Discovery of Novel Inhibitors Targeting the Macrophage Migration

Apr 9, 2014 - Jiangsu Key Laboratory of Translational Research and Therapy for Neuro-Psycho-Diseases and College of Pharmaceutical Sciences and. §...
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Discovery of Novel Inhibitors Targeting the Macrophage Migration Inhibitory Factor via Structure-Based Virtual Screening and Bioassays Lei Xu,†,∥ Yu Zhang,‡,∥ Longtai Zheng,‡ Chunhua Qiao,‡ Youyong Li,§ Dan Li,† Xuechu Zhen,*,‡ and Tingjun Hou*,†,§ †

College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, People’s Republic of China Jiangsu Key Laboratory of Translational Research and Therapy for Neuro-Psycho-Diseases and College of Pharmaceutical Sciences and §Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, People’s Republic of China

‡

S Supporting Information *

ABSTRACT: Macrophage migration inhibitory factor (MIF) is involved in regulation of both the innate and the adaptive immune responses and is regarded as an attractive antiinflammatory pharmacological target. In this study, molecular docking-based virtual screening and in vitro bioassays were utilized to identify novel small-molecule inhibitors of MIF. The in vitro enzyme-based assay identified that ten chemically diverse compounds exhibited potent inhibitory activity against MIF in the micromolar regime, including three compounds with IC50 values below 10 μM and one with an IC50 value below 1 μM (0.55 μM); the latter is 26-fold more potent than the reference compound ISO-1. The structural analysis demonstrates that most of these active compounds possess novel structural scaffolds. Further in vitro cell-based glucocorticoid overriding, chemotaxis, and Western blotting assays revealed that the three compounds can effectively inhibit the biological functions of MIF in vitro, suggesting that these compounds could be potential agents for treating inflammatory diseases.

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INTRODUCTION Macrophage migration inhibitory factor (MIF) is an immunoregulatory and proinflammatory cytokine and can be released by macrophages and T-cells.1,2 As a cytokine, MIF functions as a critical regulator of inflammation and a central upstream mediator of innate immune response3 and is believed to be a biomarker for different diseases that have an inflammatory component.4 Moreover, extensive clinical studies highlight the important role of MIF in tumorigenesis.5,6 Therefore, MIF is considered as a viable therapeutic target for treating inflammatory diseases and tumors. Apart from its physiological and pathophysiological activities, MIF is found to act as a D-dopachrome tautomerase,7 a phenylpyruvate tautomerase,8 or a thiol-protein oxidoreductase.9 MIF is a homotrimer, with three 114-residue monomers associating to form a symmetrical trimer, and the catalytic active site is located between two adjacent monomers of the homotrimer.10 Although the relationship between the catalytic activity and biological functions of MIF is still not quite clear, targeting the tautomerase activity of MIF using small-molecule inhibitors has already been recognized as an attractive strategy for attenuating MIF proinflammatory activity and deactivating its biological activity in vitro and in vivo.11,12 To date, a variety of small-molecule inhibitors of MIF have been reported. These inhibitors inactivate MIF tautomerase activities via at least five © 2014 American Chemical Society

different mechanisms: (i) binding to the active site; (ii) allosteric inhibition; (iii) covalent modification of the catalytic Pro1 residue; (iv) disruption of the active site via the compound-induced dissociation of the active trimer; (v) stabilization of the MIF monomer and prevention of its reassociation to form the active trimer.13 However, the majority of these MIF inhibitors are not ideal for pharmaceutical development. For example, ISO-1, the most characterized MIF inhibitor, shows only micromolar potency,14 and the first identified MIF inhibitor NAPQI lacks specificity and is not desirable as a pharmaceutical due to covalent modification of the catalytic N-terminal proline.11 Accordingly, the discovery of potent and specific MIF inhibitors is quite important to develop new pharmaceutical therapies for MIF-associated diseases. Molecular modeling techniques, especially molecular docking, have been extensively employed to identify lead compounds for different drug targets.15−20 However, the successful examples of the use of in silico methods for designing or finding potent MIF inhibitors are quite limited.12,21−23 Therefore, in this study, we initiated a docking-based virtual screening and assaying campaign to determine potent and diverse lead compounds. The in vitro Received: December 11, 2013 Published: April 9, 2014 3737

