The conceptual basis for structure-based drug design was formulated 100 years ago by Emil Fischer.1 His "lock and key" hypothesis is a constantly recurring leitmotif in mod- ern drug design, and yet, it is only recently that we have been in possession of detailed descriptions of the "locks", i.e., the biochemical targets to which potent and selective
*To whom all correspondence should be addressed. We dedicate this review to the memory of Dr. George deStevens who encouraged us to write it and who
was extremely supportive during its preparation.
Medicinal Research Reviews, Vol. 16, No. 1, 3-50 (1996) 0 1996 John Wiley & Sons, Inc. CCC 0198-6325/96/010003-48
4 BOHACEK, MCMARTIN, AND GUIDA
drugs (the "keys") must fit. Success has been achieved in the past by exploring the shape of the "keys," i.e., by structure/activity analysis of a series of experimental test ligands. However, this approach can yield only limited information about the "lock," and a large amount of work is required to achieve useful results by such experimental trial and error.
Three-dimensional structures obtained by X-ray crystallography and NMR spectroscopy contain a quantity of information that is orders of magnitude greater than that which can be obtained by building pharmacophores, even when a well studied set of ligands is available. This increased information introduces the possibility of directly designing drugs based on a detailed model of the target binding site, a possibility that raises a number of questions:
1. Is high-resolution information about a target binding site really helpful in the
2. What changes are likely to occur in the rate of drug discovery and in the quality
3. What techniques can fully utilize the new types of information? 4. What are the limitations of this approach?
design of new drugs?
of the products reaching the market?
In spite of initial skepticism, the design of novel, potent therapeutic agents based upon 3-dimensional structural information is now a reality. With increasing frequency, the structure of the target binding site or that of a closely related analog is available prior to embarking upon a drug design project. Thus, structure-based drug design (i.e., the design of drugs based on the 3-dimensional structure of a biomacromolecule) has emerged as a powerful tool for drug discovery. Recent reviews*-4 have presented numer- ous examples in which the utility, not simply the potential, of structure-based drug design has been amply demonstrated. An account of this activity has even appeared in the popular literature.5
Of course, one must ask, how does one measure the success of a project involving structure-based drug design? One measure might be a reduction in the number of compounds synthesized and tested in order to produce a clinical candidate. Often, however, the mere existence of a candidate drug in human clinical trials is taken as an end-point for structure-based drug design, which some believe is proof of the validity of
Regine S. Bohacek received her Ph. D. in Physical Chemistry from Rutgers University in 1987. Her thesis dealt with a configurational analysis of a synthetic polymer using the statistical mechanical methods developed by Flory. Shehasapplied thesemethods in acomputerprograin forde novostructure-baseddrugdesign. Dr. Bohacek is currently engaged in computer-aided moleculnr design at Ciba in Summit, N . 1. Her interests include: methods forestimatingenzyme inhibitor bindingenergies, thedesign of new drugs based on the3-dimensional structureof binding sites or inhibitor templates, and zinc metallo-proteases and their inhibitors.
Colin McMartin is a senior scientist in the Biophysical Chemistry group at Ciba in Summit, N . 1. where he is engaged in the development and application of computer methods for drug design. Previously, he led a group of biologists and biochemists investigating pharmacokinetics, metabolism, and bioavailability of pep- tides at Ciba in Horsham, Sussex, U . K. He received his B . A. in Chemistry with honors from Cambridge University in the U . K. His interests include: development of new structure prediction methods, methods for estimating enzyme inhibitor binding energies, and the design of new drugs based on the three-dimensional structure of binding sites andlor inhibitor templates.
Wayne C. Guida is Executive Director of Core Drug Discovery Technologies at Ciba in Summit, N . J. Previously, he had been an Associate Professor of Chemistry at Eckerd College in St. Petersburg, Florida. In 1985-1986, while on sabbatical leave, he was a Senior Research Fellow at Columbia University. He is one of the co-authors of the MacroModel molecular modeling program, which was developed in Professor Clark Still's laboratory at Columbia. Prior to joining the faculty at Eckerd College, he received his Ph. D. degree in Organic Chemistryfrom the University of South Florida and was a post-doctoral fellow at Duke University. His recent research has focused on structure-based drug design and computational methods for conformational analysis.
STRUCTURE-BASED DRUG DESIGN 5
Clone and express protein 1 Generate Structure
Purified Protein
3-D Database Searching Libraries Compound
1 I
Drug Design Combinatorial Libraries
in vivo Potency
Drug Design
in viva
Combinatorial Libraries 1
[Drug) Figure 1. Drug discovery and design flow chart.
this novel approach. Although we would argue whether this is the appropriate end- point, it is a fact that the number of new chemical entities in the clinic for which structure-based drug design has played a key role continues to grow.5 The list includes medications for T-cell lymphoma, psoriasis, tumor suppression, viral infection (includ- ing HIV), and glycoma.
It is clear that structure-based drug design plays an important role in drug discovery. In fact, a new paradigm for drug discovery and design is beginning to emerge in which contributions from rational drug design (which includes structure-based methods), tra- ditional screening and synthesis, and the newer combinatorial approaches are coupled. In Fig. 1, we illustrate the synergy among these various techniques. This review will demonstrate that structure-based drug design has been successfully used at every step, from lead finding to final optimization, in the discovery of novel drugs. For example, searching of 3-dimensional databases for molecules that can dock to a target binding site has already contributed to lead finding. The optimization of these and other leads has been accomplished using structure-based methodology. Moreover, structure-based drug design techniques have contributed to in vivo potency optimization by serving as a guide for those modifications to a molecule that might enhance bioavailability without destroy ing in vitro potency. In addition, new methods are now emerging that will make even
6 BOHACEK, MCMARTIN, AND GUIDA
more puissant use of the information inherent in a high-resolution structure of a binding site. For example, computer-based de novo structure-based drug design techniquesh-** have already begun, and will continue to contribute to the generation of novel lead candidates. We are confident that structure-based drug design techniques will, for the foreseeable future, play a role of increasing importance in drug discovery.
Prior to embarking upon a structure-based drug design project, it is necessary to have available either the 3-dimensional structure of the target biomacromolecule or the 3-di- mensional structure of a closely related analog. If the structure of an homologous protein is available, for example, it can either be used directly, or converted to a model of the target by using homology modeling techniques.3 Advances in both X-ray crystallogra- phy and NMR spectroscopy have yielded a number of target structures of therapeutic relevance over the past few years. Nonetheless, molecular modeling is necessary to use this information for drug design. In some cases, the contribution of molecular modeling has been largely confined to visualization of a ligand complexed to the target binding site using computer graphics. Whereas, in other cases, computational chemistry techniques have made a significant contribution to the design activities. Examples exist where computer-assisted molecular modeling has played an essential role in the design of poten- tial ligands that are both sterically and chemically compatible with the binding site of a target biomacromolecule. It is the combination of molecular modeling, structural biolo- gy, and chemical synthesis that has allowed structure-based drug design to be reduced to practice.
In this review, we describe the art and practice of structure-based drug design by focusing on the design techniques that were actually used for its successful application to drug discovery. We analyze sixteen selected recent examples of the design of com- pounds based on the coordinates of a target binding site (or those of a related analog) that have resulted from either protein X-ray crystallographic analysis or NMR spec- troscopic analysis. We identify the procedures that were used in these examples, and evaluate their role in the design process. We conclude with a discussion of emerging techniques, such as prediction of potency and de novo drug design, which, we believe, will allow the enormous potential of structure-based drug design to be fully realized.
Finally, we point out that structure-based drug design is a multifaceted endeavor that requires a collaborative venture among scientists from a variety of disciplines including computational chemistry, synthetic organic chemistry, medicinal chemistry, molecular biology, X-ray crystallography, and NMR spectroscopy; each activity contributes to the success of a structure-based drug design project. Nonetheless, in this review we high- light the actual and potential role of computational chemistry and molecular modeling.
11. EXAMPLES
A. Carbonic Anhydrase Inhibitors (Merck)
Recently reported work on inhibitors of carbonic anhydrase illustrates the iterative use of experimental and computational techniques.3 The results indicate the importance of conformational strain energy for determining free energies of binding.
The conversion of carbon dioxide to bicarbonate by carbonic anhydrase is a critical step in the secretion of aqueous humor. Thus, inhibitors of this enzyme can lower ocular pressure and may be of benefit in glaucoma. The starting point for the further develop- ment of compounds was MK-927. This compound has high water solubility and an appropriate partition coefficient to enable rapid penetration into the ocular tissues after topical application.
X-ray structures for complexes of carbonic anhydrase I1 with the two enantiomers of
STRUCTURE-BASED DRUG DESIGN 7
MK-927
MK-927 have been determined, and this was the starting point for compound optimiza- tion. The S-enantiomer (MK-417) has a Ki of 0.61 nM and is about 100 times more potent than the R-enantiomer (Ki = 71 nM). Since the two isomers will have the same solvation energies, it is clear that this difference must result from differences in conformational energy of the structure docked in the enzyme binding site. The docking geometries of the two isomers are shown in Fig. 2. This figure shows how the shape of the binding site strongly determines the docking geometries. The sulfonamide is rigidly locked in a very small pocket at the back of the site. The ring system and side chain lie across a larger cavity, but the positions of the sulfone sulfur and oxygen atoms and the iso-butyl methyl groups are strongly determined by way they fit with precision into cusp-like niches in the cavity as indicated by the accessible surface (see Fig. 2).
Before designing compounds, the experimental data was subjected to computational analysis and an hypothesis was formed that would explain observed differences in potency. While the contacts of the two inhibitors with the enzyme are rather similar, the internal geometries have small but energetically significant differences. Energy differ- ences due to internal geometry were computed using ab initio molecular orbital quantum
S-enantiomer (MK-417) Ki = 0.61 nM
R-enantiomer Ki = 71 nM
Conformational Energy Difference of R- vs. S-enantiorner = 2 KcaVmol
8 BOHACEK, MCMARTIN, AND GUIDA
Figure 2. Stereo images illustrating the docking of two isomers of MK-927; (A) S-enantiomer in green; (8) R-enantiomer (pink) and S-enantiomer (green). The figure was prepared for illustration purposes by docking the compounds into a structure of a carbonic anhydrase complex obtained from the Brookhaven Protein Data Bank.12.13 The mesh in this and other figures shows an accessible surface and indicates where ligand atoms in good van der Waal's contact with the binding site should lie.I4 Mesh colors show the ideal properties of ligand atoms: yellow: hydrophobic; red: hydrogen-bond accepting; blue: hydrogen-bond donating. For accurate pic- tures of the X-ray results for the MK-927 compound, the reader is referred to the original study.3
STRUCTURE-BASED DRUG DESIGN 9
TABLE I
Computed
Compound nM T (degrees) (Kcal/mol) Ki Dihedral Angle Energy Difference
0.37 140 0.0
5.5
2.0
15.3
175
153
168
1.5
0.5
1.5
mechanics calculations (3-21 G*, Gaussian 88). The dihedral angles of the bond connec- ting the sulfonamide group to the thiophene ring (N-M-S) were 150" for the S-enantiomer and 170" for the R-enantiomer, the difference resulting in an energy for the S isomer that was more favorable by about 1 kcal/mol. A similar energy difference was found comparing the anti geometry for the N-C bond of the iso-butyl side chain of the S isomer to the gauche geometry of the R enantiomer. The combined energy difference of approximately 2 Kcal/mol accounts for an appreciable part of the observed potency ratio of 1OO: l .
This conclusion is important in a general sense, since there has been some controversy over whether strain energies should be considered as significant determinants of poten- cy. This study is unusual, since other factors that might influence potency are relatively
10 BOHACEK, MCMARTIN, AND GUIDA
similar for the two molecules. Examples where it is possible to cleanly isolate one of the multiple factors that determine potency and study it with reliable experimental and computational methods are fairly rare but essential for the further development of de- pendable structure-based design methodology (see Discussion).
Both isomers are docked in a pseudo-axial conformation. Ab initio quantum mechanics calculations show that this conformation has an energy of about 1 Kcal/mol higher than the pseudo-equatorial conformation. Ab initio quantum mechanics calculations also showed that if a methyl group is introduced in the 6 position, the conformational preference is removed. This modification was, therefore, made and, at the same time, to conserve the physicochemical properties of the molecule, two methyl substituents were removed from the iso-butyl side-chain.
The methyl group in the 6 position (Table I) introduces a second chiral center resulting in four possible stereoisomers, all of which were synthesized and assayed. The com- pounds differed in potency, the most potent offering a modest improvement over the starting structure. X-ray analysis showed that each compound had a slightly different docked conformation, the major differences being in the N-M--S dihedral angle. The data is summarized in Table I.
Once again, evaluation of conformational strain energy by ab initio quantum mechan- ics calculations provided an explanation for the difference in potencies. The two trans isomers had a calculated energy difference of 1.5 kcal/mol due to changes in the NSCS dihedral, which could account for most of the 20-fold difference in potency. The cis isomers had a smaller difference in potency and the calculated energy difference was 1.0 kcal/mol.
Further series of compounds were synthesized to explore the effects of varying chain length at the 6 position and the nitrogen. The results have been reviewed previously.3 Although the gain in potency reported in the carbonic anhydrase work is modest, the results show the value of repeated experimental structure determinations at each cycle of drug optimization for confirming hypotheses and further extending our knowledge of the factors that determine potency. The use of optical antipodes is very elegant, because it eliminates factors between the molecules such as solvation energy and, thus, focuses attention on differences in docking mode and internal conformational energy.
B. Cyclosporin Analogs (Harvard University)
Cyclosporin A, 1, is an immunosuppressant, which, by binding to two proteins simul- taneously, inhibits the proliferation of T lymphocytes. Thus Cyclosporin A has found widespread use in the clinic for organ transplantation. The goal of a study undertaken by Alberg and Schreiber15 was to design a cyclosporin A (CsA) analog with increased binding affinity to both cyclophilin and calcineurin by conformationally restricting por- tions of CsA.
