Successful in Silico Discovery of Novel ... - ACS Publications

J. Med. Chem. 2005, 48, 3203-3213

3203

Successful in Silico Discovery of Novel Nonsteroidal Ligands for Human Sex Hormone Binding Globulin Artem Cherkasov,*,‡ Zheng Shi,‡ Magid Fallahi,§ and Geoffrey L. Hammond§ Division of Infectious Diseases, Faculty of Medicine, University of British Columbia, Heather Pavilion, 2733, Heather Street, Vancouver, British Columbia V5Z 3J5, Canada, and Department of Obstetrics and Gynaecology, BC Research Institute for Children’s and Women’s Health, University of British Columbia, Vancouver, British Columbia V5Z 3J5, Canada Received November 11, 2004

Using “in silico” drug design methodologies, we have discovered several nonsteroidal compounds of natural origin that bind to human sex hormone binding globulin (SHBG) with affinity constants of 0.1 × 106 to 1.2 × 106 M-1. The computational solutions we developed involved pharmacophore-aided database search, virtual protein-ligand docking, and structure-activity modeling with “inductive” QSAR descriptors. By screening 23 836 natural substance structures, we identified 29 potential SHBG ligands, and eight of these bound the protein in vitro. These nonsteroidal ligands belong to four classes of molecular scaffolds with several available substitution positions that could allow chemical modification to enhance SHBG-binding activity. Interestingly, one of these compounds is structurally similar to a dicyclohexane derivative that binds to rat SHBG and causes azospermia when administered to male rats. Taken together, the in silico strategy we have developed will aid in the discovery of nonsteroidal ligands of SHBG with novel pharmacological properties. Introduction Sex hormone-binding globulin (SHBG) is a glycoprotein in blood plasma that is produced primarily by the liver.1 Expression of the SHBG gene in the testis of several mammals also gives rise to a protein commonly known as the testicular androgen binding protein (ABP), which is thought to play a key role in sperm maturation.2 Plasma SHBG and testicular ABP bind biologically active androgens and estrogens and play a critical role in regulating the access of these sex steroids to their target cells.1-3 In addition to binding steroids with high affinity, SHBG has been reported to interact directly with plasma membranes of cells in some tissues in a ligand-dependent manner and to thereby stimulate intracellular signaling pathways that alter cell growth and/or function.4 Numerous human diseases such as endometrial cancer,5 ovarian dysfunction,6 male and female infertility,7 osteoporosis,8,9 diabetes,10 and cardiovascular diseases11 are associated with abnormal levels of SHBG in plasma. Many of these disease processes, or the health problems associated with them, can be attributed to abnormalities in the plasma distribution and bioavailability of the endogenous sex steroid ligands of SHBG. In cases where the disease can be attributed to the limited activities of sex steroid, such as osteoporosis, the identification of high-affinity nonsteroidal SHBG ligands with no intrinsic biological properties of their own might represent a means of enhancing the bioavailability and activities of endogenous sex steroids. In the current study we therefore employed conventional “in silico” drug design technologies such as docking- and pharmacophore-based virtual screening, * To whom correspondence should be addressed Phone: 604-8754588. Fax: 604-875-4013. E-mail: [email protected]. ‡ Division of Infectious Diseases. § BC Research Institute for Children’s and Women’s Health.

as well as several recently developed “in house” molecular modeling solutions, to establish a virtual screening method for nonsteroidal compounds that could effectively displace endogenous sex steroids from the human SHBG steroid-binding site. Virtual screening methods are key approaches in modern computer-aided drug design. They involve analyzing electronic collections of chemical structures by means of various computational tools with the goal of identifying manageable subsets of compounds that have higher chances of being active against the desired biological target(s). Virtual screening is thus considered an effective complement for experimental high-throughput assays and is being used increasingly in modern drug discovery practices.12-14 There are two types of virtual screening: structure-based docking, which requires detailed information about the three-dimensional structure of the target’s binding site; ligand-based techniques (such as pharmacophore modeling) that rely on preexisting knowledge of compounds with biological activities of interest.12-14 For “in silico” discovery of nonsteroidal SHBG ligands, we employed both structurebased and ligand-based techniques and our recently developed QSAR (quantitative structure-activity relationships) solutions.15-17 We focused our efforts on screening databases of natural compounds because SHBG is known to interact with some plant-derived agents18 and because such collections are typically rich in biologically active substances.19 The following three-stage lead discovery procedure was implemented to identify potential ligands of the human SHBG steroid-binding site from collections of natural molecules. The first stage involved the use of existing data on known ligands of SHBG20 to develop several pharmacophore models that were then used to screen an electronic collection of natural compounds. As a result, we identified a smaller set of natural deriva-

10.1021/jm049087f CCC: $30.25 © 2005 American Chemical Society Published on Web 04/02/2005

3204

Journal of Medicinal Chemistry, 2005, Vol. 48, No. 9

Cherkasov et al.

