Computer-aided identification of novel 3,5-substituted rhodanine derivatives with activity against Staphylococcus aureus DNA gyrase

Bioorganic & Medicinal Chemistry 22 (2014) 2176–2187

Contents lists available at ScienceDirect

Bioorganic & Medicinal Chemistry journal homepage: www.elsevier.com/locate/bmc

Computer-aided identification of novel 3,5-substituted rhodanine derivatives with activity against Staphylococcus aureus DNA gyrase Malela M. Werner a, Zhiyu Li b, Randy J. Zauhar a,⇑ a b

Department of Chemistry and Biochemistry, University of the Sciences, 600 South 43rd Street, Philadelphia, PA 19104, United States Department of Pharmaceutical Sciences, University of the Sciences, 600 South 43rd Street, Philadelphia, PA 19104, United States

a r t i c l e

i n f o

Article history: Received 9 November 2013 Revised 6 February 2014 Accepted 14 February 2014 Available online 27 February 2014 Keywords: Rhodanine derivatives Staphylococcus aureus DNA gyrase Antimicrobial Docking Molecular dynamics Multiple linear regression model Interaction frequency

a b s t r a c t Methicillin resistant Staphylococcus aureus (MRSA) is among the major drug resistant bacteria that persist in both the community and clinical settings due to resistance to commonly used antimicrobials. This continues to fuel the need for novel compounds that are active against this organism. For this purpose we have targeted the type IIA bacterial topoisomerase, DNA gyrase, an essential enzyme involved in bacterial replication, through the ATP-dependent supercoiling of DNA. The virtual screening tool Shape Signatures was applied to screen a large database for agents with shape similar to Novobiocin, a known gyrase B inhibitor. The binding energetics of the top hits from this initial screen were further validated by molecular docking. Compounds with the highest score on available crystal structure of homologous DNA gyrase from Thermus thermophilus were selected. From this initial set of compounds, several rhodaninesubstituted derivatives had the highest antimicrobial activity against S. aureus, as determined by minimal inhibitory concentration assays, with Novobiocin as the positive control. Further activity validation of the rhodanine compounds through biochemical assays confirmed their inhibition of both the supercoiling and the ATPase activity of DNA gyrase. Subsequent docking and molecular dynamics on the crystal structure of DNA gyrase from S. aureus when it became available, provides further rationalization of the observed biochemical activity and understanding of the receptor–ligand interactions. A regression model for MIC prediction against S. aureus is generated based on the current molecules studied as well as other rhodanines derivatives found in the literature. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Hospital acquired MRSA (Methicillin resistant Staphylococcus aureus) or HA-MRSA was first reported in the 1960s. Since then Abbreviations: PNP, purine nucleoside phosphorylase; MESG, 2-amino-6-mercapto-7-methylpurine ribose; GLIDE XP, grid-based ligand docking with energetics extra-precision; OPLS, optimized potentials for liquid simulations; SPC water, simple point charge water; NPT ensemble, isothermal–isobaric ensemble; RMSD, root–mean–square deviation; #rtvFG, number of reactive functional groups; SASA, total solvent accessible surface area (SASA) in square angstroms using a probe with a 1.4 Å radius; FOSA, hydrophobic component of SASA (SASA on N, O, and H on heterocarbons); QPlog Po/w, predicted octanol/water partition coefficient; Qlog PS, predicted aqueous solubility, log S; PISA, p (carbon and attached hydrogen) component of SASA); Glob, globular descriptor, (4 pr2)/SASA, where r is the radius of a sphere with a volume equal to the molecular volume. Globularity is 1.0 for a spherical molecule; #acid, number of carboxylic acid groups; ACxDN^.5/SA, index of cohesive interaction in solids; QPlog BB, predicted brain/blood partition coefficient; #ringatoms, number of atoms in a ring; QPPCaco, prediction of non-active transport permeation in nm/sec for gut–blood barrier through Caco-2 cells model; #metab, number of likely metabolic reactions; MH broth, Mueller Hinton broth; CFU, colony forming unit; CLSI, Clinical and Laboratory Standards Institute. ⇑ Corresponding author. Tel.: +1 2155968691. E-mail address: [email protected] (R.J. Zauhar). http://dx.doi.org/10.1016/j.bmc.2014.02.020 0968-0896/Ó 2014 Elsevier Ltd. All rights reserved.

community acquired MRSA or CA-MRSA has seen an increase, especially in the 1990s.1 In 2005, it was estimated that the number of deaths due to MRSA in the United States was similar to the number of deaths due to AIDS, tuberculosis and viral hepatitis combined.2–4 The prevalence of MRSA which affects mortality, quality of living and health care costs points to the continued need for new and effective antimicrobials with a spectrum of action to cover S. aureus strains resistant to current drugs regimens.5 DNA gyrase is a type IIA bacterial topoisomerase that consists of an A2B2 tetramer formed by two subunits, GyrA and GyrB. It is an attractive target for inhibiting bacterial proliferation that can lead to infections due to its specificity for prokaryotic cells. It cleaves both strands of the DNA helix and introduces negative supercoils driven by ATP hydrolysis on GyrB.6 It aids in maintaining the appropriate DNA topology during DNA processing.7 The cleavage of DNA forms a G segment which is followed by strand passage of a T-segment though the N-terminal domain or N-gate. Binding of ATP followed by hydrolysis closes the N-gate. The T segment then passes through the cleaved or G-gate after which the gate reseals, before exiting through the C terminal gate. A second ATP hydrolysis causes the N-gate to reopen and reset the enzyme for

M. M. Werner et al. / Bioorg. Med. Chem. 22 (2014) 2176–2187

new strand passage.7–9 The gyrase inhibitors that are currently approved by the FDA for clinical use in the United States are the quinolone derivatives.10 They selectively bind to the gyrase-DNA interface thereby preventing resealing of the G-segment. However, resistance to the quinolone derivatives has arisen due to emergence of mutations at the active site.11,12 The ATP binding site on GyrB is an attractive target for potential new antimicrobials for clinical use; no drugs that target this site are currently available for treatment. Novobiocin, a synthetic coumarin derivative, which competitively binds to GyrB thus inhibiting ATPase function, is a compound widely used in investigational studies.6,13 However Novobiocin has been found to cause liver toxicities14 and has been removed from clinical use by the FDA. Since then, new compounds that target GyrB have been investigated, in hopes of finding new antimicrobials for clinical use. These include, cyclothialidine,15 a natural product isolated from Streptomyces filipinesis, epigallocatechin gallate (ECGC),16 a green tea catechin, pyrrolamides,17 as well as several thiazines, pyrimidines, azole derivatives and aminobenzimidazoles with dual inhibition of gyrase and topoisomerase IV.18 Recently other groups have discovered compounds with the rhodanine moiety which showed evidence of antimicrobial activity.19–21 Our study identified small molecules similar in shape to Novobiocin using the virtual screening tool Shape Signatures.22 This initial phase was followed by screening for candidates through computational docking of the molecules to the GyrB ATP binding site from the available crystal structure from Thermus thermophilus, homologous to the S. aureus enzyme and the compounds with the highest score from this secondary screen were purchased for activity validation. The biological activities of the screened compounds

2177

were validated by determining their minimal inhibitory concentration on different strains of S. aureus. Their biochemical activity was validated through relaxation assays to determine changes in supercoiling, and through ATPase assay to determine changes in ATP hydrolysis. Subsequent work with the crystal structure from DNA gyrase from S. aureus led to an improved understanding of key binding site features. A preliminary multiple linear regression model was generated based on the molecules that we screened for antimicrobial activity as well as other rhodanine derivatives published by Xu,19 Chen,20 and Brvar.21 Figure 1 depicts the work flow followed in this study. 2. Results 2.1. In silico screening and docking of potential GyrB inhibitors on homologous enzyme from T. thermophilus Following Shape Signature screening for 10,000 molecules similar in shape as Novobiocin, docking experiments were performed to predict binding affinity between the screened ligands and the ATP-binding site of DNA gyrase, as a predictor of in vitro inhibition. The crystal structure of DNA gyrase from T. thermophilus,23 a homologue of S. aureus, 1KIJ was used for screening docked poses as a crystal structure for DNA gyrase for S. aureus was not available at the onset of the study. Further justifications for the use of the homologue 1KIJ as a binding target to screen for compounds with activity on DNA gyrase from S. aureus included a homology of 58% and the similarity between both enzymes in key residues in the ATP binding site on gyrase B where Novobiocin binds, as depicted in Supplemental Figure 1A. The rhodanine derivatives all had high

Figure 1. Study flow chart. The investigation for new inhibitors against S. aureus and S. aureus DNA gyrase was initiated with Shape Signature to screen a library of compounds with shape identical to the know inhibitor Novobiocin. This was followed by screening with docking onto the homologous crystal structure for DNA gyrase from T. thermophilus to predict binding affinity to DNA gyrase. Validation of the in vivo and the in vitro activity of the screened candidates were performed through antimicrobial sensitivity testing and DNA gyrase relaxation assays, respectively. Retrospective computational analysis on crystal structure of DNA gyrase from S. aureus were then performed to better understand observed biological and biochemical activity. A multiple linear regression model for antimicrobial prediction was developed based on correlation between minimal inhibitory concentration (MIC) and ADME (absorption, distribution, metabolism and excretion) properties obtained through the Schrodinger tool, Qikpro.

