Integrating Computational Chemistry into a Project-Oriented

Information • Textbooks • Media • Resources edited by

Computer Bulletin Board

Steven D. Gammon

Integrating Computational Chemistry into a Project-Oriented Biochemistry Laboratory Experience: A New Twist on the Lysozyme Experiment

University of Idaho Moscow, ID 83844

Rachel R. Peterson and James R. Cox* Department of Chemistry, Murray State University, Murray, KY 42071-3346; *[email protected]

Computational and carbohydrate chemistry have become two of the most active areas of chemistry. Methods in computational chemistry (molecular modeling) have found application in many areas of chemistry and will probably become even more important in the future. It has become clear that carbohydrates play a vital role in mediating a variety of cellular processes (1–4 ). As a result, the structure and dynamics of carbohydrates, free in solution and bound to protein targets, are being intensely investigated (5–11). Computational chemistry, in conjunction with NMR methods, plays a large role in the conformational analysis of carbohydrates (12). An experiment that uses molecular modeling to investigate the structure of carbohydrates will give students insight into these emerging areas of chemistry. Carbohydrates have long been studied in the undergraduate biochemistry laboratory. However, most of the carbohydrate-based experiments are focused on traditional methods of separating and identifying various types of saccharides. A more modern treatment of carbohydrates that reflects the explosion of glycoscience needs to be implemented in the biochemistry laboratory. This can be accomplished in the laboratory experience by bringing together molecular modeling, carbohydrate structure, and the recognition of carbohydrates by proteins. Several excellent examples of the integration of computational chemistry into the chemistry curriculum have appeared in this Journal (13–18). The approach described in this report is different in that we sought to integrate the modeling into a project-oriented experience that would fit into our biochemistry laboratory course. Recently, there has

CH3

CH3 O H HO HO H2C H OH

C

40

13

OH

O

H H2C

H NH 4

31

O

NH

O

39O

32

HO NH C H 3C

C

H 41 O

HO O

66

58

H2C H 67 OH

OH

been emphasis on a biochemistry lab experience that is project based and resembles a research project (19–22). When we looked for an integrated biochemistry laboratory experience to adapt, it was clear that lysozyme would be the enzyme of choice (19, 20, 23). This enzyme is easily purified and many of the major biochemical techniques can be demonstrated in the purification protocol. Lysozyme was also attractive because a previous report outlined a detailed protocol (19). Therefore, we adopted an established protocol for lysozyme isolation and quantitation and added computational and visualization components. The structure of egg-white lysozyme is well known and its crystal structure appears in many biochemistry textbooks (24–27). The structure of lysozyme has also been solved with a substrate-like inhibitor, tri-N-acetylchitotriose (NAG)3 (28). With this structure in hand, students were asked to compare the structure of the bound ligand to a low-energy structure generated through computation. They were also asked to study many protein–carbohydrate complexes using molecular visualization programs such as RasMol or Protein Explorer. It is important for students to understand how proteins can recognize carbohydrates through the formation of various types of intermolecular forces. This module of the project-oriented experience satisfied our desire to introduce molecular modeling and visualization in the biochemistry laboratory and to clearly show how these methods are quite different, yet complementary. In this report, the conformational analysis is an example of molecular modeling, and the use of computer software to visualize protein– carbohydrate interactions is an example of molecular visualization. We feel that the integration of this module in a projectoriented biochemistry laboratory can be the first step for many schools to include molecular modeling and visualization in the biochemistry curriculum. We hope to enhance our own computational facilities and offer a more comprehensive course in structural biochemistry that will further involve the use of computers to investigate the structure of biomolecules.

O

Figure 1. The structure of tri- N-acetylchitotriose (NAG)3. Atoms involved in the conformational analysis are labeled and represent torsion 1 (H41–C32–O66–C58), torsion 2 (H67–C58–O66–C32), torsion 3 (H13–C4–O39–C31), and torsion 4 (H40–C31–O39–C4). Numbers were assigned in HyperChem.

Experimental Procedures

Protein Biochemistry The isolation, purification, and quantitation of lysozyme from egg white has been well documented and will not be described in this report. We followed the protocols of Hurst et al. (23) and Wolfson et al. (19), where lysozyme was purified and characterized using established methods.

