclude all water and nonprotein ligand molecules, display the overall peptide in a ribbon format, but alter the display style of three particular amino acids to either a spacefill or ball-and-stick display style. These three amino acids also had to be labeled with either their three-letter or one-letter codes using the computer program. A printed copy of the image was required for the exam answer.

96 (45/47)

The accession numbers of five different amino acid sequences and asked to perform a COBALT-based multiple alignment on the sequences. A screenshot of the result was required for the exam answer. A PDB ID code of a protein and were asked to produce a three-dimensional image of the protein using either JSmol or Protein Workshop. A printed copy of the image was required for the exam answer.

100 (47/47)

100 (47/47) The amino acid sequence of human insulin and were asked to identify the protein using BLASTP on their computers.

Method of Assessment, On an Exam Question, Students Were Given: Pedagogical Goals, Students Will Be Able To:

Identify an unknown protein when given an amino acid sequence using a BLASTP algorithm. Use the COBALT multiple alignment tool to align several amino acid sequences. Visualize, rotate, and zoom in-and-out of the X-ray crystal structure data of a protein using the PDB file of that protein and either a JSmol embedded viewer or the Protein Workshop program. Annotate their protein image produced in Goal 3 such that they can change the display style of individual amino acids, label said amino acids individually, exclude water molecules and specific subunits or ligands, and alter the overall display style of the protein. 1

b a

Table 2. Key Pedagogical Goals and Assessment Methods for the Introductory Biochemistry Course

Students Passing the Assessment, %c

Journal of Chemical Education

Figure 1. Structure of the annotated Protein X (green fluorescent protein), visualized using Protein Workshop, that students analyze and produce during the laboratory experiment. This result, as well as screenshot images of the BLASTP search and the multiple alignment task that students will obtain during the laboratory experiment, is provided in the Supporting Information: Instructor Information. Image used with permission; see ref 12.



RESULTS AND DISCUSSION Students that perform this laboratory experiment perform three separate bioinformatics modules: a BLASTP search to determine the identity an amino acid sequence of unknown origin, a COBALT-based multiple alignment that compares the amino acid sequence of several different proteins, and a protein visualization module that allows students to view and annotate the three-dimensional structure of a protein given X-ray crystal coordinates in the form of a PDB identifier. This laboratory experiment has been used for four consecutive years in four different biochemistry-based courses. Each course introduced the experiment at different points within the students’ biochemistry learning curve, and in all cases, students responded positively to the activity and were able to complete the tasks with minimal instructor assistance. Students have completed the lab in an introductory biochemistry course that required general and organic chemistry classes as a prerequisite for two consecutive years (95 students), in two advanced biochemistry laboratory courses that required an introductory biochemistry course as a prerequisite (20 students), and in a general, organic, and biochemistry course for nonmajors that had no chemistry prerequisites (6 students). This laboratory experiment has been used most often in a first-semester introductory biochemistry course, in which students have already completed general and organic chemistry courses as a prerequisite. For this course, the laboratory experiment has been assigned for two consecutive years, in a total of four different sections of the course, with each section averaging between 20 and 25 students. During the firstsemester introductory biochemistry courses, the students performed the laboratory experiment at home on their own computers after a 30−50 min orienting lecture in class. This constituent required little to no assistance in completing the tasks successfully. The experiment was also used in two different advanced biochemistry laboratory courses and was piloted once during the laboratory component of a first-year C

