J. Med. Chem. 2009, 52, 4087–4090 4087 DOI: 10.1021/jm8015987
*To whom correspondence should be addressed. For N.F.: phone, þ39 02 503 18321; fax, þ39 02 503 18284; e-mail,
[email protected]. For A.C.: phone, þ39 02 503 14480; fax, þ39 02 503 14476; e-mail,
[email protected]. a Abbreviations: GEF, guanine nucleotide exchange factor; GAP, GTPase activating protein; GDI, GDP dissociation inhibitors; PAK, p21-activated kinase; MMP, matrix metalloproteinase; FCS, fetal calf serum; SMC, smooth muscle cell; DMEM, Dulbecco’s modified Eagle’s medium; DB, database; AD4, Autodock4; MD, molecular dynamics; MM-PBSA, molecular mechanics Poisson-Boltzmann surface area.
cells permeability,9 inflammatory response,10 generation of reactive oxygen species,11 and regulation of nitric oxide synthase.12 Thus, a pharmacological inhibition of Rac1 may represent an important pharmacological tool to better understand the role of this protein in atherogenesis. To obtain a specific inhibitory action on a biological function of a Rho protein, two potential strategies can be pursued: (1) the development of pharmacological antagonists of Rho effectors; (2) the identification of small molecules capable of interfering with the interaction between Rho protein and their GEFs. Recently, starting from the structure-function information on Rac1-GEF interaction and by using a computer based virtual screening of the National Cancer Institute database, Zheng et al. have identified compound NSC2376613 (compound 1, Figure 1) as a specific inhibitor of a subset of GEF binding to Rac1 and therefore Rac1 activation. In the present study, we have utilized a virtual screening strategy to discover new classes of Rac1 inhibitors. Starting from the ZINC database (DB), we generated a chemically diverse 3D conformational DB of druglike molecules as described in the Supporting Information (SI). The adopted procedure allowed generation of a chemically diverse subset of about 1million molecules with an acceptable loss of chemical information with respect to the starting DB (Figure S2, SI). A high throughput fragment based conformational search allowed the generation of multiple conformers for each molecule, and the obtained diverse 3D conformational DB was then used for the virtual screening. The crystal structure of Rac1 in complex with 1 has been recently published in a patent application.14 The complex is characterized by a cleft in the protein surface where the inhibitor lies (Figure S3A, SI). The binding character of the cleft is mainly influenced by four hydrophobic residues, namely, Val 36, Leu 67, Leu 70, and Pro 73, by the two mild polar residues Trp 56 and Tyr 64, and by Ser 71 which acts as a hydrogen bond (HB) acceptor for the NH at position 2 of the pyrimidine ring of 1. On such bases, a pharmacophore model aimed at a primary filtering of the diverse 3D conformational DB was generated starting from the bound structure of 1. Only those features explicitly involved in the interaction of 1 with the receptor cleft were included in the model, resulting in two hydrophobic features mimicking the aminic side chain and two HB donor projections, corresponding to the NH atoms interacting with Leu70 and Ser71, separated by an aromatic feature (Figure S3B, SI). The fulfillment of at least four features was requested during the pharmacophore filtration. All molecules fitting the pharmacophore with a rootmean-square deviation (rmsd) less than 1.0 were selected, for a total of 1024 compounds showing an rmsd with the pharmacophore of 0.1-1.0. Considering that the calculated log P(o/w) for 1 was 3.3, the resulting DB was further filtered by selecting compounds with a computed log P(o/w) ranging between 1.5 and 4 for a total of 643 molecules that were finally processed through docking experiments. The crystal structure of Rac1 in complex with 1 was used to derive a suitable computational model for the receptor to be used in molecular docking. To improve the screening accuracy, a consensus strategy was adopted by using two different docking software, MOE and Autodock4 (AD4), and a total of
r 2009 American Chemical Society
Published on Web 06/15/2009
Virtual Screening Approach for the Identification of New Rac1 Inhibitors Nicola Ferri,*,† Alberto Corsini,† Paolo Bottino,‡ Francesca Clerici,‡ and Alessandro Contini*,‡ †
Dipartimento di Scienze Farmacologiche, Universit a degli Studi di Milano, Via Balzaretti 9, 20133, Milano, Italy, and ‡Istituto di Chimica Organica “A. Marchesini”, Universit a degli Studi di Milano, Via Venezian 21, 20133, Milano, Italy Received December 18, 2008 Abstract: Rac1 protein is implicated in several events of atherosclerotic plaque development and represents a new potential pharmacological target for cardiovascular diseases. In this paper we describe a pharmacophore virtual screening followed by molecular docking calculations leading to the identification of five new Rac1 inhibitors. These compounds were shown to be more effective than the reference compound NSC23766 in reducing the intracellular levels of Rac1-GTP, thus supporting this approach for the development of new Rac1 inhibitors.