JChemEd.chem.wisc.edu • Vol. 78 No. 11 November 2001 • Journal of Chemical Education

1551

Information • Textbooks • Media • Resources

Conformational Analysis All calculations were performed in HyperChem 5.0, running on a PIII Gateway Computer with 256 MB RAM. Geometry optimizations were carried out in vacuo using molecular mechanics calculations with the MM+ force field (29) and the conjugate gradient algorithm (Polak–Ribiere). An excellent overview of the various algorithms used for energy minimizations, including Polak–Ribiere, can found in Leach (30). The students built (NAG)3 (Fig. 1) using the saccharide builder in the ChemPlus module of HyperChem and carried out an initial geometry optimization to a root mean square (rms) gradient of 0.01 kcal mol ᎑1 Å᎑1. Torsional driving was used to probe the conformational space available to the carbohydrate. There are four different torsion angles around the glycosidic bonds of the carbohydrate that can be used in the analysis (Fig. 1). To obtain a complete and thorough conformational analysis of the carbohydrate, all four torsion angles around the glycosidic bonds should be used in the analysis. However, this would take a great deal of time and computer resources and the results would be difficult to process and visualize. Therefore, we decided to choose one torsion angle from each glycosidic bond in (NAG)3. A number of torsion angle combinations are possible: torsion 1 versus torsion 3, torsion 1 versus torsion 4, torsion 2 versus torsion 3, or torsion 2 versus torsion 4. The number of students may dictate the number of torsion angle combinations performed. The first time this lab was implemented was with a very small group of students, who worked as one team. In this experiment, torsion 1 was driven against torsion 4 and no other combinations were utilized. In subsequent semesters, students have been organized into two teams; one team drives torsion 1 versus torsion 4 and the other drives torsion 2 versus torsion 3. In the future, more computers will be available and we will have students work individually and explore many of the torsion angle combinations. The comparison of the results from an expanded experiment will be a positive aspect of the laboratory we have not yet explored. Ultimately, the number of different angles to study should be based on the number of students in the lab and the availability of computational resources. The remaining portion of this section only describes the experiment where torsion 1 and torsion 4 are utilized; however, this protocol can be used with any combination of torsion angles. Torsion 1 (H41–C32–O66–C58) and torsion 4 (H40–C31– O39–C4) were driven against each other in 10° increments with a total of 36 increments. At each pair of angles, (NAG)3 underwent a restrained geometry optimization to a rms gradient of 0.001 kcal mol ᎑1 Å᎑1. A force constant of 250 kcal mol ᎑1 deg᎑2 was used to keep the correct pair of angles during the optimization. The energy of each of the 1,296 conformers generated in the analysis was calculated and a potential energy surface was generated. The lowest energy conformer from the torsional driving was identified and underwent an unrestrained geometry optimization to an rms gradient of 0.001 kcal mol ᎑1 Å᎑1. This refined conformation will henceforth be referred to as the theoretical conformation. HyperChem was also used to superimpose (overlay) the theoretical conformation of (NAG)3 and the lysozymebound conformation of (NAG)3 to check for structural similarity. All nonhydrogen atoms in the two molecules were involved in the overlay calculation. 1552

Figure 2. A ball-and-stick representation of the lowest energy structure of (NAG)3 (theoretical conformation) that resulted from the conformational analysis. Dashed lines indicate potential H-bonds.

Figure 3. The potential energy surface generated in the conformational analysis of (NAG)3.

Molecular Visualization Although this biochemistry laboratory module centered around computational chemistry, the recognition of carbohydrates by proteins was an integral part of the experience. Since the complex between lysozyme and (NAG)3 has been thoroughly examined (19, 31, 32), students were asked to find other protein–carbohydrate complexes solved through X-ray or NMR methods. The students searched for these complexes in the Protein Data Bank, a site maintained by The Research Collaboratory for Structural Bioinformatics (RCSB, http://www.rcsb.org), which is a rich source of protein structures. Students downloaded the coordinate files (.pdb or .ent) and loaded them into RasMol (http://www.openrasmol.

