Structural Evidence of N6-Isopentenyladenosine As a New Ligand of

Brief Article pubs.acs.org/jmc

Structural Evidence of N6‑Isopentenyladenosine As a New Ligand of Farnesyl Pyrophosphate Synthase Mario Scrima,†,∥ Gianluigi Lauro,†,∥ Manuela Grimaldi,† Sara Di Marino,‡ Alessandra Tosco,† Paola Picardi,† Patrizia Gazzerro,† Raffaele Riccio,† Ettore Novellino,‡ Maurizio Bifulco,†,§ Giuseppe Bifulco,*,† and Anna Maria D’Ursi*,† †

Dipartimento di Farmacia, Università degli Studi di Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Salerno, Italy Dipartimento di Farmacia, Università di Napoli “Federico II”, Via D. Montesano 49, 80131 Napoli, Italy § Dipartimento di Medicina e Chirurgia, Università degli Studi di Salerno, Via Allende, 84081 Baronissi, Salerno, Italy ‡

S Supporting Information *

ABSTRACT: N6-isopentenyladenosine (i6A), a modified nucleoside belonging to the cytokinin family, has shown in humans many biological actions, including antitumoral effects through the modulation of the farnesyl pyrophosphate synthase (FPPS) activity. To investigate the relationship between i6A and FPPS, we undertook an inverse virtual screening computational target searching, testing i6A on a large panel of 3D protein structures involved in cancer processes. Experimentally, we performed an NMR investigation of i6A in the presence of FPPS protein. Both inverse virtual screening and saturation transfer difference (STD) NMR outcomes provided evidence of the structural interaction between i6A and FPPS, pointing to i6A as a valuable lead compound in the search of new ligands endowed with antitumoral potential and targeting FPPS protein.

■

INTRODUCTON N6-Isopentenyladenosine is a modified nucleoside (Figure 1), formed by an adenosine harboring an isopentenyl chain derived

enzyme involved in the mevalonate pathway and in downstream proteins prenylation that appears deregulated in many tumors.4 Indeed, i6A exerts antiproliferative effects in thyroid K-ras (KiMol) transformed cells and untransformed, FRTL-5 wild-type cells. These effects were due to the inhibition of FPPS, both in the expression and in the activity. After i6A treatment, FPPS expression was decreased more significantly in thyroid normal cells than in tumor cells, while i6A effects on the activity of the enzyme were more efficacious in tumor cell system. FPPS inhibition was also correlated to the inhibition of downstream prenylation of proteins involved in cell proliferation. The antiproliferative action of i6A was corroborated also in an in vivo system, because the growth of murine xenograft (where tumoral KiMol cells were implanted subcutaneously) that resulted was inhibited by its treatment.5 More recently, another evidence about the ability of i6A to modulate FPPS expression and activity has been reported.6 On natural killer cells, i6A at lowest concentrations (sub 1 μM) was able to stimulate directly the proliferation and the cytotoxic activity vs tumor cells by the induction of the expression and the activity of FPPS. The enzyme FPPS is a key enzyme in the mevalonate, isoprenoid biosynthesis pathway, and it is the target of

Figure 1. Chemical structure of i6A (a), zoledronic acid (b), and FPPS allosteric ligand7 (c).

from dimethylallyl pyrophosphate in the N6 position. It belongs to the cytokinin family, involved in control of many processes in plants. Interestingly, it represents the unique cytokinins found also in mammals, bound to tRNA (tRNA) or as free nucleoside.1−3 In humans, many biological actions, both in vitro and in vivo, including antitumoral effects, can be attributed to i6A. Although its precise mechanism of action has not been fully clarified, several possible biochemical targets have been identified. Among these, i6A is able to modulate the activity of farnesyl pyrophosphate synthase (FPPS), a key © 2014 American Chemical Society

Received: June 9, 2014 Published: September 3, 2014 7798

dx.doi.org/10.1021/jm500869x | J. Med. Chem. 2014, 57, 7798−7803

Journal of Medicinal Chemistry

Brief Article

Figure 2. (a) Docking model of i6A in the FPPS binding site in the presence of IPP (i6A colored by atom type: C, green; O, red; N, blue; H, light gray. IPP colored by atom type: C, magenta; O, red; P, orange; H, light gray); residues interacting with i6A are depicted in licorice (colored by atom type: C, gray; O, red; N, blue; H, light gray; molecular surface in transparent gray). (b) 2D interactions panel showing interactions between i6A and residues in the FPPS binding site.