The structure of CsA bound to cyclophilin has been determined by NMR.16.17 The structure of CsA in the unbound state has also been determined in chloroform solution by NMR18.19 and in the solid state by X-ray crystallography.18 Interestingly, it was found that the bound conformation differs markedly from both of these unbound conformations.20J
Similar findings have been reported for another immunosuppressant, FK506. The structure of FK506 complexed to the FK506 binding protein (FKBP) has been determined by X-ray crystallography to 1.7 A.22 This bound conformation of FK506 is very different from the unbound conformation both in the solid state23 and in chloroform solution.24
However, more recently, the conformation of an FK506 analog in aqueous solution has
STRUCTURE-BASED DRUG DESIGN 11
1 CyclosporinA
been shown to be similar to the bound conformation of FK506.25 Apparently, the bound conformation of FK506 and its close analogs must preexist to some extent in aqueous solution. Based on the X-ray crystal structure of a CsA/Fab complex26 and kinetic evi- dence,27 it has been suggested that a similar situation occurs for cyclosporin.
The conformationally restricted cyclosporin analog described here was carefully de- signed so that any modifications would not interfere with either the binding to cyclo- philin or calcineurin. The residues in contact with cyclophilin were known from the X-ray structure of CsA bound to cyclophilin. Those residues that do not affect binding to calcineurin had previously been identified by the synthesis of cyclosporin analogs. The modifications of cyclosporin that do not affect immunosupressive activity28,29 are in- ferred to be in positions not interfering with the binding to calcineurin. Therefore, the segments of CsA chosen for alteration were those not in contact with cyclophilin and those that can be modified without loss of immunosupressive activity and, thus, pre- sumed not to be in contact with calcineurin. Two residues of CsA, L-Ala7-D-Alas, met these criteria and were selected for modification. The conformation of the backbone of these residues is significantly different in the bound and solution conformations. The program CAVEAT30 from Cambridge Structural Database31 was used to identify a rigid core structure that would fix the positions of the amine substituent and a carboxyl moiety of L-Ala7-D-Ala8 in the same relative orientations as found in the cyclophilin bound structure. CAVEAT suggested a bicyclic heterocycle (2), and this heterocycle was incorporated into cyclosporin A resulting in TCsA (3).1
The binding affinity of the conformationally restrained analog (3), TCsA, to cyclophilin (K i = 2 nM) was three times better than that of cyclosporin (1, Ki = 6 nM). Since this modified portion of the molecule is not expected to bind to cyclophilin, it is hypothe- sized that the increase in binding energy is entirely due to entropic effects. The affinity
12 BOHACEK, MCMARTIN, AND GUIDA
H Y
H
3 TCsA
of the complex of the conformationally constrained cyclosporin analog and cyclophilin with calcineurin is 2-3 times that of the cyclosporin-cyclophilin complex for calcineurin, i.e., the K, is 78 nM for the constrained analog vs. 198 nM for cyclosporin.
This work shows that the structure of the ligand bound to the receptor can provide valuable information for the rational design of more potent ligands. The authors re- marked that the relatively small increases in potency indicate the complexity of the problems of modeling and predicting the effects that conformational constraints will have on binding affinity. However, we speculate that a possible reason for the small increase in potency could also be attributed to the hypothesis that the bound conforma- tion of CsA is similar to the conformation in aqueous solution (as is the case for FK506) and that rigidifying a conformation that is already stabilized in solution may not gain much in binding free energy.
C. HIV-1 Protease Inhibitors
HIV-1 protease is an aspartic protease that is encoded in the HIV viral genome. The substrate specificity of HIV-1 protease is not clear; the enzyme makes a number of highly specific cleavages with the gag and gag-pol polyprotein at sites spanning remarkably heterogeneous amino-acid sequences and no consensus sequence for HIV protease has yet been deduced.
The protease is essential for proper virion assembly and maturation, and inactivation of HIV-1 protease by inhibition or mutation has been shown to lead to the production of immature, noninfectious viral particles. Therefore, HIV-1 protease has become a major target in the quest for an effective agent to combat the HIV virus.
Undoubtedly, the structure most used for the design of enzyme inhibitors is that of HIV protease. The number of enzymelinhibitor complexes for which the 3-dimensional structure has been determined continues to climb and is, at this date, estimated to
STRUCTURE-BASED DRUG DESIGN 13
exceed 200. Molecular modeling has been used extensively in an attempt to discover drugs effective in halting this dreaded viral infection.
In this review, we will discuss only a few of the most recent examples (up to November 1994) illustrating some of the different techniques that have been successfully imple- mented in this area.
1. Design of Nonpeptide Cyclic Ureas that are Potent, Bioavailable lnhibitors of HIV Protease (DuPont-Merck)
A variety of compounds that display excellent inhibition of HIV protease has been designed. However, most of these retain substantial peptidic character and are not sufficiently bioavailable. The goal of an investigation at DuPont-Merck was the design of smaller inhibitors that would retain potency with improved pharmacokinetic profiles.32 The strategy employed was that 1) by including a moiety in the inhibitor that would form the same interactions as the water molecule found in the high-resolution HIV protease crystal structures, and 2) by designing more conformationally constrained,
TABLE I1 HIV Protease Inhibitors - DuPont-Merck
4
5
6
7
4.7
0.31
0.27
14 BOHACEK, MCMARTIN, AND GUIDA
cyclic inhibitors, it would be possible to design smaller inhibitors that would retain potency but display improved oral biooavailability. From the extensive HIV inhibitor SAR, it was known that C2-symmetric diols imparted significant potency. Therefore, scientists at DuPont-Merck also wanted to include this feature in their design. Because there was no crystallographic information about how diols bind to the protease when the project was initiated, they used the X-ray structure of an hydroxyethylamine inhibitor bound to HIV-1 protease33 and used the distance geometry program DGEOMN to devel- op several pharmacophores. From this model, they constructed a simple 3-dimensional database query specifying the positions of hydrophobic groups that bind to the S1 and S1' subsites of the enzyme and either one or two hydrogen bond donors or acceptors to bind to the catalytic aspartates. This query was used to search a subset of the Cambridge Structural Database,31 which had been converted for use with MACCS-3D,35 yielding structure 4 (Table 11). MACCS3D allows the user to search a 3-dimensional database to locate structures that meet user-defined geometrical constraints. Not only did structure 4 show that a tetra-substituted benzene ring could properly position substituent groups to interact with aspartates 25 and 25', it also possessed an additional oxygen that matched the position of the key structural water molecule. However, because a benzene ring might not be the ideal scaffold to properly position its substituents to reach the enzyme's hydrophobic pockets, molecular modeling was used to optimize the type of ring in a stepwise fashion from a benzene ring, to a substituted cyclohexanone, to the larger cycloheptanone, and finally to a 7-membered cyclic urea, 5. Additional modeling was performed to determine the ideal stereochemistry and conformation for substituents attached to the cyclic urea with and without substituents on the nitrogens. The confor- mational preferences were later confirmed from small molecule X-ray crystallography. The actual potencies of cyclic ureas with various stereochemistry correlated well with those predicted by the model. In the final structure, the N-substituted cyclic urea has substituents with stereochemistry predicted to be optimal by the model: 4R, 5S, 6S, and 7R. This structure appears to be an ideal, rigid scaffold to position groups into the S1, Sl', S2, and S2' pockets as well as to place the hydroxyl groups to interact with the catalytic aspartates. In addition, the model showed the urea carbonyl oxygen occupying the position of the key structural water molecule. As predicted by the model, compound 6 binds to the enzyme with high affinity ( K ; = 4.7 nM).
However, compound 6 exhibited only modest antiviral activity. Molecular modeling indicated that there was room for a larger group in the S2/S2' subsite and, therefore, naphthalene substituents were introduced. This compound, 7, showed a 10-fold im- provement in potency ( K ; = 0.31 nM) probably due to the increased hydrophobic con- tacts. Unfortunately, this enhanced enzyme inhibitory activity did not translate into improved antiviral activity, presumably because of the lipophilicity of the naphthalene groups. Additional modeling suggested the incorporation of a hydrogen bonding group. The naphthalene rings were replaced by p-hydroxymethylphenyl substituents resulting in compound 8. Compound 8 combines excellent potency against HIV protease ( K , = 0.27 nM), and against viral replication, with good oral bioavailability. This compound is a specific HIV protease inhibitor showing no inhibition of renin, pepsin, or cathepsin D. This specificity is hypothesized to stem in part from the observation that these enzymes do not have a structural water analogous to that found in HIV protease and mimicked by cyclic urea 8. The X-ray crystal structures of ten cyclic ureas complexed to HIV protease have been determined. The crystal structure of compound 7 complexed to HIV protease was refined to the highest resolution (1.8 A, final R factor = 19.5%), and is depicted in
STRUCTURE-BASED DRUG DESIGN 15
TABLE I11 HIV Protease Inhibitors - Upjohn
Compound ~~
Potency
9 (warfarin)
10
11
IC,, = -30 pM
K, = 1 pM
Ki = 0.5 p M
OH \
12 K, = 0.038 p M
OH \
the article.32 The X-ray structure shows that 7 forms interactions with the enzyme pre- dicted by molecular modeling.
2. Optimization of a Coumarin Lead from Random Screening 1 (Upjohn)
The group at Upjohn36 began this study with a lead from targeted screening. A set of 5000 dissimilar compounds from the Upjohn compound library were screened for HIV protease activity. Compound 9 (Table III), warfarin, was identified as a weak inhibitor (ICs0 - 30 pM). Based on this finding, a search for compounds similar to 9 was con- ducted, resulting in compound 10, which has a Ki of 1 FM. Both compounds 9 and 10 have already been used as therapeutic agents in humans and exhibit high oral bio- availability and low clearance, making these compounds especially promising leads.
To begin the optimization and design process, the X-ray crystal structure of compound 10 complexed with HIV protease was determined to 2.5 8, resolution. The inhibitor was found to exist in two orientations related by a 180" rotation. The C-4 hydroxyl group was shown to hydrogen bond with the two catalytic aspartic acid residues, whereas the two oxygen atoms of the lactone formed hydrogen bonds with the backbone HN of Ile 50 and Ile 50'. The a-ethyl group and the a-phenyl ring were found near the S1 and S2 subsites, respectively.
16 BOHACEK, MCMARTIN, AND GUIDA
Analysis of this enzyme inhibitor complex showed that the position of the benzene ring of the coumarin moiety does not readily allow for the addition of substituents that will extend into the S2' subsite. Therefore, the fused benzene ring was expunged and side chains were added to the remaining 4-hydroxy-2-pyrone ring yielding compound 11. The X-ray crystal structure of 11 complexed to HIV protease was determined to 2.3 A resolution and showed that, despite replacement of the coumarin benzene ring, the hydrogen bonding pattern of 11 remained the same as that of compound 10. Also, as anticipated, the phenethyl group at C-6 was located near the S2' subsite. Next, an ethyl group was added at the position alpha to C-6 (compound 12). The ethyl group was designed to occupy the S1' pocket and to move the phenyl ring even closer into the S2' subsite. This compound can be viewed as having pseudosymmetric substitutions with the four substituents occupying the S2, S1, Sl', and S2' pockets. These modifications led to substantial improvements in the binding affinity. Compound 12 was found to inhibit HIV-1 protease with a K , of 38 nM. This compound is a mixture of four stereoisomers, all of which were isolated. All were found to inhibit HIV, the best with a K j of 14 nM. Compound 12 also inhibits HIV-2 ( K j = 32 nM), has excellent properties such as selec- tivity for HIV protease, antiviral activity in HIV-lIIIB infected MT4 and H9 cells, good oral bioavailability, and is synthesized in three chemical steps from readily available starting materials. Compound 12, U-96988, has entered phase I clinical trials.
3. Optimization of a Coumarin Lead from Random Screening 11 (Parke-Davis/ NCI-Frederick Cancer Research and Development Center)
Another example of the use of high-throughput screening, molecular modeling, X-ray crystallography, and synthesis to discover potent HIV protease inhibitors was carried out jointly at Parke-Davis and at the NCI-Frederick Cancer Research and Development Center.37
A nonpeptide, low micromolar ( K j = 1.0 FM) coumarin derivative, 13 (Table IV), was
TABLE IV HIV Protease Inhibitors - Parke-Davis/NCI
Compound IC, (PM)
13
14
15
&.XI 2.3
1.7
0.52
STRUCTURE-BASED DRUG DESIGN 17
TABLE V HIV Protease Inhibitors - Parke-Davis/NCI
Compound Ki (CLM)
17
on
16
?"
1.1
0.7
0.051
discovered using high-throughput screening techniques. Compound 13 was modeled in the binding site of HIV using the X-ray structure of HIV-1 protease complexed with the inhibitor, MVT-101. Aut~dock,~* a computer program designed to dock molecules into binding sites, was used to position compound 13 into the active site. The position of this ligand was further optimized using energy minimization. In this study, the movable flaps of HIV were "presented in a closed position" and simulations were carried out with and without the conserved water molecules, H20301 and H,OCAT. The crystal structure of 13 complexed with HIV protease was subsequently determined to 3.0 A resolution, revealing two binding modes, one of which was similar to the compound modeled without the water molecules.