tives that met the stringent structural requirements imposed by these models. The second stage involved using the same set of known SHBG ligands to train a QSAR model that utilizes a machine-learning algorithm to distinguish known SHBG ligands from other chemicals. The resulting structure-activity model was then applied to rank the pharmacophore-identified compounds for their potential binding affinity toward SHBG. During the third stage of the “in silico” study, we conducted rigorous virtual docking of selected molecular structures into the SHBG steroid-binding pocket. Finally, those compounds that ranked highly by the QSAR model and/or were favored by the virtual docking were subjected to experimental testing of their potencies as SHBG ligands in vitro. Results Database of Natural Substances. Using various public and commercial databases, we assembled a collection of natural compounds (molecules found in nature or their close synthetic analogues/derivatives) totalling more than 48 000 substances. We selected only those compounds with well-defined chemical structures that could be purchased in sufficient quantities for subsequent analysis in vitro. Those chemicals that satisfied typical “druglikeness” criteria21 were placed in a separate group of “druglike” natural substances. This group included 23 836 molecules with molecular weights within the 200-500 Da range and that possess 1-5 hydrogen bond donors (OH, NH, SH groups) and 1-10 hydrogen bonds acceptors (O, N, S atoms). They also had less than 8 rotating bonds, 1-3 aromatic rings, or/ and 1-5 cyclic systems, a total polar surface area of less than 140 Å2, and a hydrophobicity of below log P ) 8.0 (the corresponding druglikeness filters have been implemented within the MOE (Molecular Operation Environment) package22). For each of these 23 836 “druglike” natural substances, we generated up to 100 distinct conformations using the Catalyst 4.8 software.23 The resulting data set, reflecting the conformational space of the “druglike” natural derivatives, was then subjected to pharmacophore-based virtual screening. Pharmacophore-Based Virtual Screening for Novel SHBG Ligands. A pharmacophore represents a set of space-positioned molecular features determining the ability of a ligand to bind to its biological target. A pharmacophore model can thus be developed by superimposing the molecular structures of specific ligands against their known affinities for the same target, and this allows the common structural features responsible for binding interactions to be defined. In the case of SHBG, we developed three major pharmacophore models illustrated graphically in Figure 1. Pharmacophore A was developed from the structures of 5R-dihydrotestosterone (DHT) and its close derivatives (Figure 1a). This pharmacophore model included nine major features: three hydrophobic/aromatic sites, one hydrogen bond donor/acceptor point with three donor/acceptor projection points, and one hydrogen bond acceptor with two acceptor projection points. Pharmacophore B was generated by aligning the crystallographic configurations of DHT and estradiol inside the steroid-binding pocket of SHBG, and it utilized eight basic features: three hydrophobic/aromatic centers, one H acceptor and one H-bond donor, two donor projection

Figure 1. Developed pharmacophores A-C superimposed on the structure of DHT: (a) pharmacophore A; (b) pharmacophore B; (c) pharmacophore C.

points, and one volume exclusion feature restraining the molecular boundaries (Figure 1b). The nine-featured pharmacophore C relied on the flexible alignment of a large number of known ligands of human SHBG,20 which resulted in the identification of the following common features: three hydrophobic/aromatic centers, one hydrogen donor site with two donor projection points, and one H-bond acceptor with two projection points (Figure 1c).

Virtual Screening for Novel SHBG Ligands

Journal of Medicinal Chemistry, 2005, Vol. 48, No. 9 3205

Table 1. Test Results for Different Pharmacophore Hypothesesa model

feature

total hits

steroids

nonsteroids

unknown

inactive

true hit rate

recovery rate

false hit rate

A B C

9 8 9

9 12 22

9 10 11

0 1 4

0 1 1

0 0 6

1 0.92 0.68

0.56 0.69 0.94

0 0 0.27

a True hit rate ) number of true hits/total number of hits. False hit rate ) number of false hits/total number of hits. Recovery rate ) number of true hits/total number of true hits.

Table 2. “Inductive” QSAR Descriptors Used To Rank the Pharmacophore-Identified Potential SHBG Binders QSAR descriptor

definition

Average_EO_Neg

QSAR descriptor

Arithmetic mean of electronegativities of atoms with negative partial charge

Most_Pos_Sigma_mol_i

Average_EO_Pos

Arithmetic mean of electronegativities of atoms with positive partial charge Arithmetic mean of hardnesses of all atoms of a molecule

Smallest_Neg_Hardness

Arithmetic mean of softnesses of atoms with negative partial charge Arithmetic mean of softnesses of atoms with positive partial charge Iteratively equalized electronegativity of a molecule Molecular softnessssum of constituent atomic softnesses Largest atomic hardness among values for negatively charged atoms Largest atomic hardness among values for positively charged atoms Largest value of steric influence Rs(moleculefatom) in a molecule Largest negative atomic inductive parameter σ*(atomfmolecule) or atoms in a molecule Largest (by absolute value) negative group inductive parameter σ*(moleculefatom) for atoms in a molecule Largest partial charge among values for positively charged atoms Largest positive atomic inductive parameter σ*(atomfmolecule) for atoms in a molecule

Smallest_Rs_i_mol

Sum of all positive group inductive parameters σ*(moleculefatom) within a molecule Smallest atomic hardness among values for negatively charged atoms. Smallest atomic hardness among values for positively charged atoms Smallest value of atomic steric influence Rs(atomfmolecule) in a molecule Smallest value of group steric influence Rs(moleculefatom) in a molecule Atomic softness of an atom with the most negative charge Atomic softness of an atom with the most positive charge Sum of hardnesses of atoms with negative partial charge Sum of hardnesses of atoms with positive partial charge

Average_Hardness Average_Neg_Softness Average_Pos_Softness EO_Equalized Global_Softness Largest_Neg_Hardness Largest_Pos_Hardness Largest_Rs_mol_i Most_Neg_Sigma_i_mol Most_Neg_Sigma_mol_i

Most_Pos_Charge Most_Pos_Sigma_i_mol

definition

The adequacy of the resulting pharmacophore models was evaluated by applying them to a test collection of compounds with high, moderate, or zero binding affinity to SHBG. The performance in relation to each pharmacophore model was assessed through the hit- and recovery-rate parameters (for details, see Materials and Methods). The results indicated that pharmacophore A was the most successful in identifying steroidal substances (high-affinity ligands), while it tended to miss moderately active nonsteroidal compounds (see Table 1). Pharmacophore B identified most steroids in the test set in addition to some nonsteroidal SHBG binders, while pharmacophore C generated the largest number of hits including a substantial fraction of SHBG binders in the test set and numerous nonactive substances (see Table 1). These pharmacophore models were then used as datamining instruments of varying stringency. By applying them in different combinations to conformer sets generated for the 23 836 “druglike” natural substances, we identified 201 initial hits including 96 known steroidlike and 105 nonsteroidal compounds (corresponding to the entries 283-388 in Supporting Information). QSAR Hit Ranking. To prioritize the identified 105 nonsteroidal chemicals for experimental testing, we