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stock solution. The final concentration of DMSO in the reaction buffer was less than 0.25%. RAW 264.7 mouse macrophage was grown and maintained in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 5% heat-inactivated fetal bovine serum (FBS) and gentamicin (50 g/mL) at 37 °C and 5% CO2. Recombinant human MIF was expressed in E. coli and purified as previously described.13 Measurement of MIF Tautomerase Activity. Tautomerase activity of MIF was assessed as previously described.31 We modified the reaction conditions for performing this assay in 96-well plates. For the preparation of L-dopachrome methyl ester, equal volumes of L-3,4dihydroxyphenylalanine methyl ester (8 mM) and sodium metaperiodate (16 mM) were mixed and incubated for 5 min at room temperature. Then the L-dopachrome methyl ester (30 μL) was added to a 96-well plate containing hMIF (120 nM) in 10 mM potassium phosphate buffer and 0.5 mM EDTA, pH 6.2. For the measurement of the inhibitory effect of compounds on the tautomerase activity of MIF, the various concentrations of compounds were added to the 96-well plate containing hMIF (120 nM) and incubated for 30 min prior to the addition of the L-dopachrome methyl ester. The air bubbles in the plates were removed, and the absorbance was measured for 3 min at 475 nm using a Tecan Infinite M1000 microplate reader (Tecan Group Ltd.). Inhibitory Patterns of Compounds 1, 2, and 9. Each compound was dissolved in DMSO and added with different concentrations to the cuvette containing 120 nM huMIF incubated for 30 min prior to the addition of different concentrations of the Ldopachrome methyl ester. Tautomerase activity was assessed using nonlinear regression analysis by Prism4 (GraphPad Prism). Ki (the inhibition constant) was determined by nonlinear regression against the competitive equation using Prism4 (GraphPad Prism). Chemotaxis Assay. MIF-triggered chemotaxis assay was performed using CIM-16 well plates with an xCELLigence RTCA-DP instrument (Roche Diagnostics GmbH, Mannheim, Germany) as previously described.32 Briefly, the recombinant MIF was loaded into the lower chamber of the CIM-16 plate with or without MIF tautomerase inhibitors. RAW 264.7 macrophage cells were seeded into the upper chamber at 20 000 cells/well in serum-free medium. The CIM-16 well plates were transferred to the RTCA-DP machine and were monitored every 15 min for 4 h. Data analysis was carried out with the RTCA software 1.2. Enzyme-Linked Immunosorbent Assay (ELISA). The TNF-α content in microglial culture supernatants was measured as previously described33 by specific ELISA using rat monoclonal antimouse TNF-α antibody as the capture antibody and goat biotinylated polyclonal antimouse TNF-α antibody as the detection antibody (ELISA development reagents, R&D Systems). The biotinylated anti-TNF-α antibody was detected by sequential incubation with streptavidin− horseradish peroxidase conjugate and chromogenic substrates. Determination of Cell Viability. Cell viability was determined by the 3-(4,5-dimethylthiazol-2-yl)-2,5- diphenyltetrazolium bromide (MTT) assay. RAW 264.7 macrophages were seeded in triplicate at a density of 5 × 104 cells/well on a 96-well plate. The cells were treated with glycocalyixns and LPS for 24 h. MTT was added to each well, and the cells were incubated for 4 h at 37 °C. After the culture media were discarded, DMSO was added to dissolve the formazan dye. The optical density was measured at 540 nm. Western Blot Analysis. Cells were lysed in triple-detergent lysis buffer [50 mM Tris−HCl, pH 8.0, 150 mM NaCl, 0.02% sodium azide, 0.1% sodium dodecyl sulfate (SDS), 1% Nonidet P-40, 0.5% sodium deoxycholate, 1 mM phenylmethanesulfonyl fluoride (PMSF)]. The protein concentration in the cell lysates was determined using a protein assay kit (Bio-Rad, Hercules, CA). An equal amount of protein from each sample was separated by SDS− polyacrylamide gel electrophoresis (12% gel) and transferred to Hybond ECL nitrocellulose membranes (Amersham Biosciences, Piscataway, NJ). The membranes were blocked with 5% skim milk and sequentially incubated with primary antibodies [polyclonal rabbit antihuman extracellular signal-related kinase (ERK) 1/2 and phosphERK1/2 (Cell Signaling Technology Inc., Beverly, MA)] and

MIF tautomerase assay discovered 10 structurally diverse molecules with potent inhibitory activity in the micromolar range. Among the ten active molecules, three hits show IC50 values of less than 10 μM and the best one shows an IC50 value of less than 1 μM. Furthermore, the three most potent MIF inhibitors were chosen for the cell-based glucocorticoid overriding and chemotaxis assays. The experimental results indicated that the three compounds with good inhibitory potency against the tautomerase activity of MIF can also inhibit the biological functions of MIF in vitro, suggesting that these two activities are closely linked and these compounds could be potential agents for treating inflammatory diseases.

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MATERIALS AND METHODS

Docking-Based Virtual Screening. The crystal structure of MIF in complex with the substrate p-hydroxyphenyl pyruvate (HPP) (PDB entry 1CA7)24 was chosen as the template for molecular docking with the Glide module in the Schrödinger molecular simulation package (version 9.0).25 The ligand residing in the active site between chains A and B was maintained, while the other two ligands in the crystal structure were deleted. All crystallographic water molecules were removed, and the missing hydrogen atoms were added. Then the complex was submitted to a series of restrained minimizations to relieve static clashes using the OPLS2005 force field26 and the Protein Preparation Wizard in Schrödinger.25 The critical residue Pro1, which is postulated to function as a catalytic base, was protonated according to the experimental measurement and theoretical calculations.27,28 As for the grid generation and ligand docking procedures, the default Glide settings were adopted. Before the chemical libraries were screened, the accuracy of the Glide docking based on different scoring modes for distinguishing the inhibitors from noninhibitors of MIF was evaluated (part 1 in the Supporting Information). The Specs and ChemBridge databases were used as the source for screening. For each molecule in the Specs and ChemBridge databases, the ionized state at pH 7.4 and tautomers and stereoisomers (maximum number of 30) were generated with the LigPrep module in Schrödinger.25 The final virtual library includes ∼1.1 million structures. All structures were docked and scored by the Glide high-throughput virtual screening (HTVS) mode, and the 200 000 top-ranked structures from ChemBridge and the 100 000 top-ranked structures from Specs were saved. Then the saved structures from the previous step were redocked and scored by the Glide standard precision (SP) mode, and the 100 000 top-ranked structures from ChemBridge and the 50 000 top-ranked structures from Specs were saved. Finally, the chosen structures by SP were redocked and scored by the Glide extra precision (XP) scoring mode. The top 2000 molecules ranked by the Glide XP scoring mode were saved and filtered by the REOS rules.29 REOS is a hybrid method developed at Vertex Pharmaceuticals that combines a set of functional group filters with some simple counting schemes analogous to those in the “rule of five” to eliminate compounds with toxic, reactive, or otherwise undesirable moieties. To maximize the chemical diversity of the collected compounds for bioassays, the 942 molecules after application of the REOS rules29 were clustered into 150 clusters on the basis of the Tanimoto distance computed from the FCFP_4 fingerprints by the Find Diverse Molecule protocol in Discovery Studio 2.5.30 Among the top 150 molecules chosen from the individual clusters, 147 compounds that are available for purchase from Specs and ChemBridge (purity >95%; Table S1, Supporting Informaton) were submitted to experimental testing. Reagents and Cell Culture. Bacterial lipopolysaccharide (LPS) (Escherichia coli serotype 055:B5), L-3,4-dihydroxyphenylalanine methyl ester, and sodium metaperiodate were purchased from Sigma-Aldrich (St. Louis, MO). (S,R)-3-(4-hydroxyphenyl)-4,5-dihydro-5-isoxazole acetic acid methyl ester (ISO-1) was purchased from Merck Calbiochem (Darmstadt, Germany, purity ≥95%). These compounds were dissolved in dimethyl sulfoxide (DMSO) as a 10 mM 3738

