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Teaching with Technology
James P. Birk
Microcomputer Applications in Biochemistry
Arizona State University Tempe, AZ 85287
C. Stan Tsai Department of Chemistry and Institute of Biochemistry, Carleton University, Ottawa, ON K1S5B6, Canada;
[email protected] The application of microcomputers will certainly become an integral part of the biochemistry curriculum. Unfortunately, a quick survey shows that there is not a single comprehensive text suitable for classroom use in this subject. The few available texts either deal with specialized topics or are written primarily for research purposes (1–4 ). There is a need to develop course material that can be used to teach undergraduate students the application of microcomputers in biochemistry. Such a course would teach not only the biochemical principles but also the skills required for using application programs to solve practical problems. The course should raise students’ awareness of the applicability of microcomputers in biochemistry and increase their interest in the subject matter. With these objectives, I have developed a new lecture/ workshop course on the application of microcomputers in biochemistry that was offered to our chemistry, biochemistry, and biotechnology undergraduate students. However, the course can be modified and offered concurrently as a supplement to general biochemistry.
application programs to solve biochemical problems, as listed in Table 2. One assignment in each module typically involves searching the literature to acquire experimental data for the workshop. The students are expected to submit (i) hard copies of their reports and (ii) the diskettes listing files, and to demonstrate the workshop to the instructor if requested at the completion of each module. Application Programs and Their Usage The course is designed to expose the students to as many programs of varied applications as possible with a minimum expenditure. Therefore, an extensive search was made on the Internet to evaluate suitable PC-based freeware for undergraduate teaching. The following criteria were adapted. 1. The application programs must match the biochemical topics and the instructional level. 2. The programs must be easy to implement and operate yet reasonably versatile and stable. 3. The programs must come, preferably, with user’s manuals or help menus.
Course Organization The course content is organized into a number of modules as shown in Table 1. The one-semester course consists of one hour of lecture (tutorial session) and three hours of workshop per week. In the lecture, the principles of computational biochemistry and the use of application software are discussed; the workshop deals with the practical use of various
4. There must be a possibility of continuing support for the programs.
Table 1 lists the application programs selected and their sources. Each student was given an account to access Internet resources to acquire assigned freeware such as DynaFit, Gepasi, Cn3D, KineMage, RasMol and ProGraph. Two Web
Table 1. Course Content and Application Software ModTitle ule 1 Acquisition of software and retrieval of databases 2 3 4
Statistical analysis of biochemical data Chromatographic and spectroscopic peak fittings Ligand–biomacromolecule interactions
5
Analysis of enzyme kinetic data
6 7 8 9 10
Metabolic simulation Analysis of DNA sequences Analysis of protein sequences Construction of recombinant DNA Visualization and identification of 3D structures of proteins
11
Energy minimization of biomolecules
12
Molecular similarity of proteins: topology and animation
Application Software Internet SPSS PeakFit DynaFit EnzFit Leonora Winzyme Gepasi OMIGA OMIGA OMIGA Cn3D KineMage RasMol PC Spartan CS Chem3D ProGraph KineMage
Source of Software Various ftp and WWW sites: http://www.ncbi.nlm.nih.gov/Entrez and http://www.genome.ad.jp/dbget SPSS Science Jandel (SPSS Science) http://www.biokin.com/dynafit Biosoft Cornish-Bowden (3) Biosoft http://gepasi.dbs.aber.ac.uk/softw/gepasi.html Oxford Molecular Group Oxford Molecular Group Oxford Molecular Group http://www.ncbi.nlm.nih.gov/Structure/CN3D/cn3dwin.html http://www.faseb.org/protein/index.html http://www.umass.edu/microbio/rasmol/getras.htm Wavefunction, Inc. Cambridgesoft Corp. www.ask.uni-karlsruhe.de/asksam/sampages/htmltxt/profilegraph.html http://www.faseb.org/protein/index.html
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sites, Entrez (5) and DBGet (6 ) were recommended to the students for retrieving DNA sequences, protein sequences, and 3-D coordinates of proteins in module 1. The statistical and curve-fitting packages are standard installations at most university computer facilities and are readily available to the students. Curve fitting with spreadsheets has been reported (7, 8). We used SPSS for the statistical analyses of biochemical data dealing with estimation of the standard error, ANOVA analysis, and linear regression analysis in module 2. The Jandel (SPSS) software PeakFit was employed to analyze spectroscopic bands and chromatographic peaks in module 3. Common peak-fit routines such as Gaussian, Lorentzian, and Voigt functions (spectroscopy) as well as Giddings and exponentially modified Gaussian functions (chromatography) were used (9–11). For module 4, EnzFit and DynaFit (12) were applied to analyze coenzyme– dehydrogenase and drug–receptor binding data. In module 5, Cleland’s nomenclature for enzyme kinetics (13) was introduced. Leonora (3) or Winzyme was used to analyze kinetic data for uni- and bi-substrate reactions. The students were asked to propose preliminary kinetic mechanisms based on initial rate analysis. The concept of metabolic control analysis (14 ) was introduced in module 6. A general pathway simulator (Gepasi) (15) was used to simulate the oxidative phase of the pentose phosphate pathway (16 ).