Smallest_Pos_Hardness

Smallest_Rs_mol_i Softness_of_Most_Neg Softness_of_Most_Pos Sum_Neg_Hardness Sum_Pos_Hardness Total_Charge

Sum of absolute values of partial charges on all atoms of a molecule

Total_Charge_Formal

Sum of charges on all atoms of a molecule (formal charge of a molecule) Sum of softnesses of atoms with negative partial charge

Total_Neg_Softness

Total_Pos_Softness Total_Sigma_mol_i

Sum of softnesses of atoms with positive partial charge Sum of inductive parameters σ*(moleculefatom) for all atoms within a molecule

processed them with a recently developed QSAR approach utilizing “inductive” molecular descriptors.15-17,24-27 These parameters represent a novel distinct group of QSAR descriptors that cover a broad range of bound atoms and molecules whose properties vary in relation to their size, polarizability, electronegativity, compactness, mutual inductive and steric influence, and distribution of electronic density.24-27 The “inductive” QSAR descriptors have been used in our previous studies for building “druglikeness” and “antibioticlikeness” models16 and for creating QSAR solutions for the antimicrobial activity of cationic peptides.17 Detailed information on the “inductive” descriptors can be found elsewhere.15-17,24-27 In the current study, we used “inductive” descriptors in combination with the method of artificial neural networks (ANN) to rank the 105 nonsteroidal compounds selected according to their potential binding affinity to human SHBG. To build a predictive QSAR model, we assembled a set of more than 70 compounds known to interact with SHBG (entries 1-78 in Supporting Information) and compiled a set of about 200 chemicals28 with unknown affinities to SHBG as “negative” controls for the model (entries 79-282 in Supporting Information). For all 282 molecules, we calculated

3206


Cherkasov et al. Table 3. Parameters of Sensitivity, Specificity, Accuracy, and Positive Predictive Value for the Developed ANN-Based QSAR Models on the Training (a) and Testing (b) Sets with the Varying Threshold Parameters cutoff

Figure 2. Configuration of the artificial neural network containing 28 input, 8 hidden, and 1 output nodes and used for ranking the potential SHBG leads.

28 independent “inductive” QSAR descriptors that are characterized in greater detail in Table 2. The rationale for building a QSAR model for SHBG ligands was such that these 28 molecular parameters could be used as independent variables for describing SHBG binding affinity. Thus, within the training set, the SHBG binders (entries 1-78) were assigned a dependent parameter with a value of 1.0, while the compounds from the “negative control” set (entries 79-282) were all assigned zero. To relate the “inductive” descriptors to the binary (1|0) SHBG binding criteria for the 282 molecules studied, we employed a standard back-propagation ANN configuration consisting of 24 input, 8 hidden, and 1 output nodes (see Figure 2). For effective training and subsequent validation of the ANN, we used the training set of 188 molecules randomly selected as representing 2/3 of the 282 molecules under investigation. In each training run, the remaining 94 compounds under investigation were used as the testing set to assess the predictive ability of the model.

specificity

sensitivity

accuracy

ppv

0.5/0.5 0.6/0.4 0.7/0.3

(a) Training on 66% of the Data 1 0.98 0.999 0.993 0.9107 0.970 0.979 0.892 0.955

0.999 0.981 0.943

0.5/0.5 0.6/0.4 0.7/0.3

(b) Testing on 33% of the Data 0.95 0.840 0.917 0.95 0.840 0.917 0.95 0.840 0.917

0.875 0.875 0.875

In each of 20 independent training and validating testing runs of the ANN, the corresponding false/true positive and negative predictions were estimated using a 0.50 cutoff for the output. In other words, ANN outputs greater than 0.50 were considered as positive predictions. The results demonstrated that the ANN achieved up to 99% accuracy in distinguishing SHBG binders within the training sets and 92% accuracy within the testing sets. The results for various cutoff parameters averaged over 20 independent validation runs are shown in Table 3. These data clearly illustrate that the “inductive” QSAR descriptors allowed us to distinguish with confidence the compounds in the testing and training sets that bind to SHBG from those that do not. The ANN-based binary QSAR model we created was applied to the 105 molecules identified previously by the pharmacophore model-based search for potential SHBG ligands (entries 283-388 in Supporting Information). The molecular structures of all 105 molecules were processed to calculate 28 “inductive” descriptors that were then passed through the pretrained ANN. The resulting network outputs have been assembled in Supporting Information, and it can be seen that some candidate molecules are ranked very highly by the model. We anticipated that the corresponding topranked substances would represent very good candidate ligands of human SHBG, and we selected 22 compounds (molecules from Table 4 with network outputs above 0.1) for purchasing and in vitro testing. At the same time, we applied a rigorous ligand-protein docking procedure to all 105 pharmacophore-identified compounds to support the lead selection by the QSAR model and to expand the set of selected compounds. Structure-Based Virtual Screening. Several human SHBG crystal structures have been solved and deployed to the Protein Data Bank.29 These include cocrystal structures of the protein with different steroid ligands, as well as steroid-binding data for numerous human SHBG mutants that can be exploited in the structure-based design of high-affinity SHBG ligands.30-33 These published crystal structures also demonstrate that SHBG binds androgen and estrogen molecules in different (opposite) orientations (see Figure 3 for more details) and show that there are two major hydrogen bonding “anchors” within the SHBG steroid-binding site: one involving the Ser42 residue that binds to the C3 carbonyl group in C19 steroids (androgens) and to the 17β-OH group of C18 steroids (estrogens); another formed by the side chains of Asp85 and Asn82, which binds functional groups at the C3 and C17 positions of C18 and C19 steroids, respectively. These two “anchors”