Three parts of the molecule were modified to optimize the structure: the side chain (especially around the phenoxy oxygen), the phenyl group, and the coumarin ring. Since, according to the crystal structure of the complex of 13 with HIV protease, the phenoxy oxygen did not appear to interact with any polar enzyme atoms, a series of analogs were synthesized involving modifications of this atom and the length of the chain. However, it was found that an oxygen occupying a position near the phenoxy oxygen of 13, i.e., 14, was necessary for potency. It was concluded that the oxygen probably forms a water mediated hydrogen bond with the enzyme. The GRID program39 was used to suggest possible positions of this water molecule, and a water positioned to hydrogen bond both with the NH of Asp29 and the inhibitor oxygen was postulated. Modifications of the chain length did not lead to more active compounds. The X-ray crystal structure showed that the phenyl group interacts with ArglO8, and changing the chain length would alter this apparently optimal contact. Attempts to improve the inter- actions with Arg 108 were not successful. Substitutions at the 7 position of the coumarin ring were explored resulting in the most potent inhibitor, 15, which has a hydroxyl group at that position. Again, the model did not show any interactions between this
18 BOHACEK, MCMARTIN, AND GUIDA
hydroxyl group and polar enzyme atoms. Using GRID, the position of a water molecule to mediate a hydrogen bond between the hydroxyl and the carbonyl of Gly148 could be identified. In conclusion, the authors indicated that the particular binding mode of this lead made it difficult to extend the structure to form additional favorable contacts with the enzyme.
More recently, another study from Parke-Davis and NC140 described the optimization of structure 16 (Table V), another compound identified by random screening. To predict the binding mode of compound 16, a number of docking experiments were conducted using molecular dynamics as implemented in the program SYBYL (Version 5.5, available from Tripos Associates, St. Louis, MO) and the Monte Carlo-based docking program, Autodock.38 In order to optimize the position of the P1’ phenyl group to better fit into the S1‘ enzyme subsite, analogs with 1-3 carbons between the sulfur and the benzene ring were synthesized. A 2-3 fold enhancement in binding affinity was observed. Com- pound 17, with a one carbon spacer, has a Ki of 0.7 pM. The binding mode of 17 was also studied using the molecular modeling techniques mentioned above. Subsequently, the X-ray structure of 17 complexed to the enzyme was determined and confirmed the modeling results. The lactone moiety in the pyrone structure was found to displace water molecule-301 (the critical water molecule usually found in the crystal structure of HIV protease/inhibitor complexes). The lactone formed hydrogen bonds with Ile50 and Ile150. The enol hydroxyl interacted with the two catalytic aspartic acid residues, Asp25 and Asp125.
Subsequent molecular modeling studies indicated that a tether could be added to the 6-aryl ring to reach the S1 subsite. Therefore, an analog with a OCH,COOH substituent at the para position of the benzene ring located at the 6 position of the pyrone ring, which would position a negatively charged group to interact with Arg 8, was designed and synthesized. This compound, 18, was found to inhibit HIV protease with a Kiof 0.051 KM.
4. Optimization of a Lead Obtained by Screening Renin lnhibitors (Merck)
Screening of renin inhibitors, which had previously been prepared at Merck, identi- fied compound 19 (Table VI) as a potent inhibitor of HIV-1 protease (Ki = 1.0 nM). Although a potent inhibitor, 19 was not effective in inhibiting viral infection in H9 cell cultures. Therefore, 19 was modified resulting in two series of compounds. The best of the first series was 20.41 Compound 20 is a very potent HIV inhibitor (1C5, = 0.03 nM) with no renin activity and the ability to prevent the spread of viral infections in cell cultures. Compound 21 was the best compound in the second series42 with an IC,, of 0.3 nM. It has antiviral activity similar to 20.
Although 20 and 21 are very potent inhibitors, they lack appropriate solubility and a good pharmacokinetic profile and, therefore, a new study was undertaken to design inhibitors to improve these properties.43 By incorporating the P1’ group of a Roche inhibitor (22), it was hypothesized that the new compound (23) would be more water soluble and that the conformational restrained decahydroisoquinoline would decrease the entropy change upon binding to the enzyme.
The design process began with the X-ray structure of 22 complexed with HIV pro- tease. Compound 21, the lead compound, was modeled into the active site using 22 as a guide. The position of 21 was optimized using energy-minimization. Next, compound 23 was modeled to see if it would occupy the same space and form the same impor- tant interactions as the potent known inhibitors 21 and 22. An excellent superimposi-
STRUCTURE-BASED DRUG DESIGN 19
TABLE VI HIV Protease Inhibitors - Merck
19 L364,505
20
21 L685,4324
22 Ro-31-8959
23
24 L-735,524
H I N
Boc-PhePhr' 7 0
Q
d
1.0
0.03
0.3
7.8
0.56
20 BOHACEK, MCMARTIN, AND GUIDA
tion was found, and compound 23 was synthesized. Compound 23 was found to inhibit HIV with an IC,, of 7.8 nM, but was weak in inhibiting the spread of viral infections in the cellular assay. However, because 23 possessed a favorable pharmacokinetic profile, further optimization was carried out to modify the physical properties while maintaining potency. The result of this optimization was L-735,524 (24), a compound undergoing extensive human clinical trials (Phase 11). The crystal structure of a closely related analog of 24 complexed with HIV protease was determined to 2.15 A resolution and confirmed the modeling studies.
5. Optimization of a Penicillin Lead from Random Screening (Glaxo)
Screening at Glaxo led to the discovery of a crude sample of a penicillin dimer from which, through an “artifact of the purification process,” a potent inhibitor, 25 (Table VII), with an IC,, of 60 nM was discovered.& This compound was subsequently optimized leading to several different series of potent inhibitors with good properties.45.46 The X-ray crystal structure of a close analog of compound 25, in which the methyl esters were replaced by ethyl amides, was determined to 2.5 A resolution47 and was used by one of the groups at G l a ~ o ~ ~ to design more potent inhibitors with lower molecular
TABLE VII HIV Protease Inhibitors - Glaxo
Compound Potency
26
27
0
0
IC,, = 0.15 p,M
(continued)
STRUCTURE-BASED DRUG DESIGN 21
TABLE VII (Continued)
Compound Potency
28
29
30
31
32
IC, > 170 KM
IC, = 0.082 FM
IC, = 1.9 pM
IC, = 4.6 p M K, = 0.25 nM
IC, = 3.8 nM
22 BOHACEK, MCMARTIN, AND GUIDA
weight and, hence, an improved in uivo profile. Therefore, monomeric analogs of com- pound 25 were designed.
The monomer of 25, compound 26, was found to be inactive against the enzyme, and scientists at Glaxo speculated that at least one hydroxyl group to interact with the catalytic aspartic acids and a hydrophobic group to bind to the S1 subsite would be necessary for activity. A series of compounds that incorporated these groups was syn- thesized resulting in compound 27 (1C5, = 0.15 pM). The significant difference in poten- cy between 27 and 28, which differed only in the configuration of the P1' benzyl group, was rationalized by inspection of the model. The "active" R enantiomer showed that the benzyl group could fit into the S1' pocket, whereas for the "inactive" S isomer this was not possible, These modeling results were later confirmed by the X-ray structure of 27 bound to the enzyme determined to 2.2 A r e s ~ l u t i o n . ~ ~
Inspection of the crystal structure of the analog of 25 complexed to HIV protease suggested the importance of inhibitor carboxamide carbonyl oxygens in the center of the molecule, which hydrogen bond with H,O 301, which in turn hydrogen bonds with the NH of Ile 50 and Ile 50'. Compounds 27 and 28 cannot fully partake of these interactions, since they have only one carbonyl group in this part of the binding site. From the crystal structure, it was apparent that an additional carbonyl group separated by a 4-atom spacer would be ideal. Therefore, a statine isostere was incorporated into the computer model of 27. The carbonyl group was positioned in the model to form the desired hydrogen bonds and the second amide group was adjusted to form potential hydrogen bonds with the amide NH and the carbonyl of Gly27'. The structure was then subjected to energy minimization in the static enzyme structure, which included the crystal- lographic waters using the Insight/Discover molecular modeling software (available from Biosym Technologies, Inc., San Diego, CA). Subsequently, compounds 29 and 30, where synthesized and, 29, with an IC,, = 0.082 pM, did exhibit improved potency. From the model, it was further postulated that a lipophilic amine could reach the S2' subsite. A series of compounds were synthesized with a variety of hydrophobic groups in the P2' position. The best of these were 31 and 32. Although both are potent HIV inhibitors with an IC,, of 4.6 nM and 3.8 nM, respectively, only compound 31 exhibited good antiviral activity in vim. Compound 31 had excellent selectivity, thus achieving the goal of a potent, low-molecular-weight HIV protease inhibitor.
General Structure of Statine Analogs
These compounds are interesting in that they differ from the statine analogs. Statine analogs have the S stereochemistry at the asymmetric center from which the P1 side chains are attached. In compounds 31 the attachment is in the R configuration and the lipophilic group binds not to the S1 subsite but to the S1' subsite. The crystal structure of
STRUCTURE-BASED DRUG DESIGN 23
TABLE VIII HIV Protease Inhibitors - Glaxo
Compound
33, 34
22 RO-31-8959
35 JG-365
36
0
3.1 pM 1.5 pM
Ac-Ser-Leu-AsnHH
OH 0 -He-Val-OMe
0
9.0 nM
23 nM
32 complexed with HIV protease was subsequently determined and "confirms the for- mation of the major interactions designed to optimize potency."48
Although very potent, these compounds displayed a poor pharmacokinetic profile and, therefore, further optimization was undertaken.49 The first targets of this new study were compounds 33 and 34 (Table VIII). The modeling of these compounds was based on the X-ray structure of a closely related analog of the earlier penicillin derivative, 25, complexed to HIV protease. In addition, the position of the proline amide was modeled using the P1' proline of JG365 (35)50from the structure of JG365 bound to synthetic HIV-1 pr0tease.~3The biological results for 33 and 34 were disappointing (IC50 of 3.1 and 1.5 pM, respectively) when compared to JG365 (35), which has an IC,, of 0.5 nM, indicating that the interactions that 33 and 34 make with the enzyme must be far from optimal.
Increasing the ring size of the P1' group by substituting a pipecolic acid for the proline did not result in the expected increase in potency. Modeling the pipecolic acid analogs in the manner described above did reveal that the pipecolic acid did not form any major hydrophobic interactions with the enzyme. The decahydroisoquinoline moiety in the P1' site suggested by the Roche inhibitor, Ro-31-8959, (22), was incorporated resulting in
24 BOHACEK, MCMARTIN, AND GUIDA
compound 36. In the model, improved hydrophobic interactions were observed between the decahydroisoquinoline group and the enzyme, which did result in a substantial increase in potency. Compound 36 was found to inhibit HIV protease with an IC,, of 23 nM.
Compounds 33, 34 and 36 were also modeled using the binding mode of the Roche inhibitor, 22, as described in the communication of Krohn and co-workers.51 As in the case when the JG365 structure was used as a template, the corresponding groups of 36 and the Roche inhibitor formed similar interactions with the enzyme. Unfortunately, co- crystallization studies with these types of inhibitors were unsuccessful and, therefore, the modeling results could not be verified.
Although further optimization was carried out using molecule modeling, compound 36 remained the most potent of the series in the enzyme and cellular assays. Unfor- tunately, the pharmacokinetic profile of 36 was still poor, and the further development of the penicillin-derived HIV protease inhibitors was abandoned.
6 . Optimization of the C-Terminus of a Known lnhibitor (Agouron)
This work52 is an example of the iterative use of molecular modeling and X-ray crystal- lography at Agouron for the design of novel C-terminal inhibitors. The lead compound
TABLE IX HIV Protease Inhibitors - Agouron
Compound K,
Q 38
39
40
0.0018 p M
1.67 pM
0.033 pM
STRUCTURE-BASED DRUG DESIGN 25
for this work was compound 37 (Table IX), a known inhibitor, which was modeled in HIV using the X-ray structure of HIV-1 protease complexed with the inhibitor, MVT-101. Using this structure, the C-terminal Val-Val methyl ester, which occupies the S2' and S3' subsites of the protein, was replaced by a diphenylhydramine amide derivative in which the two phenyl groups fill the 52' and S3' side-chain pockets. The compound, 38, was synthesized and found to have a Ki of 1.67 pM. The crystal structure of 38 complexed with HIV protease was solved. This structure compared favorably with the modeled structure. A more elaborate inhibitor was designed, 39, which replaces the P3' phenyl group with an indole ring designed so that the NH moiety of the indole would form a hydrogen bond with the carbonyl oxygen of Gly 48. This compound, synthesized as a mixture of diastereomers, was found to be eight times more potent than compound 38 with a Ki of 0.20 pM. Again, the X-ray structure was determined and compared to the model structure. The indole was found to bind to the S3' subsite; however, the indole NH did not make the predicted hydrogen bond but instead formed a water-mediated H-bond to the Gly 27 carbonyl oxygen. At this point, the N-terminal alanines were replaced by a moiety reported by Roche, i. e., an asparagine-2-quinoline-carbonyl substi- tuent.
Further optimization was carried out to see if substituents on the P2' phenyl group could be designed to use the extra space revealed by the X-ray structure. Placing a methyl or trifluoromethyl group in the meta position resulted in compound 40, which showed small enhancements in binding affinity.
Subsequently, in order to test the free-energy perturbation method for computing binding energies, calculations were performed on these compounds. Good correlation was found between the calculated results and those experimentally determined.53
7. Design of a Macrocyclic Inhibitor Based on a Known Protease Inhibitor (Marion Merrell Dow)
Screening of the Marion Merrell Dow protease inhibitor library resulted in the discov- ery of 41 (Table X), a difluorostatone in which hydration of the fluorinated ketone is believed to generate mimics of the tetrahedral intermediate. The lead compound was optimized using traditional medicinal chemistry methods (no reported use of the struc- tural information)54,55 resulting in 42, a potent HIV-1 protease inhibitor with a Ki of 5 nM and an improved selectivity index (ratio of EC,, for inhibition of cellular growth of MT, cells infected with HIV-1 to toxicity).