DOI: 10.1021/acs.jchemed.6b00533 J. Chem. Educ. XXXX, XXX, XXX−XXX

Journal of Chemical Education

Laboratory Experiment

laboratory experiment take place during class time so that instructor assistance is immediately available. The most recent year (2016) in which this laboratory experiment was assigned to the first-semester biochemistry lecture students also identified and assessed four key pedagogical goals for this audience (Table 2). These goals included determining the identity of an amino acid sequence, performing a multiple alignment of several peptide sequences, and visualizing and annotating a protein using one of two 3-D viewer platforms when provided a PDB code containing X-ray crystal coordinates of a particular protein. The assessment methods were the successful completion of these tasks on an examination that occurred approximately one month after the completion of the laboratory experiment. The questions on the examination used different amino acid sequences and proteins than the original laboratory experiment but allowed the use of the same tools and algorithms (BLASTP, COBALT, JSmol viewer (or Protein Workshop)) for the analysis and visualizations. As can be seen in Table 2, 100% of the students (47 students total; 2 different classes of 23 and 24 students) were able to achieve the first and third pedagogical goals, identifying an unknown protein with a BLAST search and visualizing the structure of a protein using a 3-D viewing platform. In addition, 96% of students were able to complete the multiple alignment, while the annotation of the protein structure proved more challenging to students, with 15% of students failing at this task one month after completing the initial experiment. This result is not too surprising considering the annotation of a protein in either the JSmol viewer or Protein Workshop requires remembering several specific steps and scripting commands that are not intuitive, even for a very technologically savvy audience. There were a handful of difficulties that students encountered during this experiment, many of which are made more explicit in the instructor handout in the Supporting Information. First, the Protein Workshop program is not Macintosh-compatible, so Macintosh computer users will need to exclusively use the JSmol embedded viewer for the protein visualization tasks. Additionally, several components on the computer running these programs must be set appropriately: pop-up blockers must be turned off, and Java must be installed and appropriately updated. For these reasons, if using university computers for the completion of this experiment, the appropriate updates and settings should be checked on each computer by the instructor prior to beginning the experiment. Lastly, students often had a difficult time finding the correct links on the Web site pages in the initial student handout. Screenshots and arrows in the student handout in the Supporting Information have been provided to alleviate this issue. In conclusion, this laboratory experiment has been completed by a wide variety of undergraduate students that varied extensively in the breadth and depth of their biochemistry education. Students were able to successfully employ three different bioinformatics software programs to mine protein sequence databases, pairwise align multiple amino acid sequences, and visualize and annotate an X-ray crystal structure of a given protein using protein databank coordinates, with very little to no previous exposure to bioinformatics. For these reasons, this experiment is suitable not only for undergraduates taking their first introductory biochemistry course, but also for more advanced biochemistry students and potentially for undergraduate students who have had only

undergraduate general, organic, and biochemistry course for nonscience and health science majors. During the two more advanced biochemistry laboratory courses, in which a prerequisite was completion of the first semester of a two-semester third-year undergraduate biochemistry sequence, this laboratory experiment was given during laboratory time for completion. One of the courses was purely a biochemistry laboratory course (13 students) in which students worked together in pairs on the experiment, and the other advanced course was a lecture and laboratory special topics course (7 students) in which students completed the experiment individually. Because of one semester of biochemistry being a prerequisite in these courses, all students had prior knowledge of biological macromolecule structures and functions, and the 13-student laboratory course was given no preliminary lecture on bioinformatics prior to the experiment, whereas the special topics course students were given a 30 min bioinformatics lecture prior to the assignment. Very little instructor assistance was required in these courses, with the advanced laboratory students who had not received an orienting 30 min lecture requiring more assistance during the experiment. Finally, this experiment was completed by six undergraduate students, as a makeup laboratory exercise, in a general, organic, and biochemistry course for nonscience and health science majors. These first-year undergraduate nonmajor students also received a 30 min orienting lecture on bioinformatics prior to the assignment and completed the assignment during laboratory course time (the laboratory component of the course). These primarily first-year undergraduate students were able to complete most of the tasks but required significant amounts of assistance from the instructor. From these observations, a 30−50 min orienting lecture of bioinformatics is recommended, though not required, before assigning this laboratory experiment to any subset of undergraduate biochemistry students. Additionally, if assigned to a primarily first-year undergraduate or nonmajors audience, it is recommended that the experiment be completed during class to maximize instructor assistance. However, assigning the experiment as a “take-home” assignment worked very well in the introductory biochemistry lecture course that had general and organic chemistry prerequisites. A survey of the four sections of the first-semester biochemistry students completing the laboratory experiment indicated that the completion of this experiment took between 2−4 h for 79% of the participants, took less than 2 h for 11% of the participants, and more than 4 h for 10% of the participants. The students were assessed using the instructor grading key provided in the Supporting Information, and for the firstsemester biochemistry lecture course, 88 out of the 95 students received an A or a B on the assignment. The remaining seven students received a C+ or lower on the laboratory experiment. The majority of the students responded positively to the laboratory and felt that it was at an appropriate level of difficulty and imparted valuable knowledge. The six students who completed the laboratory in the general, organic, and biochemistry nonmajors course did not do as well on the task, with these students completing the laboratory within 3 h and achieving grades ranging from a B+ to a C− on the assignment. Because of the reduced science preparation of the students in the nonmajors course, it is highly recommended that a 30−50 min orienting lecture on bioinformatics be covered prior to completion of this laboratory experiment and that the D