Table 4. Nonsteroidal Substances Selected for Experimental Testing Based on Their QSAR Ranking (Column 3) and/or Consensus Docking Score (Column 4)a

a Columns 5 and 6 reflect experimentally determined percentage on [3H]DHT substitution in the SHBG binding assay and the calculated ligand-SHBG association constants. Yellow color marks those compounds that satisfy QSAR activity test. Blue color represents compounds potentially active according to the docking. Green color designates the most active chemicals resulting in more than 35% reduction of the radiolabeled DHT.

therefore interact selectively with functional groups of androgen and estrogen molecules with respect to their unique orientations within the SHBG steroid-binding site.30

In addition to differences in the orientation of androgens and estrogens within the SHBG steroid-binding site, there are several other challenges associated with the virtual docking of small compounds into this hy-

3208 Journal of Medicinal Chemistry, 2005, Vol. 48, No. 9

Cherkasov et al.

Figure 4. Relationship plot between experimental association constant (log Ka) and the consensus docking score.

Figure 3. Crystal structure of human SHBG containing DHT or estradiol in the steroid-binding pocket: (a) human SHBG steroid-binding pocket occupied by DHT; (b) human SHBG steroid-binding pocket occupied by estradiol.

drophobic pocket. One is that the conformation of the SHBG steroid-binding site may change upon ligand binding in an as yet undefined manner, and another relates to the fact that the presence of a Zn ion can disorder a polypeptide loop covering the human SHBG steroid-binding site and alter its ligand binding specificity.34 Thus, the previous studies of the crystal structure of SHBG complexes demonstrated that Zn2+ is positioned at the ligand entry point of the steroid-binding pocket.33 It has also been experimentally confirmed that the presence of a zinc ion does not have any impact on binding of C19 steroids. On the other hand, it reduces the SHBG affinity of estrogen and its derivatives that bind to the active site in a different orientation. In fact, the site-directed mutagenesis studies demonstrated that Zn2+ causes reorientation of the Asn65 side chain in the SHBG binding pocket that otherwise can form a strong H bond with the C3 oxygen of estradiol in the absence of the metal. Taking these factors into consideration and to capture all possible implications of Zn2+ presence in the active site, we developed a consensus docking strategy utilizing four protein structures, some of which include the zinc

Figure 5. Superposed structures of DHT molecules docked into SHBG using Glide software (green) with bound DHT configuration defined from the crystal structures (red).

atom while others do not, namely, the PDB entries 1D2S, 1F5F, 1KDM, and 1LHU that correspond to the human SHBG cocrystallized with DHT (1D2S) or with estradiol (1LHU). All protein structures were preprocessed for docking by removing water molecules and reconstructing hydrogen atoms (see “Materials and Methods” for more details). We used the Glide 2.7 program35 to fit 32 SHBG binders with experimentally determined association constants (corresponding to entries 1-8, 10-12, 1416, 18-21, 28, 32, 33, 38, 41, 44, 46, 51, 53 from Supporting Information) into the four different crystal structures of SHBG. On the basis of the docking results for all four structures, we derived an averaged consensus score, which was plotted in Figure 4 against known ligand-SHBG association constants (Ka) taken from the literature.20 As can be seen in Figure 4, the r value for the correlation is above 0.81, and this indicates that the docking protocol reproduced the experimental protein binding affinities with fairly good accuracy. Figure 5 illustrates the docked DHT structure and shows that it is very closely aligned with the known orientation of DHT within the crystal structure (1D2S) of the SHBG steroid-binding site. It was also established that on average a compound demonstrates good binding affinity to SHBG when the corresponding docking score is below


Figure 6. Distribution of the docking consensus score for pharmacophore-identified compounds and randomly derived molecular structures.

the -7.5 threshold. Figure 6 illustrates the distribution of the consensus docking scores among known SHBG binders and compounds with no known SHBG binding affinity (entries 82-151 from Supporting Information, which have been docked as negative controls). These results demonstrate that the consensus docking score we have developed is a useful guide for selecting potential SHBG ligands for experimental testing. We therefore applied this procedure to 105 pharmacophoreselected nonsteroidal structures and docked them into the four crystal structures of SHBG. The established consensus docking scores assembled in Table 4 indicate that this set of 105 compounds might contain a large number of potential SHBG binders, with more than half of the candidates scoring below the -7.5 threshold and several molecules yielding a docking score below -9.0 (see Figure 6). The docking scores also demonstrated that most of the molecules selected using the QSAR ranking could


be docked into SHBG steroid-binding site very precisely, and these were selected as prime candidates for testing of their SHBG binding properties in vitro. On the other hand, some compounds (9808, 823, 1372, 5605, 1924, 298, 422), which were not identified by the QSAR model, demonstrated very good docking potential (consensus scores below -7.5), and these were also selected for in vitro testing. In total, 29 nonsteroidal compounds were purchased and subjected to in vitro testing (all presented in Table 4). In Vitro Testing of Potential SHBG Ligands. All 29 compounds selected by the QSAR ranking and virtual docking experiments were screened for their ability to interact with the SHBG steroid-binding site in vitro. The screening assay involved a modification of an established competitive steroid ligand-binding assay that employs tritium-labeled DHT ([3H]DHT) as the radiolabeled ligand (see Materials and Methods for details). The initial screen of compounds was conducted at a single high concentration (approximately 200 µM), and the results (presented in Table 4) demonstrate that eight nonsteroidal compounds (696, 2593, 2228, 2623, 6767, 8590, 5636, and 5597) displaced 35-95% of the [3H]DHT from the SHBG steroid-binding site. These compounds, and a compound (5098) with no activity in the screening assay (i.e., a negative control), were then selected for a more detailed analysis of their ability to compete with [3H]DHT from the human SHBG steroidbinding site relative to known concentrations of the physiologically most important androgen (testosterone) and estrogen (17β-estradiol). The resulting competitive displacement curves generated using these test compounds (see Figure 7) illustrate that their potencies as SHBG ligands are very much in line with their rank potencies obtained in the preliminary screening assay (Table 4), with the most effective competitors being 5597, 2623, and 2593. For these compounds an IC50 could be calculated from the plot shown in Figure 7.

