An Effective Virtual Screening Protocol To Identify Promising p53

Article pubs.acs.org/jcim

An Effective Virtual Screening Protocol To Identify Promising p53− MDM2 Inhibitors Paolo Tortorella,† Antonio Laghezza,† Milena Durante,‡ Isabel Gomez-Monterrey,§ Alessia Bertamino,∥ Pietro Campiglia,∥ Fulvio Loiodice,† Simona Daniele,⊥ Claudia Martini,⊥ and Mariangela Agamennone*,‡ †

Dipartimento di Farmacia-Scienze del Farmaco, Università “A. Moro” Bari, Via Orabona 4, 70125 Bari, Italy Dipartimento di Farmacia, Università “G. d’Annunzio” Chieti, Via dei Vestini 31, 66100 Chieti, Italy § Dipartimento di Farmacia, Università “Federico II” Napoli, Via D. Montesano 49, 80131 Napoli, Italy ∥ Dipartimento di Farmacia, Università di Salerno, Via G. Paolo II 132, 84084 Fisciano, Italy ⊥ Dipartimento di Farmacia, Università di Pisa, Via Bonanno 6, 56100 Pisa, Italy ‡

S Supporting Information *

ABSTRACT: The p53−MDM2 interaction is a well-known protein−protein contact, and its disruption is a key event for p53 activation and induction of its oncosuppressor response. The design of small molecules that can block the p53−MDM2 interaction and reactivate the p53 function is a promising strategy for cancer therapy. To date, several compounds have been identified as p53−MDM2 inhibitors, and X-ray structures of MDM2 complexed with several ligands are available in the Brookhaven Protein Data Bank. These data have been exploited to compile a hierarchical virtual screening protocol. The first steps were aimed at selecting a focused library, which was submitted in parallel to docking and pharmacophore model alignment. Selected compounds were subjected to inhibition assays of both cellular vitality (MTT) and p53−MDM2 interaction (ELISA and co-immunoprecipitation), disclosing four nanomolar inhibitors.

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INTRODUCTION Protein−protein interactions (PPIs) are ubiquitous in biological systems and play pivotal roles in almost all biological processes; it has been estimated that there are approximately 650 000 contacts into cells.1 Aberrant, inappropriate, or poorly regulated PPIs are involved in different pathological conditions,2 and therefore, the ability to restore their physiological roles through modulation of specific PPIs could provide interesting opportunities for pharmaceutical intervention. A long debate is still ongoing on the druggability of protein− protein contacts3,4 because usually the contact surfaces are flat, large, and solvent-exposed. However, it has been demonstrated that PPIs can be considered druggable if it is possible to identify a “hot spot”.5 As a confirmation of PPI druggability, there are several examples of successful research projects aimed at identifying PPI inhibitors (P2I2) with promising biological activity, and several compounds are now in clinical trials for six targets.6 Among the druggable PPIs, certainly the p53−MDM2 interaction represents a well-known and studied example.7 P53, also called “the guardian of the genome”,8 responds to stress signals by triggering cell-cycle arrest and cell death by apoptosis, inhibiting tumor development. Inactivation of p53 by mutation occurs in about half of all human tumors, while those that retain wild-type p53 often acquire an alternative mechanism for its inactivation, largely through the intervention of MDM2 protein.9 © XXXX American Chemical Society

This protein controls the p53 level through a direct binding interaction that neutralizes p53 transactivation activity, exports nuclear p53, and targets it for degradation via the ubiquitylation− proteasomal pathway.10 Negative regulation of p53 by MDM2 in cancer cells impairs the stability and activities of p53, and therefore, blocking the p53−MDM2 interaction offers an opportunity for cancer therapy.11 After the first identification of nutlin by high-throughput screening,12−14 the tractability of the MDM2−p53 interaction as a drug target has been demonstrated, and a number of classes of potent small-molecule inhibitors have been developed.7,12 The binding mode of different small molecules targeting MDM2 has been disclosed by structural data: all of the known ligands mimic the three key residues of p53, i.e., Trp23, Leu26, and Phe19, which occupy the hydrophobic cleft of MDM2 in a “thumb−index−middle finger” conformation (Figure 1). All of the ligands share a highly conserved binding mode with a strict alignment of the hydrophobic groups occupying the three hot spots. The availability of an ever-growing number of X-ray structures15 and inhibitor classes allow the application of several computational approaches for the identification and optimization of p53−MDM2 inhibitors. Wang and co-workers identified Received: December 22, 2015

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DOI: 10.1021/acs.jcim.5b00747 J. Chem. Inf. Model. XXXX, XXX, XXX−XXX

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Journal of Chemical Information and Modeling

initial steps of the VS protocol were inspired by the observation that P2I2 usually occupy a different region of chemical space with respect to more traditional enzyme inhibitors and receptor agonists/antagonists.25,26 The virtual libraries therefore contain a high number of unsuitable compounds as P2I2. For this reason, to limit the waste of CPU time spent screening inappropriate compounds, a focused virtual library more consistent with the P2I2 property profile was selected by a two-step filtering procedure. These processes greatly reduced the number of virtual hits that were submitted to parallel docking and pharmacophore model screening. Final visual analysis of the highest-rated compounds afforded the final selection of 33 compounds tested in the MTT assay. The competition of the most active compounds with p53 was also assessed via enzymelinked immunosorbent assay (ELISA) and co-immunoprecipitation experiment. The obtained results confirm the efficiency of this VS protocol, which can be generally applied to the identification of other P2I2.

