In the Classroom
Receptor Surface Models in the Classroom: Introducing Molecular Modeling to Students in a 3-D World Werner J. Geldenhuys,† Michael Hayes, Cornelis J. Van der Schyf,† and David D. Allen*† Department of Pharmaceutical Sciences, School of Pharmacy, Texas Tech University Health Sciences Center, Amarillo, TX 79106; *
[email protected] Sarel F. Malan Pharmaceutical Chemistry, School of Pharmacy, North-West University, Potchefstroom, 2520, South Africa
University chemistry instructors realize that it can be difficult to teach students concepts to translate two-dimensional chemical drawings into three-dimensional (3-D) stereochemical objects (1, 2). Going one step beyond this challenge in complexity, involves teaching 3-D interactions of drug molecules with biological proteins, such as receptors, in the human body. In teaching these concepts, most university instructors utilize computer programs depicting a protein into which the student is required to insert a drug, a process also known as docking. Even though such programs can be very sophisticated, some students still have difficulty relating computer screen visuals to 3-D concepts important for medicinal chemistry or pharmacology, such as orienting chemical groups important for interaction correctly inside the interacting macromolecule. In silico molecular modeling as a drug design paradigm is often used in the pharmaceutical industry to develop new drugs and is taught in many medicinal chemistry or drug discovery courses in universities. A case example would be the use of indirect methods of drug visualization. These methods would, for example, be applicable in cases where no Xray crystal structure of the target protein is available. In the absence of a defined crystal structure or high-resolution NMR data amenable to the elucidation of such large structures, indirect methods that correlate structure with the activity of biologically interesting compounds, such as comparative molecular field analysis (CoMFA) (3) can be used. CoMFA is a 3-D quantitative structure–activity relationship (QSAR) method where a series of compounds evaluated for affinity at a specific drug target is aligned and the resultant steric and electrostatic fields represented graphically. This graphical representation renders insight as to where modification of the compound structure may be beneficial for improved activity, especially in cases where the protein structures are not available for a priori prediction of activity. The present article describes a simple, novel, and generally applicable method to demonstrate structure–activity associations of a group of biologically interesting compounds in relation to receptor binding. This approach would be useful for undergraduate and graduate students in medicinal chemistry, computer modeling, or pharmaceutical sciences programs. The approach involves the utilization of a combination of computer molecular modeling (in silico), and plastic 3-D molecular models. This method enables students to acquire a feel for the relationship between what is shown on the computer monitor and “real life”. † Current address: Northeastern Ohio Universities College of Pharmacy, Rootstown, OH 44272-0095.
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Materials and Methods
PC Model Molecular modeling was accomplished as previously described (4). To show students how indirect drug design methods could be used, we chose as drug target the blood–brain barrier choline transporter (BBB-ChT), a drug vector for delivery of drugs to the brain. Compounds utilized in this study included a series of choline analogues with affinity for the BBB-ChT. The structures of these test compounds are shown in Table 1. For the modeling, many computer programs are available and it would be up to the instructor to decide which software to use, based on functionality and availability. A comprehensive review of open-source molecular modeling software programs, many of which may be useful for instructors to use, has recently been published (5). We used the molecular modeling software program SYBYL 6.91 (6) on a Silicon Graphics Octane computer to build the compounds. CoMFA analysis was performed using the default settings in the program. A 3-D coefficient map was generated showing areas surrounding the compounds that were favored 80% for steric (in green) and electrostatic (in blue) effects and 20% disfavored steric (in yellow) and electrostatic effects (in red). The green-colored areas of the map therefore would indicate
Table 1. Structures of Compounds Used To Construct the Receptor Surface Model, listed with Ki Values R1
R2
Nⴙ
CH2R4
R3
Cpd
Ki/µM
R1
R2
R3
R4
1
(CH2)7CH3
CH3
CH3
CH2OH
2
(CH2)5CH3
CH3
CH3
CH2OH
3
(CH2)3CH3
CH3
CH3
CH2OH
40
4
CH3
CH3
CH3
CH2OH
42
H
Active 2.1 2.3
Inactive 5
H
H
CH2OH
903
6
CH2CH3
CH2CH3 CH2CH3
CH2OH
1903
7
CH3
CH3
CH3
4698
CH3
Note: Data were obtained from in situ rat brain perfusion studies (4). Inhibition of brain choline uptake was expressed as Ki values for each compound.