Rho GTPases of the Ras superfamily are involved in the regulation of multiple cell functions and have been implicated in the pathogenesis of cardiovascular diseases and cancer.1 The Rho family comprises 22 genes encoding at least 25 proteins in humans, including the Rho, Rac, and Cdc42, proteins playing a fundamental role in cell biology.2 All Rho family members bind GTP, and most exhibit GTPase activity and cycle between an inactive GDP-bound form and an active GTP-bound form. This cycling is finely regulated by three groups of proteins: the guanine nucleotide exchange factors (GEFsa) as activators; the GTPase activating proteins (GAPs) and GDP dissociation inhibitors (GDIs) as negative regulators. When bound to GTP, Rho GTPases interact with their downstream effectors, which include protein kinases, regulators of actin polymerization, and other proteins with adaptor functions. The selective interaction of the different Rho GTPases with a variety of effectors determines the final outcome of their activation. For example, the interaction of Rac1 with p21-activated kinase (PAK) family of proteins regulates lamellipodia formation and membrane ruffling.3-5 We have recently demonstrated that the activation of Rac1, but not RhoA, in human aortic smooth muscle cells (SMCs) through the engagement of R2β1 integrin by type I collagen induces the expression of matrix metalloproteinase 1 (MMP1) and MMP2, an event that may contribute to atherosclerotic plaque rupture.6 Emerging evidence also points out the pivotal role of Rac1 in many other aspects of atherosclerotic plaque development, such as cell proliferation,7,8 endothelial
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Ferri et al. Table 1. Rac1 Inhibitory Activity of the Most Promising Compounds Identified by Virtual Screening Determined by G-LISA Assay
Figure 1
three scoring functions (London dG and affinity, implemented in MOE, and the default AD4 scoring function). Results obtained by MOE and AD4 virtual screenings were compared, and only those compounds for which all the computed docking energies were better than those obtained for 1 (affinity < -4.2, London dG < -7.5, AD4 binding energy 45% inhibitory effect. In particular, 5 showed the strongest inhibitory action with 65.6% of reduction in the amount of Rac1-GTP compared to control cells (Table S1, SI). For these selected compounds, we performed a concentration-dependent experiment to determine the IC50 on Rac1 activity. Human SMCs were incubated with increasing concentrations of compounds from 5 to 100 μM for 4 h. Then Rac1 activity was stimulated by the addition of PDGF-BB (final concentration of 10 ng/mL) for 2 min and the intracellular levels of Rac1-GTP were determined by G-LISA assay. All the compounds reduced the intracellular levels of Rac1GTP in a concentration-dependent manner (Table 1 and Figure S5, SI). From the calculated IC50, 4 showed the most potent inhibitory action with an IC50 of 12.2 μM, followed by 5 with 24.1 μM. To confirm the inhibitory activity of these five identified compounds, we performed the classical pull-down assay for Rac1 GTP under the same experimental conditions utilized for the G-LISA assay. All the compounds, at 25 μM, significantly reduced the ratio Rac1-GTP/Rac1 in cultured SMCs, with 4 having the most potent inhibitory action (-86.0 ( 4.5%; see Figure S6, SI). The selectivity of action of these compounds was also tested by performing pull-down assays for Cdc42 and RhoA proteins, under the same experimental conditions utilized for Rac1 pull-down experiments. Compounds 2, 3, and 5 showed some inhibitory activity toward Cdc42, although none of the
Figure 2. Inhibitory action of 4 and 5 on Tiam1 and Rac1 interaction (see SI for experimental details).