2178

M. M. Werner et al. / Bioorg. Med. Chem. 22 (2014) 2176–2187

Glide XP24–27 docking scores on 1KIJ in the 9 and 10 kcal/mol range, close to that of Novobiocin as shown in Table 1. Other molecules that were also purchased based on also having the highest docking scores with Glide XP24–27 or GOLD28,29 on 1KIJ are depicted in Supplemental Tables 1 and 2. These structures however had lower antimicrobial activity in general than the three rhodanine derivatives in Table 1 and were not considered for further screening.

Table 2 Antimicrobial activity of test compounds. Minimal inhibitory concentrations (MIC) of the test rhodanine compounds and Novobiocin control were determined against two strains of S. aureus, ISP 794 and carolina, as described in the methods section. Novobiocin’s MIC was consistently lower than 1.27 lM. Rhod-1 had the lowest MIC of the 3 rhodanines followed by Rhod-2 and Rhod-3. In general the MIC values tended to be higher against the Carolina S. aureus strain than the ISP 794 strain. MIC values were reported as the average and standard deviation of 4 readings (duplicated MIC for each compound on two separate days) Molecule

MIC against ISP 794 S. aureus

MIC against carolina S. aureus

2.2. Determination of antimicrobial activity of compounds

Novobiocin

One of the goals of our study was to discover compounds with activity against the infectious agent, S. aureus through the mechanism of inhibiting DNA gyrase activity. We therefore conducted antimicrobial assays of the promising compounds from docking results to determine the potential therapeutic ability of the computationally screened compounds. We first tested the biological activity of the highest scoring compounds before further confirmation of their biochemical activity. Table 2 summarizes the minimum inhibitory concentration of the rhodanine compounds, which exhibited the greatest antimicrobial activity among the initially acquired and tested compounds, with Novobiocin as the positive control. Of the rhodanine derivatives tested, Rhod-1 was the most active against S. aureus with an average minimum inhibitory concentration (MIC) of 4.7 lg/ml (13.5 lM) against ISP 794 and 9.4 lg/ml (27 lM) against Carolina strains of S. aureus. Rhod-2 and Rhod-3 also exhibited antimicrobial activity against S. aureus but were less active with MIC of 12.5 lg/ml (31 lM) and 75 lg/ml (159 lM) against the strain, ISP 794, respectively, as shown in Table 2. The MIC values of other compounds which

Rhod-1

<0.8 ± 0 lg/ml (<1.27 ± 0 lM) 4.7 ± 1.8 lg/ml (13.5 ± 5.2 lM) 12.5 ± 0 lg/ml (31 ± 0 lM) 75 ± 28.9 lg/ml (159 ± 61.2 lM)

<0.8 ± 0 lg/ml (<1.27 ± 0 lM) 9.4 ± 3.6 lg/ml (27 ± 10.4 lM) 18.8 ± 7.2 lg/ml (46.5 ± 17.9 lM) 75 ± 28.9 lg/ml (159 ± 61.2 lM)

Rhod-2 Rhod-3

were tested from this initial screen are included in Supplemental Tables 1 and 2. The microbiological activities of the rhodanine compounds were also tested against Escherichia coli; however their MIC values were greater than100 lg/ml. This seems to attest to a potential specificity of the compounds for gram positive bacteria such as S. aureus. 2.3. Inhibition of gyrase relaxation activity The rhodanine derivatives were selected for further validation of their biochemical activity due to their higher antimicrobial

Table 1 Chemical structures of the compounds used in this study. Rhod-1 ((5Z)-3-(2-ethylphenyl)-5-(3-hydroxybenzylidene)-2-thioxo-1,3-thiazolidin-4-one), Rhod-2 (3-[(5Z)-5-(2chlorobenzylidene)-4-oxo-2-thioxo-1,3-thiazolidin-3-yl]-3-phenylpropanoic acid), and, Rhod-3 (2-[(5E)-5-{[5-(2,5-dichlorophenyl)furan-2-yl]methylidene}-4-oxo-2-thioxo-1,3thiazolidin-3-yl]butanedioic acid), which all contained a rhodanine moiety, were purchased for further validation after screening with Shape Signatures and docking on homologous DNA gyrase from T. thermophilus using Novobiocin as the query molecule Molecule

Compound structure

Glide XP 1KIJ T. thermophilus

Novobiocin

10.69

Rhod-1

10.26

Rhod-2

9.76

Rhod-3

10.25

M. M. Werner et al. / Bioorg. Med. Chem. 22 (2014) 2176–2187

2179

Figure 2. Inhibition of S. aureus gyrase supercoiling activity by rhodanines. All reaction lanes include 150 ng of relaxed DNA and 2 mM ATP. All lanes except lane 1 include 2 units of S. aureus DNA gyrase in the absence of inhibitor (lane 2) and in the presence of inhibitors (lanes 3 through 17). Novobiocin is present in lanes 3–5 at 300, 100 and 30 nM, Rhod-3 in lanes 6–9, Rhod-2 in lanes 10–13 and Rhod-1 in lanes 14 to 17 each at 500, 160, 50 and 20 lM, respectively.

activity in the preliminary activity validation compared to other molecules (see Supplemental Tables 1 and 2), especially Rhod-2 and Rhod-1. The relaxation assay was performed to validate the ability of the rhodanine derivatives to inhibit supercoiling of relaxed DNA by S. aureus DNA gyrase compared to the known inhibitor Novobiocin, as measured by changes in mobility of DNA through an electrophoretic gel. Novobiocin had several orders of magnitude higher activity against DNA gyrase than the rhodanine derivatives (Fig. 2). Rhod-3 inhibited DNA supercoiling the most among the rhodanine derivatives, with full inhibition at the 500 and 160 lM levels and with evidence of activity still present at the 20 lM levels. Rhod-2 exhibited a similar activity profile but with less potent inhibition at 20 lM. Rhod-1 had less activity against S. aureus DNA gyrase, with lowest concentration evidence of inhibitory activity at 160 lM. All four rhodanine derivatives along with Novobiocin were tested for activity against supercoiling by DNA gyrase from E. coli. They did not have significant activity against the enzyme from E. coli as shown in Supplemental Figure 2. 2.4. Inhibition of gyrase ATPase activity The mechanism by which the activity of DNA gyrase was inhibited was evaluated using an ATPase assay, which can provide direct evidence that the rhodanines bind and displace ATP from its binding site on DNA gyrase, and thus confirm our docking and molecular dynamics evaluation of ligand–receptor interactions. Because the two rhodanines, Rhod-2 and Rhod-3, exhibited the most activity in the relaxation assay they were analyzed for ATPase activity along with Novobiocin as the positive control. It was assumed that Rhod-1 would not have significant activity against the ATPase activity of DNA gyrase due to its poorer in vitro activity against the enzyme, compared to Rhod-2 and Rhod-3. The ATPase activity of S. aureus DNA gyrase in the presence of inhibitors was coupled with conversion of MESG to ribose 1-phosphate and 2-amino-6mercapto-7-methylpurine by PNP30 as described in the Section 5. Absorbance readings were corrected for the background absorbance of Rhod-2 and Rhod-3 in water at 360 nm. After correcting for background absorbance, the assay confirmed the inhibition of gyrase ATPase activity by Rhod-3 and Rhod-2. The experiments were successfully repeated in triplicate and the results are reported in Figure 3 normalized for DNA gyrase ATPase activity. Novobiocin, Rhod-3 and Rhod-2 showed a dose dependent inhibition of ATPase activity, with Novobiocin having greater activity against the enzyme. Novobiocin was approximately 500 times