Figure 7. Displacement curves for test compounds used in the in vitro competition assay to determine the relative binding affinities of human SHBG ligands. The amount of [3H]DHT bound to SHBG in the presence of increasing concentrations of competitor ligands (B) is expressed as a percentage of the amount of [3H]DHT bound to SHBG in the absence of competitor ligand (Bo).

3210


Cherkasov et al.

Table 5. Structures of the Three Most Active Nonsteroidal Lead Compounds Superimposed by the Flexible Alignment with DHT Molecule (Second Column) and an Estrogen Molecule (Third Column) and the Corresponding Values of the SHBG Dissociation Constants and RBA Parameters

a

Structures of the steroids are labeled in red; the structures of the identified leads are green.

These IC50 values (5597, 13.6 µM; 2623, 22.4 µM; 2593, 124.5 µM) could then be compared with those of testosterone (15.6 nM) and estradiol (61.6 nM). These data provide a measure of the relative binding affinities (RBAs) of the top three test compounds compared to testosterone or estradiol, and their RBAs are shown in Table 5 along with their structural alignments against those of testosterone or estradiol produced by the MOE flexible alignment routine22. Although small differences in the association rate constants (Ka) for testosterone and estradiol have been reported, they have been determined previously20 under the same conditions (i.e., at 4°C) as those employed in this study and are as follows: testosterone, Ka ) 1.1 × 109 M-1; estradiol, Ka ) 6 × 108 M-1. Thus, it is also possible to estimate the Ka values for the most active test compounds based on their RBA values (see Table 5). The parallelism of the competitive displacement curves for 2623 and the natural steroids (Figure 7) is also indicative that this compound is completely soluble at high concentrations and behaves in essentially the same way as a steroid ligand with respect to its kinetics of binding. By contrast, the crossing of the displacement curve for 5597 with that of 2623 (see Figure 7) indicates that 5597 may not be completely soluble at high concentrations, and this could tend to underestimation of the potency of 5597 in the assay. Discussion The competitive displacement curves in Figure 7 illustrate the range of potencies of various compounds as assessed in the competitive steroid-binding assay. The affinities of all of these compounds are more than 2 orders of magnitude lower than estradiol. However, the potencies of 2623 and 5597 exceed those of other synthetic and natural nonsteroidal compounds with

endocrine disrupting properties and which typically bind to SHBG steroid-binding site with association constants in the range of 0.02 × 105 to 8 × 105 M-1.18,36,37 To date, the highest association constant for a nonsteroidal ligand of SHBG (Ka ) 3.2 × 106 M-1) has been reported for (-)-3, 4-divanyllyltetrahydrofuran, which belongs to the class of natural lignans,18 and the calculated Ka (1.20 × 106 M-1) for compound 5597 is very close to that value. Moreover, the structure of 5597 is much easier to produce synthetically, compared to a lignan, and provides more opportunities for further structural modifications that could increase binding affinity to SHBG. Figure 8a shows a superimposition of the molecular structures of 5597 and (-)-3,4-divanyllyltetrahydrofuran. This not only illustrates that the overall shapes of the two molecules are similar but also shows that the critical chemical groups required for SHBG binding can be positioned very closely in space. It can also be expected that the nonsteroidal compounds we have identified reside within the SHBG steroid-binding pocket in essentially the same way as the physiologically important sex steroid ligands. Figure 8b shows the structure of 5597 docked into the active site of SHBG and superimposed against bound DHT. In this orientation, 5597 reproduces all critical binding features of a sex steroid. Interestingly, two of the nonsteroidal ligands of SHBG we have identified (2623 and 2593) are structurally very similar but clearly differ in their binding affinities. The main structural difference between them is the presence of a methoxy group adjacent to a hydroxyl group on a phenolic ring structure, which seems to be associated with an increase in binding affinity. This is interesting because there is a very similar difference in the RBAs of estradiol and 2-methoxyestradiol for human SHBG,20 and this suggests that the phenolic ring structures of compounds 2623 and 2593 may reside within the SHBG steroid-


Figure 8. Structural alignment of a compound 5597 with other SHBG ligands: (a) structure of the most active identified nonsteroidal SHBG blocker (green scaffold) 5597 flexibly aligned with (-)-3,4-divanyllyltetrahydrofuran, the best known nonsteroidal SHBG blocker (scaffold colored by elements); (b) structure of 5597 in its SHBG-docked configuration (green) superimposed with the experimentally determined orientation of DHT in the SHBG binding site (red); (c) structure of 5597 (green scaffold) flexibly aligned with dicyclohexyl derivative previously demonstrating high binding affinity toward the rat ABP protein (scaffold colored by elements).

binding site in the same orientation as ring A of an estrogen molecule. The structure of one of the most active compounds (5597) we have identified as a ligand of human SHBG is also quite similar to a dicyclohexane derivative (Figure 8c features a superimposition of their structures) that binds with relatively high affinity to the rat