Figure 1. MDM2 (solid transparent surface) in complex with (A) the p53 α-helix (green cartoon), (B) the key p53 residues (green sticks) and (C) nutlin (sticks with magenta C atoms).

a series of spiro-oxindole small molecules as MDM2−p53 interaction inhibitors using a structure-based de novo design strategy.16,17 Recently some papers have been published reporting the application of virtual screening (VS) workflows for the discovery of new scaffolds.18−24 The present study aimed to identify new compounds that can block the interaction of the N-terminal domain of p53 with MDM2. To achieve this goal, we implemented an effective VS protocol exploiting the large amount of available data about known inhibitors and X-ray structures of their complexes. The

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METHODS Selection and Preparation of the Known Ligand Data Set. Structures of inhibitors from the literature and patents were manually built using the Build facility in Maestro version 9.3.27

Table 1. MDM2 X-ray Structures Cocrystallized with Ligands 1−9

PDB code

ligand

resolution (Å)

ref

1RV1 1T4E 3JZK 3LBK 3LBL 3TU1 4DIJ 4ERE 4ERF

1 2 6 4 3 7 5 8 9

2.3 2.6 2.1 2.3 1.6 1.6 1.9 1.8 2.0

14 30 31 32 32 33 34 35 35

B


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carried out after the generation of a maximum of 100 conformations for each compound using ConfGen (default parameters) and evaluation of the alignment score with atom type and maximum similarity normalization. Ligand 6 was selected as the template for the following shape-based screening of the virtual library. Virtual hits filtered in the previous step were passed through this protocol after generation of the 3D structure, tautomers, protomers at pH 7.4, and stereoisomers using LigPrep.27 The conformational model for each compound was calculated with ConfGen as previously explained. Compounds with shape scores of >0.5 were submitted to the following steps. Pharmacophore Hypothesis. The pharmacophore model was generated using the Develop Pharmacophore Model tool in Phase.27 The model was built using both the KLD and X-ray ligands. The conformational model was generated using MacroModel as described before; X-ray structures of the complexed ligands were maintained in their experimental geometries. Inhibitors with pIC50 ≥ 7 were used to build the model. A common pharmacophore hypothesis matching at least 10 out of 20 active compounds was sought, selecting a seven-site hypothesis with variants comprising Acceptor (A), Donor (D), Hydrophobic (H), and Ring aromatic (R) features. The default search parameters were applied. The selected hypothesis was manually modified by adding a H-bond donor feature (D) matching the NH of the indole ring in the Trp23 pocket. Receptor-based excluded volumes were placed on the backbone atoms of residues at a cutoff distance of 5 Å from the ligand and added to the eight-site hypothesis. The Generate Phase Database module of Phase was used to prepare the database of the virtual library. One hundred conformers for each molecule were generated with ConfGen using the automatic setup protocol. The screening process was managed using the Find Matches to Hypotheses tool, saving molecules that matched at least four out of eight sites and imposing the matching of the aromatic feature in the Trp23 site and one among the other two sites; the excluded volumes were also used. Docking. A preliminary cross-docking study was carried out to identify the most effective MDM2 3D structure in the prediction of the experimental binding pose. The calculation was performed on the previously described complexes exploiting the Virtual Screening Workflow available in Maestro. The Glide Grid Generation protocol was applied to each structure. Both standard precision (SP) and extra precision (XP) methods were used when applying the default parameters. The docking region was defined by the X-ray coordinates of the ligand in the 3JZK structure. The center of mass of the ligand was considered as the center of the cubic box (20 × 20 × 20) where docked poses were retrieved. Ten poses were saved for each ligand. The best-performing MDM2 3D structure (3JZK) was used for the following structure-based virtual screening. The virtual library was submitted to the Virtual Screening Workflow, which allows filtering of compounds in each step: 10% of all states were kept from the HTVS step and passed to the SP; 50% of all good score compounds were submitted to XP calculations using the OPLS2005 force field. The final selection was carried out by visual inspection of the highest-ranking compounds. Biological Assays. MTT Assay for Cell Viability. All of the cell lines (human breast adenocarcinoma cell line MCF-7, human nonsmall cell lung carcinoma cell line H1299, and human colon carcinoma cell lines HCT116 and HCT116 p53−/−) were grown in Dulbecco’s Modified Eagle’s Medium supplemented