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Table 1. Experimentally Determined Half-Maximal Inhibitory Concentrations (IC50), XP Docking Scores, Ranks, and Properties Predicted by QikProp compd ISO-1 1 2 3 4 5 6 7 8 9 10

IC50 (μM) 14.41 0.55 7.43 25.77 23.06 45.54 74.49 85.56 22.57 7.47 102.12

± ± ± ± ± ± ± ± ± ± ±

1.59 0.76 0.87 1.41 1.36 1.66 0.04 1.93 1.35 0.87 2.01

scorea

rank

MWa

log Pb

log Sc

PCacod

similaritye

−9.27 −9.07 −10.64 −8.57 −9.38 −9.39 −9.23 −8.71 −9.08 −9.88

313 423 31 804 263 430 331 670 415 113

235.24 376.43 394.24 304.30 417.26 366.43 448.85 230.26 465.32 370.38 344.34

1.08 4.69 4.03 2.54 3.90 4.60 3.26 3.35 3.34 3.89 1.35

−2.88 −6.33 −4.95 −3.57 −4.78 −5.71 −5.06 −3.15 −5.94 −5.20 −3.62

228.56 67.35 237.25 53.94 66.70 188.69 34.84 277.57 32.80 60.52 19.51

0.24 0.26 0.22 0.23 0.25 0.24 0.26 0.20 0.30 0.44

a

Molecular weight 130.0−725.0. bPredicted octanol/water partition coefficient. cPredicted aqueous solubility, S, mol/L. dPredicted Caco-2 cell permeability, nm/s (500, great). ePairwise Tanimoto similarity indices based on the FCFP_4 fingerprints for each inhibitor with the known MIF inhibitors.

Figure 1. Concentration-dependent inhibition of MIF tautomerase activity by three active molecules. horseradish peroxidase-conjugated secondary antibodies (antirabbit and antimouse, Sigma) followed by enhanced chemiluminescence detection (Bio-Rad, Hercules, CA). All experimental results are expressed as the mean ± SD. The data were analyzed by one-way ANOVA and the Student Newman Keul post hoc analysis using the SPSS program (version 14.0). A value of p < 0.05 was considered statistically significant. Molecular Dynamics (MD) Simulations. The MD simulations were employed to investigate the binding patterns of three inhibitors (compounds 1, 2, and 9) with the best inhibitory potency against the tautomerase activity of MIF. The docked structures of the inhibitors in complex with MIF were used as the initial structures for MD calculations. The general AMBER force field (gaff)34 and ff99SB force field35 were used for the inhibitor and MIF, respectively. A production run for 10 ns was performed using the NPT ensemble under a target temperature of 300 K and a target pressure of 1 atm. Coordinate trajectories were saved every 5 ps for the whole MD runs. The sander program in AMBER1136 was used for the molecular mechanics (MM) optimization and MD simulations. Details on ligand and protein

preparation, MM optimization, and settings for MD simulations are available in the Supporting Information. MM/GBSA Binding Free Energy Decomposition Analysis. To quantitatively discern the contribution of each residue to inhibitor binding, the total binding free energies for three potent inhibitors were further decomposed into individual residue contributions by using the molecular mechanics/generalized born surface area (MM/GBSA) free energy decomposition analysis in AMBER11.37−42 The binding interaction of each inhibitor−residue pair comprises four terms: the van der Waals (ΔEvdw) and electrostatic (ΔEele) interactions between the inhibitor and each residue in the gas phase and the polar (ΔGGB) and nonpolar (ΔGSA) contributions of the desolvation free energy. Details on MM/GBSA free energy decomposition analysis are available in the Supporting Information.

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RESULTS AND DISCUSSION Docking-Based Virtual Screening and Measurement of MIF Tautomerase Activity. Prior to virtual screening, the 3739

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Figure 2. Chemical structures of ISO-1 and 10 active compounds identified by docking-based virtual screening and enzyme-based assay.

Figure 3. Nonlinear regression analysis and Lineweaver−Burk plot to determine the inhibitory constant (Ki) and type of inhibition. Concentration− response curves of compounds 1, 2, and 9 versus substrate L-dopachrome methyl ester against huMIF are shown. Ki was calculated with Graphpad Prism 4. Each Lineweaver−Burk plot reveals the competitive inhibition pattern of compounds (a) 1, (b) 2, and (c) 9 with huMIF. Data are presented as the mean ± SEM.

other two scoring schemes, a hierarchical protocol was designed for docking-based virtual screening: all structures were docked and scored by the Glide HTVS scoring mode first, and then the saved structures from the previous step were redocked and scored by the Glide SP and XP scoring mode sequentially. The 147 molecules by applying the REOS filtering and structural clustering were chosen for a series of biological assays. Eight out of the 147 purchased compounds could not be tested due to poor solubility in DMSO/ethanol/water. The other 139 compounds were then submitted to L-dopachrome tautomerase assay, which measures the inhibitory effect of these compounds on MIF tautomerase activity. For the initial screening, all compounds (soluble in DMSO) were tested at

performance of the Glide docking based on different scoring modes (HTVS, SP, and XP) was evaluated by distinguishing the inhibitors from noninhibitors of MIF. According to the evaluation results shown in the Supporting Information, the distributions of the Glide scores for the inhibitors and noninhibitors given by any of the three scoring modes are significantly different. Indicated by the p-values and AUC values (Figure S1, Supporting Information), the Glide SP and XP scoring modes show better discrimination power than the HTVS scoring mode to distinguish the inhibitors from noninhibitors, and the Glide XP scoring model performs slightly better than the SP scoring mode. Since the XP scoring mode needs considerably more computational cost than the 3740