A study was initiated to further optimize 42 using molecular modeling and crystal- lographic data.56 The goal of this study was to increase the binding affinity of 42 through additional favorable enzyme/ligand interactions, decreased molecular weight, and intro- duction of conformational constraints designed to cause the solution conformation to be similar to the bound conformation. Compound 42 was modeled in the HIV protease binding site using the structure of HIV protease bound to MVT-101.57 Compound 42 was docked using the inhibitor MVT-101 as a guide. After adjustments of bad van der Waals interactions by hand, the complex was subjected to 50 ps of molecular dynamics using the Insight/Discover software (available from Biosym Technologies, Inc., San Diego, CA). This was followed by solvating the complex with two concentric shells of water. The positions of the water were optimized using energy minimization followed by subsequent molecular dynamics. The resulting structure shows phenyl rings A and B, outside of the enzyme cavity in solvent, engaging in unfavorable hydrophobic/ hydrophilic interactions. It was postulated that these rings could be eliminated and that the P1 phenyl group could be connected to the P3 portion of the molecule by formation
26 BOHACEK, MCMARTIN, AND GUIDA
TABLE X HIV Protease Inhibitors - Marion Merrel Dow
Compound Potency (K,)
41
42 MDL 73,669
43
0.
0.6 p,M
5 nM
20 nM
of a macrocycle, compound 43. This idea had been successfully applied to the design of macrocyclic potent pepsin inhibitors.58 Compound 43 was modeled in the active site of HIV protease using the protocol described above, subsequently synthesized, and it was found to inhibit HIV with a Ki of 20 nM. NMR studies were performed to determine if the macrocyclic portion of a close analog of 43 would show a solution conformation similar to the bound conformation predicted by the model. Parts of the molecule in the model of the complex corresponded well to the NMR data, whereas other sections of the molecule could not be explained by the information determined by NMR studies. In addition, in uacuo molecular dynamics simulations were performed on 43 in order to qualitatively define its dynamical behavior. Subsequently, the X-ray crystal structure of 43 complexed to HIV was determined, and it was reported that those parts of the molecule that had been characterized by NMR and found to be stable in the dynamics simulations correspond well to X-ray structure, while remaining parts of the molecule appeared to be a time average of two conformations defined by the dynamics calcula- tions.
The authors do not address reasons for the lack of significant increase in binding affinity of the macrocycle vs. the lead compound. We note, however, that Professor Daniel Rxh has suggested that if a linear inhibitor already spends considerable time in solution in a conformation close to the binding conformation, then introducing confor- mational constraints may not lead to large gains in binding energy.59
STRUCTURE-BASED DRUG DESIGN 27
D. NEP Inhibitors-Neutral Endopeptidase, 24.11 (Ciba)
In this example, the 3-dimensional structure of the target binding site was not known and, therefore, the structure of the homologous enzyme, thermolysin, was used to successfully design novel potent NEP inhibitors.
Atrial natriuretic factor (ANF) is a cardiac hormone that has been shown to play an important role in regulation of electrolyte levels and in the suppression of renin and aldosterone secretion.60-62 It has been shown that infusion of ANF produces rapid natriuresis, diuresis, and lowering of blood pressure.63,@ The rapid metabolism of this 28 amino acid peptide makes ANF itself unsuitable as a dmg.65 However, with the discovery of neutral endopepidase (NEP) 24.11 as the major enzyme responsible for the clearance of ANF,66,67 inhibitors of NEP have been designed to potentiate the beneficial effects of ANF by retarding its degradation.
NEP 24.11 is a zinc metalloprotease that catalyzes the hydrolysis of an amide bond on the amino side of a hydrophobic residue. Pozsgay et a1.68 suggested that the optimal substrate would have a P2-Pl-Pl' sequence of Phe-Gly(or A1a)-Phe(or Leu), and Hersh and Morihara found that Leu would be ideal at the P2' site.69
The 3-dimensional structure of NEP 24.11 has not as yet been determined. However,
TABLE XI NEP Inhibitors - Ciba
Compound G o
44
45
46 CGS 25155
47 CGS 26670
HS.,. ..q? 0 COOH
TLN: 9.1 nM (Ki)
NEP: 3.0 nM TLN: 2.5 p M
NEP: 0.8 nM
RCO0" NEP: 1.0 nM TLN: 68 nM
HS'
28 BOHACEK, MCMARTIN, AND GUIDA
the X-ray crystal structure of thermolysin, a zinc metalloprotease, which is similar to NEP in the primary sequence of the active site, substrate specificity, and inhibitor SAR, has been successfully used as a model for NEP. In earlier work,70 the group at Pfizer used thermolysin in the initial stages of the design of candoxatrilat, whose prodrug candoxatril is under clinical evaluation as a potential therapy for congestive heart failure. Later, the X-ray crystal structure of a candoxatrilat analog complexed to thermolysin was determined and verified the model.71
Here we summarize the more recently published work carried out at Ciba. MacPher- son, et al.72 designed novel, potent, orally active NEP inhibitors using the X-ray struc- ture of thermolysin (see Table XI). Examination of the crystal structure of thermolysin complexed with a transition state inhibitor, 44, showed that the two leucine side chains occupied the S1’ and S2‘ subsites.73J4 These two subsites from a connected cavity in this enzyme. This observation suggested that the side chains could be connected to form a macrocycle, resulting in structure 45. Molecular modeling was then used to test this hypothesis by docking the four stereoisomers of compound 45 into the binding site of thermolysin. The docking was carried out by using Monte Carlo-like perturbation with energy minimization to search for low energy ~onformations.~5 All but the R,R isomer had low energy conformations, which formed good interactions with the enzyme. Upon synthesis, the compounds were found to be very potent inhibitors of NEP and some- what weaker inhibitors of thermolysin. The S,R isomer inhibited NEP and thermolysin with an IC,, of 3 nM and 2.5 pM, respectively.
Compound 45 was subsequently optimized for oral availability resulting in CGS25155, 46, an orally active, novel, potent (IC, = 0.8 nM) NEP inhibitor. The crystal structure of this compound in thermolysin was subsequently determined, revealing that this com- pound has two binding modes, one of which is very similar to that predicted by molecu- lar modeling76 (see Fig. 3).
In another study at Ciba, benzo-fused macrocycles were designed to inhibit NEP. To prioritize the synthesis of these difficult-to-prepare compounds, macrocyclic compounds
Figure 3. CGS 25155, 46, an orally active potent NEP inhibitor (IC, = 0.8 nM) docked using molecular modeling techniques in the binding site of thermolysin.
STRUCTURE-BASED DRUG DESIGN 29
containing 10- to 15-membered rings were first modeled in thermolysin. The model showed that an 11-membered macrocycle, 47, formed the most favorable interactions with thermolysin. Compounds with 12- and 13-membered rings also appeared favorable in the model. The 11-, 12-, and 13-membered macrocycles were subsequently synthe- sized and found to be potent inhibitors of NEP and thermolysin. In fact, the 11- membered macrocycle inhibited thermolysin with an IC,, of 68 nM, making it one of the most potent thiol thermolysin inhibitors yet reported.
Clearly, the ideal situation is to have available the 3-dimensional structure of the actual target enzyme. However, this example illustrates that the structure of an homologous enzyme that is sufficiently similar to the actual target can be a useful model for structure- based drug design.
E. Dual Inhibitors of ACE and NEP (Ciba)
The design of dual ACE/NEP inhibitors at Ciba provides an additional example of a structure-based drug design project in which the 3-dimensional structures of the target enzymes have not as yet been reported. In this study, an homologous enzyme was used to simulate one of the target enzymes and a 3-dimensional inhibitor composite template was used as a model for the other.
ACE (angiotensin-converting enzyme, EC 2.4.15.1) catalyzes the hydrolysis of angio- tensin I, a decapeptide, resulting in formation of angiotensin 11, a potent vasoconstrictor. Angiotensin I1 is also involved in the release of the sodium-retaining steroid, al- dosterone. Both factors result in an increase in blood pressure. The inhibition of these processes by ACE inhibitors is one of the most effective therapeutic approaches for the treatment of hypertension and congestive heart fa i l~re .~7 As pointed out in Section D, inhibition of NEP has been shown to potentiate the beneficial effects of the peptide ANF and, therefore, NEP inhibitors are being investigated clinically for their ability to induce ANF-like effects.78 Recently, preclinical results with combinations of ACE and NEP in- hibitors have indicated possible synergistic effects.79-82 Therefore, a number of research groups have been working to exploit some of the similarities of the two enzymes for the design of a single compound capable of inhibiting both enzymes.
Like NEP and thermolysin, ACE is a zinc metalloprotease. ACE is a dipeptidyl carb- oxypeptidase, usually cleaving the C-terminal dipeptide residues of its substrates. ACE is rather nonspecific, although it does not favor substrates with terminal dicarboxylic acids or a proline in the penultimate position.83
Scientists at Ciba have used an X-ray crystal structure of thermolysin as a model for NEP and a 3-dimensional ACE inhibitor composite template to aid in the design of dual ACE/NEP inhibitors.84 Initially, two potent ACE inhibitors previously discovered at Ciba were combined to form a composite ACE inhibitor template, 48 (see Table XII), defining the P1’ and P2’ regions of the inhibitor. This template was used during the early phases of the project to model macrocyclic dual ACE/NEP inhibitors.85 As inhibitors were de- signed that incorporated residues designed to bind in the S1 subsite, a more comprehen- sive template was required. Therefore, four conformationally constrained, potent ACE inhibitors were used to enlarge the initial template. A zinc atom was added to the zinc chelating group of each inhibitor. The computer program, TFIT,86 was used to super- impose all four inhibitors. This program identified low-energy conformations, which allowed the zinc, the hydrophobic groups, the amido carbonyl, and the C-terminal carboxylic acid group of each inhibitor to be superimposed onto the equivalent groups of the other inhibitors.
30 BOHACEK, MCMARTIN, AND GUIDA
TABLE XI1 ACEiNEP Inhibitors - Ciba
Compound IC50
48 Initial ACE Template
49 Thiorphan
46 CGS 25155
50 Benazaprila t
51
52
H S q COOH
H S f N -coon
0
0
0
HS
HS
COOH
ACE: 0.8 pM NEP: 6 nM
ACE: 29.7 pM NEP: 0.8 nM
ACE: 6.1 nM NEP: inactive
ACE: <50 nM NEP: 2.7 nM
ACE: 40 nM NEP: 48 nM
STRUCTURE-BASED DRUG DESIGN 31
A single molecule capable of inhibiting these two enzymes must possess those func- tional groups required for tight binding to each enzyme. The models predict that the conformation that an inhibitor must adopt to bind to ACE differs from the one required for binding to NEP. Thus, an inhibitor must be able to adopt a conformation so that it can bind to the S1, Sl', and S2' subsites of ACE and to the Sl', S2', and S3' subsites of NEP. In the investigation at Ciba, inhibitors were proposed possessing conformationally re- stricted sections designed to bind within certain areas in the binding site of each en- zyme. These structural units were connected by a flexible hinge, allowing the entire molecule to adopt conformations complementary to the active site of each enzyme. The 3-dimensional computer models were used as an aid in this design process.
The design strategy is outlined in Table XII. Comparison of two potent NEP inhibitors, thiorphan 49 and the macrocycle 46, suggests that NEP can tolerate two different posi- tions for the C-terminal carboxylic acid. The situation is different in ACE; thiorphan is a moderate inhibitor of ACE; but, the macrocycle has little binding affinity for ACE, indi- cating that the position of the C-terminal carboxylic acid is much more critical in ACE. However, there are potent ACE inhibitors such as benazeprilat, 50, in which the zinc chelating group is further removed from the terminal C-terminal carboxylic acid than in thiorphan. Therefore, combining thiorphan and benazeprilat led to compound 51. To further improve the potency of 51, a fused proline residue was incorporated into the C-terminal portion of the molecule as suggested by the original template (48). Since P-thiols such as 51 usually display poor pharmacokinetic properties, and we also wanted to improve the in vivo stability of the thiol, the thiol was moved from the p to the a position. a-Thiols had been previously shown to inhibit ACE.87 Thus, these two mod- ifications led to compound 52. When docked and optimized using energy minimization in the thermolysin model, this molecule formed good interactions with the enzyme atoms. Thiol 52 could also be readily superimposed onto the ACE inhibitor template. When synthesized, 52 was found to be a potent inhibitor of ACE (IC5" = 40 nM) and NEP
Figure 4. Tricyclic a-thiol dual ACD/NEP inhibitor, 52, bound to thermolysin. In pink is the conformation predicted by molecular modeling. In green is the conformation subsequently determined by X-ray crystallogra- phy. Relevant atoms of the thermolysin binding site are shown in darker colors.
32 BOHACEK, MCMARTIN, AND GUIDA
(IC50 = 48 nM) and, to a lesser extent, of thermolysin (IC50 = 1.6 pM). Scientists at both Marion Merrell Dow8* and Bristol Myers Squibb89-91 have also reported on potent dual ACE/NEP inhibitors containing the a-thiol functionality.
Because no 3-dimensional structure of an a-thiol bound to a zinc metalloprotease had been reported, there was some concern about the reliability of the modeling of compound 52 in thermolysin. Therefore, the X-ray crystal structure of thermolysin complexed with 52 was determined at a resolution of 1.9 A. The close agreement between the conformation predicted by molecular modeling to that determined experimentally (see Fig. 4) validated the model and supported the hypothesis for the binding mode of 52 in NEP.M
F. PNP Inhibitors (Ciba, BioCryst, UAB, SRI)
In a collaborative venture between scientists at BioCryst Pharmaceuticals, Ciba Phar- maceuticals, the University of Alabama at Birmingham, and Southern Research Institute, inhibitors of the enzyme purine nucleoside phosphorylase (PNP) were designed using structure-based techniques.92-96 Since this work has previously been reviewed in this journal,97 we describe it here only briefly.
PNP is essential for purine salvage and catabolism, and is an important modulator of T-cell proliferation. The enzyme catalyzes the reversible phosphorolysis of the purine nucleosides guanosine and inosine (and their 2' deoxy analogs) and inhibitors of PNP are potentially useful as therapeutic agents in host-graft rejection following organ trans- plantation, and in T-cell proliferative disease such as T-cell lymphoma.