DOI: 10.1021/acs.jchemed.6b00533 J. Chem. Educ. XXXX, XXX, XXX−XXX

Journal of Chemical Education

Laboratory Experiment

Biol. Educ. 2014, 42 (1), 68−78. (b) Centeno, N. B.; Villà-Freixa, J.; Oliva, B. Teaching structural bioinformatics at the undergraduate level. Biochem. Mol. Biol. Educ. 2003, 31 (6), 386−391. (c) Counsell, D. A review of bioinformatics education in the UK. Briefings Bioinf. 2003, 4 (1), 7−21. (4) (a) Kossida, S.; Tahri, N.; Daizadeh, I. Bioinformatics by example: from sequence to target. J. Chem. Educ. 2002, 79 (12), 1480− 1485. (b) Leon, D.; Uridil, S.; Miranda, J. Structural analysis and modeling of proteins on the Web: an investigation for biochemistry undergraduates. J. Chem. Educ. 1998, 75 (6), 731−734. (c) Likić, V. A. Computer programming and biomolecular structure studies: A step beyond internet bioinformatics. Biochem. Mol. Biol. Educ. 2006, 34 (1), 1−4. (d) McDougal, O. M.; Cornia, N.; Sambasivarao, S. V.; Remm, A.; Mallory, C.; Oxford, J. T.; Maupin, C. M.; Andersen, T. Homology modeling and molecular docking for the science curriculum. Biochem. Mol. Biol. Educ. 2014, 42 (2), 179−182. (e) Parslow, G. R. Commentary: Molecular visualization tools are good teaching aids when used appropriately. Biochem. Mol. Biol. Educ. 2002, 30 (2), 128− 129. (f) Yoshida, S.; Watanabe, H. Robust salivary gland-specific transgene expression in Anopheles stephensi mosquito. Insect Mol. Biol. 2006, 15 (4), 403−410. (g) Pastrana-Rios, B. Exploring the Structure-Function Relationship of Macromolecules at the Undergraduate Level. J. Chem. Educ. 2004, 81 (6), 837−840. (h) Roy, U. Structural biology of tumor necrosis factor demonstrated for undergraduates instruction by computer simulation. Biochem. Mol. Biol. Educ. 2016, 44 (3), 246−255. (i) Roy, U.; Luck, L. A. Molecular modeling of estrogen receptor using molecular operating environment. Biochem. Mol. Biol. Educ. 2007, 35 (4), 238−243. (j) Sansom, C. E.; Smith, C. A. Computer applications in biomolecular sciences. Part 2: bioinformatics and genome projects. Biochem. Educ. 2000, 28 (3), 127−131. (k) Dabrowiak, J. C.; Hatala, P. J.; McPike, M. P. A molecular modeling program for teaching structural biochemistry. J. Chem. Educ. 2000, 77 (3), 397−400. (l) Magana, A. J.; Taleyarkhan, M.; Alvarado, D. R.; Kane, M.; Springer, J.; Clase, K. A survey of scholarly literature describing the field of bioinformatics education and bioinformatics educational research. CBE Life Sci. Educ. 2014, 13 (4), 607−623. (5) (a) Hayes, J. M. An Integrated Visualization and Basic Molecular Modeling Laboratory for First-Year Undergraduate Medicinal Chemistry. J. Chem. Educ. 2014, 91 (6), 919−923. (b) Lowery, M. S.; Plesniak, L. A. Some Like It Cold: A Computer-Based Laboratory Introduction to Sequence and Tertiary Structure Comparison of ColdAdapted Lactate Dehydrogenases Using Bioinformatics Tools. J. Chem. Educ. 2003, 80 (11), 1300−1302. (c) Petrovic, D.; Zlatovic, M. Modeling Human Serum Albumin Tertiary Structure To Teach Upper-Division Chemistry Students Bioinformatics and Homology Modeling Basics. J. Chem. Educ. 2015, 92 (7), 1233−1237. (d) Rodrigues, R. P.; Andrade, S. F.; Mantoani, S. P.; Eifler-Lima, V. L.; Silva, V. B.; Kawano, D. F. Using Free Computational Resources To Illustrate the Drug Design Process in an Undergraduate Medicinal Chemistry Course. J. Chem. Educ. 2015, 92 (5), 827−835. (e) Ship, N. J.; Zamble, D. B. Analyzing the 3D structure of human carbonic anhydrase II and its mutants using deep view and the protein data bank. J. Chem. Educ. 2005, 82 (12), 1805−1808. (f) Inlow, J. K.; Miller, P.; Pittman, B. Introductory bioinformatics exercises utilizing hemoglobin and chymotrypsin to reinforce the protein sequencestructure−function relationship. Biochem. Mol. Biol. Educ. 2007, 35 (2), 119−124. (g) Hati, S.; Bhattacharyya, S. Incorporating modeling and simulations in undergraduate biophysical chemistry course to promote understanding of structure-dynamics-function relationships in proteins. Biochem. Mol. Biol. Educ. 2016, 44 (2), 140−159. (6) (a) Protein BLAST Query Page; National Library of Medicine, 2017. http://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM= blastp&PAGE_TYPE=BlastSearch&Link_LOC=blasthome (accessed January 2017). (b) Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E. The Protein Data Bank. Nucleic Acids Res. 2000, 28 (1), 235−242. (c) Papadopoulos, J. S.; Agarwala, R. COBALT: constraint-based