testicular ABP and causes azospermia when administered to sexually mature rats.38,39 This dicyclic derivative does not bind with high affinity to human SHBG,38,39 and this likely reflects species differences in the topography of the SHBG steroid-binding site, which undoubtedly contribute to the unique steroid-binding properties of SHBGs in different species.20 The identification of 5597 as a human SHBG ligand indicates that other structurally related compounds probably exist with even higher affinities for human SHBG and that they could be predicted by applying the computational solutions we have developed. In this context, very good consensus docking scores of below -8.2 were obtained for seven of the eight novel SHBG ligands we have identified. Despite the fact that the virtual docking approach resulted in numerous false positive predictions of SHBG ligands, three inactive compounds (3838, 1898, and 2099) were correctly rejected by the virtual docking. Although several other compounds (3305, 4944, 5607, 1824, 2835, 5537, 5098, 1235, 4819, 479, and 938) were falsely predicted as good SHBG binders by the QSAR model, QSAR ranking did not produce any false negative predictions. All six compounds (9808, 823, 1372, 5605, 1924, 298, 422) that scored below the 0.10 threshold in the QSAR model were selected for in vitro testing because of acceptable docking scores, but they did not demonstrate significant binding activity. The QSAR model assigned very significant network outputs above 0.5 to four of the most active compounds that produce a >35% reduction of [3H]DHT binding to SHBG in the screening assay (compounds 696, 2593, 2623, 2228). This model also resulted in output values above 0.10 for four other molecules (6767, 8590, 5636, 5597) that were subsequently shown to bind to SHBG. By contrast, it would seem that the ANN tended to overrank chemical structures having several nonaromatic rings because all 11 top-ranked substances contain two or more of them. However, this was not unexpected because the ANN was trained on natural steroids (containing several conjoined aliphatic rings) as the strongest SHBG binders. On one hand, this provides several advantages because the condensed polycyclic compounds we have identified (2593, 696, 2228, and 2623) all demonstrated good SHBG binding properties. On the other hand, the ANN did not rank some of the most active substances, such as 5597, 5636, 6767, and 8590, very highly, and this is like due to the fact that those molecules contain aromatic groups that may resemble nonsteroidal structures used as the “negative control” in the ANN model training. Nonetheless, the trained ANN model ranked the corresponding chemicals structures highly enough to select them for purchasing and testing. Overall, on the basis of the results of experimental testing, we conclude that QSAR ranking allowed efficient prioritization and selection of the chemicals. At the same time, one should keep in mind that the usage of ANN does not allow interpreting contributions from individual QSAR descriptors. On another hand, the developed binary QSAR model is exceptionally fast compared to docking and other “in silico” approaches; it represents an excellent complementary technique that enhances the power of pharmacophore methods. It can

3212


Cherkasov et al.

also be coupled to future studies with programs for combinatorial synthesis/library design to carry out the rounds of lead optimization. In upcoming studies we will also attempt to develop QSAR solutions operating by statistical rather than machine-learning algorithms that will provide more insight into the SAR. Nonetheless, it should also be stressed that the “in silico” procedure we have developed in the current work has already allowed a more than 25% recovery rate of novel submicromolar inhibitors of DHT binding to SHBG, and this significantly exceeds standard success rates of conventional “in silico” protocols.

1 for more details). The corresponding parameters for the three pharmacophores are presented in Table 1. Conformation Generation. We have utilized the Catalyst 4.8 package to generate multiple conformations of known SHBG ligands as well as compounds from the database of natural substances. We used the default parameters of the Catalyst 4.8 software in combination with the FAST algorithm;23 default is 20 kcal/mol energy range, and the maximum number of generated conformations per structure is set to 100. QSAR Modeling. The MOE package has been used to optimize all molecular structures.22 The “inductive” descriptors have been computed by the MOE custom written scripts for all compounds studied. The ANN training has been created and used by the SNNS package.40 During the learning phase, we used a learning rate of 0.8 and a learning update threshold of 0.2, while the input patterns were shuffled. The number of hidden nodes varied from 2 to 20, and we have conducted 20 independent training and testing runs for each configuration. The corresponding statistical parameters featured in Table 3 represent the values averaged over those 20 training/testing runs. As for the limits of applicability of the developed QSAR model, since it was based on rigorously cross- validated ANN, we expected it to work for any organic molecule as long as its 34 “inductive” QSAR descriptors (presented in Table 4) fit the range of “inductive” parameters used for the ANN training. Docking. The Maestro suite41 was used to prepare protein structures for docking, which was conducted using the Glide 2.7 program35 with default settings. The protein charges were assigned by the MMFF94 molecular mechanics force field method,42 and the binding pocket of SHBG was identified as 6.5 Å surrounding the natural ligand DHT from the crystal structure 1D2S. SHBG Ligand-Binding Assay. An established competitive ligand binding assay was used to determine the binding affinities of test compounds to human SHBG in relation to those of testosterone and estradiol.43 In brief, the assay involved mixing 100 µL aliquots of diluted (1:200) human pregnancy serum containing approximately 1 nM SHBG, which was pretreated with dextran-coated charcoal (DCC) to remove endogenous steroid ligand, with 100 µL of tritiumlabeled DHT ([3H]DHT) at 10 nM as the labeled ligand. For the screening assay, triplicate aliquots (100 µL) of a fixed amount (200 µM) of test compound were added to this SHBG/ [3H]DHT mixture and incubated overnight at room temperature, followed by a 10 min incubation with 500 µL of a DCC slurry at 0°C and centrifugation to separate SHBG-bound from free [3H]DHT. Compounds that displaced more than 35% of the [3H]DHT from the SHBG in this assay were then diluted serially, and triplicate aliquots (100 µl) of known concentrations of test compounds were used to generate complete competition curves by incubation with the SHBG/[3H]DHT mixture, and separation of SHBG-bound from free [3H]DHT, as in the screening assay. The amounts of [3H]DHT bound to SHBG at each concentration of competitor ligand were determined by scintillation spectrophotometry and plotted in relation to the amount of [3H]DHT bound to SHBG at zero concentration of competitor. From the resulting competition curves, IC50 concentrations could be calculated if displacement of more than 50% of [3H]DHT from SHBG was achieved. The association constants (Ka) have been calculated from the relative binding affinity parameters (RBA) using Ka(DHT)/ [(1 + R)/RBA - R], where Ka(DHT) ) 0.98 × 109 M-1 is the association constant of the DHT and R (0.05) is the ratio of bound to free tritium-labeled DHT at 50% displacement in the assay.