Figure 3. Inhibition of S. aureus gyrase ATPase activity by rhodanines. The ATPase activity of S. aureus DNA gyrase was evaluated in the presence of Novobiocin and the rhodanine inhibitors Rhod-2 and Rhod-3. The control DNA gyrase activity is represented in darker blue far right bar. Novobiocin concentrations are in the nM range and representated as 300, 100, 30 and 10 nM in red, green, purple and light blue striped-bars respectively. Rhod-3 and Rhod-2 are represented in the lM range as 300, 100, 30 and 10 lM in red, green, purple and light blue solid-bars respectively. The inhibition of ATPase activity is dose dependent.

more active than Rhod-3 at the 300 and 100 lM levels and Rhod-2 at the 300 lM level. Rhod-3 was slightly more active than Rhod-2, especially at 300 and 100 lM levels. Rhod-3 lowered ATPase activity of DNA gyrase to 23%, 23%, 66%, and 84% at concentrations of 300, 100, 30 and 10 lM respectively or an approximate IC50 of 30 lM. At same concentrations, Rhod-2 lowered the activity of DNA gyrase to 25%, 52%, 76%, 84%, respectively or an approximate IC50 of 100 lM. 2.5. Computational analysis of rhodanine derivatives on DNA Gyrase from S. aureus Once the X-ray structure of DNA gyrase from S. aureus, pdb code 3TTZ,17 became available computational analysis of Novobiocin and the rhodanine derivatives were performed on the more suitable target for further understanding of observed activity on the S. aureus DNA gyrase enzyme. These included docking evaluations using induced fit docking followed by quantum mechanicspolarized ligand docking, and molecular dynamics simulations.

2180

M. M. Werner et al. / Bioorg. Med. Chem. 22 (2014) 2176–2187

Visualization of each ligand poses against an electrostatic potential map of the 3TTZ receptor generated using Pymol31 shows that all 4 ligands fit in a well-defined pocket on 3TTZ adjacent to a crevice containing Arg144, as depicted in Figure 4. 2.5.1. Induced fit docking Induced fit docking analysis of the rhodanine derivatives and Novobiocin on 3TTZ were performed with the Schrodinger suite Induced fit docking protocol32–34 to account for the more realistic receptor side chain and backbone movements whereby protein movements allow binding site to better fit ligand binding mode, which is not the case in standard rigid docking where the receptor binding site is held rigid while the ligand is free to move. Table 3 summarizes the results using this protocol. Novobiocin ( 14.128 kcal/mol) had the highest Glide XP score from IFD in agreement with experiment. This was followed by Rhod-3 ( 11.318 kcal/mol) and Rhod-2 ( 9.571 kcal/mol) in agreement with the experimentally observed activity rank on DNA gyrase, despite the higher magnitude in the difference between Rhod-3 and Rhod-2 docking scores compared to experimental activity. Rhod-1 ( 9.116 kcal/mol) had the lowest docking score in agreement with low experimental activity in biochemical assay. 2.5.2. QM-polarized ligand docking Induced fit docking was followed by Schrodinger’s quantum mechanics-polarized ligand docking protocol (QPLD)35,36 in which charges were assigned to the ligand using the quantum mechanical ligand charge assignment for docking with Glide XP. This method was used to further improve charge approximation of the ligand in the vicinity of the receptor. The results of the QM docking are represented in Table 3. Both methods predicted the superiority of Novobiocin in binding. The docking scores for each ligand were closely matched between the IFD method and the QM-polarized ligand docking method as shown in Table 3. The docking score trend is seen in relaxation and ATPase activity of the compounds against DNA gyrase from S. aureus.

Table 3 Protein docking scores for Novobiocin and rhodanine derivatives onto crystal structure of DNA gyrase from S. aureus, pdb code 3TTZ. Glide XP docking scores were generated using induced fit ligand docking and QM-polarizable ligand docking from Schrodinger Inc. Induced fit docking was used to allow flexibility of the docking site. QM-polarizable ligand docking was used to assign more accurate quantum mechanical charges on the ligands in the field the receptor Compound name Novobiocin Rhod-3 Rhod-2 Rhod-1

Glide XP from IFD 14.128 11.318 9.571 9.116

Glide XP QM (SE-QM) 14.488 11.055 9.584 9.318

ATPase IC50 100 nM 30 lM 100 lM >300 lM

2.5.3. Molecular dynamics simulations on DNA gyrase from S. aureus Molecular dynamics simulations were performed for 50 ns on DNA gyrase from S. aureus to obtain a dynamic picture and understanding of the interaction between ligands and receptor, using Desmond.37,38 Importantly, the complexes remained intact over the course of long dynamics trajectories, indicating that all of the predicted binding geometries are both energetically favorable and kinetically stable. The frequencies of the different types of interactions including hydrogen bonds, hydrophobic, ionic and water bridge interactions are represented in Figure 5. The interactions occurring more than 10% or 30% of the time between ligand and receptor are depicted in Figure 6. Figures 5 and 6 were generated with the Desmond simulation interaction tool.38 During the course of the 50 ns simulation, the most sustained interaction between Novobiocin and the receptor is hydrogen bond with ASP 81 (97% of the time). This interaction is also seen with Rhod-1, although at a much lower frequency (13% of the time), and also with Rhod-2 (84% of the time). Novobiocin also forms recurrent interactions with SER 55 (84% of the time) which are also found in Rhod-1 but at a much lower frequency. Rhod-3 does not form a bond with ASP 81, like the other ligands. It does however form an electrostatic interaction with

Figure 4. Novobiocin and rhodanine derivatives complexed with DNA gyrase from S. aureus, pdb code: 3TTZ. Novobiocin (A), Rhod-1 (B), Rhod-2 (C), and Rhod-3 (D) in tube mode, fit in the well-defined ATP binding pocket of DNA gyrase. Electrostatic potential surface of the binding site is shown with blue representing positive electrostatic potential, and red representing negative potential. Hydrophobic regions are colored light grey. Crystallographic waters close to the binding site are represented in stick mode. The images were captured with Pymol.31

M. M. Werner et al. / Bioorg. Med. Chem. 22 (2014) 2176–2187

2181

Figure 5. Simulation interaction diagram for ligand–protein interaction in the course of the molecular dynamics simulation. The frequency of the interaction between Novobiocin, Rhod-1, Rhod-2, Rhod-3 and binding site residues are represented as histogram generated with Desmond32 simulation interaction diagram. The relative frequency for each interaction is displayed on the Y-axis. The protein–ligand interaction include hydrogen bonds, hydrophobic, Ionic and water bridges as color-coded in the legend.

Arg 84 which is not present with the other ligands. Common among all four ligands is a hydrophobic interaction with LEU 102. As depicted in Figure 5 however, Rhod-1 has more solvent exposure through its ethyl benzene throughout the course of the simulation than Rhod-2 and Rhod-3 who benefit from phobic enclosure of their hydrophobic group by ILE 175 and LEU 103. Rhod-2 has a benzene group that is unfavorably solvent exposed but this might be further compensated through formation of water and water-bridge interactions.

Rhod-3, they did not have any Jorgensen rule of three violations39 either. Novobiocin had three violations of each of these two rules. Finally, from the standpoint of excretion and safety, the rhodanine derivatives had no reactive functional groups (compared to two for Novobiocin) and fewer predicted metabolites, with Rhod-1 having the least.