The diversity-based selection was carried out in Canvas version 1.5;27 hashed radial binary fingerprints were calculated for all of the ligands, and a diversity-based selection was carried out, taking into account the activity range (10 compounds for each order of magnitude were selected). Default settings were applied in cases where parameters are not specified. The protonation state at pH 7.4 was generated for all structures using the cxcalc tool of Chemaxon.28 The obtained structures were minimized to a derivative convergence of 0.05 kJ Å−1 mol−1 using the Polak−Ribiere conjugate gradient (PRCG) minimization algorithm, the OPLS2005 force field, and the generalized Born/surface area (GB/SA) water solvation model implemented in MacroModel version 9.9.27 Conformational searches were carried out on all of the minimized ligand structures, applying the mixed-torsional/low-mode sampling, the OPLS2005 force field, the GB/SA water solvation model, and the automatic setup protocol in order to obtain the global minimum geometry of each molecule, which was then used in all of the following studies. Nine ligands whose X-ray structures in complex with MDM2 were retrieved from the Protein Data Bank (PDB) (see below) were added to the data set in their experimental conformations after the structures were properly fixed by correction of bond orders and addition of hydrogen atoms. The structures and activity data of the known ligand data set (KLD) are reported in Table S1 in the Supporting Information. Selection and Preparation of the 3D Structure of MDM2. X-ray structures of MDM2 complexes with small molecules were downloaded from the PDB.29 Nine complexes with resolution of ≤2.6 Å were retrieved (Table 1). Each MDM2 structure was prepared using the Protein Preparation Wizard in Maestro,27 which allows elimination of ligands and water molecules, fixing of bond orders, addition of hydrogen atoms, computation of residue protonation states, optimization of the H-bonding network, and relaxation of the structure with a constrained minimization. The so-prepared structures were submitted to subsequent cross-docking studies. Virtual LibraryPurchased Compounds. A total of 2 500 000 2D compounds were collected from the vendors Asinex, Enamine, and IBS. The purchased molecules had a declared purity >92% as assessed by NMR and/or HPLC/MS methods (see the Supporting Information). A second batch of the active compounds resulting from the MTT screening was purchased to confirm the activity and structure identity. Virtual Screening Protocol. Property-Based Filter. The property-based filter was carried out by applying a recursive partitioning procedure set up in Canvas.27 To this end, LigFilter and Physicochemical properties of the 113 compounds composing the KLD were calculated. The collection was divided among active (pIC50 > 5) and inactive (pIC50 < 5) compounds. The ensemble model procedure was applied by random selection of the training set (70% of the whole data set). The 2D structures of the virtual library were projected in the obtained model after the calculation of LigFilter and Physicochemical properties in Canvas. Only compounds classified in the “active” group were submitted to the following steps. Shape-Based Filter. The 3D shape filtering was carried out using the Shape screening tool available in Maestro.27 The crystallographic coordinates of a ligand were used as the template: each of the nine ligands in its crystallographic conformation was used in turn as the template to align the remaining eight X-ray ligands and the KLD. The alignment was C


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Figure 2. Workflow of the VS protocol.

with 10% fetal bovine serum, 10 units mL−1 penicillin, 100 mg mL−1 streptomycin, and 2 mM L-glutamine in a 5% CO2 atmosphere at 37 °C. Cells were seeded at a density of 1−5 × 104 cells/well into 96-well flat bottom culture plates containing 50 μL of the test compounds (from 100 μM to 100 nM final concentration) in a final volume of 100 μL. Test compounds were dissolved in dimethyl sulfoxide (DMSO) (1% final concentration; DMSO carrier had no effect on cell proliferation). Control wells lacked inhibitor. After 48 h of incubation at 37 °C in a 5% CO2 atmosphere, 3-(4,5-dimethylthiazol-2-yl)-2,5diphenyltetrazolium bromide (MTT) (5 mg mL−1 stock solution) was added to a final concentration of 0.5 mg mL−1. Background absorbance was measured in six wells of cells, which were lysed with Triton X-100 (0.1% v/v final concentration) immediately prior to the addition of MTT reagent. After incubation under the same conditions for a further 3−4 h, the culture medium was removed, and the insoluble product was dissolved by the addition of 100 μL of solvent (1:1 v/v DMSO/ EtOH). The absorbance of each well was measured at 570 nm using a PerkinElmer Victor V3 plate reader. Cell growth inhibition was then calculated using eq 1: V% =

A − Ab × 100% Ac − A b

where V% is the percentage of cell viability, A is the absorbance of the treated culture, Ab is the absorbance of the background control, and Ac is the absorbance of the control culture. IC50 values were determined from dose−response curves using GraphPad PRISM version 5.0. Studies of Dissociation of the Native MDM2−p53 Complex. The abilities of new compounds to dissociate the native MDM2−p53 complex were tested using a quantitative sandwich enzyme immunoassay technique on crude cell lysates from glioblastoma (U343 MG) cells.36 Cells were washed twice in icecold phosphate-buffered saline, collected by centrifugation, and resuspended in lysis buffer (20 mM Tris HCl, 137 mM NaCl, 10% glycerol, 1% NONIDET40, 2 mM EDTA, pH 8) containing 1% protease inhibitor cocktail (Sigma-Aldrich, Milan, Italy). The optimal composition of the lysis buffer and reaction conditions were determined in preliminary experiments.36,37 The wells of a 96-well plate were precoated with a mouse full-length antiMDM2 antibody (sc-965, Santa Cruz Biotechnology, 1:50 in 0.05% poly-L-ornithine) overnight at room temperature. Cell lysates (20 μg in a final volume of 100 μL) were preincubated with DMSO (control) or different compound concentrations for 10 min at room temperature and then transferred to the precoated wells for 60 min. The assay is termed “cell-free” because the putative MDM2 inhibitor is added directly to the MDM2−p53 complex. After three quick washes with PBS/ Tween 0.05% to remove unbound MDM2, each well was