fundamental drawback of molecular docking methods. Although notable advances were made in the past few years, most docking software still lack accuracy both in sampling and scoring components, and this can dramatically influence the results of an in silico screening. This issue is complicated when more proteins are considered, mainly because sampling and associated approximated scoring depend both from ligand and target variability. The overall result is that data extracted from one system, e.g., ligands vs one specific target, are not immediately comparable to those related to the other targets, leading to an unproductive selection of false positive and more importantly to an exclusion of false negative results. A better enrichment could be obtained using accurate but computationally demanding methods for the calculation of the binding affinities, such as free energy perturbation (FEP), thermodynamic integration (TI), linear interaction energy (LIE) or MMGBSA and MM-PBSA. It is otherwise clear that the application of these approaches is convenient only considering a few number of ligand−protein systems but become arduous when one deals with a screening procedure in which various systems should be considered. For these reasons, we previously successfully reduced this problem by introducing a mathematical manipulation of molecular docking results8,11−13 (see Computational Details). In the present report, i6A was tested on a panel of 296 3D structures of proteins involved in cancer processes, and 50 “blanks” were used for the normalization of the docking results (see Computational Details). After the normalization of the predicted binding affinities (see Table S1, Supporting Information (SI)) a ranking was obtained, from the most promising targets (higher V values) to the worst ones (lower V values). FPPS was identified in the first two positions among the 296 proteins investigated, considering that we built two models of this target starting from the same crystallized structure (PDB code: 1ZW5)14 but differing in the absence or the presence of cocrystallized ligand isopentenyl pyrophosphate (named in the panel as f pps_ipp and f pps_no_ipp,

bisphosphonates for treatment of bone-related disorders. The major group of FPPS inhibitors are nitrogen-containing bisphosphonates (N-BP) used in clinical treatment of osteoporosis diseases, Paget’s disease, and more recently, metastatic bone-related tumors and cancer. In view of the implication of FPPS in cancer related pathways, it is considered an interesting target, prompting the search of new specific anticancer compounds. On the other hand, the employment of bisphosphonates FPPS inhibitor in different tumor or infective diseases is limited by their adverse pharmacokinetic properties. An extended ongoing research of new FPPS inhibitors with improved pharmacokinetic properties has recently led to the identification of a new set of allosteric modulators.7 In the hypothesis that i6A may be able to modulate the activity of farnesyl pyrophosphate synthase through a direct structural interaction, and given the implication of i6A and FPPS in cancer related pathways, we performed an inverse virtual screening of i6A on a large panel of 3D protein structures involved in cancer processes. Experimentally, we performed an NMR investigation of i6A in the presence of FPPS protein. Both our inverse virtual screening and STD NMR outcomes provide evidence that FPPS protein may be a molecular target for i6A compound. On these basis, our data give the first evidence that i6A may be a valuable lead compound in the search of FPPS inhibitor to be used as anticancer therapeutics.

■

RESULTS AND DISCUSSION Inverse Virtual Screening. Inverse virtual screening is an in silico approach,8−10 allowing the analysis of different binding hypotheses between a single ligand and a high number of targets by means of molecular docking experiments. It represents a fast and inexpensive approach for the identification and selection of possible ligand−receptor favorite complexes among a set of numerous other theoretical possibilities. In previous attempts,8 we faced the problem of the scarce reliability in predicting the binding affinities, representing the 7799