Software from the Oxford Molecular Group, OMIGA, was selected to carry out genomic and proteomic analyses in modules 7, 8, and 9. Deoxyribonucleic acid and protein sequences were either provided to the class or retrieved from Entrez by the students. From the DNA sequences, the workshop of module 7 performed transcription, translation, mapping of open reading frames and alignment of nucleotide sequences. From the protein sequences, the workshop of module 8 conducted reverse translation, alignment of amino acid sequences, analysis of proteolytic sites, and prediction of protein secondary structures. In module 9, analysis of restriction sites and construction of a recombinant DNA were carried out. Assignments for the workshop of module 10 were provided to the students on diskettes containing 3-D structures of biomacromolecules. Three molecular graphic programs, Cn3D (17), KineMage (18), and RasMol (19), were applied to visualize and identify structural motifs of proteins, the coordination of the active-site metal ion of metalloenzymes, and characteristics of DNA–protein complexes. Two PC-based commercial molecular modeling programs, PC Spartan and CS ChemDraw–Chem3D package were employed in module 11 to calculate the single point energy and to perform energy minimization by molecular mechanics. The objective of this module is to introduce the concept of using an empirical force
Table 2. Assignments for Workshops ModTopic ule
Problems
1
Acquisition of freeware Retrieval of databases
Dynafit, Gepasi, ClustalW, Cn3D, KineMage, and Rasmol Nucleotide sequences for a genome and a circular vector, amino acid sequence of an enzyme
2
Linear regression ANOVA analysis QSAR analysis
Inhibition of cultured leukemia cells with methotrexate derivatives Treatment of HIV infection with antiretroviral agents Literature search (J. Med. Chem.)
3
Liquid chromatography Spectral analysis HPLC
Chromatographic peak fitting of seven protein components Spectroscopic peak fitting of FAD chromophore Literature search (J. Chromatogr.)
4
NADP-enzyme titration Drug–receptor binding Application of DynaFit
Titration of dimeric dehydrogenase with NADP+ Binding of a hypothetical drug to a tetrametric receptor Literature search (J. Biol. Chem., Biochemistry, Anal. Biochem.)
5
Uni-substrate reaction Bi-substrate reactions Inhibition studies
Kinetic analysis of esterase catalysis Kinetic analyses of hexokinase, G6P dehydrogenase, and 6-phosphogluconate dehydrogenase catalysis Literature search (Biochemistry, Biochem. J., Arch. Biochem. Biophys.)
6
Pentose phosphate pathway Pentose phosphate pathway Ethanol oxidation
Simulation of the oxidative phase of the pathway at constant ATP and NADP concn with varying D-glucose concn Effect of varying ATP and NADP concn on the oxidative phase of the pathway at constant D-glucose concn Literature search (J. Biol. Chem., Biochem. J., Arch. Biochem. Biophys.)
7
Identification of ORF Sequence alignment Application of ClastalW
Reverse translation of calcitonin and identification of open reading frame of calcitonin gene from procalcitonin Nucleotide sequence alignment of tRNA genes and identification of codons/anticodons Nucleotide sequence alignment of tRNA genes
8
Sequence analysis Motif analysis Sequence alignment
Post-transcriptional processing of proopiomelanocortin into ACTH, LPHs, MPHs, and endophins Translation of yeast ADH1 gene, proteolysis, and identification of the active site Amino acid sequence comparison of c- and g-type lysozymes and alignment of the active site residues
9
Restriction map Recombinant DNA Recombinant DNA
Identification of restriction sites for glucagon from preglucagon Insertion of glucagon gene into a circular vector Restriction and insertion of an enzyme gene into a circular DNA
10
Molecular graphic Molecular modeling Visualization
Identification of structural domains of proteins Identification of the active sites of alcohol dehydrogenase and Fe-superoxide dismutase Characterization of DNA–protein interactions
11
Energy minimization Geometric optimization Molecular mechanics
Energy minimization of oxidized versus reduced FMN Energy minimization and comparison of conformational differences among maltose, cellobiose, and isomaltose Effect of different empirical force fields in the MM energy minimization of oxytocin
12
Secondary structure
Literature search (J. Mol. Biol., J. Theor. Biol.) for empirical parameters and their use to predict secondary structures of alcohol dehydrogenases Overlap of FMN structures accompanied conformational changes in the redox reactions of flavodoxins Comparison of conformational differences by the KineMage animation of monomeric, tetrameric and synthetic ubiquitins