Journal of Chemical Education • Vol. 78 No. 11 November 2001 • JChemEd.chem.wisc.edu


org) or the Chime-based Protein Explorer (http://www. proteinexplorer.org) to visualize the structures. Chime is a plugin that is used with a Web browser to interactively view and manipulate molecules. It can be downloaded from the Web site of MDL Information Systems, Inc. (http://www.mdli.com). The students in the laboratory were asked to find specific examples of noncovalent interactions between a protein and a bound carbohydrate. Student presentations at the end of the semester highlighted the many types of interactions that exist between proteins and carbohydrates. Results and Discussion

Conformational Analysis We have only carried out experiments in which torsion 1 was driven against torsion 4 or torsion 2 was driven against torsion 3. The low-energy structures that resulted from these two independent torsional driving calculations were very similar with respect to torsion angles and energy (less than 1 kcal/ mol difference). This was not surprising, since torsional driving probed a significant amount of conformational space available to the carbohydrate (1,296 conformations). A clear advantage of torsional driving is that the resulting low-energy structure should be independent of the initial structure of the molecule under investigation owing to the large number of conformations generated and evaluated. This has certainly been the case in our experiments. Each time the experiment is performed, the students build (NAG)3 from scratch, not relying on previously built structures. The experiment involving torsion 1 and torsion 4 has been repeated many times with great reproducibility. For the sake of brevity, only the results obtained when torsion 1 and torsion 4 were used will be discussed. The structure of (NAG)3 that resulted from the conformational analysis (theoretical structure) is shown in Figure 2. Most of the flexibility in carbohydrates occurs around the glycosidic linkages (33, 34). Therefore, the conformation in Figure 2 is a reasonable approximation of an unbound (or average) conformation of (NAG)3 and worked well in this computational module. The potential energy surface generated from the torsional driving experiment is shown in Figure 3. This gave the students a visual representation of a conformational analysis and provided a better understanding of the relationship between conformation and energy. The lysozyme-bound and theoretical structures of (NAG)3 have been overlaid in Figure 4. The coordinates for the lysozyme-bound (NAG)3 were obtained from the Protein Data Bank with the pdb code 1HEW. The rms deviation for the overlaid structures (only nonhydrogen atoms considered) was 0.68 Å. These two conformations also have similar values for torsion 1 and torsion 4. The angles in the theoretical structure were 20° (torsion 1) and 0° (torsion 4) compared to 30° (torsion 1) and 357° (torsion 4) in the lysozyme-bound structure. These results suggest that free and lysozyme-bound (NAG)3 adopt similar but distinct conformations. Although the theoretical structure of (NAG)3 in Figure 2 will most likely not be the favored conformation in aqueous solution, students need to consider the flexibility of carbohydrates free in solution and bound to macromolecules such as proteins. In aqueous solution, carbohydrates will adopt conformations that maximize H-bonding opportunities

Figure 4. A stereopair of the superposition of the lysozyme-bound (black) and theoretical (gray) structures of (NAG)3. All nonhydrogen atoms in the two structures were used in the rms overlay calculation. For clarity, hydrogens are not shown in the figure.

among polar groups (intramolecular interactions) or with water molecules (intermolecular interactions). The fact that carbohydrates can be quite flexible in solution suggests that these two types of interactions are of similar energy. However, when bound to a protein, carbohydrates will often interact with specific residues in a binding pocket. This can restrict mobility and force the carbohydrate away from the conformations populated in solution (lowest energy conformations) and into an important bioactive conformation. An excellent example of this can be found in reactions catalyzed by sugar kinases, where the orientation of the carbohydrate is key to the regiochemistry of the reaction. Usually, only one hydroxyl group of the carbohydrate is reactive while several other hydroxyl groups remain unmodified. Another aspect of this module currently under development is a comparison of the energy of the theoretical structure and the enzyme-bound structure. Simply calculating and comparing the energy of the two conformations (with added hydrogens) in Figure 4 is not very revealing. The energy of the theoretical structure is lower, but the students understand that this is because the interactions between the carbohydrate and the protein are not included in the calculation for the enzyme-bound structure. We are developing a method by which the amino acid side chains of lysozyme that are known to interact with the carbohydrate are included in the energy calculation. This will reinforce the idea that the formation of intermolecular interactions in the protein–carbohydrate complex compensate for the loss of weak interactions and entropy when the complex is formed. As mentioned below, this module has a great deal of flexibility and certain instructors may wish to emphasize this part of the module while others may want to focus only on the geometry of the structures. Another attractive feature of this computational module is that it can be expanded in many ways, thus meeting the needs of a variety of instructors and institutions. NMR studies on (NAG)3 will allow students to produce a more refined solution structure of the carbohydrate and learn how NMR data is used to complement molecular modeling in structure determination. Other instructors may wish to use a variety of force fields or modeling packages for the molecular mechanics calculations. A comparison of the conformations of (NAG) 3 derived from the different conformational analyses (with or without NMR data) will give students unique insight into the struggles of researchers in this area. A discussion of the anomeric effect will also fit nicely into this module.