Brief Article

respectively). Moreover, a third FPPS 3D structure in the panel of targets (named f pps_allo) was related to the search of putative allosteric binders,7 but in this case no promising spots with i6A were detected (position no. 143 in the final ranking, Table S1, SI). Overall, these data strengthened the hypothesis of i6A interaction in the canonical binding site of FPPS. V values (normalized predicted binding energies) related to f pps_ipp/i6A and fpps_no_ipp/i6A interactions were 1.511 and 1.223, respectively. To elucidate the molecular basis behind this significant difference, we carefully investigated the binding modes of i6A in the two cases. Surprisingly, in both the cases, the same preferred binding mode of i6A in the FPPS binding site was observed (Figure S1, SI). Therefore, this result indicates a preferential orientation of i6A that is not influenced by the absence/presence of cocrystallized FPPS ligand IPP, and the same predicted binding energy values (BE) found in the two cases (BEfpps_ipp = −9.5 kcal/mol, BEfpps_no_ipp = −9.4 kcal/mol) confirmed these data. On the contrary, this similar trend on the two forms of the protein was not observed for the 50 “blanks” used for the normalization, for which better affinity values were generally found for the form of the protein without IPP, mainly due to the larger space in the binding site. Obviously, this entailed a better averaged predicted binding affinity value calculated for the “blanks” against f pps_no_ipp if compared to fpps_ipp, causing the related distinct V values calculated for i6A. Docking analysis revealed a favorable accommodation of i6A in the binding site of FPPS through a large set of both hydrophobic and polar interactions (Figure 2). Sugar moiety of i6A establishes a network of H-bonds with Asp118 and Asp261 and polar interactions with Asp121, Ser123, Thr125, Arg126, Thr274, and Lys271. Adenine core of the molecule is oriented between the three Mg ions cocrystallized with FPPS, making polar contacts with Lys214, Asp257, and H-bonding with Gln254. Isopentenyl part of the molecule is placed in the deeper region of the binding site, and hydrophobic interactions are observable with Phe113, Leu114, and Tyr218. In general, i6A is able to occupy the limited space in the FPPS binding site, showing a very good shape complementarity with the protein counterpart, and its molecular dimensions are compatible with the accommodation of the deeper part of the binding site maintaining fundamental interactions (see Figure S2, SI). Furthermore, flexible docking calculations for i6a on the topranked targets were subsequently performed to refine the obtained results (residues chosen as flexible listed in Table S4, SI) and confirmed the binding modes from the semiflexible docking procedure (considering proteins as rigid, as employed in the inverse virtual screening study; Figure S3, SI). NMR Experiments. NMR analysis of FPPS−i6A interaction was based on saturation-transfer difference (STD) and WaterLOGSY NMR experiments.15,16 STD and WaterLOGSY are powerful NMR techniques that enable the identification of protein−ligand binding sites and the determinations of protein−ligand dissociation constants (KD). NMR sample containing 8 μM of FPPS was titrated with i6A to have STD build-up at protein−ligand molar ratios: 1:10, 1:20, 1:30, 1:50, 1:70, and 1:100. For each titration point, STD experiments were carried out using different saturation times (0.50, 1.00, 1.50, 2.00, 3.00, 4.00, and 5.00 s).17 Figure 3 shows STD and WaterLOGSY NMR spectra recorded at 1:100 FPPS−i6A molar ratio (2s saturation time). Standard 1H monodimensional and 2D COSY NMR experiments allowed the 1H chemical shift assignment of i6A proton signals (Figure

Figure 3. (A) WaterLOGSY, (B) 1D 1H NMR spectrum, (C) STD NMR spectra of FPPS−i6A, 1:100 molar ratio.

S5, SI). Both STD and WaterLOGSY NMR spectra, shown in Figure 3, evidence significant variations in intensity for the H11, H12, H14, and H15 signals belonging to the i6A isopentenyl moiety. H2 and H8 protons of the adenine ring are moderately perturbed, and weak STD effects are also observable on the protons belonging to the ribose sugar. The data extracted from the STD experiments indicate that the isopentenyl moiety and the purine ring of i6A are directly involved in the interaction with FPPS binding pocket, while the ribose portion may undertake interactions that are less evident in the current experimental conditions. The quantitative estimation of STD effects in experiments collected at different protein−ligand ratios and saturation times conditions allowed the calculation of i6A−FPPS binding constant KD, according to the methodology recently developed by Angulo et al.17 Using this procedure, the calculation of protein−ligand affinities is independent from contingent experimental factors, such as STD saturation time, ligand residence time in the complex, and the intensity of the signal.17 Table 1 reports KD Table 1. Dissociation Constants Calculated by Isotherms of Initial Growth Rates of STD-AF Values of the Protein− Ligand Systems Studied Herein protein−ligand system proton proton proton proton

H2/H8 H12 H11 H14−H15

KD (from STD-AF initial slopes) [mM] 2.02 2.21 0.55 1.21

± ± ± ±

0.31 0.32 0.06 0.12

for the single i6A protons involved in the binding with FPPS. In agreement with the previous qualitative evaluation, protons H11, H12, and H14−H15 of the isopentenylic moiety and the proton H2 of purine ring show the lowest values of KD. The mean value of the KD calculated lead for FPPS is in the mM range. To provide further experimental validation of the inverse virtual screening prediction, we recorded STD experiments on a sample containing 1:100 Bcl-x(L)−i6A (data not shown). Bclx(L) is a protein involved in the cancer process18 identified in the position 151 among the 296 proteins analyzed (Table S1, SI) in the computational procedure. Confirming the inverse 7800