Structural overlap Molecular animation
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Information • Textbooks • Media • Resources
field to evaluate relative local minimum energies of biomolecules; therefore each assignment consists of paired compounds. In the module 12, 2-D comparison of homologous proteins was undertaken with ProGraph. This involved a modification of the amino acid parameter table consisting of the desired empirical conformational parameters (20). Three-dimensional comparison of conformational differences by the similarity overlap and animation were generated with KineMage (18, 21). Concluding Remarks This course was developed to teach microcomputer applications and biochemical principles to chemistry and biochemistry undergraduate students. Although it was conceived as an independent course for students who had taken general biochemistry, most modules of the course reinforce subject matter and can be used as supplementary teaching material for general biochemistry. The course, however, extends the topics beyond the boundary of general biochemistry. For instance, the peak-fitting routines in module 3, Cleland’s nomenclature in module 5, the metabolic simulation of module 6, and the energy minimization of module 11 are normally not included in the general biochemistry textbooks but are useful to the practice of modern biochemistry. To provide course content sufficient for one semester, selected commercial software programs were used to complement freeware. Two areas of the course specifically depend on commercial software, namely genomics (OMIGA) and energy minimization (PC Spartan or Chem3D). Not all of the relevant PC-based freeware programs are addressed here. Some of them may be added or used to replace current ones. For examples, we are evaluating the possibility of replacing the DynaFit program with the LIGAND program (22). To simulate and fit kinetic data, KinSim and FitSim (23) are useful. ClustalW (24 ) can be employed to align sequences. Metabolic databases (25) can be added to fill the missing links in the course. Similarly, metabolic control analysis (14 ) can be carried out with the metabolic simulator MIST (26 ). Aside from economical reasons, the use of freeware has the distinct appeal of portability, so that the students are able to continue and complete their assignments after the workshop period. Therefore, the use of freeware in undergraduate teaching merits our consideration.
Acknowledgment I would like to thank all the authors who provide these highly useful application programs for free distribution. Without their effort and generosity, the development of this course would not have been possible. Literature Cited 1. Atkinson, D. E.; Clarke, S. G.; Rees, D. C. Dynamic Model in Biochemistry; Benjamin/Cummings: Menlo Park, CA, 1987. 2. Microcomputers in Biochemistry; Bryce, C. F. A., Ed.; IRL: Oxford, UK, 1992. 3. Cornish-Bowden, A. Analysis of Enzyme Kinetic Data; Oxford University Press: Oxford, UK, 1995. 4. Doucet, J.-P.; Weber, J. Computer-Aided Molecular Design; Academic: San Diego, CA, 1996. 5. Hogue, C. W. V.; Ohkawa, H.; Bryant, S. H.; Trends Biochem. Sci. 1996, 21, 226. 6. Kanehisa, M. Trends Biochem. Sci. 1997, 22, 442. 7. Machuca-Herrera, J. O. J. Chem. Educ. 1997, 74, 448. 8. Harris, D. C. J. Chem. Educ. 1998, 75, 119. 9. Jansson, P. A. Deconvolution with Applications in Spectroscopy; Academic: San Diego, CA, 1984. 10. Brown, P. R.; Hartwick, R. A. High Performance Liquid Chromatography; Wiley: New York, 1989. 11. Evans, M.; Hastings, N.; Peacock, B. Statistical Distributions; Wiley: New York, 1993. 12. Kuzmie, P. Anal. Biochem. 1996, 237, 260. 13. Cleland, W. W. Biochim. Biophys. Acta 1963, 67, 104. 14. Fell, D. A. Biochem. J. 1992, 286, 313. 15. Mendes, P. CABIOS. 1993, 9, 563. 16. Tsai, C. S.; Chen, Q. Biochem. Cell Biol. 1998, 76, 107. 17. Hogue, C. W. V. Trends Biochem. Sci. 1997, 22, 314. 18. Richardson, D. C.; Richardson, J. S. Protein Sci. 1992, 1, 3. 19. Sayle, R. A.; Milner-White, E. J. Trends Biochem. Sci. 1995, 20, 374. 20. Chou, P. Y.; Fasman, G. D. Adv. Enzymol. 1978, 45, 45. 21. Richardson, D. C.; Richardson, J. S. Trends Biochem. Sci. 1994, 19, 135. 22. Munson, P. J.; Rodbard, D. Anal. Biochem. 1980, 107, 220. 23. Dang, Q.; Frieden, C. Trends Biochem. Sci. 1997, 22, 317. 24. Thompson, J. D.; Higgins, D. G.; Gibson, T. J. Nucleic Acids Res. 1994, 22, 4673. 25. Karp, P. D. Trends Biochem. Sci. 1998, 23, 114. 26. Ehlde, E.; Zacchi, G. CABIOS, 1995, 11, 201.
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