0
HO O H H O O H
+ +
Using the X-ray crystal structure of the human PNP/ guanine complex determined to 2.8 A resolution, it was possible to obtain potent inhibitors for this enzyme. Neverthe- less, one of the initial obstacles in the design of PNP inhibitors was the inability to accurately predict the bound geometry of potential inhibitors prior to their synthesis. Since all of the compounds synthesized in this investigation were guanine analogs, this "docking" problem was reduced to the determination of the conformational preferences of groups attached to the purine ring of these analogs in the PNP binding site. By using Monte Carlo/energy minimization conformational search procedures,75,94 low-energy conformers of potential inhibitors could be located in the PNP binding site. Synthesis of the most promising compounds and X-ray crystal structure determination of the com- plexes confirmed that, in all cases, one of the lowest energy conformers compared
STRUCTURE-BASED DRUG DESIGN 33
Figure 5. Low energy conformers of 9-cyclohexylmethyl-9-deazaguanine in the binding site of PNP. A phos- phate ion is shown in purple. The pastel-colored molecules show several of the possible docking modes discovered using a search procedure based on Monte Carlo torsion perturbation with energy minimization.
favorably with the X-ray derived structure. It was found that the Monte Carlo/ energy minimization method as implemented in the MacroModel/ BatchMin software98 was useful in the design of novel PNP inhibitors, since it provided a means for estimating how potential (but unsynthesized) inhibitors might dock to the enzyme binding site. This information provided a basis for estimating the relative binding affinities of these inhibitors using the complementarity surface14 (see the Discussion section).
One might assume that a disadvantage of the Monte Carlo/ energy minimization procedure is that it generates a collection of low-energy conformers rather than a single conformer (i.e., the structure observed crystallographically). However, this ensemble of conformers effectively maps the space available to molecules bound to the active site of an enzyme and has proven to be useful for the design of PNP inhibitors. For example, by comparing the low energy conformers of 9-cyclohexylmethyl-9-deazaguanine (a rela- tively potent PNP inhibitor) in the binding site of PNP, the adamantyl analog 9-(2- adamantylmethyl)-9-deazaguanine was suggested. This molecule was synthesized and was observed to inhibit PNP with good binding affinity (see Fig. 5).
Recently, the Monte Carlo /energy minimization method has been extended and the studies mentioned above have been repeated so that orientational as well as conforma- tional sampling was performed.99 Thus, the molecule is allowed to translate and rotate during the Monte Carlo step. The sampling is quite efficient, and the geometries located (from poorly docked starting structures) compare well with crystallographically derived models. This procedure can be used for the complete docking (translational, rotational, plus conformational sampling) of ligands in binding sites.
The PNP design effort resulted in a number of 9-deazaguanine analogs that include the ones shown below, which possess IC,,s in the 5-50 nM range. Using molecular modeling techniques, molecules (see below) were designed to take advantage of the major binding sites in PNP discovered using X-ray crystallography. The 9-deazaguanine moiety forms key hydrogen bonds within the purine binding site, including one be- tween Asn-243 and the N-7 hydrogen, which is only present in 9-deaza analogs. Substi- tuents at R, were designed to fill the hydrophobic binding site and substituents at R, were designed to fill the hydrophobic binding site and substituents at R, were designed to interact with the phosphate binding site. By incorporating these design features, some of the most potent PNP inhibitors yet discovered were produced.
R, = Aryl, Cycloalkyl, and Heterocyclic Substituents
R2 = H, CH,CH2OH, CH,CN, CH2COOH
R2 R1
G. Sialidase Inhibitors (Monash University, CSIRO, Glaxo)
The design of inhibitors of the enzyme sialidase by investigators at Monash University (Australia), CSIRO (Australia), and Glaxo (UK) provides another example of the success- ful application of structure-based drug design techniques to the design of enzyme inhibi- tors with therapeutic relevance. 100 Sialidase, which is also known as neuraminidase, cleaves terminal a-ketosidically linked sialic acids from glycoproteins, oligosaccharides, and glycolipids. It is expressed on the surface of the influenza virus and is thought to play a role in the exit of viral particles from infected cells. It is also believed to aid in the movement of the virus through the mucus lining of the respiratory tract. Thus, interest in the design of potent inhibitors for this enzyme arises from the belief that these compounds would serve as antiviral agents for influenza.
Based upon the X-ray crystal structure of sialidase (2.8 A structure of influenza virus A/Tokyo/3/67 sialidase complexed with sialic acid and sialic acid analogs101,102), the most potent influenza virus sialidase inhibitors yet reported were designed. An investigation of the binding site of the enzyme using standard molecular graphics techniques and using the GRID computer program39 aided in the design of novel inhibitors with excel-
STRUCTURE-BASED DRUG DESIGN 35
H OH
Sialic Acid (N-Acetyl-neuraminic acid)
lent binding affinities. GRID allows the user to explore the binding site of an enzyme with various “functional groups” in order to locate energetically favorable binding re- gions for these groups. A carefully parameterized molecular mechanics force field is used to estimate the binding energy, displayed as contours, of the various probes. Thus, with the aid of GRID, a protonated primary amine probe was used to suggest that replacement of a hydroxyl group on the previously known inhibitor Neu5Ac2en (53) by an amino substituent would yield a more potent analog. This molecule (54) was synthe- sized and it was found to e an excellent sialidase inhibitor [Ki = 50 nM; A/Tokyo/3/67 (N2) sialidase]. Replacement of the hydroxyl group with a guanidino substituent pro- vided an even more potent compound 155, Ki = 0.2 nM; A/Tokyo/3/67 (N2) sialidase].
It & R,= H OH
H3C- C - N COO‘ \ CH20H
” R2
This example stresses the utility of the structure-based approach. The availability of the X-ray crystal structure of sialidase allowed extremely potent inhibitors to be de- signed in essentially one step using a previously known inhibitor as a lead.
H. Thymidylate Synthase Inhibitors
The enzyme thymidylate synthase (TS) catalyzes the conversion of deoxyuridylate monophosphate to thymidylate monophosphate (dTMP) via a methylation reaction in which 5,lO-methylenetetrahydrofolate is involved as a co-factor. TS is involved in the sole, rate-limiting pathway for the biosynthesis of dTMP.
Inhibitors of TS have been shown to possess broad spectrum activity as antiprolifera- tives and antitumor agents, and, thus, have become attractive candidates for cancer
36 BOHACEK, MCMARTIN, AND GUIDA
0
TS 2-03m-u - FH4 -
0
OH I OH
durn dTMP
chemotherapy. TS has also represented an attractive target for structure-based drug design, since structural information about the TS binding site has been available for some time now.
1. Optimization of Thymidylate Synthase lnhibitors (Agouron)
The X-ray structures of E . coli TS in its apo form and as the ternary complex were determined several years ago to moderately high resolution103-106 (2.0-2.5 A), and scien- tists at Agouron Pharmaceuticals have been able to utilize protein crystallography and molecular modeling in an iterative fashion to design potent inhibitors of this enzyme, yielding at least one compound that is currently in phase I1 clinical trials as an anti- tumor agent.3~107
The initial goal of the effort to obtain potent, bioavailable inhibitors of TS involved the structure-based modification of known TS inhibitors such as 56 (see Table XIII). This compound possesses excellent potency (Ki = 8 nM; human TS) but, nonetheless, has liabilities. It is actively transported into cells, which may lead to drug resistance that has been observed for a number of classical antifolates such as 56. Thus, the optimization of lipophilic analogs of 56 was sought in order to afford inhibitors that would cross the cell membrane by passive diffusion. Structure-based lead optimization was employed, using crystallographic analysis, of inhibitors complexed to the E . Coli enzyme, which was available in sufficient quantities for iterative studies. Synthesis of 57, in which the p-COGlu moiety had been removed, yielded a compound that possessed a Ki of 2.2 p M against human TS and, thus, had lost 2.4 orders of magnitude in binding affinity. Examination of the X-ray crystal structure of 58, the closely related 2-amino analog of 56, bound to E. coli TS revealed the existence of a hydrophobic binding site near the meta position of the phenyl substituent of 58. Synthesis of 59 produced an inhibitor with a Ki of 0.39 pM against human TS and its binding to the E . coli enzyme was as predicted. Aided by crystallographic analysis of the TS binding site, it was postulated that a large hydrophobic substituent, which also possessed an electron withdrawing group, posi- tioned at the para position of the phenyl group of 57 would lead to enhanced binding. Using semi-empirical MO calculations (AMl/MNDO) to search conformational space for appropriate low-energy conformers in vucuo, it was postulated that compound 60 should not incur appreciable strain energy upon binding to TS. Compound 60 was synthesized and it possessed a Ki of 13 nM against human TS. Thus, potency was nearly restored to that of the initial lead compound.
2. De novo Design of ThyrnidyIate Synthase Inhibitors (Agowon)
In a second example, which the authors have described as de novo design,3,107,10* potent TS inhibitors were designed for the cofactor binding site using the crystal struc- ture of E . coli TS complexed with 5-fluoro-2’-deoxyuridylate. The GRID39 software was used to locate hydrophobic regions in the binding site by employing a methyl probe. It was observed that a naphthalene ring would achieve good overlap between the aromatic ring and the GRID contour map. Using the naphthalene ring as a template, hydrogen bonding groups were envisioned at the 1- and 8-positions to provide chemical comple- mentarity for an aspartic acid in the binding site and for a bound water molecule. This and further analysis lead to the design of a benz[cd]indole ring substituted at the 6-posi-
38 BOHACEK, MCMARTIN, AND GUIDA
TABLE XIV Thymidylate Synthase Inhibitors - Agouron
~
Compound Ki
62
63
1.6 FM
34 nM
2 nM
tion with a disubstituted nitrogen (see Table XIV). A benzyl group was used as one of the substituents and it was substituted at the 4-position with a piperazine sulfonamide in order to impart water solubility to the molecule. Analysis of the binding site indicated that the piperazine ring should be oriented toward solvent. Synthesis of compound 61 afforded a TS inhibitor that possessed a K j of 1.6 pM against human TS. X-ray analysis indicated that it was bound to the enzyme in a fashion similar to the one predicted by the modeling studies. Improvements in the binding affinity of this lead inhibitor were made using iterative structure-based drug design techniques to afford compound 62 and 63 with Kjs of 34 nM and 2 nM (human TS), respectively.
3 . Discovery of Thymidylate Synthase lnhibitors Aided by 3 - 0 Database Searching (UCSF)
Another example109 of the application of structure-based drug design techniques to the discovery of TS inhibitors involves database searching using the DOCK computer program*10-”* developed in Tack Kuntz’s laboratory at the University of California, San Francisco, and the commercial software MACCS-3D35 (available from MDL Information Systems, Inc., San Leandro, CA). MACCS-3D allows one to search a 3-dimensional database in order to find structures that meet user-imposed 3-dimensional constraints (e.g., distances between key functional groups). The DOCK program can also be used to search 3-dimensional databases, but it operates by locating structures that are compati- ble with a target binding site whose 3-dimensional structure is known. The DOCK program can be used to ”score” potential inhibitors with respect to their approximate binding affinity.
For the design of TS inhibitors, .DOCK was employed to search the Fine Chemicals Directory (which is distributed by MDL Information Systems, Inc. and is now referred to as the Available Chemicals Directory) to locate molecules with potential affinity for the
STRUCTURE-BASED DRUG DESIGN 39
Sulisobenmne Phenolphthalein
ICso = 15 PM
TS binding site. The X-ray crystal structure of L. casei TS determined to 2.3 8, resolution was used for the docking experiments performed with DOCK. The MACCS3D program was then used to search the Fine Chemicals Directory to locate structures that were similar to sulisobenzone, one of the molecules that had been found by DOCK. DOCK was then used to dock and score each of the molecules located by MACCS3D. Using this methodology, phenolphthalein analogs were found that inhibit TS in the low micromo- lar range. It was suggested that these compounds could serve as potential leads for the synthesis of novel, more potent analogs.
Interestingly, when the X-ray structure of the sulisobenzone/TS complex was deter- mined to 2.5 /! resolution in two different buffer solutions, two different binding modes were observed, which depended upon the buffer. Both structures differed from the one suggested by DOCK and revealed an alternate binding region within the active site of TS. The composite binding region was used to dock and evaluate the compounds found as a result of the MACCS3D search. When the X-ray structure of the phenolphthalein/TS complex was determined, it was found that the binding mode of phenolphthalein was similar to the one suggested by DOCK.
111. DISCUSSION
The examples covered in this and other reviewsz-4 show that structure-based design is a fertile approach to drug discovery that has enormous potential. It should be pointed out that the literature in this area is already very extensive, and, as a result, reviews have not yet appeared that are fully representative of a rapidly growing field. The numerous examples covered in the present review, drawn only from the most recent literature, suggest a state of exponential growth for this approach to drug discovery.
It is clear that a large number of successful new approaches are currently being devel- oped in which structural information is employed for the design of drugs. We have the impression of an area of science in rapid transition, which offers considerable scope and opportunity to scientists who are comfortable working at the interface between disci- plines and have imagination and initiative. At present, there is no single well-beaten path to success. Rather, there are a number of different ways in which experimental data, or in some cases hypotheses, about the interactions between potential drugs and their host binding site are being used to catalyze, inspire, and focus the search for biologically active compounds. In this sense, the practice of structure-based drug design is somewhat of an art and we have attempted in this review to provide a flavor of the state of the art in this endeavor.
40 BOHACEK, MCMARTIN, AND GUIDA
The process of drug discovery can be conveniently divided into two stages: (1) the discovery of novel lead compounds and (2) the optimization of these compounds. A suitable lead will typically have a binding affinity in the micromolar range and should also be chemically accessible so that modifications can readily be made. Formerly, in the absence of the structure of the bound ligand, the easiest leads to convert to a bioavailable drug were small and not too flexible. For example, it would have been very difficult to optimize a linear hexapeptide lead compound. Where high-quality structural informa- tion is available, this task becomes approachable. Once the structure of the complex of a ligand with a binding site is known, smaller analogs with very different physicochemical properties can be designed. Some examples are described in Section IIC of this review.