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a concentration of 50 μM. The prototypical MIF inhibitor ISO1, a previously reported tautomerase inhibitor in the dopachrome-based assay, was used as the reference control, and DMSO (1%, v/v) was used as the vehicle control. The compounds revealing greater than 25% inhibition of MIF activity were retested to exclude false positives. In total, 10 compounds that showed more that 25% inhibition on MIF tautomerase activity in initial screening were subjected to further study for determining the IC50 values of inhibiting activity. As shown in Table 1 and Figure 1, among them, three hits have IC50 values of less than 10 μM and are more potent than the control inhibitor, ISO-1 (IC50 = 14.4 μM). Compound 1 exhibited the most potent inhibiting activity with IC50 below 1 μM (0.55 μM). The other seven compounds with IC50 values in the range of 22.6−102.1 μM are less active than the control inhibitor. In our experiments, the Ki value of ISO-1 was about twice that reported in the previous study.14 This difference may attribute to the condition of enzymatic reaction, such as the concentration of the substrate.43 The concentration of L-3,4dihydroxyphenylalanine methyl ester (8 mM) used for preparation of the substrate in the present study was higher than that in the previous study (2.4 mM),12 which resulted in the increase of the IC50 value of the compounds. In addition, the activity of recombinant MIF may also influence the Km value, which may ultimately lead to the variation of the value of IC50. In fact, Balachandran et al. reported that the IC50 of ISO-1 was more than 100 μM in their experimental conditions.44 The chemical structures of the 10 active molecules are shown in Figure 2. The structural diversity of the 10 identified MIF inhibitors provided a valuable alternative series for ongoing lead optimization. Next, we further analyzed the type of inhibition and determined the inhibition constant (Ki). The Lineweaver− Burk plots of the dose−response curves revealed that the three compounds 1, 2, and 9 were competitive inhibitors of MIF (Figure 3). Structural Analysis of the Potent Inhibitors. To evaluate the novelty of these 10 inhibitors with respect to known MIF inhibitors, the pairwise Tanimoto similarity indices based on the FCFP_4 fingerprints for these 10 inhibitors with the known MIF inhibitors obtained from the BindingDB database were calculated through the Find Similar Molecules by Fingerprints protocol in Discovery Studio 2.5.30 The Tanimoto coefficient is the well-known method of choice for the computation of fingerprint-based similarity in terms of a distance measure, giving values in the range of zero (no bits in common) to unity (all bits the same).45 The results show that these inhibitors with the exception of compound 10 (0.20− 0.30, 0.44 for compound 10) have low Tanimoto similarities with the known MIF inhibitors from BindingDB (Table 1). Accordingly, these inhibitors can be considered to be structurally novel. Moreover, as shown in Table 1, all these inhibitors satisfy the druglikeness rules defined by Qikprop.23 Predicted Binding Patterns of the Three Most Potent Inhibitors against MIF Tautomerase Activity. The 10 inhibitors, with the exception of compound 3, show similar binding poses predicted by molecular docking. The most potent inhibitor, compound 1 (IC50 = 0.55 μM), represents a unique chemotype, and it is composed of a biphenyl and a benzoic acid side chain linked by the polar group. In addition, compound 9 with the common polar group exhibits high MIF tautomerase inhibitory activity (IC50 = 7.47 μM). As shown in Figure 4, the docked poses of these two inhibitors are quite similar, and both of them can form the aryl−aryl interaction

Figure 4. Solvent-accessible surfaces of the binding pocket of MIF for compounds (a) 1, (c) 2, and (e) 9 and the binding poses of compounds (b) 1, (d) 2, and (f) 9 (the carbon atoms of these compounds are colored in green).

with Tyr95 in T-shaped geometry and the cation−π interaction with the protonated Pro1, as well as the hydrophobic interactions with His62, Ser63, Ile64, Val106, Phe113, etc. (Figure S2, Supporting Information). Besides, the acylamino and carboxyl groups of compound 1 are predicted to form Hbonds with the phenolic ring of Tyr36. The modeling suggests that the 4-methoxybenzoic acid side chain of compound 9 forms a parallel, stacked arrangement with Tyr36, and it also forms H-bonding interactions with Ile64 (Figure 4f). Thus, according to the chemical structures of these two inhibitors, we speculate that the two aromatic rings, with one aromatic ring possessing a carboxy group, linked by a polar group may be an important structural feature of MIF inhibitors (Table S2, Supporting Information). It seems that the addition of a hydroxyl group at position 4 or 5 of the aromatic ring, which was demonstrated to form a key H-bond with Asn97 in a previous study,46 may be favorable to enhance the inhibitory activity against MIF (Figure S3, Supporting Information). Compound 5 also possesses two aromatic rings, and one aromatic ring has a carboxyl group, but the linker is a carbon− sulfur bond. Compared with compounds 1 and 9, compound 5 has decreased bioactivity. It is quite possible that it possesses a relatively smaller polar linker and cannot form a H-bond with Ile64. Compounds 2, 3, and 8 have an identical structural moiety, 2-(2-methoxyphenoxy)acetic acid. As shown in Figure 4, although these three inhibitors have common structural 3741

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Figure 5. Inhibitory effect of compounds on MIF glucocorticoid overriding activity. The RAW 264.7 macrophage cells were seeded in triplicate at a density of 5 × 104 cells/well on a 96-well plate. The cells were pretreated for 30 min with dexamethasone (DEX; 0.1 μM) and recombinant MIF (0.2 μM) in the presence or absence of compounds (20 μM) prior to LPS (0.1 μg/mL) stimulation. (a) At 16 h after the LPS stimulation, the amounts of NO in the supernatants were measured using Griess reagents. (b) At 4 h after the LPS stimulation, the amounts of TNF-α in the supernatants were measured using ELISA. (c) Cell viability was determined by MTT assay. The data are expressed as the mean ± SD (n = 3) and are representative of the results obtained from three independent experiments. Key: (*) p < 0.05, (**) p < 0.01, (***) p < 0.001, as compared with the LPS + DEX + MIF group. (d) RAW 264.7 macrophage cells were incubated with 0.1 μg/mL LPS in the presence or absence of compounds (40 μM) for 24 h. The amounts of nitrite in the supernatants were measured using Griess reagent. The data are expressed as the mean ± SD (n = 3) and are representative of the results obtained from three independent experiments. Key: (**) p < 0.01, (***) p < 0.001, as compared with the LPS alone treatment.