2.6. Bioavailability and toxicity calculations

The current experiment and subsequent computational analysis have allowed us to better understand the interactions between the ligands under study and their target enzyme. The results of the computational analysis further attests to the predictive power of computational simulations in ligand activity. However, one of the barriers to identifying potent actives is that docking simulations do not take into consideration a compound’s bioavailability and permeation across the bacterial cell wall. In an effort to better understand and predict antimicrobial activity, a multiple linear regression model was generated with the Schrodinger tool, Strike,41 using as input ADME (absorption, distribution, metabolism and excretion) descriptors computed using the Schrodinger tool, QikProp.39 Out of 50 available descriptors, a subset of 8 showed highest correlation with experimental antimicrobial activity, and were used to generate a predictive model with MIC as the target activity. The MIC values were obtained using results from Xu,19 Chen,20 and Brvar21 for other rhodanine derivatives, along with the rhodanines from this study and the other molecules from the initial screening included in Supplemental Table 1. The results of the regression analysis are shown in Table 5. The R–Square value for the regression model measuring the correlation between predicted and experimental MIC is 0.8788. The large F-factor and the low P-value (P < 0.05) show the regression model is statistically significant and not obtained by chance. After cross validation with n-leave out analysis over 10 cycles, q2 was 0.7989. The close values

In an effort to predict bioavailability and other factors related to drug efficacy, we examined the physico-chemical and ADME (absorption, distribution, metabolism and excretion) properties of the ligands previously docked, using the Schrodinger tool, QikProp39 (Table 2). Table 4 summarizes the Qikpro descriptor calculations. The QPPCaco descriptor measures membrane permeability, and is an indirect measure of cell wall penetration. We observed that among the rhodanine derivatives, Rhod-1 had the highest value at 1260.88 followed by Rhod-2 at 238.19 and Rhod-3 which had the lowest QPPCaco value at 8.05. The trend in the QPPCaco value mirrored antimicrobial activity within this set of rhodanine derivatives with Rhod-1 having the lowest MIC. Novobiocin, did not follow this trend however as other contributors to antimicrobial activity might be at play as discussed later. All three rhodanines had a small molecular weight compared to Novobiocin, and as depicted in Table 4, there was direct correlation between molecular weight and predicted % oral human absorbance in the order Rhod-1, Rhod-2, Rhod-3 and Novobiocin, with the smaller molecules, Rhod-1 and Rhod-2 predicted to have 100% absorption. The QPlog Po/w values for the rhodanine compounds were higher than that of Novobiocin (which explained their greater solubility in DMSO than water). The rhodanines did not have any Lipinski rule of five violations40 and with the exception of

2.7. Linear regression analysis for prediction of minimum inhibitory concentration based on known actives

2182

M. M. Werner et al. / Bioorg. Med. Chem. 22 (2014) 2176–2187

Figure 6. Schematic representation of ligand–protein contact frequency in the course of the molecular dynamics simulation. Represented are the interactions that occurred more than 10% of the time between the protein, Novobiocin, Rhod-1 and Rhod-3, and more than 30% of the time for Rhod-2. These diagram where generated with Desmond32 simulation interaction diagram.

of R–Square and q2 shows that the results of the regression model are robust and do not overfit the training data. Interestingly enough, the Qikprop descriptor, QPPCaco, whose trend seemed to

mirror antimicrobial activity for the rhodanine derivatives in this study was not selected in the optimal subset generated. As seen with Novobiocin, different factors might be at play and results

2183

M. M. Werner et al. / Bioorg. Med. Chem. 22 (2014) 2176–2187

Table 4 ADME (absorption, distribution, metabolism and excretion) predictors for drug-like properties of screened compounds through Schrodinger QikProp33 program. mol_MW = molecular weight of the molecule. QPlog Po/w = predicted octanol/water partition coefficient. #rtvFG = number of reactive functional groups. #metab = number of likely metabolic reactions. QPPCaco = prediction of non-active transport permeation in nm/sec for gut–blood barrier through Caco-2 cells model. Rule of Five = Lipinski rule of Five: mol_MW <500, QPlog Po/w <5, donorHB 65, accptHB 610. Rule of Three = Jorgensen’s rule of Three: QPlog S > 5.7, QPPCaco >22 nm/s, #primary metabolites <7 molecule

mol_MW

QPlog Po/w

#metab

QPPCaco

#rtvFG

Percent human oral absorption

Rule of Five

Rule of Three

Novobiocin Rhod-1 Rhod-2 Rhod-3

612.63 341.44 403.90 472.31

2.40 4.39 4.85 4.50

8 2 3 3

9.376 1260.88 238.19 8.05

2 0 0 0

19.50 100 100 69.5

3 0 0 0

3 0 0 2

Table 5 Results for Multiple linear regression (MLR) model statistics for predicted versus experimental minimal inhibitory concentrations (MIC). The predicted MIC of 46 compound including the rhodanine derivatives from this study, the compounds in Supplemental Table 1 and other rhodanine derivatives with antimicrobial activity found by Xu,19 Chen,20 and Brvar,21 was generated with a multiple linear regression model using for independent variables the Qikpro33 descriptors: #acid = number of carboxylic acid groups. Dipole = computed dipole moment of the molecule. PISA = p component of the SASA (total solvent accessible surface area). ACxDN^.5/SA = index of cohesive interaction in solids. glob = Globularity descriptor. QPlog S = predicted aqueous solubility. QPlog BB = Predicted brain/blood partition coefficient. #ringatoms = number of atoms in a ring. The model was generated with Strike35 from the Schrodinger suite. The statistics of the regression model, the coefficients and T values for the variables a well as the cross-validation test with leave-2 out over 10 cycles are reported S.D.

R–squared

F(8.0,37.0)

MLR regression statistics 50.6382 0.8788

33.5

Variable

Std. Err.

Coefficient

MLR regression coefficients and T-values Intercept 4.19E + 03 #acid 6.28E + 01 dipole 8.26E + 00 PISA 1.84E + 00 ACxDN^.5/SA 1.01E + 04 glob 4.19E + 03 QPlog S 1.46E + 02 QPlog BB 6.43E + 01 #ringatoms 4.74E + 01

7.84E + 02 2.70E + 01 3.00E + 00 1.81E-01 3.07E + 03 8.34E + 02 2.00E + 01 3.28E + 01 4.08E + 00

P 1.11E-14

3. Discussion

T 5.3406 2.3289 2.7547 10.1765 3.3104 5.0292 7.2703 1.96 11.6259

Cross validation leave-2-out results over 10 cycles q2 RMS 0.7989

for QPPCaco may vary for different compounds with different substituents. The variables included in the model are listed and described in Table 5. Figure 7 shows the results of the predicted versus experimental data. The predicted versus actual MIC values for Rhod-1, Rhod-2 and Rhod-3 as well as ZINC09010005 are highlighted in the figure. Rhod-2 is predicted to have a lower MIC than Rhod-3, however the result for Rhod-1 is higher than expected. ZINC09010005 is predicted to have an MIC very close to experiment. In general as shown in Figure 7, the model seems to do a better job in predicting compounds with MIC values >100 lg/ml than lower MICs. It can therefore serve as a good preliminary screening tool against compound that would have MIC >100 lg/ml. We note that is a preliminary model, which can be improved with even greater number of active antimicrobials.

58.8624

Figure 7. Multiple linear regression (MLR) model for predicted versus experimental minimal inhibitory concentrations (MIC). The predicted MIC for the rhodanine derivatives, the compounds in Supplemental Table 1 and other rhodanine derivatives with antimicrobial activity found by Xu,19 Chen,20 and Brvar,21 was generated with a multiple linear regression model using for independent variables the Qikpro33 descriptors listed in Table 5. The experimental versus predicted MIC values for Rhod-1, Rhod-2, Rhod-3, and ZINC09010005 are highlighted in the plot. In general, as seen with the less active compound ZINC09010005, the model seemed to be more accurate at predicting compounds with high MIC values, above 100 lg/ml.

The emergence of bacterial drug resistance in community and hospital settings points to the continued need to discover new chemical entities with antimicrobial activity. Shape Signatures has been used previously to screen for compounds with shape similar to known actives using descriptors generated by a ray-tracing method.22 Our goal was to find small compounds with similar shape but potentially less toxicity than the known DNA gyrase inhibitor, Novobiocin. This method has allowed us to identify several potentially-active compounds from the ZINC chemical library.42 For our initial secondary in silico screen we used a homologue, DNA gyrase from T. thermophilus, pdb code 1KIJ,23 as the molecular docking target, due to the lack of availability of an equivalent welldefined binding pocket structure for S. aureus at the onset of the study. While not identical to the sequence of S. aureus, this homologue has proved to support adequate predictions for the rhodanine derivatives, as shown by subsequent docking results using the recently available crystal structure of DNA gyrase from S. aureus (pdb code 3TTZ17). The induced fit docking scores and the QMpolarized docking scores for the S. aureus structure follow the same trend as our previous results, while evincing better correlation with biochemical assay. Antimicrobial assay served as the first activity validation method for the acquired compounds. Among several compounds from the computational screen that were assayed for antimicrobial activity (see Supplemental Tables 1 and 2 for further listing), we noticed that the Rhodanine derivatives Rhod-1 and Rhod-2 evinced the most activity against the bacteria with MIC values of 4.7 and 12.5 lg/ml respective. We therefore decided to evaluate all three rhodanine derivatives further for biochemical activity. Rhod-3 and Rhod-2 exhibited activity against supercoiling of DNA by DNA gyrase with evidence of activity for both at the 50 lM level and with activity for Rhod-3 still seen at the 20 lM level. Both Rhod-3 and Rhod-2 also exhibited ATPase activity against DNA gyrase from S. aureus. Subsequent docking the rhodanine derivatives on the available crystal structure of DNA gyrase from S. aureus, pdb code 3TTZ in