Lead optimization is usually necessary to obtain compounds that are sufficiently po- tent and have appropriate bioavailability characteristics. Leads may also require optimi- zation to improve selectivity, although generally, as more potent drugs with highly optimized binding characteristics are developed, they can be expected to be more selec- tive. Since lower doses will be required, potent compounds are less likely to have side- effects arising from actions unrelated to the mechanism of action.
Although the distinction between lead discovery and lead optimization is somewhat arbitrary, it will serve in this discussion to distinguish between two different aspects of structure-based design:
1. The use of structural information to suggest and plan a series of small changes intended to optimize the structure of a weakly active compound. The structural information may help to eliminate steps with little chance of success and may encourage or justify the spending of resources on more difficult chemical syn- theses of compounds that may have improved properties.
2. The design or discovery of radically different molecules to provide new leads. Ideally, these should be capable of optimization to highly potent compounds. Within the drug industry it is also important that they should have sufficient novelty to be patentable.
A. Optimization of Lead Compounds
Structure-based optimization must be based on a 3-dimensional model of a docked starting compound. This model can be hypothetical or based on computational or experi- mental methods.
At present, optimization of a lead compound is more reliable when there is an experi- mental structure of the complex of that compound with the binding site. In fact, once a round of optimization has occurred (compounds made and tested for potency), it is often useful to obtain experimental structures for new compounds to be used as leads for further optimization. Even more informative results can be obtained using a series of compounds of differing potency for experimental structure determination so that accu- rate docking modes can be obtained for each compound. From these results, it may be possible to learn in detail the factors that seem to be responsible for potency (see, for example, Section IIA on carbonic anhydrase, and Section IIH-3 on thymidylate syn- thase). These studies may not be essential for a specific optimization task, but they are of tremendous value for the advancement of the science of structure-based drug design.
In addition, the experimental results obtained at high resolution can give information about ordered water molecules and about small but potentially important changes in the enzyme binding site. They may also reveal a totally unexpected change of docking mode (see Sections IID and IIH-3), which potentially could be utilized for further optimization.
STRUCTURE-BASED DRUG DESIGN 41
Ideally, computational methods would provide the information currently gained by itera- tive cycles of synthesis and structure determination. This would greatly accelerate the optimization process by reducing the number of compounds that need to be synthesized and the time-consuming process of further structure determinations. There are, in fact, a considerable number of methods for docking.113 Reasonable results have been obtained in some cases. However, in general, these methods cannot be relied upon to always produce the correct result. Despite this drawback, it can be very helpful to perform docking studies to select promising candidates from a variety of molecules that require lengthy synthesis. A good example, from our laboratories, of the effective use of a thorough docking study is work on benzo-fused macrocyclic NEP inhibitors (see Section IID), where the study helped to decide which of a large series of cyclic structures to synthesize. A similar example has also been described in Section IIF of this review on docking of PNP inhibitors. We believe computer docking methods are most reliable when the binding site is relatively rigid and the docking geometry is largely determined by steric complementarity. These methods are less effective at predicting the correct geometry when a number of alternative docking geometries of low energy is possible.
Although experimental docked geometries may differ from those obtained from a computational study, it is probable that, whenever a computation shows a binding mode with high steric and chemical complementarity, the molecule will indeed bind. A lower energy binding mode may be possible, but the computational method can still be ex- pected to act as an effective filter for active compounds. For example, as described in Section IIH-3, a molecule (sulisobenzone) selected by DOCK as a candidate for inhibition of thymidylate synthase was subsequently shown by X-ray crystallography to have a binding mode different than the one predicted using DOCK. In fact, two binding modes were found, depending on the buffer, and a composite of both of the complexes was then used for further database searching resulting in inhibitors with IC,,s in the low p.M range.
A number of considerations are relevant to the process of optimization. They are discussed here in approximate order of ease and reliability. In general, the effects of changes that lower conformational energy, or that avoid bad van der Waals contacts, can be predicted with reasonable confidence. Those based on nonbonded interactions in- volving electrostatic and solvation energies are difficult to predict and less accurate.
1 . Improvement of Complementarity to the Binding Site
Steric fit to a binding site is one of the easiest factors to estimate. Van der Waals contact distances are known with high accuracy and are, in fact, assumed as invariants (along with bond angles and bond lengths) in the process of structure refinement from experi- mental data. Chemical complementarity can be reliably assessed at the level of simple scoring of hydrogen bonding and hydrophobic contacts. 14 However, subtle differences due to charge distribution in the molecule or to solvation effects are more difficult to predict. A reasonable approach is to use broad complementarity criteria to design a series of conipounds that can be made and experimentally tested.
Improvements in complementarity based on small changes can usually be proposed by simply looking at the docked complex on a high-resolution graphics workstation. We have found the display of a solvent-accessible surface of the binding site, represented as a grid, to be very helpful in this process. The surface can also be colored to indicate chemical complementarity. A coloring algorithm based on experimental data has proven to be reliable in our hands.14 A different approach, which has been used successfully in a
42 BOHACEK, MCMARTIN, AND GUIDA
number of cases, is the application of the program GRID39 to identify and graphically display positions in the binding site where a variety of functional and hydrophobic probe groups can be expected to bind (see Sections IIG and IIH-2). Although it can be argued that these methods are not essential for drug design, one should not underesti- mate their ability to guide and stimulate the creative process by clearly displaying oppor- tunities for improving the binding affinity of the ligand.
2. lmprovement of Conformational Properties
Where the docked structure is strained or has a high degree of flexibility, it may be possible to introduce changes that make the molecule less strained or more rigid. The likely effects of these changes, which are estimated by computational conformational analysis of the starting compound and the modified compounds, can usually be pre- dicted with some confidence. Typically, molecular mechanics is used for this purpose. In cases where the force-field parameters are inadequate (e. g., with unusual functional groups, highly conjugated systems, or strained rings) it may be desirable to perform quantum mechanics calculations (see, for example, Sections IIA and IIH-1). Semi- empirical and/or ab initio calculations are now accessible using a workstation of modest cost.
3 . Improvement of Bioavailability
It may be necessary to change physicochemical properties such as octanol/water parti- tion coefficient and water solubility in order to improve bioavailability. Usually, it is important to achieve this with minimal loss in potency. The structure of a docked com- plex can be used: (a) by designing potency-neutral changes in parts of the molecule in contact with solvent or (b) selecting parts of the molecule that, although in contact with the site, do not seem to be important for binding. A number of examples of successful optimization of bioavailability have been cited in this review (see Section IIC).
B. Finding New Leads
screening methods that do not require structure information. A number of different approaches are available. To provide perspective, we start with
1. High Throughput Screening of Coinpound Libraries
Compounds are tested experimentally for ability to inhibit binding or an enzyme catalyzed reaction. The process is indicated in Fig. 6. The advantage of this method is that it can be applied in the absence of any structural knowledge and that "hits," once validated, are known with confidence to be active. With the development of combina- torial libraries, it is becoming possible to screen large numbers of compounds. However, as indicated in Fig. 6, the universe of potential molecules is very large, and it is by no means certain that libraries of synthetic compounds, whether combinatorial or not, will adequately sample this universe.
Experience has shown that it is often possible to obtain lead compounds through high throughput screening. It is not clear, however, that this process will always find leads or that it will find the best leads in terms of ease of synthesis, ease of optimization, bioavailability, and selectivity.
STRUCTURE-BASED DRUG DESIGN 43
Figure 6. Schematic illustration of a search for lead compounds by screening. Molecules are assumed to be arranged so that similar molecules are close to each other. A subset of molecules containing up to 30 C, N, 0, and S atoms may have more than 1060 members=. Contours show regions where the molecules are potent inhibitors for a specific (in this case hypothetical) binding site.
2. Computer-Based Screening of Structural Databases
This process, exemplified by the program DOCK,"0-11* can be used to limit the number of candidates and, thus, accelerate the physical screening process or to find a small number of compounds that can be purchased or synthesized and tested for po- tency. It uses the structure of a target binding site and a database with the 3-dimensional structures of a collection of molecules.
aAlthough the number of possible molecules is difficult to estimate accurately, simple considerations show that it must be very large! Consider growing a linear molecule an atom at a time and choosing a carbon, nitrogen, oxygen, or sulfur atom at each position. Some of these atoms can be doubly or triply bonded, but not all combinations of atoms are chemically stable, and some multiple bonds will only be possible in nonlinear structures, i.e., a C=O group. Assuming a very approximate average choice multiplicity of 6, then 630 or 2 X
1023 molecules could be grown containing 30 atoms. Now consider the ways of introducing branching or cyclization into the resulting structure. Closure of rings with three or more atoms involves selecting two atoms to form a bond and could be achieved in 30'28/2 ways. Making a branched molecule could be achieved by choosing a point to cut the chain and a point in the first part of the chain to attach to the cut end of the second part of the chain (i.e., 302 ways). Not all atoms can be joined in this way. However, this will be offset by the fact that when stereochemical considerations are introduced, the number of possibilities will be expanded. Based upon these considerations, approximately 1040 molecules with up to four rings and 10 branch points could be produced from each linear chain, resulting in a very approximate estimate of 1065 molecules in total. Although this is a rough estimate, it seems likely that when all the different possible combinations of ring closure and branching are taken into account, the true number will be well in excess of 1060 and will rise steeply with increasing molecular weight.
44 BOHACEK, MCMARTIN, AND GUIDA
3. Design of New Leads (de novo Design)
New leads can be designed interactively at the workstation. As mentioned in Section IIIA on lead optimization, the use of a graphical representation of the surface of the binding site can be very helpful for this purpose. It is, however, difficult to design highly novel structures in this way, because it is very hard to visualize the effects of large changes without building and optimizing the proposed structures. Furthermore, a very large number of changes is possible. This process can be handled very effectively using completely automated computer algorithms for de novo design. The method with which we are most familiar is our own program GrowMol,6 which uses a complementarity grid based on the complementarity surface14 described earlier. This guides the growth of molecules to achieve a high degree of complementarity with the binding site. This method produces very large numbers of unique structures and has, in fact, revealed the wealth of potential inhibitors available in even a quite constricted site such as that of thermolysin.6
Figure 7 shows how, with suitable rules for growing the molecule an atom (or func- tional group) at a time, and a probabilistic rejection of new atoms based on complemen- tarity, it may in the future be possible to search the entire universe of small organic molecules for potential drugs.
De nozm computer programs differ somewhat in their philosophy, with some biased towards producing only one or a few compounds intended to be highly potent whereas
:one containing a11 possible molecules srranged vertically according lo size, msifioned so fhaf similar molecules B r a close together.
Pofenf molecules
complemenfary
Per molecule Zone 01 growth -
molecules wifh high sferic and chemical complemen W i l y to binding site
Figure 7. Algorithm for de nouo drug design. Atom-by-atom growth of molecules, complementary to a target binding site, searches the universe of organic molecules for potential drugs.
STRUCTURE-BASED DRUG DESIGN 45
other programs produce a wide range of compounds. The latter approach provides the synthetic chemist with a variety of choices, and enables compounds to be selected based on synthetic accessibility and on subjective factors such as likelihood of achieving bio- availability. Of course, rules might be devised that will further filter out the output structures to avoid difficult syntheses or compounds unlikely to be bioavailable.
An alternative approach to structure-based design is to explore possible ways of con- necting fragments selected and docked by the user. The NEWLEAD program, for exam- ple, accomplishes this readily and is easy to use.114
4. Potency Prediction
A major requirement for the effective design and optimization of lead compounds is the prediction of potency. With a reliable predictor, we will be able to scan the large number of structures produced by de novo programs and select those having potencies better than a threshold value. Potency prediction should also increase the probability of forecasting accurate docking geometries for inhibitors and eliminate some of the itera- tions in the process of optimization. Indeed, an ultimate goal would be to dispense with the distinction between lead discovery and optimization, and design potent novel mole- cules in a single step.
A large number of factors are important for determining the binding free energy, including:
1. Interaction energies (van der Waals and electrostatic) with the binding site 2. Solvation free energy changes 3. Loss of conformational entropy 4. Internal (strain) energy of the bound molecule
For a given series of molecules in a specific binding site, some of these factors may be much more important than others. For example, we found that for a series of thermo- lysin inhibitors, a simple complementarity score, based on the number of hydrogen bonding and hydrophobic contacts, gave a very high correlation with potency.6~14 Al- though the scoring method is simple, the parameters defining hydrophobic contacts and hydrogen bonds were critical to the success of the method and were based on a careful study of complementarity in proteins.
Very different results were obtained in the work, cited earlier on carbonic anhydrase (Section IIA), which clearly shows that internal strain energy is the predominant factor for the series of compounds reported.3
In another example reported recently,115 a high degree of correlation was achieved between the calculated intermolecular interaction energy (van der Waals plus electrosta- tic energies), for a series of HIV-1 protease inhibitor complexes and the observed i n vitro potency. The inhibitors had been subjected to energy minimization in the HIV protease binding site. This methodology was subsequently used to predict the potency of poten- tial HIV protease inhibitors in advance of their synthesis and testing for biological activity.
Each of these methods accounts for only a subset of the terms listed above, which account for the total free energy of binding and can be used with confidence only when a learning set of compounds is available. A method is needed that takes into account all the factors listed above and can be applied without further parameterization to any inhibitor in any binding site.
46 BOHACEK, MCMARTIN, AND GUIDA
Computational methods that involve free energy simulations,1*6,117 which explicitly account for the factors mentioned above, are poten t i d y capable of reproducing accurate relative binding free energies. However, at present, the range of drug design problems to which they can be successfully applied appears to be quite limited. Free energy perturbation calculations, for example, are computationally demanding and the CPU time required to ensure convergence can be excessive. Furthermore, the method is usually restricted to small differences between the pair of molecules for which one wishes to compute the difference in binding free energy. Adequate conformational sam- pling must be performed, if flexible ligands and/or large structural perturbations are involved, and this task can be prohibitive for all but the simplest of systems. Nonethe- less, these methods are continuously being improved. A recent publication,lls for exam- ple, indicates how the conformational sampling problem might be overcome by combin- ing Monte Carlo sampling with molecular dynamics, using a continuum solvation model.