overriding assay. In agreement with previous studies, treatment of RAW 264.7 cells with MIF overrode the inhibitory effect of dexamethasone on TNF-α and NO production induced by LPS, which was complementally inhibited by ISO-1 treatment (Figure 5a,b). As expected, three tested compounds fully inhibited MIF glucocorticoid overriding activity. The levels of proinflammtory mediators of these compounds were much lower than that of dexamethasone plus MIF, which could be attributed to their ability to inhibit endogenous MIF or to directly inhibit proinflammatory properties of LPS. Indeed, we found that this compound significantly inhibited NO production in LPS-stimulated RAW 264.7 cells (Figure 5d). The potency of suppression of glucocorticoid overriding activity was sequenced as follows on the basis of percent inhibition, compound 1 (213%) > compound 2 (157%) > compound 9 (131%) > ISO-1 (128%), which is consistent with the order given by tautomerase assay. MIF triggered macrophage chemotactic migration and plays a critical role in immune cell recruitment and the process of inflammation.48 Inhibition of MIF tautomerase activity by small molecules suppresses MIF-induced inflammatory responses.49 To expose the potential inhibitory effect of the compounds on MIF-induced macrophage chemotactic activity, we performed Roche xCELLigence chemotaxis assay using RAW 264.7 cells. The presence of MIF led to 250% cell migration, while the addition of hit-inactivated (H.I) MIF or DMEM medium did not alter the cell migration. The addition of compounds 1, 2, and 9 decreased the chemotactic migration to 52%, 83%, and 159% of its initial value (100% for MIF alone), respectively. Therefore, as compared to MIF alone, the presence of the

segments, their binding structures predicted by molecular docking are not well aligned in the active site of MIF. The modeling suggests that compounds 2 and 8 have similar binding patterns, and they form H-bonds with the amino group of Lys32, as well as hydrophobic interactions with Pro1, Tyr36, Ile64, Val106, Phe113, etc. (Figure S2, Supporting Information). Besides, the benzothiazole ring of compound 2 is predicted to form aryl−aryl interactions with Tyr95 in a Tshaped geometry, and compound 8 is predicted to form a Hbond with Ile64. Compound 3 is located in close proximity to the outside of the binding pocket in comparison with the other two compounds, and it can form a H-bond with Ile64. The aryl−aryl interactions between the benzothiazole ring of compound 2 and Tyr95, as well as the hydrophobic interactions with Pro1, Met2, and Val106, are favorable for ligand binding. Similarly, the addition of a hydroxyl group to the benzothiazole ring at position 4 or 5 may lead to the formation of a key Hbond with Asn97 (Figure S3, Supporting Information). Details on the analyses of the dynamic interaction patterns between MIF and compound 1, 2, or 9 are available in the Supporting Information. Cell-Based Assays for the Three Most Potent Inhibitors against MIF Tautomerase Activity. As a cytokine, MIF was shown to override the immunosuppressive effects of glucocorticoids on production of proinflammatory mediators by activated macrophages.47 Therefore, we tested whether the MIF tautomerase inhibitors identified in this study could regulate the immunoregulatory function of MIF on RAW264.7 macrophages. The three compounds 1, 2, and 9 with IC50 less than 10 μM were chosen for glucocorticoid 3742

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compounds repressed cell migration by around 4.8-, 3-, and 1.6fold. The specific MIF inhibitor ISO-1 also decreased the MIFmediated macrophage chemotactic migration by around 2.2fold (Figure 6).

Figure 6. Inhibitory effect of compounds on MIF-mediated macrophage chemotactic migration. The recombinant MIF with or without compounds was added to the lower chamber of the CIM-16 plate 30 min before addition of RAW 264.7 macrophage cells to the upper chamber. The CIM-16 well plate was transferred to the RTCA-DP machine and was monitored every 15 min for 4 h. Data analysis was carried out with RTCA software 1.2, and the numbers of migrated cells are expressed as impedance values. Heat-inactivated (H.I) MIF was used as the negative control. The relative migration rate (%) represents percent migration compared to that of the MIF alone treatment group. The data are expressed as the mean ± SD (n = 3) and are representative of the results obtained from three independent experiments. Key: (*) p < 0.05, (**) p < 0.01, as compared with the MIF alone treatment.

Figure 7. Effects of compounds on ERK activation in MIF-stimulated RAW 264.7 macrophages. RAW 264.7 macrophage cells were stimulated with MIF (0.1 μg/mL) in the presence of compounds (20 μM) that had been added 30 min before the stimulation. (a) Total cell lysates obtained 20 min after the MIF stimulation were subjected to Western blotting to assess the levels of ERK phosphorylation. (b) Quantification of expression was performed by densitometric analysis. Detection of total ERK was done to confirm the equal loading of the samples. The values are expressed as a percentage of the maximal band intensity in the culture treated with MIF alone, which was set to 100% (lane 2). The data are expressed as the mean ± SD (n = 3) and are representative of the results obtained from three independent experiments.

Previous studies have demonstrated that MIF has a proinflammatory property through activation of extracellular signal-related kinase (ERK)-1/2.50 Thus, the effect of the compounds on the ERK activation was examined. After stimulation of RAW 264.7 cells with MIF for 30 min, activation of ERK was detected by Western blot analysis using an antibody specific for phospho-ERK. Similarly, the level of ERK phosphorylation was suppressed by ISO-1 and the three tested compounds. Recombinant MIF markedly induced ERK phosphorylation by around 2.5-fold in macrophages. The presence of compounds 1, 2, and 9 suppressed the MIFinduced ERK phosphorylation to 43%, 55%, and 59%, respectively (the level of MIF was set to 100%) (Figure 7). Taken together, these results indicated that the compounds not only inhibit the MIF tautomerase activity but also impair the biological properties of MIF in vitro, suggesting that these two activities are closely associated and these compounds could be potential agents for treating inflammatory diseases.

effectively inhibit the biological functions of MIF in vitro. The novel scaffolds of these inhibitors can be utilized as the starting templates for lead optimization. The development of the derivatives of several novel hits is currently under investigation.