Conclusions The computational approach we have developed resulted in the identification of 105 prospective compounds from a collection of 23 836 natural substances that were prioritized for in vitro testing by QSAR modeling and virtual docking techniques. This procedure resulted in the identification of eight novel nonsteroidal ligands with an ability to displace the natural ligand (DHT) with the highest known affinity for human SHBG at low micromolar concentrations. We also conclude that the combination of rather stringent QSAR ranking with more “relaxed” docking criteria provides the most efficient predictive power. In fact, the results indicate that the “in silico” pipeline we have implemented correctly identifies every fourth compound we selected for in vitro testing as a micromolar inhibitor of the target protein. Moreover, the eight most active nonsteroidal SHBG ligands we have identified belong to four distinct molecular scaffolds with several available substitution positions. Hence, there is potential to improve the binding activity of these lead compounds through further chemical modification. It is also anticipated that the adopted “in silico” procedure will undergo further methodological development and enhancement: more “inductive” QSAR descriptors are underway, and more advanced machine learning and statistical techniques are being tested for QSAR modeling with “inductive” parameters (because the latter will allow interpretation of contributions from individual “inductive” descriptors and thus be a better guide for lead optimization/synthetic efforts). It is also feasible to enhance the “in silico” pipeline with such sensitive approaches as CoMFA/CoMSIA that may accelerate further leads optimization. We also plan on expanding the developed approach to other areas of therapeutics and expect that such an endeavor may lead to the identification of novel drug leads. Materials and Methods Flexible Alignment and Pharmacophore Generation. To test the adequacy of the developed pharmacophores, we have applied them to the 16 strongest SHBG binders (i.e., the compounds with >35% displacement of [3H]DHT from SHBG in the screening assay including entries 1, 2, 4, 5, 7-10, 1215, 17-19, and 21 from Supporting Information), the 16 moderately strong SHBG binders (i.e., those that displaced 10-35% [3H]DHT from SHBG in the screening assay; namely, entries 3, 6, 11, 16, 20, 28, 29, 32, 33, 38, 41, 44, 46, 51-53), and 70 nonbinders (entries 82-151). The pharmacophore performance was assessed through the predictions by the true hit rate, false hit rate, and recovery rate (see footnotes to Table

Acknowledgment. G.L.H. is a Canada Research Chair in Reproductive Health and is supported by operating grants from the Canadian Institutes of Health Research. Supporting Information Available: Listing of compounds with ANN values. This material is available free of charge via the Internet at http://pubs.acs.org.



References

(22) MOE: Molecular Operational Environment, version 2003.10; Chemical Computation Group Inc.: Montreal, Canada, 2004. (23) Catalyst, version 4.8; Accelrys Inc.: San Diego, CA, 2004. (24) Cherkasov, A. Inductive Electronegativity Scale. Iterative Calculation of Inductive Partial Charges. J. Chem. Inf. Comput. Sci. 2003, 43, 2039-2047. (25) Cherkasov, A. R.; Galkin, V. I.; Cherkasov, R. A. “Inductive” Electronegativity Scale. 2. “Inductive” Analog of Chemical Hardness. J. Mol. Struct.: THEOCHEM 2000, 497, 115123. (26) Cherkasov, A. R.; Galkin, V. I.; Cherkasov, R. A. A New Approach to the Theoretical Estimation of Inductive Constants. J. Phys. Org. Chem. 1998, 11, 437-447. (27) Cherkasov, A. R.; Galkin, V. I.; Cherkasov, R. A. “Inductive” Electronegativity Scale. J. Mol. Struct.: THEOCHEM 1999, 489, 43-46. (28) Asinex Gold Collection; Asinex Ltd.: Moscow, 2004. (29) Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E. The Protein Data Bank. Nucleic Acids Res. 2000, 28, 235-242. (30) Grishkovskaya, I.; Avvakumov, G. V.; Hammond, G. L.; Catalano, M. G.; Muller, Y. A. Steroid ligands bind human sex hormone-binding globulin in specific orientations and produce distinct changes in protein conformation. J. Biol. Chem. 2002, 277, 32086-32093. (31) Avvakumov, G. V.; Grishkovskaya, I.; Muller, Y. A.; Hammond, G. L. Crystal structure of human sex hormone-binding globulin in complex with 2-methoxyestradiol reveals the molecular basis for high affinity interactions with C-2 derivatives of estradiol. J. Biol. Chem. 2002, 277, 45219-45225. (32) Grishkovskaya, I.; Avvakumov, G. V.; Hammond, G. L.; Muller, Y. A. Resolution of a disordered region at the entrance of the human sex hormone-binding globulin steroid-binding site. J. Mol. Biol. 2002, 318, 621-626. (33) Hammond, G. L.; Avvakumov, G. V.; Muller, Y. A. Structure/ function analyses of human sex hormone-binding globulin: effects of zinc on steroid-binding specificity. J. Steroid Biochem. Mol. Biol. 2003, 85, 195-200. (34) Avvakumov, G. V.; Muller, Y. A.; Hammond, G. L. Steroidbinding specificity of human sex hormone-binding globulin is influenced by occupancy of a zinc-binding site. J. Biol. Chem. 2000, 275, 25920-25925. (35) Glide, version 2.7; Schro¨dinger Inc.: San Diego, CA, 2004. (36) Dechaud, H.; Ravard, C.; Claustrat, F.; de la Perriere, A. B.; Pugeat, M. Xenoestrogen interaction with human sex hormonebinding globulin (hSHBG). Steroids 1999, 64, 328-334. (37) Hogert, J.; Zacharewski, T. R.; Hammond, G. L. Interactions between human plasma sex hormone-binding globulin and xenobiotic ligands. J. Steroid Biochem. Mol. Biol. 2000, 75, 167176. (38) Rousseau, G. G.; Rolin Jacquemyns, C. F.; Sirett, D. A. N.; Huybrechts, M.; De Coen, J. L.; Quivy, J. I. Inhibition of steroidprotein interactions by dicyclohexane derivatives. J. Steroid Biochem. 1988, 31, 691-697. (39) Rousseau, G. G.; Quivy, J. I.; Kirchnoff, J.; Bui, X.-H.; Devis, R. Nonsteroidal compounds which bind epididymal androgenbinding protein but not the androgen receptor. Nature 1980, 284, 458-459. (40) SNNS: Stuttgart Neural Network Simulator, version 4.0; University of Stuttgart: Stuttgart, Germany, 1995. (41) Maestero; Schro¨dinger Inc.: San Diego, CA, 2004. (42) Halgren, T. A. Merck Molecular Force Field. I. Basis, Form, Scope, Parameterization and Performance of MMFF94. J. Comput. Chem. 1996, 17, 490-519. (43) Hammond, G. L.; Lahteenmaki, P. L. A versatile method for the determination of serum cortisol binding globulin and sex hormone binding globulin binding capacities. Clin. Chim. Acta 1983, 132, 101-110.