compounds reduced the amount of Cdc42-GTP protein by more than 25% and none of these were statistically significant (see Figure S6, SI). In contrast, none of the tested compounds affect the activation of RhoA in human SMCs (see Figure S6, SI). To further investigate the basic molecular mechanism of Rac1 inhibition, we tested the capabilities of 4 and 5 to directly interfere with the GEF-mediated Rac1 activation. To directly monitor the effect of 4 and 5 on this molecular event, we performed a binding assay by using Rac1 G15A agarose beads and total cell lysates from human SMCs after stimulation with PDGF-BB as a source of active Tiam1. The stimulation with PDGF-BB significantly increased the amount of Tiam1 pulled down by the active form of Rac1, and the incubation with 4 and 5 (50 μM) interfered with the Rac1-Tiam1 interaction, reducing the amount of Tiam1 recovered after the incubation with Rac1 agarose beads (Figure 2). In addition we tested the ability of these selected compounds to interfere with the binding of Rac1 to its effector Pak1. Both compounds, together with the leading 1, did not significantly interfere with Rac1-Pak1 interaction (see Figure S7, SI). In agreement with the previous study we did not observe any effect of 1 to Rac1Pak1 binding. To study a biological, relevant effect of Rac1 of the newly identified compounds, we evaluated their effect on cytoskeleton organization in 3T3 cells after stimulation with PDGF-BB. As shown in Figures 3 and S8, serum-starved 3T3 cells developed extensive lamellipodia and membrane ruffle formation together with extensive cytoskeleton rearrangement in response to PDGF-BB. In the presence of 25 μM 4 and 5, PDGF was only marginally effective in inducing lamellipodia at the cell edges. These results further indicate
Letter
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Figure 3. Effect of 4 and 5 on lamellipodia and membrane ruffling formation in 3T3 cells (see SI for experimental details). Table 2. Clustering Information, AD4 Binding Energy (kcal/mol), and MM-PBSA Absolute and Relative Free Energy of Binding (kcal/mol) compd
Nconf a
ΔGAD4 b
ΔGMMPBSA c
ΔΔGMMPBSA d
2 3 4 5 6
2 (34) 5 (6) 4 (8) 2 (29) 2 (72)
-6.7 (-6.1) -8.2 (-7.9) -8.7 (-8.2) -8.2 (-8.2) -7.4 (-7.4)
-6.9 -3.1 -6.8 -5.2 -7.3
-4.9 -3.5 -3.7 -1.4 -2.4
a
Number of clusters with six or more individuals, corresponding to the number of conformations considered for MM-PBSA calculations. The number of individuals of the best ranked cluster is reported in parentheses. b AD4 free energy of binding for the best ranked cluster and, in parentheses, the free energy of binding for the most populated cluster. c MM-PBSA free energy of binding for the most favored conformation. d MM-PBSA free energy difference between the two lowest energy conformations.
that 4 and 5 are effective in inhibiting Rac-mediated cellular events. The binding modes for the identified hits were analyzed through further docking experiments with the AD4 package. A cluster analysis of the docked conformation was conducted, and clusters with a population greater than five individuals were further considered. As all hits showed multiple binding modes (two for 2, 5, and 6, four for 4, and five for 3), the lowest energy docked geometry of each cluster was subjected to a MD simulation of 2 ns in explicit water and trajectories were postprocessed through the MM-PBSA approach, which proved to be reliable for discriminating different binding conformations.15 The MM-PBSA procedure allows a statistical evaluation of the free energies of binding through the combination of molecular mechanical energies (internal energy, van der Waals, and electrostatic interactions) with the electrostatic contribution to the solvation free energy computed through the numerical solution of the PoissonBoltzmann equation. Table 2 reports the absolute free energies of binding (ΔG) computed for the most favored geometry and the relative energy (ΔΔG) between the two lowest energy conformations computed with the MM-PBSA approach. Clustering information provides an empirical measure of the ligand configurational entropy.16,17 AD4 binding energies for the lowest energy cluster with a population greater than five individuals are also reported. Bearing in mind that ranking similarly potent compounds by standard computational methods is not trivial, energy results fit fairly well with the experimental Rac1 inhibition, also considering that the average error for MM-PBSA calculation
Figure 4. Predicted binding modes for 4 (A) and 5 (B). True hydrogen bonds are represented as magenta lines, while hydrogen interactions are depicted as green lines.