1553


area to be productive and competitive in today’s job market. The module described in this report uses molecular mechanics in a conformational analysis. Students were exposed to the fundamentals of computational chemistry and the anatomy of a molecular mechanics force field. The recognition of carbohydrates by proteins was also emphasized through the use of molecular visualization. This module enabled students to better understand and appreciate the role of noncovalent interactions in biomolecular structure and how they can influence the conformations of a ligand when bound to a protein. Acknowledgments

Figure 5. The interaction of amino acid side chains from congerin I and bound lactose. Hydrogen bonding and CH–π interactions help stabilize this protein–carbohydrate complex.

Biochemistry students at Murray State University are acknowledged for their work in investigating protein–carbohydrate interactions and participating in the project-oriented laboratory. Academic Computing and Technology Services (ACTS) at Murray State University is acknowledged for allowing us to use their technology-enhanced classroom. Literature Cited

Molecular Visualization of Protein–Carbohydrate Complexes Having students examine protein–carbohydrate complexes through molecular visualization is a nice complement to the computational aspect of the module. It is important to enhance the understanding and appreciation of noncovalent interactions in biological systems. The observations from a complex between lactose and the protein congerin I (35) are shown in Figure 5. A student identified many potential interactions that help stabilize this complex. H-bonds seem reasonable between bound lactose and Arg29, His44, and Arg75, while Trp70 is stacking against a face of the galactose residue. The interplay between Trp70 and lactose is an example of carbohydrate–arene interactions and has been observed in many types of protein–carbohydrate complexes (36– 38). In fact, a similar type of stacking interaction occurs in the complex between lysozyme and (NAG)3 (31, 32). A Chime-based Web site featuring the search for noncovalent interactions in protein systems has been developed (http:// campus.murraystate.edu/academic/faculty/ricky.cox/pi/ pi_inter.htm). This site contains links to other pages devoted to the molecular visualization of biological molecules. One prominent example is the World Index of Molecular Visualization Resources (http://www. molvisindex.org), which catalogs many tutorials on using visualization methods to teach the structural nature of molecules important in chemistry and biology. Conclusions A module for a project-oriented biochemistry laboratory was developed. It provides a unique combination of molecular modeling and molecular visualization and was developed in response to the expanding fields of computational and carbohydrate chemistry. Computational methods are widely used in chemistry, and students must gain experience in this