minimal exposure to the structure and function of biological macromolecules that may be encountered in a generaleducation-focused chemistry, biochemistry, or biotechnology course.



ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available on the ACS Publications website at DOI: 10.1021/acs.jchemed.6b00533. Student handout with extensive introductory material, step-by-step instructions, and laboratory questions (PDF, DOCX) Detailed instructions for students using the embedded JSmol viewer (PDF, DOCX) Additional instructor information with detailed information, screenshots, and a grading key (PDF, DOCX)



AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Laura Rowe: 0000-0003-4657-1194 Notes

The author declares no competing financial interest.



ACKNOWLEDGMENTS The author would like to thank Thomas Goyne for using this lab in his Biochemistry Laboratory course and for the significant editing guidance during the construction of this experiment. The author would also like to thank the students who participated in this laboratory experiment for their constructive feedback on how to improve its ease of use and efficacy. The author would also like to acknowledge the National Library of Medicine for allowing the reproduction of screenshots from their public domain website as well as the Research Collaboratory for Structural Bioinformatics. L.R. has received funding from Valparaiso University Creative Works Committee, the Indiana Space Grant Consortium (INSGCNASA), the National Science Foundation (NSF-MRI grant), and the Pittsburgh Conference Memorial National College Grant during the years that this laboratory experiment was developed.



REFERENCES

(1) Zardecki, C.; Dutta, S.; Goodsell, D. S.; Voigt, M.; Burley, S. K. RCSB Protein Data Bank: A Resource for Chemical, Biochemical, and Structural Explorations of Large and Small Biomolecules. J. Chem. Educ. 2016, 93 (3), 569−575. (2) (a) Voet, D.; Voet, J. Biochemistry, 4th ed.; John Wiley and Sons: New York, 2010. (b) Berg, J.; Tymoczko, J.; Gatto, G.; Stryer, J. Biochemistry, 8th ed.; W.H. Freeman and Co: New York, 2015. (c) Nelson, D.; Cox, M. Lehninger Principles of Biochemistry, 6th ed.; W.H. Freeman and Co: New York, 2013. (d) Garrett, R.; Grisham, C. Biochemistry, 4th ed.; Cengage Learning: Boston, 2010. (e) Appling, D.; Anthony-Cahill, S.; Mathew, C. Biochemistry Concepts and Connections; Pearson: New York, 2016. (f) McKee, T.; McKee, J. Biochemistry The Molecular Bases of Life, 6th ed.; Oxford University Press: New York, 2016. (g) Moran, L.; Horton, R.; Scrimgeour, G.; Perry, M. Principles of Biochemistry, 5th ed.; Pearson: New York, 2011. (3) (a) Badotti, F.; Barbosa, A. S.; Reis, A. L. M.; do Valle, I.́ F.; Ambrósio, L.; Bitar, M. Comparative modeling of proteins: A method for engaging students’ interest in bioinformatics tools. Biochem. Mol. E