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ASSOCIATED CONTENT

S Supporting Information *

Part 1, validation of the performance of molecular docking; part 2, MD simulations and MM/GBSA binding free energy decomposition analysis; part 3, dynamic interaction patterns between MIF and compound 1, 2, or 9; Table S1, ranks, docking scores, database sources, IDs, and corresponding numbers in Table 1 of the virtual screening hits after clustering for bioassays; Table S2, structures and docking scores for inhibitor 1 and the six designed derivatives; Table S3, binding free energy contributions of the key binding-site residues for the three potent inhibitors calculated by the binding energy decomposition (kcal/mol); Figure S1, distributions and ROC curves of the Glide docking scores to distinguish the inhibitors from noninhibitors of MIF on the basis of the (a, b) HTVS, (c, d) SP, or (e, f) XP scoring mode; Figure S2, schematic depiction of the major interactions of inhibitor 1 (a), inhibitor 2 (b), and inhibitor 3 (c) with the MIF-averaged structure over the last 4 ns MD trajectory (generated by the LIGPLOT

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CONCLUSION In this study, docking-based virtual screening and bioassays were employed to discover novel inhibitors against the MIF tautomerase activity. The enzyme-based assay shows that ten molecules have inhibitory activity in the micromolar regime, including three hits with IC50 values below 10 μM and one hit as low as 0.55 μM. The binding patterns of the three best hits were explored by molecular docking, MD simulations, and free energy decomposition. Moreover, the in vitro cell-based glucocorticoid overriding and chemotaxis assays revealed that the three compounds with IC50 values of less than 10 μM can 3743

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program51); Figure S3, schematic representation of the major interactions of the derivative of inhibitor 1 (a) and inhibitor 2 (b) with MIF; Figure S4, (a) inhibitor−residue interaction spectrum for the MIF−compound 1 complex, (b) nonpolar interaction (ΔEvdw + ΔGSA) spectrum for the MIF−compound 1 complex, and (c) polar interaction (ΔEele + ΔGGB) spectrum for the MIF−compound 1 complex; Figure S5, (a) inhibitor− residue interaction spectrum for the MIF−compound 2 complex, (b) nonpolar interaction (ΔEvdw + ΔGSA) spectrum for the MIF−compound 2 complex, and (c) polar interaction (ΔEele + ΔGGB) spectrum for the MIF−compound 2 complex; Figure S6, (a) inhibitor−residue interaction spectrum for the MIF−compound 9 complex, (b) nonpolar interaction (ΔEvdw + ΔGSA) spectrum for the MIF−compound 9 complex, and (c) polar interaction (ΔEele + ΔGGB) spectrum for the MIF− compound 9 complex. This material is available free of charge via the Internet at http://pubs.acs.org.

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(5) Hagemann, T.; Robinson, S. C.; Thompson, R. G.; Charles, K.; Kulbe, H.; Balkwill, F. R. Ovarian cancer cell-derived migration inhibitory factor enhances tumor growth, progression, and angiogenesis. Mol. Cancer Ther. 2007, 6, 1993−2002. (6) Meyer-Siegler, K. L.; Iczkowski, K. A.; Leng, L.; Bucala, R.; Vera, P. L. Inhibition of macrophage migration inhibitory factor or its receptor (CD74) attenuates growth and invasion of DU-145 prostate cancer cells. J. Immunol. 2006, 177, 8730−8739. (7) Sugimoto, H.; Taniguchi, M.; Nakagawa, A.; Tanaka, I.; Suzuki, M.; Nishihira, J. Crystal structure of human D -dopachrome tautomerase, a homologue of macrophage migration inhibitory factor, at 1.54 Å resolution. Biochemistry 1999, 38, 3268−3279. (8) Rosengren, E.; Åman, P.; Thelin, S.; Hansson, C.; Ahlfors, S.; Björk, P.; Jacobsson, L.; Rorsman, H. The macrophage migration inhibitory factor MIF is a phenylpyruvate tautomerase. FEBS Lett. 1997, 417, 85−88. (9) Kleemann, R.; Kapurniotu, A.; Frank, R. W.; Gessner, A.; Mischke, R.; Flieger, O.; Jüttner, S.; Brunner, H.; Bernhagen, J. Disulfide analysis reveals a role for macrophage migration inhibitory factor (MIF) as thiol-protein oxidoreductase. J. Mol. Biol. 1998, 280, 85−102. (10) Sun, H.-W.; Bernhagen, J.; Bucala, R.; Lolis, E. Crystal structure at 2.6-A resolution of human macrophage migration inhibitory factor. Proc. Natl. Acad. Sci. U.S.A. 1996, 93, 5191−5196. (11) Senter, P. D.; Al-Abed, Y.; Metz, C. N.; Benigni, F.; Mitchell, R. A.; Chesney, J.; Han, J.; Gartner, C. G.; Nelson, S. D.; Todaro, G. J. Inhibition of macrophage migration inhibitory factor (MIF) tautomerase and biological activities by acetaminophen metabolites. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 144−149. (12) Cournia, Z.; Leng, L.; Gandavadi, S.; Du, X.; Bucala, R.; Jorgensen, W. L. Discovery of human macrophage migration inhibitory factor (MIF)−CD74 antagonists via virtual screening. J. Med. Chem. 2009, 52, 416−424. (13) Ouertatani-Sakouhi, H.; El-Turk, F.; Fauvet, B.; Cho, M.-K.; Karpinar, D. P.; Le Roy, D.; Dewor, M.; Roger, T.; Bernhagen, J.; Calandra, T. Identification and characterization of novel classes of macrophage migration inhibitory factor (MIF) inhibitors with distinct mechanisms of action. J. Biol. Chem. 2010, 285, 26581−26598. (14) Lubetsky, J. B.; Dios, A.; Han, J.; Aljabari, B.; Ruzsicska, B.; Mitchell, R.; Lolis, E.; Al-Abed, Y. The tautomerase active site of macrophage migration inhibitory factor is a potential target for discovery of novel anti-inflammatory agents. J. Biol. Chem. 2002, 277, 24976−24982. (15) Sheng, C.; Zhang, W. Fragment informatics and computational fragment-based drug design: an overview and update. Med. Res. Rev. 2012, 33, 554−598. (16) Yang, P.; Wang, L.; Feng, R.; Almehizia, A. A.; Tong, Q.; Myint, K.-Z.; Ouyang, Q.; Alqarni, M. H.; Wang, L.; Xie, X.-Q. Novel triaryl sulfonamide derivatives as selective cannabinoid receptor 2 inverse agonists and osteoclast inhibitors: discovery, optimization, and biological evaluation. J. Med. Chem. 2013, 56, 2045−2058. (17) Deng, J.; Feng, E. G.; Ma, S.; Zhang, Y.; Liu, X. F.; Li, H. L.; Huang, H.; Zhu, J.; Zhu, W. L.; Shen, X.; Miao, L. Y.; Liu, H.; Jiang, H. L.; Li, J. Design and synthesis of small molecule RhoA inhibitors: a new promising therapy for cardiovascular diseases? J. Med. Chem. 2011, 54, 4508−4522. (18) Kolb, P.; Huang, D.; Dey, F.; Caflisch, A. Discovery of kinase inhibitors by high-throughput docking and scoring based on a transferable linear interaction energy model. J. Med. Chem. 2008, 51, 1179−1188. (19) Shen, J.; Tan, C.; Zhang, Y.; Li, X.; Li, W.; Huang, J.; Shen, X.; Tang, Y. Discovery of potent ligands for estrogen receptor β by structure-based virtual screening. J. Med. Chem. 2010, 53, 5361−5365. (20) Zhou, H.; Liu, L.; Huang, J.; Bernard, D.; Karatas, H.; Navarro, A.; Lei, M.; Wang, S. Structure-based design of high-affinity macrocyclic peptidomimetics to block the menin-mixed lineage leukemia 1 (MLL1) protein−protein interaction. J. Med. Chem. 2013, 56, 1113−1123.

AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. Phone: +86-512-65880369. *E-mail: [email protected] or [email protected]. Phone: +86-571-8820-8412. Author Contributions ∥

L.X. and Y.Z. contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This study was supported by the National Science Foundation of China (Grants 21173156, 81130023, 81373382, and 81372688) and the National Basic Research Program of China (973 program, Grants 2012CB932600, 2009CB522000, and 2011CB5C4403). Support from Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutes and a grant from the Jiangsu Science and Technology Commission (BY2011131) are also appreciated.

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ABBREVIATIONS USED MIF, macrophage migration inhibitory factor; LPS, lipopolysaccharide; ISO-1, (S,R)-3-(4-hydroxyphenyl)-4,5-dihydro-5-isoxazole acetic acid methyl ester; DMSO, dimethyl sulfoxide; DMEM, Dulbecco’s modified Eagle’s medium; FBS, fetal bovine serum; ELISA, enzyme-linked immunosorbent assay; SDS, sodium dodecyl sulfate; ERK, extracellular signalrelated kinase; REOS, rapid elimination of swill; MD, molecular dynamics; MM/GBSA, molecular mechanics/generalized Born surface area; MM, molecular mechanics; GAFF, general AMBER force field

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REFERENCES

(1) Morand, E. F.; Leech, M.; Bernhagen, J. MIF: a new cytokine link between rheumatoid arthritis and atherosclerosis. Nat. Rev. Drug Discovery 2006, 5, 399−411. (2) Xu, L.; Li, Y.; Sun, H.; Zhen, X.; Qiao, C.; Hou, T. Current developments of macrophage migration inhibitory factor (MIF) inhibitors. Drug Discovery Today 2013, 18, 592−600. (3) Calandra, T.; Roger, T. Macrophage migration inhibitory factor: a regulator of innate immunity. Nat. Rev. Immunol. 2003, 3, 791−800. (4) Grieb, G.; Merk, M.; Bernhagen, J.; Bucala, R. Macrophage migration inhibitory factor (MIF): a promising biomarker. Drug News Perspect. 2010, 23, 257−264. 3744