1554

1. Lee, Y. C.; Lee, R. T. Acc. Chem. Res. 1995, 28, 321–327. 2. Sears, P.; Wong, C.-H. Chem. Commun. 1998, 1161–1170. 3. Sears, P.; Wong, C.-H. Angew. Chem., Int. Ed. Engl. 1999, 38, 2300–2324. 4. Lis, H.; Sharon, N. Chem. Rev. 1998, 98, 637–674. 5. Landersjo, C.; Stenutz, R.; Widmalm, G. J. Am. Chem. Soc. 1997, 119, 8695–8698. 6. Cox, J. R.; Serpersu, E. H. Biochemistry 1997, 36, 2353–2359. 7. Geyer, A.; Hummel, G.; Eisle, T.; Reinhardt, S.; Schmidt, R. Chem. Eur. J. 1996, 2, 981–989. 8. Casset, F.; Peters, T.; Etzler, M.; Korchagina, E.; Nifantev, N.; Pérez, S.; Imberty, A. Eur. J. Biochem. 1996, 239, 710–719. 9. Scheffler, K.; Ernst, B.; Katapodis, A.; Magnani, J. L.; Wang, W. T.; Weisman, R.; Peters, T. Angew. Chem., Int. Ed. Engl. 1995, 34, 1841–1844. 10. Jiménez-Barbero, J.; Asensio, J. L.; Canada, F. J.; Poveda, A. Curr. Opin. Struct. Biol. 1999, 9, 549–555. 11. Coxon, B.; Sari, N.; Mulard, L. A.; Kovac, P.; Pozsgay, V.; Glaudemans, C. P. J. J. Carbohydr. Chem. 1997, 16, 927–946. 12. Woods, R. J. Glycoconjugate J. 1998, 15, 209–216. 13. Gasyna, Z. L.; Rice, S. A. J. Chem. Educ. 1999, 76, 1023–1029. 14. Kantardjieff, K. A.; Hardinger, S. A.; Van Willis, W. J. Chem. Educ. 1999, 76, 694–697. 15. Martin, N. H. J. Chem. Educ. 1998, 75, 241–243. 16. Saiz, E.; Tarazona, M. P. J. Chem. Educ. 1997, 74, 1350–1354. 17. Lee, M. J. Chem. Educ. 1996, 73, 184–187. 18. Shusterman, G. P.; Shusterman, A. J. J. Chem. Educ. 1997, 74, 771–776. 19. Wolfson, A. J.; Hall, M. L.; Branham, T. R. J. Chem. Educ. 1996, 73, 1026–1029. 20. Deal, S. T.; Hurst, M. O. J. Chem. Educ. 1997, 74, 241–242. 21. Craig, P. A. J. Chem. Educ. 1999, 76, 1130–1135. 22. Markwell, J. J. Chem. Educ. 1993, 70, 1018–1019. 23. Hurst, M. O.; Keenan, M. V.; Son, C. C. J. Chem. Educ. 1992, 69, 850–851. 24. Voet, D.; Voet, J. G.; Pratt, C. W. Fundamentals of Biochemistry, 1st ed.; Wiley: New York, 1999.

Journal of Chemical Education • Vol. 78 No. 11 November 2001 • JChemEd.chem.wisc.edu

Information • Textbooks • Media • Resources 25. Garrett, R. H.; Grisham, C. M. Biochemistry, 2nd ed.; Saunders: New York, 1999. 26. Nelson, D. L.; Cox, M. M. Lehninger Principles of Biochemistry, 3rd ed.; Worth: New York, 2000. 27. Stryer, L. Biochemistry, 4th ed.; Freeman: New York, 1995. 28. Cheetham, J. C.; Artymiuk, P. J.; Phillips, D. C. J. Mol. Biol. 1992, 224, 613–628. 29. The MM+ force field is derived from the MM2 force field (Allinger, N. L. J. Am. Chem. Soc. 1977, 99, 8127) and is a product of Hypercube, Inc. 30. Leach, A. R. Molecular Modelling: Principles and Applications; Longman: Singapore, 1996. 31. Nishio, M.; Hirota, M.; Umezawa, Y. The CH/p Interaction: Evidence, Nature, and Consequences; Wiley: New York, 1998.

32. Cox, J. R. J. Chem. Educ. 2000, 77, 1424–1428. 33. Rutherford, T. J.; Partridge, J.; Weller, C. T.; Homans, S. W. Biochemistry 1993, 32, 12715–12724. 34. Coxon, E. E.; Coxon, J. M.; Boswell, D. R. Aust. J. Chem. 1998, 51, 397–401. 35. Shirai, T.; Mitsuyama, C.; Niwa, Y.; Matsui, Y.; Hotta, H.; Yamane, T.; Kamiya, H.; Ishii, C.; Ogawa, T.; Muramoto, K. Structure 1999, 7, 1223–1233. 36. Poveda, A.; Jiménez-Barbero, J. Chem. Soc. Rev. 1998, 27, 133–143. 37. Davis, A. P.; Wareham, R. S. Angew. Chem., Int. Ed. Engl. 1999, 38, 2978–2996. 38. Elgavish, S.; Shaanan, B. Trends Biochem. Sci. 1997, 22, 462–467.


1555

Integrating Computational Chemistry into a Project-Oriented

Recommend Documents