computer series, 172 Examining Host-Guest Interactions with an Integrated Spreadsheet1 Molecular-Modeling Program T. W. Hanks, R. Hallford, and G. Wright Furrnan University Greenville,SC 29613 Molecular Modeling as a Teaching Tool One of the primary challenges for using new technologies in the classroom is to use them appropriately. I n order for a new system to gain widespread acceptance, there must be obvious benefits that cannot be achieved through more conventional methodologies. Molecular modeling is an area that many academicians almost instinctively regard as a teaching tool of enormous potential ( I ) , but until there are compelling examples of its use (Z),adoption of the technique will be limited. In this paper, we describe a combination molecular modelingispread-sheet software package. We then discuss a student exercise in host-guest chemistry in which modeling is used as a tool for the investigation of intermolecular interactions. Finally, we present results from student reports as a means of evaluating the effectiveness of the method.
JAMES P. BIRK Arizona State Univesity Tempe, AZ 85287
also can lead to better predictions of transition states and to the design of new chemical reactions. Project Leader Project Leader is a "molecular spreadsheet" introduced by CAChe Scientific in late 1993.' I t works in conjunction with the other modules of the CAChe modeling suite. These include adaptations of Allinger's MMX forcefield ( 6 ) and a dynamics program based upon it, Extended Hiickel (71, MOPAC and ZINDO (8)semi-empirical molecular orbital programs as well a s tools for building structures and viewing calculated orbitals and surfaces (9). I n addition, the programs PlogW and PlogP (10) are available. These predict the water solubility and octanollwater partition coefficients, respectively. Project Leader is able to invoke these modules a s required to calculate values for spreadsheet cells. As with any other spreadsheet, one may enter data into a cell manuaily, derivi other infrrmnrim irom this data, or corrtht(, thc infomiation in one group of cells w i ~ hinformation from another group. project ~ e a d e differs r from other spreadsheets in that a valid data type may be a molecular structure file consisting of atom types, their spatial coordinates and any information generated from the computational methods employed. Figure 1shows a portion of a Project Leader spreadsheet used later in this paper. In this example, each cell in column one contains structural data for a guest molecule. Column two contains experi-
Host-Guest Binding The terms "host" and "guest" were introduced by Cram in the late 1970's (3). Hosts are relatively large mblecu~est h a t have multiple, converging binding sites capable of interacting with other molecules. I n biological terms, hosts a r e receptor sites. Guest molecules t e n d to be smaller and have binding sites that diverge. Guests play the role of s u b s t r a t e s o r inhibitors i n chemical and biological systems. When a host envelops a guest, the resulting structure is termed a "complex". Anumber of interac- Figure 1. Portion of a Project Leader spreadsheet tions can be important to the stamentally determined binding energies, while the remainbility of a complex, including (4): ing columns contain calculated molecular properties exsteric effects tracted from the structure files. ion pairing hydrogen bonding A Modeling Exercise for Advanced Undergraduates hydrophobic effects The modeline laboratorv that follows is used in our Adaryl-aryl interactions vanced Structure and Reactivity course, designed primarsolvent effects ily for Senior undergraduates and Masters students. The entropy effects, such as host "preorganization"( 5 ) course serves approximately eight students a year, all of whom have taken a t least one course in physical chemistry The study of the factors that enable a host to strongly and three in organicheaction chemistry. Because we are and selectively bind a guest is a central one in the field of integrating molecular modeling into our cnrriculum a t molecular recognition. The same interactions also are keys every level, most students already will have used the modto the rational design of new drugs, catalysts, and materieling computers in several different courses and in varying als. A better understanding of intermolecular interactions deerees of so~histication. To prepare for the laboratory experiment, the class is first ' C A C ~Scientific, ~ P. 0. Box 500 MIS 13-400. Beaverton, OR givenafive-hour series oflectureltutorials on the operationof 97077. the programs, the appropriate use of the various computa-
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tional modules and some warnings as to the limitations of the methodologies. Every student has a workstation and is able to spend additional time outside of the formal sessions in order to master a topic. These sessions familiarize the students with the types of predictions that the modeling programs can make and give examples of how such information can be applied to real chemical problems. Creating a Model of a Host-Guest Complex: The Assignment
I n the winter of 1994,we introduced the following experiment in which modeling became a tool for investigating the subtleties of a particular hoskguest system. To do this, students were instructed to: 1. Model a series of guest molecules, calculating a variety of
molecular properties far each.