of the absolute free energy of binding has been reported to fall between 1.5 and 2.5 kcal/mol.15 However, an equal or higher accuracy can be expected in discriminating different conformations of the same molecule, and for this reason a prediction of the preferred binding mode can be safely made for 2-4, where the energy difference between the two lowest energy conformations is between 3.5 and 4.9 kcal/mol. The ΔΔG computed for 5 is low (1.4 kcal/mol), suggesting that two binding conformations are possible. However, the inspection of the MD trajectories showed that the two starting geometries converged to similar binding modes (an rmsd of 0.8 A˚ was calculated between the two docked ligand geometries), differing only for the orientation of the benzopyrazole moiety (see Figure S4, SI). It is worth noting that in AD4 calculations for 5 and 6 the best ranked cluster of conformations coincided with the most populated cluster (for 6 the exceptional result of 72 conformations were obtained for the best ranked, while only six individuals populated the second ranked cluster). MM-PBSA calculated energies for the analyzed binding conformations were in net concordance with the energy ranking of the clusters of conformations obtained from the docking of hits 2, 5, and 6, while for hits 3 and 4 the second ranked cluster of conformations was predicted by the MM-PBSA procedure as the preferred binding mode, confirming AD4 as a fast and valuable tool for binding mode predictions. It should also be noted that the AD4 binding energies, which are based on an empirical scoring function, are in a rather good concordance with the IC50 experimentally obtained for 2-6. The proposed binding modes for hits 4 and 5 are depicted in Figure 4, while the binding modes for hits 2, 3, and 6 are reported in the Supporting Information. Both 4 and 5 are sulfonamide derivatives, but even though they are chemically related, different binding modes can be expected because of their large structural differences at the sulfonamidic and carboxamide moieties. Moreover, a primary inspection of the binding
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modes showed that the sulfonamidic moiety cannot be considered as a common pharmacophoric feature. Indeed, the primary sulfonamidic NH of 4 is involved in a hydrogen bond (HB) with the backbone carbonyl of Phe 37, with a donoracceptor (DA) distance of 2.5 A˚ and a donor-hydrogenacceptor (DHA) angle of 168.8°. This HB is strengthened by a second interaction observed between the Asp 57 NH and the sulfone oxygen (DA distance, 2.9 A˚; DHA angle, 174.4°). The extended aromatic portion consisting of the dimethyloxazole and the phenyl ring, bridged by the methylenoxy moiety, is well sunken in the hydrophobic cleft formed by Val 36, Ala 59, Tyr 64, Leu 67, and Leu 70, while the amidic portion remains solvent-exposed. On the other hand, the benzopyrazole ring of 5 is able to establish with the receptor two HBs, as a HB donor with the backbone carbonyl of Asp 57 (DA distance, 2.8 A˚ ; DHA angle, 156.7°) and as a HB acceptor with the Ser 71 OH group (DA distance, 2.7 A˚; DHA angle, 166.3°). A weak hydrogen interaction can also be observed between 5’s amidic carbonyl oxygen and the Lys 5 amino group (DA distance, 3.5 A˚; DHA angle, 123.5°). The arylsulfonylpiperidine moiety lies in the above-mentioned hydrophobic cleft, and no specific interactions are observed for the sulfone group, while the o-methoxy substituent is placed in a solvent-exposed position. In conclusion, we have processed the ZINC DB to obtain a reduced DB more convenient for virtual screening procedures. Pharmacophore screening, followed by molecular docking calculations performed on a consensus basis between two different software, led to the identification of five new diverse and potent Rac1 inhibitors. These molecules have been shown to be more effective in inhibiting Rac1 activation compared to the previously described inhibitor 1 in in vitro culture model. Moreover, from the data reported in the present report we can conclude that 4 and 5, similar to 1, seem to be more selective in the inhibitory action on Rac1 in comparison to Cdc42 and RhoA. This specificity was shown by the results of pull-down assays and also confirmed by the fact that 4, and by some extent 5, inhibits lamellipodia and membrane ruffle formation in 3T3 cells stimulated with PDGF-BB, a cellular response mediated by Rac1, but has no effect on stress fiber formation, an event regulated by RhoA.1 The ability of 4 and 5 to inhibit the Tiam1-Rac1 interaction in vitro also demonstrates that both compounds act with a similar mechanism of action as 1, such as a selective interference of Rac1-GEF interaction. Acknowledgment. This work was supported by the EU Grant 018671, Drug Design for Cardiovascular Diseases: Integration of in Silico and in Vitro Analysis (CARDIOWORKBENCH). The authors thank the “Consorzio Interuniversitario Lombardo per l’Elaborazione Automatica” (CILEA) for computational facilities and Giuseppe Celentano for HPLC analyses.
Ferri et al.
Supporting Information Available: Additional figures and tables, detailed molecular modeling methods, and experimental procedures for enzymatic inhibition assays. This material is available free of charge via the Internet at http://pubs.acs.org.
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