2184

M. M. Werner et al. / Bioorg. Med. Chem. 22 (2014) 2176–2187

complex with a pyrrolamide derivative,17 as well as a magnesium ion, showed a docking trend that mirrored enzymatic activity, Table 3. The presence of magnesium in the binding site of 3TTZ, a cofactor of DNA gyrase, which can form a complex with and stabilize the phosphate group of ATP,43 allowed us to better model the physiological state of the receptor. Molecular dynamics (Figs. 5 and 6) provided a more realistic picture of the protein–ligand interaction compared to static docking studies, which, while computationally efficient, neglect the dynamic character of the interaction. Notably, binding to ASP81 seems to be an important contributor to ligand function as suggested by the stable (97% of the time) hydrogen bonding interaction between the positive control Novobiocin and ASP81 in the course of the dynamics simulation. Rhod-3 seems to compensate for its lack of interaction with ASP81 through other more favorable electrostatic interactions with the receptor, including less solvent exposure and better hydrophobic enclosure than Rhod-2 and Rhod-1 as illustrated in the interaction diagram in Figure 6. While retrospective docking scores on 3TTZ were in line with relaxation and ATPase assays (Figs. 2 and 3) with Rhod-3 having the greatest in vitro inhibition of the 3 rhodanine derivatives, this was not the case for antimicrobial activity, where bioavailability concerns are central. In antimicrobial assays Rhod-1 had the highest activity with an MIC of 4.7 lg/ml (13.5 lM) against the S. aureus strain ISP 794, followed by Rhod-2 > Rhod-3 (Table 3). These results are in excellent agreement with computational predictions of membrane permeability for these compounds as suggested by the QPPCaco descriptor from Qikprop although it is a measure of passive penetration of compounds in the gut, and is not a direct measure of bacterial cell wall permeability. A study by BarrettPee and Pinder suggests that Novobiocin might be taken into the organism by active transport,44 which is not estimated by the QPPCaco descriptor (Table 5). The rhodanines derivates were tested for antimicrobial activity against E. coli but did not exhibit significant activity against the bacteria with MIC values greater than 100 lg/ml. Chen et al. have tested 21 different rhodanine derivatives against 2 different strains of E. coli and they all displayed MIC values greater than 64 lg/ml.20 This suggests a potential specificity of these derivatives for gram positive cocci, such as S. aureus which has a different cell wall composition than gram negative E. coli. The rhodanine derivatives were also tested for enzymatic activity against DNA gyrase from E. coli. They did not exhibit significant activity against the enzyme, with lack of activity seen at concentrations greater than 250 lM, Supplemental Figure 2. A sequence alignment of crystallographic DNA gyrase from S. aureus, pdb code 3TTZ,17 and from E. coli, pdb code 1KZN,45 is shown in Supplemental Figure 1B. They share a 57% homology. The ligand contact bands show that they share conserved active site residues: ASP 81 ? ASP 73; ARG 84 ? ARG 76; ARG 144 ? ARG 135; GLU 58 ? GLU 50; ASN 54 ? ASN 46; GLY 85 ? GLY 77 for S. aureus and E. coli, respectively. Studies have shown46 that mutations in E. coli DNA gyrase including ASP 73, ARG 76, GLU 50, ASN 46, and GLY 77, significantly reduced ATPase activity of the mutant protein, suggesting that these residues may be key for activity across these two organisms. Novobiocin the most active compound against the S. aureus enzyme made persistent interactions with ASP 81 in molecular dynamics simulations, which seems to be a key residue for function. Although Rhod-3, the more enzymatically active of the 3 rhodanines, did not make significant interactions with ASP 81 in the course of the simulation, it nevertheless interacted consistently with GLU 58 and ARG 84 (20% and 19% of the time, respectively), which are two other residues conserved in E. coli, and shown in mutagenesis studies46 to be

important for function. Differences in enzymatic activity of the compounds against S. aureus and E. coli could perhaps be due to other physical chemical properties of the ligand in the field of the receptor such as solvation or hydrophobic effects. While static docking provides a snapshot of the interaction between ligand and receptor, molecular dynamics provides a longer time frame and therefore more significant view of the interactions. It can therefore serve as a powerful tool for further selection of ligands with most frequent interaction with the key residues whose significance in ATPase activity was demonstrated through mutagenesis analysis46 and in this way help further enhance activity. Prediction through Qikprop39 suggested a safer ADME profile for the rhodanine compounds tested compared to Novobiocin, with no reactive functional group, no rule of five violation and better predicted human absorption (Table 2). Although these are preliminary data that require further in vivo validation, these ADME calculations suggest the potential for increased safety of the rhodanine derivatives. Although docking analysis can help predict potential hits against enzymatic target, this does not guarantee biological activity where other factors such as bioavailability and cell wall permeation can be problematic. In an effort to better screen for future compounds with antimicrobial activity against S. aureus, a multiple linear regression model was generated to predict MIC of compounds from this study as well as other rhodanine derivatives identified by Xu,19 Chen,20 and Brvar21 to possess antimicrobial activity. Coupled with ligand docking and molecular dynamics simulations, this method could help better predict compounds with antimicrobial activity. Rhodanine derivatives were efficiently identified through initial Shape Signatures screening, readily obtained and tested. Computational analysis of these compounds serve as foundation for future work aimed at increasing potency through consideration of key enzymatic molecular dynamics interaction and MIC predictions. It is our goal to achieve this while maintaining improved cell penetration and toxicity compared to Novobiocin.

4. Conclusions Using Shape Signatures we successfully screened small compounds containing the rhodanine moiety and which demonstrated activity against the S. aureus organism and DNA gyrase. Their direct activity against DNA gyrase from S. aureus was validated through relaxation and ATPase assays and mirrored computational molecular docking and dynamics simulations. Antimicrobial activity was evaluated through bacterial sensitivity testing. Rhod-1, with an MIC of 4.7 lg/ml (13.5 lM), and Rhod-2 with an MIC of 12.5 lg/ml (31 lM) against S. aureus strain ISP 794, have comparable antimicrobial activity. Rhod-3 evinced the most activity against the enzyme as later observed to have the highest docking score of the three rhodanine on S. aureus DNA gyrase. Given our new understand of key ligand–protein interactions through molecular dynamics simulation, limited modifications to any of the rhodanine that would enhance molecular dynamic interactions with receptor especially with key residues as discussed above, may dramatically improve their potency. Combined with enzymatic activity which can be readily predicted trough computational modelling, the addition of antimicrobial activity predictions related to bioavailability can help enhance screening of potent antimicrobial agents. For this purpose, we have generated a preliminary multiple linear regression model that may assist in considering potential compounds targeting S. aureus, with MIC lower than 100 lg/ml that could help further improve predictive power.