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(21) El Turk, F.; Fauvet, B.; Ouertatani-Sakouhi, H.; Lugari, A.; Betzi, S.; Roche, P.; Morelli, X.; Lashuel, H. A. An integrative in silico methodology for the identification of modulators of macrophage migration inhibitory factor (MIF) tautomerase activity. Bioorg. Med. Chem. Lett. 2010, 18, 5425−5440. (22) Winner, M.; Meier, J.; Zierow, S.; Rendon, B. E.; Crichlow, G. V.; Riggs, R.; Bucala, R.; Leng, L.; Smith, N.; Lolis, E. A novel, macrophage migration inhibitory factor suicide substrate inhibits motility and growth of lung cancer cells. Cancer Res. 2008, 68, 7253− 7257. (23) Dahlgren, M. K.; Garcia, A. B.; Hare, A. A.; Tirado-Rives, J.; Leng, L.; Bucala, R.; Jorgensen, W. L. Virtual screening and optimization yield low-nanomolar inhibitors of the tautomerase activity of plasmodium falciparum macrophage migration inhibitory factor. J. Med. Chem. 2012, 55, 10148−10159. (24) Lubetsky, J. B.; Swope, M.; Dealwis, C.; Blake, P.; Lolis, E. Pro-1 of macrophage migration inhibitory factor functions as a catalytic base in the phenylpyruvate tautomerase activity. Biochemistry 1999, 38, 7346−7354. (25) Schrödinger, version 9.0; Schrödinger, LLC: New York, 2009. (26) Kaminski, G. A.; Friesner, R. A.; Tirado-Rives, J.; Jorgensen, W. L. Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J. Phys. Chem. B 2001, 105, 6474−6487. (27) Soares, T. A.; Goodsell, D. S.; Ferreira, R.; Olson, A. J.; Briggs, J. M. Ionization state and molecular docking studies for the macrophage migration inhibitory factor: the role of lysine 32 in the catalytic mechanism. J. Mol. Recognit. 2000, 13, 146−156. (28) Swope, M.; Sun, H.-W.; Blake, P. R.; Lolis, E. Direct link between cytokine activity and a catalytic site for macrophage migration inhibitory factor. EMBO J. 1998, 17, 3534−3541. (29) Walters, W. P.; Stahl, M. T.; Murcko, M. A. Virtual screening-an overview. Drug Discovery Today 1998, 3, 160−178. (30) Discovery Studio 2.5 Guide, Accelrys Inc.: San Diego, 2009; http://www.accelrys.com. (31) Alam, A.; Haldar, S.; Thulasiram, H. V.; Kumar, R.; Goyal, M.; Iqbal, M. S.; Pal, C.; Dey, S.; Bindu, S.; Sarkar, S. Novel antiinflammatory activity of epoxyazadiradione against macrophage migration inhibitory factor inhibition of tautomerase and proinflammatory activities of macrophage migration inhibitory factor. J. Biol. Chem. 2012, 287, 24844−24861. (32) Limame, R.; Wouters, A.; Pauwels, B.; Fransen, E.; Peeters, M.; Lardon, F.; De Wever, O.; Pauwels, P. Comparative analysis of dynamic cell viability, migration and invasion assessments by novel real-time technology and classic endpoint assays. PLoS One 2012, 7, e46536. (33) Zheng, L. T.; Hwang, J.; Ock, J.; Lee, M. G.; Lee, W. H.; Suk, K. The antipsychotic spiperone attenuates inflammatory response in cultured microglia via the reduction of proinflammatory cytokine expression and nitric oxide production. J. Neurochem. 2008, 107, 1225−35. (34) Wang, J.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A. Development and testing of a general amber force field. J. Comput. Chem. 2004, 25, 1157−1174. (35) Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins: Struct., Funct., Bioinf. 2006, 65, 712−725. (36) Case, D. A.; Cheatham, T. E.; Darden, T.; Gohlke, H.; Luo, R.; Merz, K. M.; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R. J. The Amber biomolecular simulation programs. J. Comput. Chem. 2005, 26, 1668−1688. (37) Gohlke, H.; Kiel, C.; Case, D. A. Insights into protein-protein binding by binding free energy calculation and free energy decomposition for the Ras-Raf and Ras-RalGDS complexes. J. Mol. Biol. 2003, 330, 891−913. (38) Hou, T.; Li, Y.; Wang, W. Prediction of peptides binding to the PKA RII α subunit using a hierarchical strategy. Bioinformatics 2011, 27, 1814−1821.

(39) Hou, T.; Wang, J.; Li, Y.; Wang, W. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J. Chem. Inf. Model. 2011, 51, 69−82. (40) Hou, T.; Wang, J.; Li, Y.; Wang, W. Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking. J. Comput. Chem. 2011, 32, 866−877. (41) Hou, T. J.; Li, N.; Li, Y.; Wang, W. Characterization of domainpeptide interaction interface: prediction of SH3 domain-mediated protein-protein interaction network in yeast by generic structure-based models. J. Proteome Res. 2012, 11, 2982−2995. (42) Xu, L.; Sun, H.; Li, Y.; Wang, J.; Hou, T. Assessing the performance of MM/PBSA and MM/GBSA methods. 3. the impact of force fields and ligand charge models. J. Phys. Chem. B 2013, 117, 8408−8421. (43) Copeland, R. A. Evaluation of Enzyme Inhibitors in Drug Discovery: A Guide for Medicinal Chemists and Pharmacologists; John Wiley & Sons: New York, 2013. (44) Balachandran, S.; Rodge, A.; Gadekar, P. K.; Yadav, V. N.; Kamath, D.; Chetrapal-Kunwar, A.; Bhatt, P.; Srinivasan, S.; Sharma, S.; Vishwakarma, R. A. Novel derivatives of ISO-1 as potent inhibitors of MIF biological function. Bioorg. Med. Chem. Lett. 2009, 19, 4773− 4776. (45) Willett, P. Similarity-based virtual screening using 2D fingerprints. Drug Discovery Today 2006, 11, 1046−1053. (46) Dabideen, D. R.; Cheng, K. F.; Aljabari, B.; Miller, E. J.; Pavlov, V. A.; Al-Abed, Y. Phenolic hydrazones are potent inhibitors of macrophage migration inhibitory factor proinflammatory activity and survival improving agents in sepsis. J. Med. Chem. 2007, 50, 1993− 1997. (47) Calandra, T.; Bernhagen, J.; Metz, C. N.; Spiegel, L. A.; Bacher, M.; Donnelly, T.; Cerami, A.; Bucala, R. MIF as a glucocorticoidinduced modulator of cytokine production. Nature 1995, 377, 68−71. (48) Bernhagen, J.; Krohn, R.; Lue, H.; Gregory, J. L.; Zernecke, A.; Koenen, R. R.; Dewor, M.; Georgiev, I.; Schober, A.; Leng, L. MIF is a noncognate ligand of CXC chemokine receptors in inflammatory and atherogenic cell recruitment. Nat. Med. 2007, 13, 587−596. (49) Al-Abed, Y.; Dabideen, D.; Aljabari, B.; Valster, A.; Messmer, D.; Ochani, M.; Tanovic, M.; Ochani, K.; Bacher, M.; Nicoletti, F. ISO-1 binding to the tautomerase active site of MIF inhibits its proinflammatory activity and increases survival in severe sepsis. J. Biol. Chem. 2005, 280, 36541−36544. (50) Mitchell, R. A.; Metz, C. N.; Peng, T.; Bucala, R. Sustained mitogen-activated protein kinase (MAPK) and cytoplasmic phospholipase A2 activation by macrophage migration inhibitory factor (MIF). Regulatory role in cell proliferation and glucocorticoid action. J. Biol. Chem. 1999, 274, 18100−18106. (51) Wallace, A. C.; Laskowski, R. A.; Thornton, J. M. LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng. 1995, 8, 127−134.

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