(1) Hammond, G. L. Potential functions of plasma steroid-binding protein. Trends Endocrinol. Metab. 1995, 6, 298-304. (2) Joseph, D. R. Structure, function, and regulation of androgenbinding protein/sex hormone-binding globulin. Vitam. Horm. 1994, 49, 197-280. (3) Siiteri, P. K.; Murai, J. T.; Hammond, G. L.; Nisker, J. A.; Raymoure, W. J.; Kuhn, R. W. The serum transport of steroid hormones. Recent Prog. Horm. Res. 1982, 38, 457-510. (4) Nakhla, A. M.; Rosner, W. Stimulation of prostate cancer growth by androgens and estrogens through the intermediacy of sex hormone-binding globulin. Endocrinology. 1996, 137, 41264129. (5) Nisker, J. A.; Hammond, G. L.; Davidson, B. J.; Frumar, A. M.; Takaki, N. K.; Judd, H. L.; Siiteri, P. K. Serum sex hormonebinding globulin capacity and the percentage of free estradiol in postmenopausal women with and without endometrial carcinoma. A new biochemical basis for the association between obesity and endometrial carcinoma. Am. J. Obstet. Gynecol. 1980, 138, 637-642. (6) Hogeveen, K. N.; Cousin, P.; Pugeat, M.; Dewailey, D.; Soudan, B.; Hammond, G. L. Human sex hormone-binding globulin variants associated with hyperandrogenism and ovarian dysfunction. J. Clin. Invest. 2002, 109, 973-981. (7) Anderson, D. C. Sex-hormone-binding globulin. Clin Endocrinol. 1974, 3, 69-96. (8) Van Pttelgergh, I.; Goemaere, S.; Zmierczak, H.; Kaufman, J. M. Perturbed sex steroid status in men with idiopathic osteoporosis and their sons. J. Cin. Endocrinol. Metab. 2004, 89, 4949-4953. (9) Rapuri, P. B.; Gallagher, J. C.; Haynatzki, G. Endogenous levels of serum estradiol and sex hormone binding globulin determine bone mineral density, bone remodeling, the rate of bone loss, and response to treatment with estrogen in elderly women. J. Clin. Endocrinol. Metab. 2004, 89, 4954-4962. (10) Lindstedt, G.; Lundberg, P.-A.; Lapidus, L.; Lundgren, L.; Bjo¨rntrop, P. Low sex-hormone-binding globulin concentration as independent risk factor for development of NIDDM. 12-yr follow-up of population study of women in Gothenburg, Sweden. Diabetes 1991, 40, 123-128. (11) Haffner, S. M.; Katz, M. S.; Stern, M. P.; Dunn, J. F. Association of decreased sex hormone binding globulin and cardiovascular risk factors. Arteriosclerosis 1989, 9, 136-143. (12) Virtual Screening for Bioactive Molecules; Bo¨hm, H.-J., Schneider, G., Kubinyi, H., Mannhold, R., Timmerman, H., Eds.; Methods and Principles in Medicinal Chemistry; Wiley: New York, 2000. (13) Klebe, G. Virtual Screening: An Alternative or Complement to High Throughput Screening?; Kluwer: Dordrecht, The Netherlands, 2000. (14) Guner, O. F. Pharmacophore Perception, Development, and Use in Drug Design; IUL Biotechnology Series; International University Line: La Jolla, CA, 2000. (15) Cherkasov, A. “Inductive” Descriptors. 10 Successful Years in QSAR. Curr. Comput.-Aided Drug Des. 2005, 1, 21-42. (16) Cherkasov, A. Inductive QSAR Descriptors. Distinguishing Compounds with Antibacterial Activity by Artificial Neural Networks. Int. J. Mol. Sci., in press. (17) Cherkasov, A.; Jankovic, B. Application of “Inductive” QSAR Descriptors for Quantification of Antibacterial Activity of Cationic Polypeptides. Molecules 2004, 9, 1034-1052. (18) Schottner, M.; Gansser, D.; Siteller, K. M. Lignans interfering with 5 alpha-dihydrotestosterone binding to human sex hormonebinding globulin. J. Nat. Prod. 1997. 61, 119-121. (19) Newman, D. J.; Cragg, G. M.; Snader, K. M. Natural products as sources of new drugs over the period 1981-2002. J. Nat. Prod. 2003, 66, 1022-1037. (20) Westphal, U. Steroid-Protein Interaction II. Mongraphs in Endocrinology; Springer-Verlag: Berlin, 1986. (21) Xu, J.; Stevenson, J. Drug-like index: a new approach to measure drug-like compounds and their diversity. J. Chem. Inf. Comput. Sci. 2000, 40, 1177-1187.

JM049087F

Successful in Silico Discovery of Novel ... - ACS Publications

Recommend Documents