2. Compare and contrast calculated properties with experi-
mentally determined binding constants to a host malecule. 3. Note the properties that earrelate mast closely with binding and develop a theory to explain the interactions important in host-guest complexation. This was done in a report written as a short research paper. Dougherty and co-workers (11) recently have reported on the interaction of a number of small molecules to the host molecule "Pa (Fig. 2). The structure and binding energies of some 67 guest molecules from this report were provided to the students; however, the structure of "P" was not. This situation is much like that encountered i n biological systems where the active site of a particular enzyme or receptor is not known. Students were asked to choose a subset of eight to 10 guest molecules and to develop a theory a s to the nature of the host-guest interactions. The use of a subset of the data was advantageous for several reasons. First, by choosing a set of related guests, students could focus on relatively simple trends. Very diverse data sets are diff~cultto model because of the large number of interactions that might be important. Secondly, by having different subsets of the guests, each student conducted a different, but closely related "experiment". Finally, by limiting the number of guest molecules, the computational load for each experiment became manageable. Results Student Reactions
Student response to this exercise was very positive. Most seemed to fmd the experiment interesting, and they put a
c b p Figure 2. Host " P . 330
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considerable amount of effort into their reports. There were suprisindy few difficulties with the software. O ~ e r a tion of the modeling packages through the Project ~ e a d e r interface proved to be easier than running the individual programs. While the students felt that the assignment was interesting and helped them to understand a difficult problem, the quality of their work varied. Many presented highly insightful analysis, but occasionally students sacrificed chemical logic for statistical correlation. For example, one student proudly presented a perfect correlation between the electron affinity of his guests and the energy of their LUMO's. When asked to explain the significance of this finding, the student soon realized that not only were the two properties dependent, but that correlations between calculated properties said nothing about the interactions involved i n host-mest com~lexation. Follow~ngthe complet~onof thc course, the two ofus who had been subjerted to the assignment R I I and GM', agreed to refine our analysis using multilinear regression analysis and instructor feedback from our original reports. While working independently on very different classes of molecules, we came to very similar conclusions concerning the binding of guests to host "P". The following sections are excerpts from our analyses. Student Results: Binding of N-Heterocycles to Host " P
One of us chose the set of guest molecules shown in Figure 3. The guests varied greatly in their ability to bind to the host, with reported binding energies ranging from 3.8 to 8.5 eV (11). Initial geometries were obtained by drawing the structures in the Editor application and using the built-in clean-up routines. Each structure was then optimized by molecular mechanics and the resulting structure was used as input for analysis by MOPAC using AM1 parameters. Of the various molecular features available. the best correlation was found by comparing the binding energy with the partial charge on the ring substituent (in the case of quinoline (6),a ring carbon was used a s the suhstituentj. A correlation coefficient of 0.854 was obtained with this oarameter alone. We proposed that the major interaction was a donor-acceptor interaction between a n electron-ooor guest and a n electron-rich host. I n a n effort to imorove the correlation. several multinle linear regression analyses were Modest Lmprovements could be made in the correlation bv factoring i n the electron affinity, octanoVwater partition-coefficient (from the BlogP program) or the molecular volume. The
7 (5.9)
8 (3.8)
Figure 3. Eight heterocyclic guest molecules and their exponential binding energies.
use of a11 four parameters gave a correlation of greater than 0.95. These observations are consistent with those made by Dougherty in the original work (11). They note that while desolvation and hydrophobic interactions certainly play a role in the binding, these contributions are likely to be very similar for the flat aromatic systems, and therefore, play only a minor role in the relative binding energies. The rather small changes in molecular volume also played a relatively minor role, though this would be expected to change with very large substituents. Within the regime considered, increasing size resulted in greater hydrophobic interactions, but a t some point steric repulsions would begin to decrease the binding energy. Student Results: Binding to Host "P"by Alkylammonium Cations
The next subset of guests include eight ammonium cations as shown in Figure 4. A portion of the Project Leader spreadsheet used here is shown in Figure 1. These complexes had binding energies ranging from 4.8 to 6.7 eVs (11). . ., while havine a alkvlammonium head unit. The structures were drawn and optimized in the same way as the first data set. For these com~ounds.however. the choice of starting geometry would be expected to be'more critical than for the rigid aromatic systems, where local minima are less common. I t was assumed that all alkyl chains would adopt staggered conformations and six-membered rings would adopt chair-type conformations. A number of predicted features were extracted from the structure files. Some of these, such a s heat of formation,
Figure 4. Eight tetraalkylammonium guests and their experimental binding energies.