(1) D


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structure. In fact, MDM2 is subjected to an important plasticity that allows it to adapt to different binders.39 Virtual Database. The starting virtual library (almost 2 500 000 compounds) was obtained by collecting compounds from the Asinex, Enamine, and IBS vendor databases as 2D structures. At this stage, no filtering based on Lipinski’s rule of five (Ro5) was carried out.40 This choice was due to the preference for a different filtering procedure. Many drugs, in fact, have at least one violation of the Ro5, and in the present case, P2I2 can possibly present more than one violation because of their physicochemical profiles. However, the drug-likeness of the selected compounds was evaluated at the end of the protocol. Property-Based Filter. The first steps of this protocol were set up to account for the different chemical space covered by P2I2 with respect to more traditional enzyme inhibitors or receptor ligands. In order to reduce the number of compounds to be screened, we decided to eliminate molecules without adequate properties by filtering the starting virtual library through the application of two sequential filters compliant with the main properties characterizing P2I2: the physicochemical parameters and the 3D shape. A recursive partitioning (RP) protocol was implemented in Canvas27 to filter compounds with similar property profiles with respect to known inhibitors. RP is a method that builds one or more decision trees, dividing compounds on the basis of property values that best discriminate between actives and inactives. In this case, the result is a very simple and effective procedure used to rapidly classify compounds on the basis of their properties. The KLD was exploited to train the model subsequently applied for the screening. The model was generated after the calculation of the LigFilter and Physicochemical descriptors in Canvas.27 The 113 compounds were classified into two sets on the basis of their pIC50 values: compounds with pIC50 values less than 5 were classified as inactive (35 compounds), while those with pIC50 values higher than the cutoff value were collected in the active group (78 compounds). The whole set of compounds was randomly divided into a training set and a test set to assess the validity of the generated protocol. In the generation of the decision trees, the ensemble model procedure provided the best results:

incubated for 15 min with 1% bovine serum albumin to block nonspecific sites and then for 1.5 h at room temperature with a rabbit primary anti-p53 antibody (sc-6243, Santa Cruz Biotechnology, 1:250 in 5% milk). Then the wells were washed and incubated for 1 h with an anti-rabbit horseradish peroxidase conjugate antibody (1:3000 in 5% milk) and washed again. The TMB substrate kit (Thermo Fisher Scientific) allowed colorimetric quantification of the MDM2−p53 complex. Blanks were obtained by processing cell lysates in the absence of the primary anti-p53 antibody. Absorbance values at 450 nm were measured, and the background was subtracted. Sigmoid dose− response curves were generated using Graph Pad Prism 4 software, from which IC50 values of the MDM2−p53 complex were derived. MDM2−p53 Co-immunoprecipitation. The amount of MDM2−p53 complex in cells was confirmed using coimmunoprecipitation experiments.38 U87MG cells were treated with DMSO (control) or compound 38 (1 μM) for 6 h, and 1 mg of cell lysates was precleared with protein A-Sepharose (1 h at 4 °C) to precipitate and eliminate immunoglobulin G. Samples were then centrifuged for 10 min at 4 °C (14000g). The supernatants were incubated with an anti-MDM2 antibody (5 μg/sample) overnight at 4 °C under constant rotation and then immunoprecipitated with protein A-Sepharose (2 h at 4 °C). After washing, the immunocomplexes were resuspended in Laemmli solution, boiled for 10 min, resolved by SDS-PAGE (7.5%), transferred to PVDF membranes, and probed overnight at 4 °C with anti-p53 (1:300, FL-393, Santa Cruz Biotechnology) or anti-MDM2 (diluted 1:500, C-18, Santa Cruz Biotechnology). The peroxidase was detected using a chemiluminescent substrate (ECL, PerkinElmer). Densitometric analysis (OD) of immunoreactive bands was performed using ImageJ software.

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RESULTS In the present work, an attentively designed VS workflow (Figure 2) has been implemented to find new and promising scaffolds that can block the p53−MDM2 interaction. This represents one of the most studied PPIs, and the large amount of available data (both published ligands and X-ray structures) allowed the application of multiple approaches. KLD. The known ligand data set (KLD), which was collected from literature and patent reports, was assembled in order to have a reliable and representative set of active ligands with the widest structural diversity and covering a large activity range. Only compounds whose IC50 values (here reported as pIC50) were determined by the same procedure (i.e., fluorescence polarization assay) were considered in this study in order to have comparable activity data. Moreover, only compounds with defined chirality were selected in order to have a univocal match between structure and activity. The diversity-based selection with Canvas was carried out both to limit the data set dimensions without losing information and to reduce the risk of building a model biased by the prevalence of one or a few scaffolds in the data set. The final library was constituted by 113 compounds with pIC50 values in the range 2.6−9.2 and sufficient structural heterogeneity. The ligands with available crystallographic data were also added to the library to exploit knowledge-driven structure-based information. MDM2 Structures. For the structure-based approach, nine complexes with small molecules and good resolution (≤2.6 Å) were retrieved from the PDB. We focused on complexes with small molecules, neglecting those with peptides because of the strong adaptability to the ligand observed in the MDM2