There have been only a few reports of the application of free energy simulation methodology to structure-based drug design. One exception is the work by Reddy, et al.53 in which the free energy perturbation approach was employed for the design of HIV-1 protease inhibitors. Recently, a potentially more rapid semi-empirical method for estimating absolute free energies of binding based on data collected during molecular dynamics simulations has been described. * I 9 The method was applied to five endo- thiapepsin inhibitors with moderate structural diversity. It lacks the rigorous theoretical basis of free energy simulation methods and it remains to be seen whether it will be generally applicable to other systems.
A recent study120 investigated the use of multiple scoring factors to predict potencies of a number of inhibitors for a large number of enzymes. The range of enzymes for which a single scoring algorithm was able to predict potency was impressive. The enzymes included examples in which the ligand was free, as well as those where the ligand was bound to a metal ion. The standard deviation of the correlation was approx- imately 1.5 log units, corresponding to a factor of 30. The prediction of relative potencies of similar compounds can be expected to be much better. This method, which is very rapid, should provide a useful basis for prioritizing compounds prior to their synthesis. These results are encouraging and we anticipate that it will be possible to improve upon the accuracy of such methods further. A method with an accuracy of a factor of ten within a 95% confidence limit would provide a very powerful tool for computer-based design of new ligands.
IV. CONCLUSIONS
The art and practice of structure-based drug design is rapidly evolving into a legiti- mate scientific discipline. We use the term "scientific discipline" with confidence since the best work in this area currently involves:
1. Precise and reproducible experimentation 2. Use of sophisticated theoretical models, which can be rigorously subjected to
exact experimental verification
The scientific foundations for this approach were established many years ago with the development of X-ray and NMR techniques for the accurate determination of structures of macromolecular complexes and with the development of computational methods for molecular mechanics and quantum mechanics. However, the basic requirement for the
STRUCTURE-BASED DRUG DESIGN 47
successful application of these techniques to drug discovery is a well equipped, multi- disciplinary team of actively collaborating scientists. Many pharmaceutical companies invested heavily in this area at a time when this approach was as yet unproven. We are now seeing the results of this investment (see Section 11). With some notable exceptions, our impression is that most of the leading-edge research in structure-based molecular design is currently being carried out in industry.
While structure-based design is clearly proving to be of value in many drug discovery projects, the full potential of this approach has not yet been realized. At present, suc- cessful applications rely heavily on repeated cycles of potency measurement and experi- mental structure determination. In this process, the full potential of computational mo- lecular modeling methods has not yet been reached. Improved computational methods are necessary to optimally utilize available structural information. Methods for predict- ing docked geometries, conformational changes in the binding site, and binding affini- ties of novel molecules can be expected to improve. Once these operations become sufficiently reliable, the process of lead finding and optimization can be expected to be truly revolutionized.
Based on past and present accomplishments, further advances in the art and practice of structure-based drug design are expected. These advances will enable us to very rapidly obtain novel, highly potent compounds for a target binding site, once its 3-di- mensional structure is known.
REFERENCES
1. E. Fischer, Ber. Dtsch. Chem. Ges. 27, 2985 (1894). 2. J. W. Erickson and S. W. Fesik, in Annual Reports in Medicinal Chemistry M. C. Venuti, Ed., Academic Press,
3. J. Greer, J. W. Erickson, J. J. Baldwin, and M. D. Varney, 1. Med. Chem. 37, 1035 (1994). 4. C. L. M. J. Verlinde and W. G. J. Hol, Structure 2, 577 (1994). 5. C. E. Bugg, W. M. Carson, and J. A. Montgomery, Scientific American 269, 60 (1993). 6. R. S. Bohacek and C. McMartin, 1. Am. Chem. SOC. 116, 5560 (1994). 7. V. J. Gillet, W. Newell, P. Mata, G. Myatt, S. Sike, Z. Zsoldos, and A. P. Johnson, 1. Chem. I f . Comput. Sci.
8. D. A. Pearlman and M. A. Murcko, 1. Comp. Chem. 14, 1184 (1993). 9. S. H. Rotstein and M. A. Murcko, 1. Camp.-Aided Mol. Design 7, 23 (1993).
Inc., New York, 1992, p. 271.
34, 207 (1994).
10. S. H. Rotstein and M. A. Murcko, 1. Med. Chem. 36, 1700 (1993). 11. Y. Nishibata and A. ltai, 1. Med. Chem. 36, 2921 (1993). 12. E. E. Abola, F. C. Bernstein, S. H. Bryant, T. F. Koetzle, and J. Weng, in Data Commission offhe Int’l Union of
Crystallography, F. H. Allen, G. Bergerhoff, and R. Sievers, Eds., Int’l Union of Crystallography, Bonn, Cambridge, Chester, 1987, p. 107.
13. F. C. Bernstein, T. F. Koetzle, G. J. 8 . Williams, E. F. Meyer, M. D. Brice, J. R. Rodgers, 0. Kennard, T. Shimanouchi, and M. Tasumi, 1. Mol. B i d . 112, 535 (1977).
14. R. S. Bohacek and C. McMartin, 1. Med. Chem. 35, 1671 (1992). 15. D. G. Alberg and S. L. Schreiber, Science 262, 248 (1993). 16. C. Weber, G. Wider, B. von Freyberg, R. Traber, W. Braun, H. Widmer, and K. Wuthrich, Biochemistry 30,
6563 (1991). 17. S. W. Fesik, R. T. Gampe Jr., H. L. Eaton, G. Gemmecker, E. T. Olejniczak, P. Neri, T. F. Holzman, D. A.
Egan, R. Edalji, R. Simmer, R. Helfrich, J. Hochlowski, and M. Jackson, Biochemistry 30, 6574 (1991). 18. H.-R. Loosli, H. Kessler, H. Oschkinat, H.-P. Weber, T. J. Petcher, and A. Widmer, Helu. Chim. Acta. 68,
682 (1985). 19. H. Kessler, M. Kock, T. Wein, and M. Gehrke, Helu. Chim. Acta. 73, 1818 (1990). 20. W. L. Jorgensen, Science 254, 954 (1991). 21. K. Wuthrich, B. von Freyberg, C. Weber, G. Wider, R. Traber, H. Widmer, and W. Braun, Science 254,953
22. G. D. Van Duyne, R. F. Standaert, P. A. Karplus, S. L. Schreiber, and J. Clardy, Science 252, 839 1991). (1991).
48 BOHACEK, MCMARTIN, AND GUIDA
23. T. Taga, H. Tanaka, T. Goto, and S. Tada, Acta. Cryst. C43, 751 (1987). 24. D. F. Mierke, P. Schmieder, P. Karuso, and H. Kessler, Helv. Chim. Acfa. 74, 1027 (1991). 25. A. M. Petros, J. R. Luly, H. Liang, and S. W. Fesik, 1. A m . Chem. Soc. 115, 9920 (1993). 26. D. Altschuh, 0. Vix, B. Rees, and J.-C. Thierry, Science 256, 92 (1992). 27. J. L. Kofon, P. Kuzmic, V. Kishore, G. Gemmecker, S. W. Fesik, and D. H. Rich, 1. Am. Chem. Soc. 114,
2670 (1992). 28. N. H. Sigal, F. Dumont, P. Durette, J. J. Siekierka, L. Peterson, D. H. Rich, 8. E. Dunlap, M. J. Staruch,
M. R. Melino, S. L. Koprak, D. Williams, B. Witzel, and J. M. Pisano, 1. Exp. Med. 173, 619 (1991). 29. A. A. Patchett, D. Taub, 0. D. Hensens, R. T. Goegelman, L. Yang, F. Dumont, L. Peterson, and N. H.
Sigal, 1. Antibiot. 45, 94 (1992). 30. G. Lauri and P. A. Bartlett, 1. Computer-Aided. Mol. Design 8, 51 (1994). 31. F. H. Allen, J. E. Davies, J. J. Galloy, 0. Johnson, 0. Kennard, C. F. Macrae, E. M. Mitchell, G . F. Mitchell,
J. M. Smith, and D. G. Watson, 1. Chem. In!. Comput. Sci. 31, 187 (1991). 32. P. Y. S. Lam, P. K. Jadhav, C. J. Eyerman, C. N. Hodge, Y. Ru, L. T. Bacheler, J. L. Meek, M. J. Otto, M. M.
Rayner, Y. N. Wong, C.-H. Chang, P. C. Weber, D. A. Jackson, T. R. Sharpe, and S. Erickson-Viitanen, Science 263, 380 (1994).
33. A. L. Swain, M. M. Miller, J. Green, D. L. Rich, J. Schneider, S. 8. H. Kent, and A. Wiodawer, Proc. Natl. Acud. Sci. U . S . A. 87, 8805 (1990).
34. J. M. Blaney, G. M. Crippen, A. Dearing, and J. S. Dixon, Indiana University-Quantum Chemistry Program Exchange (QCPE # 590), Bloomington, IN.
35. 0. F. Guner, D. W. Hughes, and L. M. Dumont, 1. Chem. I f . Comput. Sci. 31, 408 (1991). 36. S. Thaisrivongs, P. K. Tomich, K. D. Watenpaugh, K.-T. Chong, W. J. Howe, C.-P. Yang, J. W. Strohbach,
S. R. Turner, J . P. McGrath, M. J. Bohanon, J. C. Lynn, A. M. Mulichak, P. A. Spinelli, R. R. Hinshaw, P. J. Pagano, J. B. Moon, M. J. Ruwart, K. F. Wilkinson, B. D. Rush, G. L. Zipp, R. J. Dalga, F. J. Schwende, G. M. Howard, G. E. Padbury, L. N. Toth, Z . Zhao, K. A. Koeplinger, T. J. Kakuk, S. L. Cole, R. M. Zaya, R. C. Piper, and P. Jeffrey, 1. Med. Chem. 37, 3200 (1994).
37. E. A. Lunney, S. E. Hagen, J. M. Domagala, C. Humblet, J. Kosinski, B. D. Tait, J. S. Warmus, M. Wilson, D. Ferguson, D. Hupe, P. J. Tummino, E. T. Baldwin, T. N. Bhat, B. Liu, and J. W. Erickson, 1. Med. Chem. 37, 2664 (1994).
38. D. S. Goodsell and A. J. Olson, Proteins: Struct., Fund . , Genet. 8, 195 (1990). 39. P. J. Goodford, /. Med. Chem. 28, 849 (1985). 40. J. V. N. V. Prasad, K. S. Para, E. A. Lunney, D. F. Ortwine, J. 8 . Dunbar, D. Ferguson, P. J. Tummino, D.
Hupe, B. D. Tait, J. M. Domagala, C. Humblet, T. N. Bhat, 8. Liu, D. M. A. Guerin, E. T. Baldwin, J. W. Erickson, and T. K. Sawyer, 1. A m . Chem. Soc. 116, 6989 (1994).
41. J. P. Vacca, J. P. Guare, S. J. deSolms, W. M. Sanders, E. A. Giuliani, S. D. Young, P. L. Darke, J. Zugay, I. S. Sigal, W. A. Schleif, J. C. Quintero, E. A. Emini, P. S. Anderson, and J. R. Huff, 1. Med. Chem. 34,1225 (1991).
42. T. A. Lyle, C. M. Wiscount, J. P. Guare, W. J. Thompson, P. S. Anderson, P. L. Darke, J. A. Zugay, E. A. Emini, W. A. Schleif, J. C. Quintero, R. A. F. Dixon, I. S. Sigal, and J. R. Huff, 1. Med. Chem. 34, 1228 (1991).
43. B. D. Dursey, R. B. Levin, S. L. McDaniel, J. P. Vacca, J. P. Guare, P. L. Darke, J. A. Zugay, E. A. Emini, W. A. Schleif, J. C. Quinterno, J . H. Lin, I.-W. Chen, M. K. Holloway, P. M. D. Fitzgerald, M. G. Axel, D. Ostovic, P. S. Anderson, and J. R. Huff, 1. Med. Chem. 37, 3443 (1994).
44. D. C. Humber, N. Cammack, J. A. V. Coates, K. N. Cobley, D. C. Orr, R. Storer, G. G. Weingarten, and M. P. Weir, 1. Med. Chem. 35, 3080 (1992).
45. D. C. Humber, M. J. Bamford, R. C. Bethell, N. Cammack, K. Cobley, D. N. Evans, N. M. Gray, M. M. Hann, D. C. Orr, J. Saunders, B. E. V. Shenoy, R. Storer, G. G. Weingarten, and P. G. Wyatt, 1. Med. Chem. 36, 3120 (1993).
46. D. S. Holmes, R. C. Bethell, N. Cammack, I. R. Clemens, J. Kitchin, P. McMeekin, C. L. Mo, D. C. Orr, 8. Patel, I. L. Paternoster, and R. Storer, 1. Med. Chem. 36, 3129 (1993).
47. A. Wonacott, R. Cooke, F. R. Hayes, M. M. Hann, H. Jhoti, P. McMeekin, A. Mistry, P. Murray-Rust, 0. M. P. Singh, and M. P. Weir, 1. Med. Chem. 36, 3113 (1993).
48. H. Jhoti, 0. M. P. Singh, M. P. Weir, R. Cooke, P. Murray-Rust, and A. Wonacott, Biochem. 33, 8417 (1994).
49. J. Kitchin, R. C. Bethell, N. Cammack, S. Dolan, D. N. Evans, S. Holman, D. S. Holmes, P. McMeekin, C. L. Mo, N. Nieland, D. C. Orr, J. Saunders, B. E. V. Shenoy, 1. D. Starkey, and R. Storer, 1. Med. Chem. 37, 3707 (1994).