M. M. Werner et al. / Bioorg. Med. Chem. 22 (2014) 2176–2187

5. Experimental section 5.1. Materials Novobiocin, the positive control was acquired through Fluka analytical; the rhodanine derivative Rhod-3 (2-[(5E)5-{[5-(2,5-dichlorophenyl)furan-2-yl]methylidene}-4-oxo-2-thioxo1, 3-thiazolidin-3-yl]butanedioic acid) was acquired through Vitas-M Laboratory, LTD; the rhodanine derivatives, Rhod-1 ((5Z)-3-(2-ethylphenyl)-5-(3-hydroxybenzylidene)-2-thioxo-1,3thiazolidin-4-one) and Rhod-2 (3-[(5Z)-5-(2-chlorobenzylidene)4-oxo-2-thioxo-1,3-thiazolidin-3-yl]-3-phenylpropanoic acid) were acquired through Innovapharm. Stock solutions were prepared by dissolving the compounds in DMSO. ISP 79447–49 S. aureus was kindly provided by D. C. Hooper (Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital). Carolina S. aureus was kindly provided by J. Johnson (Department of Biological Sciences, University of the Sciences in Philadelphia) who previously obtained the strain from Carolina Biologicals. E. coli MG165550 was obtained from ATCC. Relaxed DNA, pUC19 and E. coli DNA gyrase were obtained through New England Biolabs. S. aureus DNA gyrase enzyme, accompanying buffers, as described in the methods section, and ATP were acquired through Topogen. The EnzChek phosphate assay kit containing PNP and MESG31 were obtained from Molecular Probes. 5.2. Computer-aided molecular screening 5.2.1. Molecule screening with Shape Signature Potential DNA gyrase inhibitors were generated by using onedimensional Shape Signatures22 (these encode only shape information) to screen the NCI ZINC database, retaining 10,000 molecules similar in shape to Novobiocin. The tool was applied by generating a shape-based descriptor for Novobiocin, encoded as a probability distribution (histogram) of ray-trace segment lengths. This was then used as a query against a custom database of Shape Signatures generated for the ZINC library (version 10). 5.2.2. Docking screen of potential GyrB inhibitors on homologous enzyme from T. thermophilus Potential candidates from the Shape Signatures screening were initially docked into the X-ray structure of DNA gyrase from T. thermophilus, PDB 1KIJ23 because of the lack of a defined X-ray crystal structure of the DNA gyrase from S. aureus at the outset of the study. T. thermophilus 1KIJ has a 58% homology to S. aureus DNA gyrase and close similarity in binding site residues as depicted in S1. Top scoring compounds from this initial screen were acquired.

2185

allowing re-orientation of amide bonds, ring corners, pyramidal nitrogens, planar R-NR1R2 groups, and protonated carboxylic acids. 5.2.2.2. Glide docking24–27. The molecules initially identified as similar to Novobiocin were docked against 1kij using Glide from the Schrödinger suite preceded by ligand and protein preparations steps described below. Ligand preparation: Glide docking was preceded by a ligand preparation step using the Schrödinger LIGPREP51 tool to ensure low energy 3-dimensional structures. The default force field used in the minimization was OPLS-2005. The general treatment of the ligands included neutralizing, desalting, and generating all possible tautomers, stereoisomers, ring conformers and protonation states consistent with neutral pH (with up to 32 structures per ligand retained). Protein preparation: A protein preparation step using the Schrödinger Protein Preparation Wizard52,53 then followed to refine the crystallographic structure, 1kij in complex with Novobiocin, to relieve strains and make it more suitable for modeling calculations. The preprocessing step included assignment of proper bonds and bond orders, adding hydrogen, and deleting waters beyond 5 Å of the ligand. For docking purposes, seven crystallographic waters closest to Novobiocin were retained. The preprocessing step was followed by optimization of the hydrogen bonding network to improve the orientation of hydroxyl (or thiol) groups, the terminal amide groups in asparagine and glutamine, and the protonation states of histidine residues in the X-ray structure. The final protein preparation step was refinement of the structure through minimization using the default force field, OPLS-2005. Ligand docking: A receptor grid centered on the reference Novobiocin was generated to specify the docking site for the ligands prepared through LigPrep.51 The ligands were docked using Glide to evaluate and rank the binding modes generated. All 10,000 ligands were initially docked using the standard precision docking mode (Glide SP), without incorporation of restrictive penalties, for rapid identification of ligands that can readily bind. Of these, the top 1000 where then docked using the extra precision Glide scoring scheme (Glide XP) for more extensive sampling of docked poses, or binding modes, with added enforcement of scoring penalties based on physical chemical principles, such as solvent exposure hydrophobic enclosures and stacking. In both docking approaches (each of which assumes a rigid receptor), ligand flexibility was added by allowing conformations of amide bonds to vary and by sampling ring and nitrogen conformations. 5.3. Antimicrobial activity validations

5.2.2.1. GOLD docking28,29. The molecules screened with Shape Signatures were further docked against the crystal structure of gyrase B of T. thermophilus (protein databank entry 1kij) an enzyme homologous to S. aureus DNA gyrase as a secondary screen before decision to purchase. The crystal structure of this protein in complex with Novobiocin. GOLD docking tools were applied using the Hermes interface to generate predicted binding modes for the Shape Signatures hit compounds and to evaluate their fitness at the binding site. The center of the volume available for GOLD docking was determined by averaging the atomic coordinates of Novobiocin as found in the experimental complex. Active site waters were retained for inclusion in docking calculations. The default settings for docking were used. GOLD employs a genetic-algorithm approach for docking; the default parameters included a population size of 100 and evolution of the initial population for 100,000 generations. Flexibility of the ligand was included by

We acquired 20 of the most promising compounds selected based on virtual screening and initial Glide docking score on the crystal structure of DNA gyrase from T. Thermophilus, (pdb entry 1KIJ), from several vendors, and measured their antimicrobial activity by determining minimum inhibitory concentrations (MIC) against three bacterial strains following a procedure adapted from Zgoda and Porter.54 Two strains of Staphylococcus aureus, ISP 79447–49 and Carolina (Carolina Biologicals), as well as Escherichia coli MG1655,50 were grown overnight in Mueller Hinton (MH) broth to O.D.600 of 0.5. Bacteria grown overnight were further diluted to OD of 0.05 in MH broth. Eight two-fold serial dilutions of the compounds in 100 lL/well MH broth were performed followed by addition of 100 lL of bacteria. The final concentrations from the dilutions were 100, 50.0, 25.0, 12.5, 6.25, 3.13, 1.56, and 0.781 lg/ml. The plates were incubated at 37 °C for 24 h and the

2186

M. M. Werner et al. / Bioorg. Med. Chem. 22 (2014) 2176–2187

MIC was evaluated through spectrometry reading at 750 nm as well as visual inspection. Streak dilutions on TSA plates with 0.1 ml bacteria from 10 2 dilutions and 10 4 dilutions of growth batch in normal saline were performed to determine colony forming units (CFU) and the plates were also incubated for 24 h at 37 °C. The resulting CFU were 3.4  106 CFU/ml and 5.5  106 CFU/ml for S. aureus ISP 794 and Carolina strain respectively and 6.1  106 CFU/ml for E. coli strain MG1655.50 The minimum target 5  105 CFU/ml was therefore met for all species tested. Each compound was tested against each microbe in duplicate and on two different days for which averages and standard deviations were recorded. Compounds from Supplemental Table 1 which were tested first, were purchased from vendors listed in the ZINC database website. They were then diluted in DMSO at concentrations of 2 or 4 mg/ml before outsourcing for antimicrobial testing. Minimal inhibitory concentration assays were performed by Antimicrobial Test Laboratories, LLC, (Round Rock, TX). Tests were performed against two strains of bacteria, S. aureus ATCC653855,56 and MRSA ATCC33592.57,58 The bacteria were grown in tryptic soy broth followed by MIC determination in Mueller–Hinton (MH) broth. A target inoculum of 5  105 CFU was used. The total reaction volume was 300 lL with 150 lL constituting the test compound and the remaining 150 lL MH broth. Serial two-fold dilutions were performed in 96-well plates in duplicate and incubated for 24 h at 36 ± 1 °C.

360 nm for the product. The change of absorbance at 360 nm allows us to quantitatively determine the amount of inorganic phosphate consumed in the reaction. The ATPase assay requires two steps: the DNA gyrase assay which generates phosphate and the PNP reaction which consumes the phosphate. The gyrase assay, using S. aureus DNA gyrase, was performed as described in the relaxation protocol with the same buffer and potassium glutamate contents. For this assay the reaction mixture contained 4 U of gyrase, 4 mM Phosphate, 100 ng of relaxed DNA pUC19, and indicated concentration of compounds as shown in Figure 7. The gyrase relaxation reactions were incubated for 1 h in a water bath at 37 °C. Twenty microlitre of the gyrase reaction products were added to 30 lL of the PNP reaction buffer in 96-well plates for a total reaction volume of 50 lL. The PNP reaction buffer added to each gyrase reaction product was comprised of PNP, MESG, and 20 buffer from the phosphate kit. The combined gyrase and PNP reactions were incubated for 30 min at room temperature then read in a spectrometer with absorbance set to 360 nm. The gyrase assay combined with the PNP reaction mix was performed in triplicate. Along with the gyrase reactions, consistent phosphate determination curves were generated as positive controls using a series of initial concentrations of inorganic phosphate in the PNP reaction mixture described above.