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In the Classroom
where sterically bulky groups may enhance interaction affinity. The blue-colored regions indicate where electronegative groups would have increased binding affinity. Log Ki values were used as the biological descriptor. These values were obtained through experimentation using the in situ rat brain perfusion technique (7, 8). Briefly, uptake of [3H]choline was determined using short perfusions of a physiological fluid into the common carotid artery of the rat. Inhibition of brain choline uptake by a competitor compound was determined and expressed as Ki (in µM; Ki is the quantity estimated to cause 50% inhibition) values for each compound. Lower Ki values indicate higher affinity for the BBB-ChT compared to choline.
Plastic Model As our plastic molecular modeling set, we used the Prentice Hall Molecular Model Set For Organic Chemistry.1 To build a plastic model that would mimic the computer model, we chose a receptor surface approach that was constructed as described in an earlier study by Walters, Pearlstein, and Krimmel, published in the 1986 (9). These authors described the use of their receptor surface model as an aid to research, but did not detail its use for educational purposes (9). The method proposes to shape—by using an appropriate moldable material—the imaginary binding pocket in a protein that would accommodate compounds known to interact with the protein in question. Aluminum foil was used by shaping the sheets around plastic molecular models (10) used in organic stereochemistry classroom instruction. Four active compounds (Ki < 100 µM) were chosen as templates and the aluminum foil was molded around these (Table 1). As described by Walters et al. (9) it is also possible to mark with foil, areas of particular importance such as hydrogenbonding acceptors and donors, ionic interactions, and hydrophobic regions. Classroom Use The instructor has the option to have students do the molecular modeling themselves (depending on class size, access to computers, and the skill level of students) or have the instructor do it and show the students the results. The students were first introduced to the background of QSAR in the classroom. To assess the usefulness of the plastic models in an educational setting, we completed the CoMFA analysis before showing the students the results. After discussion of the results of the modeling study, the students were asked to build the receptor surface model with the aluminum foil and plastic molecular models. Assessment of the usefulness of the plastic model in understanding ligand fit methods of drug design was gauged by a questionnaire. The students (N = 9) were a randomly selected cohort of graduate students enrolled in a research-based masters degree program in pharmaceutical chemistry.2 To determine students’ attitudes towards either of the models, we chose ease of understanding as the criterion for both models. Discussion The 3-D structure of a drug is important when interacting with a receptor and such interaction forms the basis of structure–activity relationship studies. Translating a two980
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dimensional representation on paper into a 3-D representation creates a challenge for chemical instructors. With this challenge in mind, we propose the use of a receptor surface model (9) in combination with plastic molecular models to illustrate how one compound is able to fit well inside a receptor (and be able to elicit biological activity when tested pharmacologically), while another compound with a poor fit would be less active (or inactive) in the same assay. Concepts important in drug fitting such as steric interactions (i.e., atoms oriented in too close proximity to one another, leading to repulsion) or hydrogen-bond interactions can be illustrated as well. The target macromolecule we selected for the current study, the blood–brain barrier choline transporter (BBBChT), is a system we have studied extensively (11, 12). In our earlier studies, the BBB-ChT has been shown to have potential utility as a drug delivery vector to the central nervous system. This transporter is of further importance in that it may have utility as a drug target in Alzheimer’s disease (12), ischemia, and traumatic brain injury (12). As such, we selected a BBB-ChT:ligand system as an interesting example to form the basis for demonstrating this novel teaching tool. At this time, the BBB-ChT has yet to be identified with molecular biology methods, and although Murakami et al. (14) have suggested that the BBB-ChT may be part of the organic cation transporter family, little is known about this protein’s 3-D structure. We initiated a 3-D-QSAR study to develop predictive models for compound binding and identified structural features important for binding to this transporter (4). In vivo experimental data were obtained from in situ rat brain perfusion studies as noted above. Using the wellestablished 3-D quantitative structure–activity relationship (QSAR) comparative molecular field analysis (CoMFA) methods (a relatively difficult technique to teach in any molecular modeling, medicinal chemistry, or drug discovery course), we developed models that gave us insight into important molecular determinants, or binding requirements, for ligand interaction with the BBB-ChT (Figure 1). Previous studies have indicated that a primary interaction occurs between the quaternary nitrogen moiety in choline and a complementary binding site on the BBB-ChT. In addition, a hydrogen-bonding site, which displays significant restriction, has been proposed. For example, replacement of the hydrogen-bond donor (hydroxyl) group with a methyl group, leads to a considerable decrease in affinity for the transporter. That is, when the hydroxyl group that interacts with a portion of the target transporter (BBB-ChT) is replaced, the binding is eliminated resulting in lower affinity or a significantly higher Ki value. The distance between the positively charged nitrogen and the hydroxyl group in compounds that may bind the BBB-ChT has also been found to be limited to approximately 3.26 to 3.30 Å (12). These models render a useful approximation for binding requirements in the BBBChT, and until the cloned blood–brain barrier transporter becomes available, the model may have significant utility in developing a predictive model for the rational design of drugs targeted to the brain, an approach we are currently evaluating. A theoretical rendering of the BBB-ChT binding site is shown in Figure 2. The receptor surface model built with aluminum foil is shown in Figure 3. The model was built using active com-
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In the Classroom A
B
Figure 2. Theoretical rendering of the blood–brain barrier choline transporter (4).