• training set: (1) Se = TP/(TP + FN) = 47/47 + 6 = 88% (2) Sp = TN/(TN + FP) = 24/24 + 3 = 88% • test set: (1) Se = TP/(TP + FN) = 22/22 + 1 = 85% (2) Sp = TN/(TN + FP) = 8/8 + 2 = 80% where Se is the selectivity, Sp is the specificity, TP is the number of true positives (correctly classified inhibitors), FP is the number of false positives (noninhibitors wrongly classified as inhibitors), TN is the number of true negatives (correctly classified noninhibitors), and FN is the number of false negatives (inhibitors wrongly classified as noninhibitors). Analysis of the decision trees built by the model allowed us to identify properties that better discriminate between actives and inactives. The most relevant properties were found to be molecular weight (MW), AlogP, chirality, and topological polar surface area (TPSA), in agreement with what was observed by Sperandio and co-workers.25,26 The good sensitivity and specificity values for the test set and its fast applicability prompted us to use this filter for our 2D virtual library. The starting library of almost 2 500 000 compounds was submitted to the property-based filter and E


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Journal of Chemical Information and Modeling greatly reduced (87% of the starting compounds were removed), limiting the number of virtual candidates submitted to the following steps. Shape-Based Filter. As already stated, another peculiar aspect of P2I2 is their defined 3D geometry. In the case of the p53−MDM2 interaction in particular, known ligands mimic the p53 α-helix, locating three hydrophobic residue side chains (Phe19, Trp23, and Leu26) in correspondence to key hot spots with a definite “thumb−index−middle finger” arrangement. Moreover, analysis of small molecules in complex with MDM2 revealed an almost perfect alignment of the ligand aromatic rings in the receptor cleft and, in particular, in the Trp23 pocket (see Figure 3). This evidence suggested the opportunity to further filter the virtual library by selecting compounds that can properly occupy the MDM2 hydrophobic cleft.

Figure 4. Shape of ligand 6 (3JZK complex) shown as a blue transparent surface.

because of unsuitable fitting in the target binding site. Another drawback is represented by the bias due to the similarity between high-score virtual hits and the model ligands. In this case, to reduce the risk of false positives and obtain diverse hits, we exploited the KLD and the X-ray data of ligands, these latter in their experimental binding conformation, to provide a structure-driven (or knowledge-driven) pharmacophore hypothesis. As Phase produces only a seven-site hypothesis, an additional site was manually added, corresponding to a usually retrieved feature, the H-bond donor in the Trp23 pocket. The customized pharmacophore model accounts for all of the main interactions between known inhibitors and MDM2: ADDHHHRR (Figure 5). The obtained hypothesis was qualitatively evaluated though its ability to correctly align ligands 1−9 and to assign higher scores to more active compounds. To have a more selective and restrictive ligand-based approach, receptor-based excluded volumes were added on the basis of the Cα positions of the residues close to the ligand (5 Å).

Figure 3. X-ray structures of ligands superimposed on the MDM2 binding cleft.

The shape-based filter was modeled on the 3D coordinates of the X-ray structure of one ligand. This latter was selected from among inhibitors 1−9 on the basis of its ability to properly align the other X-ray structures of inhibitors and the compounds of the KLD. The best-performing structure was found to be chromenotriazolopyrimidine 6 of the 3JZK X-ray complex, which allowed an optimal alignment of all of the ligands. Ligand 6 worked fine as a template, as its structure is quite simple (Figure 4), missing solubilizing side chains that do not find a definite location in the binding site. In fact, the X-ray structures of other ligands have a hydrophilic portion that fluctuates toward the solvent without fitting the MDM2 cleft; therefore, attempts also to superimpose this part of the ligand can produce unfavorable alignment. For the VS campaign, the virtual library was submitted to the generation of 3D structures and protomers, tautomers, and stereoisomers at neutral pH. For each obtained structure, a conformational model was generated with ConfGen. After alignment, compounds obtaining shape scores higher than 0.5 were selected. The cutoff value of 0.5 was chosen as the lower shape score obtained by active ligands in the KLD aligned to the template. Pharmacophore Hypothesis Generation. Pharmacophore hypothesis alignment is a well-known ligand-based approach that has been frequently applied in VS campaigns because of its speed of application. The main advantage of ligandbased virtual screening (LBVS), in fact, is the possibility to rapidly screen a large number of compounds. However, this approach can produce a lot of false positives, i.e., compounds that match the pharmacophore hypothesis but do not show activity,