5.4. Relaxation assay

Rationalization of the activity of the rhodanine derivatives in this study was performed with computational analysis on the crystal structure of DNA gyrase from the more study-relevant organism, S. aureus once it became available. The X-ray crystal structure for DNA gyrase, from S. aureus was obtained from the Protein Data Bank, PDB 3TTZ.17 3TTZ is the B subunit of DNA gyrase B from S. aureus with a high resolution of 1.63 Å. It is found in complex with a pyrrolamide and a magnesium ion.

This assay measured the conversion of relaxed DNA gyrase substrate (N0471S) to supercoiled DNA based on changes in mobility under gel electrophoresis. The reaction mixture, with total volume 30 lL, consisted of reaction buffer containing 75 mM Tris–HCL (pH 7.5), 7.5 mM MgCl2, 7.5 M DTT, 0.075 mg/ml BSA, and 30 mM KCL; 2 mM ATP; 500 mM potassium glutamate; 150 ng relaxed DNA; and 2 U S. aureus DNA gyrase. This procedure was repeated for E. coli DNA gyrase. Two lL of DMSO was included in control reactions to account for DMSO’s potential inhibitory effects on gyrase activity; drug concentrations of the rhodanine derivatives studied were added in indicated amounts. Reactions were performed in 20% glycerol brought to total volume of 30 lL. The reactions cuvettes were incubated in a 37 °C water bath for 30 min and then stopped with 10 lL loading buffer containing 50 mM EDTA, 12.5% glycerol, 2% sarkosyl and 0.05% bromophenol blue. The samples were than analyzed by gel electrophoresis using 1% agarose gel and a 1 running buffer (adjusted to pH 8.0) containing 100 mM Tris base , 42 mM sodium citrate and 50 mM EDTA. After electrophoresis for approximately 18 h at 23 V, the gel was stained with ethidium bromide and observed under UV light. 5.5. ATPase assay The ATPase activity of gyrase B was coupled with the ENZCheck phosphate detection assay containing purine nucleoside phosphorylase.30 The supercoiling activity of DNA gyrase is powered by ATP hydrolysis in which ATP is converted to ADP + Pi.6,7,9 The efficiency of DNA gyrase’s ATPase activity in the presence of different concentration of inhibitors was measured by the generation of phosphate (Pi), which was detected in the presence of MESG and PNP as a decrease in absorbance due to less generation and utilization of Pi for product formation in the presence of inhibitors. In the presence of inorganic phosphate, the substrate 2-amino-6-mercapto-7-methylpurine ribose (MESG) is converted to ribose 1-phosphate and 2-amino-6-mercapto-7-methylpurine by the enzyme, purine nucleoside phosphorylase (PNP). This enzymatic conversion shifts the maximum absorbance of the substrate MESG from 330 nm to

5.6. Computational analysis of Rhodanine derivatives on DNA gyrase from S. aureus

5.6.1. Induced fit docking Induced fit docking analysis of the rhodanine derivatives and Novobiocin on 3TTZ were performed to account for receptor flexibility, following the IFD protocol from Schrodinger Inc.32–34 Before docking, the rhodanines and Novobiocin were prepared with LigPrep51 and the protein crystal structure was prepared with the Schrodinger protein preparation wizard52,53 as described in section 5.2.2.2. The ligands were first docked onto the rigid receptor using a softened potential with van der Waals scaling of 0.8 for the protein. A maximum of 20 receptor–ligand poses with coulomb vdw score <100 and H-bond score < 0.05 were retained. Prime side chain prediction through minimization and conformational searches for each protein–ligand complex was performed on residue within 5 Å of ligand. This was followed by Glide XP re-docking of each ligand/protein pose using the default hard-potential. The estimated binding energy for the each pose is generated as the induced fit score. Top ligand pose for each ligand is reported. 5.6.2. QM-Polarized ligand docking Following Induced Fit docking, quantum mechanical polarized ligand docking using Schrodinger’s QPLD tool,35,36 was performed to improve partial charge distribution on ligand in the field of the receptor. In the first step of the process, the ligands were docked using charges generated from semi-empirical NDDO calculations. The polarized ligand charges induced by the protein were then calculated with Qsite using quantum mechanics charge calculations with the 6-31G⁄/LACVP⁄ basis set, B3LYP density functional, and ‘Ultra-fine’ SCF accuracy level. The ligand were then re-docked with new QM/MM charges. Both the initial and re-docking phases were performed with Glide XP.

M. M. Werner et al. / Bioorg. Med. Chem. 22 (2014) 2176–2187

5.6.3. Molecular dynamics simulations Dynamics simulations of molecular motion were performed using the Desmond37,38 application distributed by Schrodinger Inc. Each system, which included the protein, 3TTZ, in complex with ligand and crystallographic active site waters, was solvated with SPC water and enclosed in an orthorhombic box that was further minimized. The systems were neutralized with the addition of Cl counterions. The force field OPLS_2005 was used for all calculation. The systems were relaxed before simulation, using the default relaxation protocol. The dynamics simulations were then performed, each for a total time of 50 ns using a time step of 2 fs in the NPT ensemble, at 300°K. Trajectory snapshots were recorded every 50 ps for a total of 1000 frames. 5.7. Linear regression analysis for prediction of minimum inhibitory concentration based on known actives based on Qikprop39 descriptors A predictive multiple linear regression model was generated based on correlations between Qikprop ADME descriptors and the minimal inhibitory concentrations of the rhodanine derivatives in this study, additional rhodanine derivatives reported by Brvar, Xu and Chen, and other non-rhodanine molecules shown in Supplemental Tables 1 and 2, for a total of 47 molecules. The predictive model was generated using Schrodinger’s Strike tool.41 From the original set of selected descriptors, a subset containing the top 5 optimal Qikprop descriptors were automatically identified to build the model, and with experimental MIC values selected as the target activity property. The descriptors included for the model calculations were PISA (p component of total surface accessible area), QPlog S (predicted aqueous solubility), #ringatoms (the number of atoms in a ring), #in56 (the number of atoms in 5 or 6-membered ring), and #noncon (number of ring atoms not able to form conjugated aromatic systems). Outlier testing reported 2 compounds from the set of molecules. These 2 molecules were removed from model, for a final remainder of 45 molecules for model building. Cross validation of the regression model was performed with leave-n-out testing. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.bmc.2014.02.020. References and notes 1. 2. 3. 4.

5. 6. 7. 8. 9. 10. 11. 12. 13. 14.

Waness, A. J. Glob. Infect. Dis. 2010, 2, 49. Boucher, H. W.; Corey, G. R. Clin. Infect. Dis. 2008, 46, S344. Boucher, H.; Miller, L. G.; Razonable, R. R. Clin. Infect. Dis. 2010, 51, S183. Klevens, M. R.; Morrison, M. A.; Nadle, J.; Petit, S.; Gershman, K.; Ray, S.; Harrison, L. H.; Lynfield, R.; Dumyati, G.; Townes, J. M.; Craig, A. S.; Zell, E. R.; Fosheim, G. E.; McDougal, L. K.; Carey, R. B.; Fridkin, S. K. J. Am. Med. Assoc. 2007, 298, 1763. Filice, G. A.; Nyman, J. A.; Lexau, C.; Lees, C. H.; Bockstedt, L. A.; Como-Sabetti, K.; Lesher, L. J.; Lynfield, R. Infect. Control Hosp. Epidemiol. 2010, 31, 365. Sugino, A.; Higgins, P. N.; Brown, P. O.; Peebles, C. L.; Cozzarelli, N. R. Proc. Natl. Acad. Sci. U.S.A. 1978, 75, 4838. Sissi, C.; Palumbo, M. Cell. Mol. Life Sci. 2010, 67, 2001. Roca, J. Nucleic Acids Res. 2008, 37, 721. Gubaev, A.; Klostermeier, D. Proc. Natl. Acad. Sci. U.S.A. 2011, 108, 14085. Heddle, J.; Maxwell, A. Antimicrob. Agents Chemother. 2002, 46, 1805. Yoshida, H.; Bogaki, M.; Nakamura, M.; Yamanaka, L. M.; Nakamura, S. Antimicrob. Agents Chemother. 1991, 35, 1647. Pantel, A.; Petrella, S.; Veziris, N.; Brossier, F.; Bastian, S.; Jarlier, V.; Mayer, C.; Aubry, A. Antimicrob. Agents Chemother. 2012, 56, 1990. Lafitte, D.; Lamour, V.; Tsvetkov, P. O.; Makarov, A. A.; Klich, M.; Deprez, P.; Moras, D.; Briand, C.; Gilli, R. Biochemistry 2002, 41, 7217. Cox, R. P.; Foltz, E. L.; Raymond, S.; Drewyer, R. N. Eng. J. Med. 1959, 261, 139.