Figure 1. Contour maps of (A) CoMFA and (B) CoMSIA hydrophobic analysis. Steric fields are shown where green areas indicate regions where bulky substituents can be accommodated in a sterically favorable way and yellow, unfavorable. Electrostatic fields are shown where blue areas would favorably accommodate cationic groups. Cationic interactions in red areas would be unfavorable. Areas that favor hydrophobic interactions are indicated by yellow and areas that disfavor hydrophobic interactions, by white. Choline is shown docked into the model. (Colored version is shown in the table of contents.)
pounds (with Ki values < 100 µM; cpds 1–4 in Table 1). Importantly, in building this model, a series of active compounds is required, instead of a single active compound. This requirement is to ensure accommodation (or binding) of multiple active compounds and the exclusion of inactive compounds (those that do not bind to the active site). The model inherently assumes that all of the compounds used have affinity for the same binding site (in this case, a binding site on the BBB-ChT), that the active compounds fit well within the site, and that the inactive compounds fit poorly. Exclusion of inactive compounds may be due to poor steric interactions or the presence of certain functional groups that are located in positions that are unfavorable in allowing adequate binding to the receptor under investigation (9). The commonality between compounds 1–4 is the presence of an alkyl chain on the R1 position of the compound (see Table 1), and methyl groups on R2 and R3, as well as a common R4 group. These R1 alkyl chains fit well within the receptor surface model, with a longer alkyl chain resulting in increased affinity for the BBB-ChT. The strength of the model generated by this approach is that, using a relatively small number of compounds, it is possible to demonstrate to students the importance of steric as well as other binding interactions in small molecule binding to proteins such as receptors. This concept will enable students to acquire an understanding of how to think “three dimensionally” when investigating the interaction of drugs with targets, as well as afford them a better view of why certain groups in a series of compounds result in some compounds www.JCE.DivCHED.org
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Figure 3. Receptor surface model of the blood–brain barrier choline transporter (BBB-ChT), with (A) cpd 1 that is very active, (B) choline, cpd 4, the naturally occurring compound in the body, and (C) cpd 7, an inactive compound, shown inside the model.
being more active than others (that is, structure–activity relationships). Also, the inclusion of inactive compounds illustrates the importance of functional groups and the positioning of groups on a molecule that preclude binding of these compounds to the target. One of the limitations is that this model is greatly simplified and will not accurately account for all steric and electrostatic requirements of all compounds reported in the literature, necessitating the instructor to choose examples where our model may be useful.
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1. The CoMFA model on the PC was the easier of the two models to understand. 2. The plastic–aluminum foil model was the easier of the two models to understand. 3. I understand steric considerations in drug design better when using the PC model. 4. I understand steric considerations in drug design better when using the plastic–aluminum foil model. 5. A combination of the two models helped me understand the concept of sterics in drug design better than when I was presented with only one model.
List 1. Questions used to assess the usefulness of the plastic model.
Figure 5. Comparison between the percentage of student responses that agree with a statement that a particular model is the easier of two models to understand. *Statistical significance (Student’s t-test), P