Figure 5. Pharmacophore hypothesis ADDHHHRR used for the VS, shown aligned to its reference ligand. The features are reported as follows: Acceptor, red; Donor, cyan, Ring aromatic, amber; Hydrophobic, green. Excluded volumes are not shown for the sake of clarity. F


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Table 2. RMSD Values Obtained by Superimposing the Experimental and Docked Poses of X-ray Structures of Ligands 1−9 in Each Receptor Structure ligand

1RV1

1T4E

3LBL

3LBK

4DIJ

3JZK

3TU1

4ERE

4ERF

1 − 1RV1 2 − 1T4E 3 − 3LBL 4 − 3LBK 5 − 4DIJ 6 − 3JZK 7 − 3TU1 8 − 4ERE 9 − 4ERF average median

3.91 1.18 − 0.64 5.68 1.43 1.77 0.90 1.55 2.13 1.49

3.89 3.43 2.22 0.67 3.70 0.38 8.18 5.30 5.72 3.72 3.70

4.26 1.22 7.09 1.27 5.53 1.22 1.37 1.64 1.04 2.74 1.37

4.03 5.45 − 0.63 2.48 0.42 1.92 6.70 6.13 3.47 3.25

7.00 5.80 − 0.96 3.06 0.46 0.87 1.47 6.51 3.27 2.27

8.53 0.62 7.03 0.60 3.02 0.36 1.09 1.10 0.77 2.57 1.09

6.12 3.64 − 1.32 8.06 0.72 1.79 1.38 1.50 3.07 1.65

6.12 3.55 − 1.56 5.33 5.95 − − 1.00 3.92 4.44

5.69 5.49 − 2.43 5.40 1.70 − − 0.46 3.53 3.92

Figure 6. Residual cell vitality after 48 h of incubation in the presence of compounds 10−42 at 100 μM.

The virtual library, filtered by properties and shape, was used to generate a Phase database that was submitted to the subsequent pharmacophore-based screening. Ligands mapping at least four out of eight features were saved. All of the compounds were forced to map the aromatic ring feature

corresponding to the Trp23 residue: its interaction, in fact, is the most conserved among known ligands and mainly contributes to the p53 binding energy.41 Docking. The setup of the docking protocol was carried out through a cross-docking that was performed to select the G


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Journal of Chemical Information and Modeling Table 3. IC50 Values (in μM) of the Most Active Compounds on Four Tumor Cell Lines compound

MCF7

H1299

HCT116

HCT116 p53−/−

10 13 14 15 19 23 28 31 32 34 35 38

102.1 ± 3.0 26.6 ± 4.1 13.8 ± 4.0 29.1 ± 25.5 28.9 ± 17.2 80.3 ± 40.7 110.5 ± 12.0 30.7 ± 10.0 63.7 ± 1.8 21.6 ± 15.5 102.5 ± 3.5 6.8 ± 3.5

97.8 ± 3.1 19.1 ± 0.6 9.8 ± 0.8 11.4 ± 2.5 27.1 ± 1.4 56.5 ± 1.1 76.4 ± 4.7 13.7 ± 3.2 99.9 ± 0.2 9.5 ± 4.5 229.0 ± 14.1 14.2 ± 2.1

65.5 ± 22.6 19.6 ± 2.2 9.5 ± 2.5 8.0 ± 0.3 24.7 ± 2.2 47.3 ± 5.6 57.6 ± 0.9 13.2 ± 10.2 53.9 ± 1.2 11.9 ± 3.3 43.3 ± 15.5 13.4 ± 4.9

81.7 ± 11.7 18.2 ± 0.8 8.8 ± 1.4 7.4 ± 0.9 30.6 ± 0.9 47.9 ± 4.7 74.6 ± 12.3 12.8 ± 9.7 68.3 ± 7.4 14.8 ± 7.1 65.4 ± 6.9 12.6 ± 1.8

receptor structure that can correctly accommodate ligands 1−9. Both the Glide SP and XP protocols were applied. Cross-docking results were evaluated by comparing the experimental and docked poses in each receptor. The XP protocol performed better than the SP protocol in retrieving the experimental binding pose (the RMSD values of the XP poses are reported in Table 2). The 3JZK MDM2 structure allowed the correct positioning of seven out of nine ligands: the high RMSD value of the 1RV1 ligand (nutlin-2, 1) is due to a reversed docked pose, it is difficult to retrieve the correct binding pose for the 3LBL ligand in almost all MDM2 X-ray structures. The 1RV1 receptor presents the best average RMSD value, but the 3JZK ligand has a lower median and the lowest RMSD for five out of nine ligands. Moreover, six ligands have RMSDs lower than 1.2. Therefore, the 3JZK MDM2 structure was used to dock the virtual library by applying the docking protocol. The virtual screening was carried out on the set of compounds previously filtered (641 720) using the Virtual Screening Workflow procedure available in Maestro. This workflow allows a fast preliminary screening (HTVS) to be performed, followed by more accurate docking protocols (SP and XP). Final Selection. Docking and ligand-based methods were applied in parallel. The final selection was carried out by visual inspection of the best-ranking compounds. Almost 1000 highestranked compounds for each approach were analyzed. Only ligands with properly oriented aromatic rings, in particular in the Trp23 and Leu26 pockets, were considered. Selected compounds from each method were collected, and the most representative compounds were finally purchased (33 virtual hits; see Table S2 in the Supporting Information). Biological Assays. The 33 purchased compounds were subjected to biological assays to assess their antiproliferative activities on cancer cells and their abilities to inhibit the p53− MDM2 interaction. An MTT assay on four tumor cell lines (MCF, H1299, HCT116, and HTC116 p53−/−) at 100 μM was conducted as a preliminary screening, and the residual cell vitality after 48 h was evaluated (Figure 6). Compounds producing a residual cell vitality of less than 40% at 100 μM were subjected to determination of IC50 (Table 3). Seven ligands showed IC50 values lower than 30 μM and were subjected to an ELISA to confirm their ability to compete with p53 in the MDM2 site. Notably, four of them showed nanomolar IC50 values lower than that shown by nutlin-3 (used as a reference compound), with a final hit rate of 12% (Table 4). The ability to inhibit the p53−MDM2 interaction was confirmed by co-immunoprecipitation/Western blot experi-