2187

15. Nakada, N.; Gmunder, H.; Hirata, T.; Arisawa, M. Antimicrob. Agents Chemother. 1994, 38, 1966. 16. Gradisar, H.; Pristovsek, P.; Plaper, A.; Jerala, R. J. Med. Chem. 2007, 50, 264. 17. Sherer, B. A.; Hull, K.; Green, O.; Basarab, G.; Hauck, S.; Hill, P.; Lock, J. T.; Mullen, G.; Bist, S.; Bryant, J.; Boriack-Sjodin, A.; Read, J.; Degrace, N.; UriaNickelsen, M.; Illingworth, R. N.; Eakin, A. E. Bioorg. Med. Chem. Lett. 2011, 21, 7416. 18. Oblak, M.; Kotnik, M.; Solmajer, T. Curr. Med. Chem. 2007, 14, 2033. 19. Xu, L.; Zheng, C.; Sun, L.; Miao, J.; Piao, H. Eur. J. Med. Chem. 2012, 48, 174. 20. Chen, Z.; Zheng, C.; Sun, L.; Piao, H. Eur. J. Med. Chem. 2010, 45, 5739. 21. Brvar, M.; Perdih, A.; Hodnik, V.; Renko, M.; Anderluh, G.; Jerala, R.; Solmajer, T. Bioorg. Med. Chem. 2012, 20, 2572. 22. Zauhar, R. J.; Moyna, G.; Tian, L.; Li, Z.; Welsh, W. J. J. Med. Chem. 2003, 46, 5674. 23. Lamour, V.; Hoermann, L.; Jeltsch, J. M.; Oudet, P.; Moras, D. J. Biol. Chem. 2002, 277, 18947. 24. Glide, Version 5.8, Schrödinger, LLC, New York, NY, 2012. 25. Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shelley, M.; Perry, J. K.; Shaw, D. E.; Francis, P.; Shenkin, P. S. J. Med. Chem. 2004, 47, 1739. 26. Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L. J. Med. Chem. 2004, 47, 1750. 27. Friesner, R. A.; Murphy, R. B.; Repasky, M. P.; Frye, L. L.; Greenwood, J. R.; Halgren, T. A.; Sanschagrin, P. C.; Mainz, D. J. Med. Chem. 2006, 49, 6177. 28. Jones, G.; Willett, P.; Glen, R. C.; Leach, A. R.; Taylor, R. J. Mol. Biol. 1997, 267, 727. 29. Verdonk, M. L.; Cole, J. C.; Hartshorn, M. J.; Murray, C. W.; Taylor, R. D. Proteins 2003, 52, 609. 30. Webb, M. Proc. Natl. Acad. Sci. U.S.A. 1992, 89, 4884. 31. The Pymol Molecular Graphics System, Version 1.5.0.4 Schrödinger LLC. 32. Induced Fit Docking protocol 2013-2, Glide Version 5.9 Prime Version 3.2, Schrodinger, LLC, New York, NY 2013. 33. Sherman, W.; Day, T.; Jacobson, M. P.; Friesner, R. A. J. Med. Chem. 2006, 49, 534. 34. Sherman, W.; Beard, H. S.; Farid, R. Chem. Biol. Drug Des. 2006, 67, 83. 35. QM-Polarized Ligand Docking Protocol 2013-2; Glide Version 5.9, Jaguar Version 8.0, QSite version 5.9, Schrodinger, LLC, New York, NY, 2013. 36. Cho, A. E.; Guallar, V.; Berne, B.; Friesner, R. A. J. Comput. Chem. 2005, 26, 915. 37. Desmond Molecular Dynamics System, Version 3.1, D. E. Shaw Research, New York, NY, 2012. Maestro-Desmond Interoperability Tools, Version, 3.1, Schödinger, New York, NY, 2012. 38. Desmond Molecular Dynamics System, Version 3.5, D. E. Shaw Research, New York, NY, 2013. Maestro-Desmond Interoperability Tools, version 3.5, Schrödinger, New York, NY, 2013. 39. QikProp, Version 3.4, Schrödinger, LLC, New York, NY, 2011. 40. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Adv. Drug Del. Rev. 2001, 46, 3. 41. Strike, Version 2.3, Schrödinger, LLC, New York, NY, 2013. 42. Irwin, J. J.; Shoichet, B. K. J. Chem. Inf. Model. 2005, 45, 177. 43. Sissi, C.; Palumbo, M. Nucleic Acids Res. 2009, 37, 702. 44. Barrett-Bee, K.; Pinder, P. J. Antimicrob. Chemother. 1994, 33, 1165. 45. Lafitte, D.; Lamour, V.; Tsvetkov, P. O.; Makarov, A. A.; Klich, M.; Deprez, P.; Moras, D.; Briand, C.; Gilli, R. 2002, 41, 7217–7223. 46. Gross, C. H.; Parsons, J. D.; Grossman, T. H.; Charifson, P. S.; Bellon, S.; Jernee, J.; Dwyer, M.; Chambers, S. P.; Markland, W.; Botfield, M.; Raybuck, S. A. Antimicrob. Agents Chemother. 2003, 47, 1037. 47. Ince, D.; Zhang, X.; Hooper, D. C. Antimicrob. Agents Chemother. 2003, 47, 1410. 48. De Souza, N. J.; Gupte, S. V.; Deshpande, P. K.; Desai, V. N.; Bhawsar, S. B.; Yeole, R. D.; Shukla, M. C.; Strahilevitz, J.; Hooper, D. C.; Bozdogan, B.; Appelbaum, P. C.; Jacobs, M. R.; Shetty, N.; Patel, M. V.; Jha, R.; Khorakiwala, H. F. J. Med. Chem. 2005, 48, 5232. 49. Strahilevitz, J.; Hooper, D. C. Antimicrob. Agents Chemother. 2005, 49, 1949. 50. Edward, J. S.; Palsson, B. O. Proc. Natl. Acad. Sci. U.S.A. 2000, 97, 5528. 51. LigPrep, Version 2.4, Schrödinger, LLC, New York, NY, 2010. 52. Schrödinger Suite 2010 Protein Preparation Wizard; Epik Version 2.1, Schrödinger, LLC, New York, NY, 2010; Impact Version 5.6, Schrödinger, LLC, New York, NY, 2010; Prime Version 2.2, Schrödinger, LLC, New York, NY, 2010. 53. Schrödinger Suite 2012 Protein Preparation Wizard; Epik Version 2.3, Schrödinger, LLC, New York, NY, 2012; Impact Version 5.8, Schrödinger, LLC, New York, NY, 2012; Prime Version 3.1, Schrödinger, LLC, New York, NY, 2012. 54. Zgoda, J. R.; Porter, J. R. Pharm. Biol. 2001, 39, 221. 55. Lambert, R. J. Lett. Appl. Microbiol. 1998, 26, 288. 56. Latimer, J.; Forbes, S.; McBain, A. J. Antimicrob. Agents Chemother. 2012, 56, 3092. 57. Reischl, U.; Linde, H.; Metz, M.; Leppmeier, B.; Lehn, N. J. Clin. Microbiol. 2000, 38, 2429. 58. Huletsky, A.; Giroux, R.; Rossbach, V.; Gagnon, M.; Vaillancourt, M.; Bernier, M.; Gagnon, F.; Truchon, K.; Bastien, M.; Picard, F. J.; van Belkum, A.; Ouellette, M.; Roy, P. H.; Bergeron, M. G. J. Clin. Microbiol. 2004, 42, 1875. 59. Maestro, Version 9.5, Schrödinger, LLC, New York, NY 2013. 60. Seybold, U.; Kourbatova, E. V.; Johnson, J. G.; Halvosa, S. J.; Wang, Y. F.; King, M. D.; Ray, S. M.; Blumberg, H. M. Clin. Infect. Dis. 2006, 42, 647. 61. Kristen, M. K.; Stine, O. C.; Johnson, J. K.; Perencevich, E. N.; Shardell, M. D.; Lesse, A. J.; Gordin, F. M.; Climo, M. W.; Roghmann, M. C. Diagn. Microbiol. Infect. Dis. 2011, 70, 285.