50. D. H. Rich, J. Green, M. V. Toth, G. R. Marshall, and S. B. H. Kent, 1. Med. Chem. 33, 1285 (1990).
STRUCTURE-BASED DRUG DESIGN 49
51. A. Krohn, S. Redshaw, J. C. Ritchie, B. J. Graves, and M. H. Hatada, J. Med. Chem. 34, 3340 (1991). 52. M. D. Varney, K. Appelt, V. Kalish, M. R. Reddy, J. Tatlock, C. L. Palmer, W. H. Romines, B.-W. Wu, and
53. M. R. Reddy, M. D. Varney, V. Kalish, V. N. Viswanadhan, and K. Appelt, j . Med. Chem. 37, 1145 (1994). 54. D. Schirlin, S. Baltzer, V. Van Dorsselaer, F. Weber, C. Weill, J. M. Altenburger, B. Neises, G. Flynn, J. M.
55. D. Schirlin, V. Van Dorsselaer, C. Tamus, A. S. Tyms, S. Baltzer, F. Weber, J. M. Remy, T. Brennan, R. Farr,
56. B. L. Pologar, R. A. Farr, D. Friedrich, C. Tamus, E. W. Huber, R. J. Cregge, and D. Schirlin, J. Med. Chem.
57. M. Miller, J. Schneider, B. K. Sathyanarayana, M. V. Toth, G. R. Marshall, L. Clawson, L. Selk, S. B. H.
58. Z. Szewczuk, K. L. Rebholz, and D. H. Rich, Int. 1. Peptide Protein Res. 40, 233 (1992). 59. R. A. Wiley and D. H. Rich, Med. Res. Reviews 13, 327 (1993). 60. B. M. Brenner, B. J. Ballermann, M. E. Gunning, and M. L. Zeidel, Physiol. Rev. 70, 665 (1990). 61. P. Needleman, E. H. Blaine, J. E. Greenwald, M. L. Michener, and C. 8. Saper, Ann. Rev. Pharmacol.
62. R. E. Lang, T. Unger, and D. Ganten, I. Hypertension 5, 255 (1987). 63. A. E. G. Raine, J. G. Firth, and J. G. G. Ledinham, Clin. Sci. 76, 1 (1989). 64. J. P. Bussien, J. Biollaz, B. Waeber, J. Nussberger, G. A. Turini, H. R. Brunner, F. Brunner-Feber, H. J.
65. T. G. Yandle, A. M. Richards, M. G. Nicholls, R. Cuneo, E. A. Espiner, and J. H. Livesey, Life Sci. 38,1822 (1986). 66. J. L. Sonnenberg, Y. K. Sakane, A. Y. Jeng, J. A. Koehn, J. A. Ansell, L. P. Wennogle, and R. D. Ghai,
67. A. J. Kenny and S. L. Stephenson, FEBS Lett. 232, 1 (1988). 68. M. Pozsgay, C. Michaud, M. Liebman, and M. Orlowski, Biochemistry 25, 1292 (1986). 69. L. 8 . Hersh and K. Morihara, 1. Bid. Chem. 261, 6433 (1986). 70. J. C. Danilewicz, P. L. Barclay, I. T. garnish, D. Brown, S. F. Campbell, K. James, G. M. R. Samuels, N. K.
71. D. R. Holland, P. L. Barclay, J. C. Danilewicz, B. W. Matthews, and K. James, Biochemistry 33, 51 (1994). 72. L. J. MacPherson, E. K. Bayburt, M. P. Capparelli, R. S. Bohacek, F. H. Clarke, R. D. Ghai, Y. Sakane, C. J.
73. P. A. Bartlett and C. K. Marlowe, Science 235, 569 (1987). 74. H. M. Holden, D. E. Tronrud, A. F. Monzingo, L. H. Weaver, and B. W. Matthews, Biochemistry 26,8542
75. W. C. Guida, R. S. Bohacek, and M. D. Erion, 1. Comp. Chem. 13, 214 (1992). 76. R. S. Bohacek, J. Priestle, and M. Gruetter, unpublished results. 77. L. H. Opie, Angiotensin Converting Enzyme Inhibitors, Wiley-Liss, New York, 1992. 78. A. M. Richards, I. G. Crozier, T. Kosoglou, M. Rallings, E. A. Espiner, M. G. Nicholls, T. G. Yandle,
79. K. B. Margulies, M. A. Perrella, L. J. McKinley, and J. C. Burnett, 1. Clin. Invest. 88, 1636 (1991). 80. I. Pham, W. Gonzales, A.-I. K. El Amrani, M.-C. Fourni&Zaluski, M. Philippe, I. Laboulandine, B. P.
81. A. A. Seymour, M. M. Asaad, V. M. Lanoce, K. M. Langenbacher, S. A. Fennell, and W. L. Rogers, 1.
82. C. Krulan, R. D. Ghai, R. W. Lappe, and R. L. Webb, FASEB J. 7, A247 (1993). 83. A. A. Patchett, and E. H. Cordes, in Adv. Enzymol. A. Meister, Ed., John Wiley & Sons, New York, 1985. 84. R. S. Bohacek, S. DeLombaert, C. McMartin, J. Priestle, and M. Gruetter, unpublished results; manu-
script in preparation. 85. G. Ksander, R. S. Bohacek, R. de Jesus, A. Yuan, Y. Sakane, C. Berry, R. Ghai, and A. J. Trapani,
unpublished results, manuscript in preparation. 86. C. McMartin and R. Bohacek, unpublished results. 87. D. W. Cushman, H. S. Cheung, E. F. Sabo, and M. A. Ondetti, Biochemistry 16, 5484 (1977). 88. G. A. Flynn, D. W. Beight, S. Mehdi, J. R. Koelh, E. L. Giroux, J. F. French, P. W. Hake, and R. C. Dage, 1.
Med. Chem. 36, 2420 (1993). 89. N. G. Delaney, J. C. Banish, R. Neubeck, S. Natarajan, M. Cohen, G. C. Rovnyak, G. Huber,
N. Murugesan, R. Girotra, E. Sieber-McMaster, J. A. Robl, M. M. Asaad, H. S. Cheung, J. E. Bird, T. Waldron, and E. W. Petrillo, Bioorg. Med. Chem. Lett. 4, 1783 (1994).
L. Musick, J. Med. Chem. 37, 2274 (1994).
Remy, and C. Tarnus, Biorg. Med. Chem. Lett. 3, 253 (1993).
and D. Janowick, Biorg. Med. Chem. Lett. 4, 241 (1994).
37, 3684 (1994).
Kent, and A. Wlodawer, Science 246, 1149 (1989).
Toxicol. 29, 23 (1989).
Gomez, and E. S. Otterbein, 1. Curdiovasc. Pharmacol. 8, 216 (1986).
Peptides 9, 173 (1988).
Terrett, and M. J. Wythes, Biochem. Biophys. Res. Commun. 164, 58 (1989).
Berry, J. V. Peppard, and A. J. Trapani, j . Med. Chem. 36, 3821 (1993).
(1987).
H. Ikram, and C. Frampton, Hypertension 22, 119 (1993).
90. J. A, Robl, L. M. Simpkins, 1. Stevenson, C. Sun, N. Murugesan, J. C. Barrish, M. M. Asaad, J. E. Bird, T. R. Schaeffer, N. C. Trippodo, E. W. Petrillo, and D. C. Karanewsky, Bioorg. Med. Chem. Lett. 4, 1789 (1994).
91. J. A. Robl, L. M. Simpkins, R. Sulsky, E. Sieber-McMaster, J. Stevenson, Y. F. Kelly, C. Sun, R. J. Misra, D. E. Ryono, M. M. Asaad, J. E. Bird, N. C. Trippodo, and D. C. Karanewsky, Bioorg. Med. Chem. Lett. 4, 1795 (1994).
92. W. C. Guida, R. D. Elliott, H. J. Thomas, J. A. Secrist, Y. S. Babu, C. E. Bugg, M. D. Erion, S. E. Ealick, and J. A. Montgomery, J. Med. Chem. 37, 1109 (1994).
93. M. D. Erion, S. Niwas, J. D. Rose, S. Ananthan, M. Allen, J. A. Secrist, Y. S. Babu, C. E. Bugg, W. C. Guida, S. E. Ealick, and J. A. Montgomery, J. Med. Chem. 36, 3771 (1993).
94. J. A. Montgomery, S. Niwas, 1. D. Rose, J. A. Secrist, Y. S. Babu, C. E. Bugg, M. D. Erion, W. C. Guida, and S. E. Ealick, J. Med. Chem. 36, 55 (1993).
95. J. A. Secrist, S. Niwas, J. D. Rose, Y. S. Babu, C. E. Bugg, M. D. Erion, W. C. Guida, S. E. Ealick, and J. A. Montgomery, 1. Med. Chem. 36, 1847 (1993).
96. S. E. Ealick, Y. S. Babu, C. E. Bugg, M. D. Erion, W. C . Guida, J. A. Montgomery, and J. A. Secrist, Proc. Nutl. Acud. Sci. U. S . A. 88, 11540 (1991).
97. J. A. Montgomery, Med. Res. Reviews 13, 209 (1993). 98. F. Mohamadi, N. G. J. Richards, W. C. Guida, R. Liskamp, M. Lipton, C. Caufield, G. Chang, T.
Hendrickson, and W. C. Still, I. Comp. Chem. 11, 440 (1990). 99. W. C. Guida, and 1. Kolossvary, unpublished observations.
100. M. von Itzstein, W.-Y. Wu, G. B. Kok, M. S. Pegg, 1. C. Dyason, B. Jin, T. V. Phan, M. L. Smythe, H. F. White, S. W. Oliver, P. M. Colman, J. N. Varghese, D. M. Ryan, J. M. Woods, R. C. Bethell, V. J. Hotham, J. M. Cameron, and C. R. Penn, Nature 363, 418 (1993).
101. J. N. Varghese, J. McKimm-Breschkin, J. 8. Caldwell, A. A. Kortt, and P. M. Colman, Proteins: Struct., Funct., Genet. 14, 327 (1992).
102. A. K. J. Chong, M. S. Pegg, N. R. Taylor, and M. von Itzstein, Eur. I . Biochem. 207, 335 (1992). 103. J. S. Finer-Moore, W. R. Montfort, and R. M. Stroud, Biochemistry 29, 6977 (1990). 104. D. A. Matthews, K. Appelt, S. J. Oatley, and N. H. Xuong, 1. Mol. Biol. 214, 923 (1990). 105. D. A. Matthews, J. E. Villafranca, C. A. Janson, W. W. Smith, K. Welsh, and S. Freer, J. Mol. Bid. 214,937
(1990). 106. W. R. Montfort, K. M. Perry, E. B. Fauman, J. S. Finer-Moore, G. F. Maley, L. Hardy, F. Maley, and R. M.
Stroud, Biochemistry 29, 6964 (1990). 107. K. Appelt, R. J. Bacquet, C. A. Bartlett, C. L. J. Booth, S. Freer, M. A. M. Fuhry, M. R. Gehring, S. M.
Herrmann, E. F. Howland, C. A. Janson, T. R. Jones, C.-C. Kan, V. Kathardekar, K. K. Lewis, G. P. Marzoni, D. A. Matthews, C. Mohr, E. W. Moomaw, C. A. Morse, S. J. Oatley, R. C. Ogden, M. R. Reddy, S. H. Reich, W. S. Schoettlin, W. W. Smith, M. D. Varney, 1. E. Villafranca, R. W. Ward, S. Webber, S. E. Webber, K. M. Welsh, and J. White, J. Med. Chem. 34, 1925 (1991).
108. M. D. Varney, G. P. Marzoni, C. L. Palmer, J. G. Deal, S. Webber, K. M. Welsh, R. J. Bacquet, C. A. Bartlett, C. A. Morse, C. L. J. Booth, S. M. Herrmann, E. F. Howland, R. W. Ward, and J. White, j . Med. Chem. 35, 663 (1992).
109. 8. K. Shoichet, R. M. Stroud, D. V. Santi, I. D. Kuntz, and K. M. Perry, Science 259, 1445 (1993). 110. I. D. Kuntz, J. M. Blaney, S. J. Oatley, R. Langridge, and T. E. Ferrin, J. Mol. Biol. 161, 269 (1982). 111. R. L. DesJarlais, R. P. Sheridan, G. L. Seibel, J. S. Dixon, I . D. Kuntz, and R. Venkataraghavan, J. Med.
112. B. K. Shoichet, D. L. Bodian, and I . D. Kuntz, J. Comp. Chem. 13, 380 (1992). 113. I . D. Kuntz, E. C. Meng, and B. K. Shoichet, Acc. Chem. Res. 27, 117 (1994). 114. V. Tschinke and N. C. Cohen, J. Med. Chem. 36, 3863 (1993). 115. M. K. Holloway, J. M. Wai, T. A. Halgren, P. M. D. Fitzgerald, J. P. Vacca, B. D. Dorsey, R. B. Levin, W. J.
Thompson, J. Chen, S. J. deSolms, N. Gaffin, A. K. Ghosh, E. A. Giuliani, S. L. Graham, 1. P. Guare, R. W. Hungate, T. A. Lyle, W. M. Sanders, T. J. Tucker, M. Wiggins, C. M. Wiscount, 0. W. Woltersdorf, S. D. Young, P. L. Darke, and J. A. Zugay, J. Med. Chem. 38, 305 (1995).
Chem. 31, 722 (1988).
116. P. A. Kollman, Chem. Rev. 93, 2395 (1993). 117. P. A. Kollman, Current Opinion in Structural Biology 4, 240 (1994). 118. F. Guarnieri and W. C. Still, J. Comp. Chem. 15, 1302 (1994). 119. J. Aqvist, C. Medina, and J.-E. Samuelsson, Protein Engineering 7, 385 (1994). 120. H.-J. Boehm, J. Computer-Aided Mol. Design 8, 243 (1994).