Brief Article

of FPPS at the active site, induced observable variations of FPPS−i6A STD spectrum (Figure S6, SI). All together, these data were strongly suggestive that the binding site of i6A at FPPS protein is different from the allosteric site. Conversely, NMR data fit with the prediction of the inverse virtual screening, demonstrating that i6A binds at the active site; interestingly, the intensity of the STD effects results are coherent with the binding pose hypothesized in the docking calculation. Indeed, the protons showing the lowest values of KD (Table 1) occupy the deepest region of the binding pocket, whereas the protons of the sugar moiety evidencing weak STD effects are those more exposed to the solvent and possibly more blandly involved in the interaction with the binding site.20 Finally, a colorimetric assay21 was used to measure FPPS activity. This test performed with zoledronic acid as a positive control evidenced the ability of i6A to inhibit FPPS activity in conditions compatible with those predicted by NMR experiments. (Figures S7 and S8, SI). In this respect, these results provide evidence that the binding of i6A to FPPS is effective for an inhibition of the enzymatic activity.

virtual screening outputs, we did not observe any i6A proton affected by Bcl-x(L) presence. Moreover to verify the molecular docking prediction, suggesting the binding mode of i6A is not influenced by the absence/presence of cocrystallized FPPS ligand IPP, we recorded STD NMR experiments by titrating 1:100 FPPS− i6A sample with increasing amounts of IPP to have FPPS−IPP

■

CONCLUSIONS In the reported work, using the combination of inverse virtual screening and STD NMR experiments, we proved that FPPS is a molecular target for i6A. Computational and NMR experimental data show that i6A is able to occupy the FPPS active site, with the isopentenyl moiety and adenosyl ring possibly oriented toward the deep region of the binding site. In view of these evidence, the aforementioned biological pathways (vide infra) controlled by i6A5,6 may occur thanks to its ability to structurally interact with FPPS enzyme, and i6A is proved to be a new lead compound in the search of FPPS inhibitors. From the methodological point of view, our work demonstrates that the inverse virtual screening is a powerful technique to identify molecular target for compounds exhibiting interesting biological activities but with unknown molecular targets. In this framework, STD NMR techniques provide an excellent experimental support for the virtual screening prediction.

Figure 4. STD NMR spectra of FPPS containing both i6A and IPP (1:100 FPPS−i6A; 1:100 FPPS−IPP). On top, the STD spectrum; on bottom, the off-resonance spectrum.

molar ratios: 1:10, 1:20, 1:30, 1:50, 1:70, 1:100. Figure 4 shows H NMR spectrum of FPPS in the presence of both i6A and IPP (FPPS−i6A and FPPS−IPP 1:100 molar ratio, respectively).19 Unfortunately, H5 and H8 IPP protons were not observable in the reported experimental conditions due to the water suppression. The spectrum shown in Figure 4 confirms the participation of the previously mentioned (H11, H12, H14, and H15) protons of i6A (vide infra) to the binding with FPPS, moreover, it evidences STD effects for H14 and H6 protons of IPP. The qualitative evaluation of these STD effects at different FPPS−IPP ratios indicates a different modulation of STD effects relative to IPP with respect to i6A. These data prove that both IPP and i6A bind FPPS, their binding is characterized by different kinetic, there is no evidence of competition between the two ligands. Therefore, accordingly to the virtual docking prediction, i6A and IPP may share the FPPS binding pocket with minimal consequence for their respective binding affinities. As previously pointed out, it was recently demonstrated that the enzymatic activity of FPPS is modulated by an additional allosteric site for which a set of powerful new ligands were identified.7 To discriminate whether the detected interaction of i6A with FPPS involved the active or the allosteric FPPS binding site, we titrated the FPPS−i6A complex with increasing concentrations of compound c (Figure 1), a FPPS allosteric modulator (IC50 = 6.0 μM).7,19 Our experiments performed by adding increasing amounts of c ligand (FPPS−c molar ratios 1:10, 1:50, and 1:100) to the 1:100 molar ratio FPPS−i6A sample showed that the i6A STD effect is not influenced by the concomitant presence of c; this conversely evidenced the presence of independent STD signals reasonably due to the interaction with the allosteric binding site.7,19 On the other hand, 80 μM concentration of zoledronic acid, a known high affinity inhibitor 1