Table 4. Effect of New Hits on the Dissociation of the p53− MDM2 Complex compound

IC50 (nM)a

LEb

13 14 15 19 31 34 38 nutlin-3

>1000 18.5 ± 2.1 >1000 64.5 ± 5.6 45.6 ± 1.8 >1000 16.2 ± 1.5 108.0 ± 4.5

− 0.35 − 0.29 0.31 − 0.33 0.25

a

Concentration (in nM) leading to half-maximal inhibition of the p53−MDM2 complex. bLigand efficiency, expressed in units of kcal/ atom.

ments. Challenging U87MG cells with 1 μM compound 38 for 6 h led to a drastic reduction in the p53−MDM2 level (Figure 7), thus confirming the dissociation of the protein complex.

Figure 7. U87MG cells were incubated with 1 μM compound 38 for 8 h; following incubation, samples were immunoprecipitated using an antiMDM2 antibody. The MDM2−p53 complex and the relative input of the proteins were detected by p53 immunoblot. One representative Western blot is presented in (A). The bar graph in (B) shows the quantitative analysis of the Western blot, performed using the ImageJ program. The data represent the mean ± SEM of three different experiments. **, P < 0.001 vs control cells.

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DISCUSSION P2I2 identification offers several issues for VS protocol development due to the flexibility of the receptor and the large, flat, and hydrophobic interacting surfaces. In the present work, a VS protocol is described that attempts to account for all of these aspects with a careful setup of each step exploiting the H


Article

Journal of Chemical Information and Modeling large amount of available experimental data. In particular, we attentively selected the X-ray structure used for docking calculations and the compounds composing the known ligand data set. The latter was collected by selecting the most structurally diverse inhibitors spanning the widest activity range to have a representative and informative set of compounds that was used to train the models applied in the filtering processes and pharmacophore hypothesis generation, avoiding the bias due to redundant information. The KLD, in particular, was used for the initial filtering procedure aimed at obtaining a focused library of p53−MDM2-compliant compounds. In fact, as already mentioned, Sperandio and co-workers25,26 demonstrated that P2I2 are usually large and hydrophobic and have a definite stereochemistry occupying a different region of the chemical space with respect to more traditional drugs. Therefore, most of the compounds included in a virtual library are not suitable P2I2. In this respect, the hierarchical filtering steps were successful, as they greatly reduced the number of screened compounds, thereby enriching the library with more promising virtual hits. The RP protocol was particularly effective because it works on 2D structures and can be quickly applied without generating 3D structures and/or conformations, thus allowing it to work faster and, consequently, to screen a larger library. Most of the previously published VSs of p53−MDM2 inhibitors applied a pharmacophore-based VS as the first step20,22 or a structurebased approach with docking followed by postdocking refinement21 or MD calculations.18 The mentioned workflows therefore need to generate 3D structures and/or conformations for all of the library compounds, which requires a lot of CPU time. In fact, in all of the previous papers, a maximum of 300 000 compounds were screened, while in our work a library of 2 500 000 compounds was submitted to VS. In addition, this method greatly reduced the number of initial compounds to 13% of the starting library, eliminating unsuitable compounds at the beginning of the process. The relevance of the filtering criteria is particularly evident upon analysis of the physicochemical properties of the active compounds reported in Table 5. All of the active compounds in fact have one violation of the Ro540 due to the high AlogP value. It is therefore evident that applying a drug-likeness filtering at the beginning of the screening protocol would have removed active compounds from the virtual library, frustrating the possibility of identifying active inhibitors even with the application of an accurate pharmacophore and docking procedure. The filtering process was followed by the parallel application of both ligand- and structure-based VS methods (pharmacophore screening and docking) to exploit all of the available data concerning both ligands and MDM2 structures. These methods are usually hierarchically applied, with ligand-based screening preceding docking: ligand-based approaches, in fact, are faster than docking but provide a large number of false positives that can be reduced through docking-based screening. In this case, we decided to use them in parallel, limiting the risk of false positives using a pharmacophore model accounting for the receptor structure via application of excluded volumes. In fact, it has been demonstrated that the parallel application of the two methods improves the diversity of identified hits.42 Analyzing the structural features of active compounds, we can affirm that this objective has been reached: the identified hits have three different scaffolds, demonstrating that the parallel use of ligandand structure-based methods can actually afford diverse structures. Moreover, to better highlight the diversity of the selected ligands, Tanimoto similarities were calculated using the