DOI: 10.1021/acs.jchemed.6b00533 J. Chem. Educ. XXXX, XXX, XXX−XXX

Journal of Chemical Education

Laboratory Experiment

alignment tool for multiple protein sequences. Bioinformatics 2007, 23 (9), 1073−1079. (7) Choudhuri, S. Bioinformatics for Beginners; Elsevier Inc.: San Diego, CA, 2014. (8) (a) Jaramillo, A.; Wernisch, L.; Hery, S.; Wodak, S. J. Automatic procedures for protein design. Comb. Chem. High Throughput Screening 2001, 4 (8), 643−659. (b) Tsai, C. S. Using Computer Applications and Online Resources To Teach and Learn Pharmaceutical Chemistry. J. Chem. Educ. 2007, 84 (12), 2019−2023. (c) Kane, M. D.; Brewer, J. L. An information technology emphasis in biomedical informatics education. J. Biomed. Inf. 2007, 40 (1), 67−72. (d) Felton, M. J. Bioinformatics: The child of success. Mod. Drug Discovery 2001, 4 (1), 25−26. (9) (a) Tsien, R. Y. Constructing and Exploiting the Fluorescent Protein Paintbox (Nobel Lecture). Angew. Chem., Int. Ed. 2009, 48 (31), 5612−5626. (b) Shimomura, O. Discovery of Green Fluorescent Protein (GFP) (Nobel Lecture). Angew. Chem., Int. Ed. 2009, 48 (31), 5590−5602. (c) Chalfie, M. GFP: Lighting Up Life (Nobel Lecture). Angew. Chem., Int. Ed. 2009, 48 (31), 5603−5611. (d) Douglas, R. G.; Frischknecht, F. The 2001 uses of GFP: A review of ″Illuminating Disease″. BioEssays 2015, 37 (4), 352−353. (e) Mishin, A. S.; Belousov, V. V.; Solntsev, K. M.; Lukyanov, K. A. Novel uses of fluorescent proteins. Curr. Opin. Chem. Biol. 2015, 27, 1−9. (10) Perozzo, M. A.; Ward, K. B.; Thompson, R. B.; Ward, W. W. Xray diffraction and time-resolved fluorescence analyses of Aequorea green fluorescent protein crystals. J. Biol. Chem. 1988, 263 (16), 7713−7716. (11) (a) Bicker, A.; Dietrich, D.; Gleixner, E.; Kristiansen, G.; Gorr, T. A.; Hankeln, T. Extensive transcriptional complexity during hypoxia-regulated expression of the myoglobin gene in cancer. Hum. Mol. Genet. 2014, 23 (2), 479−490. (b) Cormack, B. P.; Valdivia, R. H.; Falkow, S. FACS-optimized mutants of the green fluorescent protein (GFP). Gene 1996, 173 (1), 33−8. (c) de Wet, J. R.; Wood, K. V.; DeLuca, M.; Helinski, D. R.; Subramani, S. Firefly luciferase gene: structure and expression in mammalian cells. Mol. Cell. Biol. 1987, 7 (2), 725−737. (d) Gregan, J.; Lindner, K.; Brimage, L.; Franklin, R.; Namdar, M.; Hart, E. A.; Aves, S. J.; Kearsey, S. E. Fission yeast Cdc23/Mcm10 functions after pre-replicative complex formation to promote Cdc45 chromatin binding. Mol. Biol. Cell 2003, 14 (9), 3876−3887. (12) Moreland, J. L.; Gramada, A.; Buzko, O. V.; Zhang, Q.; Bourne, P. E. The Molecular Biology Toolkit (MBT): A Modular Platform for Developing Molecular Visualization Applications. BMC Bioinf. 2005, 6 (21), 21.

F

DOI: 10.1021/acs.jchemed.6b00533 J. Chem. Educ. XXXX, XXX, XXX−XXX