D i p o l e Moment (debye)
dipole moment, electron affinity, HOMO energy, LUMO energy, surface area, volume and water solubility were based upon the whole molecule. Others, such as partial charge, electrophillic susceptibility and nucleophilic susceptibility, were specific to a particular atom in each molecule. We considered both the ammonium nitrogen and a carbon to that center. Unlike the former data set, the calculated partial charge of either of the two atoms evaluated showed no correlation to binding because, in most cases. these values were virtually identical. Of the considered, only dipole moment showed any discernible correlation (Fie. 5). The correlation coefficie& of 0.409 was certainly no't impressive. Combinations of dipole moment and other parameters also was generally unproductive. Addition of water solubility to the correlation improved the correlation to 0.511, but no other calculated feature proved to be of any value. (See Fig 5) Examination of the data showed that the molecules that lay furthest from the regression were the substituted cyclohexanes 10 and 11.We nronose that enereeticallv unfavorable conformational cianbes are required for optimal bindine of 10 to host "P". leadine u to a n anomalouslv low binding energy. If we disregard compound 10, the correlation coefficient iumns to 0.677 for dioole moment alone. and 0.847 incluhing dipole moment and water solubility. Excluding the doublv chareed com~ound11 raises the correlations'to 0.854 a i d 0.913 (Fig. g), respectively.
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Conclusions 1)ougherty proposed that host 'P' adopts dlfftwnr conformutlons for the N-h(~trn~cvc11c and the ammomum catlon guests (11).It is not at allsurprising that different models were proposed to explain the important binding parameters for the two classes of guests. Despite this, both models point out the importance of electronic effects and salvation. In correlational analysis, one would like to have a t least five data points per parameter in an equation (12).This requirement is ex&edkgly difficult to meet in structure-activity relationship studies because it either means over-simplif$ng a complex system or preparing and testing-an impractical number of guests. In this experiment, however, students were able to identify some important features of the host from their models and also were able to offer explanations for guests that deviated from their models. Much of chemical education, and of chemistry itself, is the communication of models ofreal molecular systems (9). Perhaps the most important lesson we can give to our students is that these models are based on incomplete data,
Regression o f d i p o l e m o m e n t and w a t e r s o l u b i l i t y
'7
/
4 1 , 4
,
, , ,
5
, , , , 6
P
7
B i n d i n g Energy dipolemoment=3.592*Binding Energy-16.054;rA2= 0.409
C = -0.160°D+ 0.178'E+4.799 T2= 0.934
Figure 5. Correlations of experimental binding energy (11) with calculated molecular features. Volume 72 Number 4 April 1995
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simplifying assumptions and experimental data t h a t i s subject to error. The value of this exercise i s i n having the student struggle with building a workable model of a particular svstem. while realizing the limitations of the tools used to Guild that model. The next logical step i n this process i s to use a student model to predict the binding- enerm -. of a n untested molecule; and then perform that experiment. The design of this exercise is i n progress. Acknowledgment The Furman Molecular Modeling Laboratory was constructed with financial assistance fmm NSF's Instrument and Laboratory Improvement Program and the Milliken Foundation. We thank Lany Trzupek for helphl conversations. Literature Cited
D.: B.:
1. See DeKock, R. L.:Madura, J. Riour, F.;Casanova, J. in ReuYus in Compufcltional Chemistry, Lipkowitl, K . Boyd, D. B., Eds.,VCH: New York, 1993,Vol.4, pp 140 and references therein. 2. Far spedicexamples of the use of molecular modeling, see recent issues of this Javrnol as well as Hehre, W . J.: Burke, L. D.: Shusterman, A. J.; Pieho, W.J.
Experiments in Compulotionol O~gonicChamistw:Wavefundion,he.: Iruine. CA, .OO?
L.:Tarnowski.
3. Kyba, E. P.: Helgemn, R. C.; Madan. K:Gokel. T T. L.; Moore, S. S.; Cram,D. J. J Am. C h m . Soe. 1971,09,25M. 4. For a recent revlew of computational insights into binding, see: Jorgensen, W . L. C h m t m c k P O r g Chem 1991.6 91. 5. Cram, 0. J.: Lehn. G.M. J Am. Cham. Soc. 1986,107,3657. 6. Berkert, U.: Ailinger, N. L. Molecular Mwhcnics; ACS Monograph 177; American Chemical Society: Washington. OC, 1982. 7. Hoffman. R. J. Chrm. Phys. 1981.30,1397. Zemer. M. C. Inorg. Cham. 1990.29, 8. Anderson, W. P.: Cundarm. R. R.: Drago, R. 1. 9. W J Chem. Edue. 1994.71,62. 10. Bodel N.;Husng, M. - J J. Pharm. Sei. 1992.81.954. 11. Keamqv. P C.; Mizoue, L. S.; Kumpf. R. A,: Forman, J.E.: McCurdy, A.; Dougherty, D.A. J. Am. Cliem. S a . 1993,115,9907. 12. Hansch, C.: Klein. T. E . A e Chem. Res 1986.10.392.