Table 5. Properties of Active Compounds

compound

MW

AlogP

HBD

HBA

14 19 31 38

401.41 446.5 427.44 453.84

5.43 5.32 5.50 6.14

1 1 1 1

2 4 2 2

MACCS fingerprints. The similarity matrix reported in Table S3 in the Supporting Information shows an average similarity of 0.41. Notably, the 3JZK ligand and receptor structures offered the best templates for both shape-based filtering and docking. This could be ascribed to the rigid conformation of the ligand, which is missing solubilizing hydrophilic chains that do not find an exact location in the MDM2 cleft and induce uncertainty in the model. The activity shown on the HCT116 p53−/− cell line in the MTT assay suggests that the p53−MDM2 complex is not the unique target of our compounds. This result is not surprising: in the literature there are examples of confirmed p53−MDM2 inhibitors that show activity on the HCT116 p53−/− cell line.43,44 Nutlin-3 demonstrated off-target action on cancer cells that has been attributed to disruption of the p73−MDM2 interaction too.45 Moreover, some p53−MDM2 inhibitors have been demonstrated to activate p53-independent pathways that may contribute to their effect on cancer cells.46,47 On the other hand, both ELISA and co-immunoprecipitation assays confirmed the interference of our ligands with the p53−MDM2 interaction. The final hit rate, accounting for the direct competition with p53 in the binding to its receptor, was 12%, which is good, even if not excellent, and is in line with the value obtained in previous VSs.18−24 However, if we consider the hit rate of submicromolar inhibitors, our protocol outperforms all other published methods, where the best result was reported by Zhuang et al.24 Moreover, none of the above methods reported the identification of four hits with two-digit nanomolar IC50 values. As usually observed for P2I2, the Ligand Efficiency value is not very high, although it is higher than that of nutlin. Moreover, the molecular weights of the active compounds are lower than 500 Da, suggesting the possibility to further optimize the identified hits. The optimization would also improve the solubility of the identified compounds. Two compounds out of the four hits (14 and 38) have a stereogenic center that can influence the binding. I


Article


Figure 8. Docked poses of active compounds (A) 14, (B) 19, (C) 31, and (D) 38 in the MDM2 cleft. The MDM2 structure is represented as a gray transparent surface. Residues close to the docked ligands are represented as gray sticks.

selection of both the ligands and the MDM2 3D structure used in ligand- and structure-based approaches. The most relevant aspect of the present work involves the application of a preliminary filtering procedure to select a small portion of the starting library compliant with the p53−MDM2 inhibitor property profile. The screening afforded a final selection of 33 compounds that were tested in MTT assays and ELISAs. Twelve compounds were active in the MTT assay, and four of them were able to interfere with the p53−MDM2 interaction at nanomolar concentrations. The results confirm the validity of the VS protocol and suggest that it can be a valuable tool to find inhibitors of other target PPIs.

In our study the racemic mixtures were tested, but the synthesis of the right enantiomer could affect the observed activity. The identified hits were checked with respect to compounds composing the KLD to evaluate the ability of our VS to identify novel compounds. Only compound 31 showed a Tanimoto similarity of 0.81 with a patented compound, while the average and median similarity indexes are lower than 0.5 for all of the compounds, demonstrating the ability of our method to retrieve new scaffolds. The putative binding mode of the active compounds in the MDM2 binding site (Figure 8) shows that all four ligands occupy the MDM2 hydrophobic cleft with their aromatic chains. Therefore, the binding is mainly driven by hydrophobic interactions, as expected. Only the NH group of compound 31 establishes a H-bonding interaction with the Leu54 carbonyl oxygen. Notably, pyrazolopyrrolidinones 14 and 38 have a different binding mode, even though they share the same scaffold. This result is not surprising: in fact, it has been verified experimentally (NMR) that multiple binding modes are possible for MDM2 ligands.48

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jcim.5b00747. Structures of compounds composing the KLD and related references, structures of purchased and tested compounds, similarity matrix of selected compounds, similarity between identified hits and the KLD, and NMR and/or purity data for tested compounds (PDF)

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CONCLUSIONS In the present work, we developed an effective virtual screening protocol to identify ligands interfering with the p53−MDM2 interaction, which is the most explored protein−protein interaction. The virtual screening was prepared by careful J


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AUTHOR INFORMATION

Corresponding Author

*Tel: +39 0871 355 4585. Fax: +39 0871 355 4911. E-mail: m. [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was accomplished thanks to the financial support of the Ministero dell’Istruzione, dell’Università e della Ricerca (MIUR 2009 20098SJX4F_005).

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An Effective Virtual Screening Protocol To Identify Promising p53

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