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Research-Level Computers and Software in the Undergraduate Curriculum Hannes J6nsson University of Washington Seattle, ~ ~ $ 8 1 9 5
Computers have become valuable research tools in all the sciences. With user-friendly software, they can enable students to explore and develop a deeper understanding of concepts and equations ( I ) . Modern workstations with graphical interfacing can combine t h e computational power for state-of-the-art calculations with hassle free program execution and analysis of results. Many research groups have workstations dedicated to research projects. However, undergraduate and even graduate chemistry classes typically do not reflect the existence of these powerful tools. Chemistry undergraduate students usually do not even have access to graphics workstations. This i s an unfortunate situation. A s t u d e n t graduating without hands-on experience with a research-level computer has not received a n up-to-date education. Furthermore. wellchosen computer calculations can complement the traditional curriculum and enhance the student's intuition and understanding. Too often, regular student assignments are limited to calculations that can be done i n a reasonable amount of time on a piece of paper, possibly with the help of a calculator, rather than calculations and graphics that best illustrate the concepts the student needs to-develop. This article briefly describes a n upper division undergraduate course entitled " ~ o m ~ u t a t i binn Chemistry" recently established a t the University of Washington. The format i s that of a laboratory course, but the students work exclusively with color graphics workstations and carry out various com~ntationalexercises. The ouroose is first of all to give the students hands-on experience with research
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Journal of Chemical Education
level computers and software. Furthermore, they get reinforcement of several difficult concepts covered in previous courses while working through the computational exercises. The course material includes. but is not limited to. calculations of electron wave function for molecules and molecular modeling with empirical force fields. The first half of the course is a t the level of algorithms, where the students enter short programs into the computer using . -a mathematical analysis program as a high-level programming language. In the second half, the students run larger, menu-driven programs, both commercial chemistq software and software developed by undergraduates in the Department. A comouter teachine facilitv with several workstations has been'established f;;r this &rpose as well as for use in homework exercises and independent ~ r o i e c t sin other undergraduate and graduate chemistT c&rses. The facility has enabled the development and implementation of new approaches to teaching. The hope is that the software and exercises developed for this special course will prove useful for demonstrations and homework assignments i n the r e m l a r undergraduate courses. The software is available through the Internet computer network. Organization of the Course This is a one-quarter, three-credit course with two lectures and one four-hour laboratory session per week. I t is one of several courses chemistry majors can choose from to fulfill a requirement of a total of 14 credits in upper (400) level chemistry courses. The prerequisite is a year ofphysical chemistry, or a t least concurrent enrollment in the last quarter of the physical chemistry sequence. The class was taught for the first time by the author in spring of 1993. The lectures cover the necessary background for the calculations. Mathematical techniaues are reviewed. the numerical method presdnted a n d the physics and chemistry of the computational exercise of each week discussed. In the laboraiory session, the student receives a handout similar in spirit to a laboratory handout for a physical chemistry laboratory course. The handout gives detailed instructions for writing short programs or simply running prewritten programs. Several questions are raised in the handouts and hints are given for further exploration by the student. Throughout t h e exercise, t h e student collects various graphs, which become the core of a written or an oral report. Part A: Introduction to Tools and Algorithms The basic techniques for solving differential equations on a comouter usine finite difference aooroximations are first presented and applied to calculations of chemical kinetics, quantum wave functions and classical trajectories. The student uses directions from a laboratory handout to Dropram short versions of these aleorithms or to modifv k c a d d to prewritten modules. ~ h i r i done s within a hig(level programming environment where the tedium of programming is reduced as compared with Fortran or C and graphical display of results and even animation is relatively easy. The program "Mathematica" from Wolfram Research Inc. was chosen for this purpose (2).The techniques illustrated are 'finite difference' approximations to differential equations (first three exercises), Fourier expansions and matrix eigenvalue problems (fourth exercise), and random numbers (fifth exercise).
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Exercises in the First Half 1. Kinetics of coupled chemical reactions. 2. Calculations of quantum mechanical wave functions and energy levels. 3. Calculations of exchange reactions using classical me-
chanics. 4. Molecular vibrations and normal modes.