■

MATERIALS AND METHODS

Computational Details. The chemical structures of investigated compounds were built with Maestro (version 9.6).22 To identify a possible three-dimensional starting model of each compounds for the subsequent docking calculations, we applied an optimization (conjugate gradient, 0.05 Å convergence threshold) of the structures. For the subsequent docking calculations, all the structures were converted in the.pdbqt format using OpenBabel software (version 2.3.2),23 adding Gasteiger charges. The 296 protein 3D models were prepared starting from the X-ray structures in the Protein Data Bank database (www.rcsb.org, Table S1, SI). Water molecules were removed, and .pdb files obtained were then processed with Autodock Tools 1.5.6 and converted in .pdbqt format, merging nonpolar hydrogens and adding Gasteiger charges. Charge deficit was spread over all atoms of related residues. Semiflexible (considering ligands as flexible and proteins as rigid, employed in the inverse virtual screening study) and flexible (considering ligands and residues in the binding sites of the topranked targets as flexible, Table S4, SI) docking calculations were performed using the Autodock-Vina software.24 In the configuration files linked to 3D structures of the proteins, we specified coordinates and dimensions along x, y, z axes of the grid related to the site of presumable pharmacological interest, with spacing of 1.0 Å between the grid points (Table S2, SI). The exhaustiveness value was set to 64, saving 10 conformations as the maximum number of binding modes. 7801



Brief Article

For all the investigated compounds, all open-chain bonds were treated as active torsional bonds. Docking results were analyzed with Autodock Tools 1.5.6. Predicted binding energies and normalized values for i6A are collected in Table S1, SI. Illustrations of the 3D models were generated using VMD software25 and Maestro.22 Normalization of Docking Results. To reduce the selection of false positive results,8,11 for each target considered we normalized each affinity value calculated for the case-study molecule (i6A), dividing it on the average value calculated on a library of 50 “blank” compounds (Table S3, SI). The averaged molecular weight calculated on all these “blanks” is quite similar to the compound of interest. Specifically, the normalization procedure complies with the following equation:

carried out using different saturation times (0.50, 1.00, 1.50, 2.00, 3.00, 4.00, and 5.00 s and different relaxation delay 1.50, 2.00, 2.50, 3.00, 4.00, 5.00, and 6.00 s). For each experiment in the frequency list (FQ2LIST), the on-resonance and off-resonance pulse were 320 and 50000 Hz, respectively. All STD experiments were performed in 25 mM d-Tris, pH 7.4, 0.5 mM MgCl2, and 25 mM NaCl with 1% dimethyl sulfoxide-d6 as a cosolvent. STD NMR experiments were performed at 25 °C as previously described.15 Briefly, two free induction decay (FID) data sets were collected in an interleaved manner to minimize temporal fluctuations with the protein irradiation frequency set on-resonance (−0.5 ppm) and off-resonance (40 ppm), respectively (sw = 6000 Hz, 16 steady state scans, 2048 transients, 4k complex points, d1 = 3 s). Protein saturation was obtained using a train of individual 50 ms long, frequency selective Gaussian radio frequency (rf) pulses separated by an interpulse delay of 1 ms. The number of selective pulses was set to 5 by an interpulse delay of 1 ms. The number of selective pulses was set to 50, leading to a total saturation time (τsat) of 2.5 s. gradient tailored excitation (WATERGATE) scheme was employed to suppress the residual water signal.26 Suppression of the background signals arising from the protein was not required. The FID acquired with off-resonance irradiation generated the reference spectrum (Ioff), whereas the difference FID (off-resonance−on-resonance) yielded the STD spectrum (ISTD = Ioff − Ion). Spectra were processed with an exponential apodization function (1 Hz line-broadening) and zero-filling to 8k complex points before Fourier transformation and baseline correction with a thirdorder Bernstein polynomial fit. The STD measurements were done in duplicate, and all data were processed and analyzed using TopSpin software (Bruker v 1.3). One-dimensional 1H NMR WaterLOGSY experiments were acquired. A reference experiment was recorded followed by the WaterLOGSY magnetization transfer spectrum. Acquisition parameters for the WaterLOGSY spectra were 256−512 scans with a mixing time of 1.4 s and a 2 s relaxation delay.

V = V0/VR where, for each target investigated, V represents the normalized binding energy value of the case-study molecule, V0 is the predicted binding affinity from docking calculations (kcal/mol) and before normalization, and VR is the average value of binding energy calculated on all the “blanks” (kcal/mol). To avoid any bias, docking affinity values poorer than −3.0 kcal/mol were ignored during the normalization process. For the case-study molecule (i6A), after the normalization a new ranking is obtained, from the most promising targets (higher V values) to the poorest ones (lower V values) (see Table S1, SI). NMR Sample Preparation. The buffer used for NMR analysis consisted of 25 mM d-Tris, pH 7.4, 0.5 mM MgCl2, and 25 mM NaCl. Unless noted, all chemicals were purchased from Microtech Srl. i6A was purchased from Iris Biotech GMBH and zoledronic acid from Sigma-Aldrich (St. Louis, MO). 3-Carboxymethyl-4,7-dichloro-1H-indole-2-carboxylic acid (ligand c in Figure 1) was synthesized according to the previously published synthetic strategy6 via a Fischer indole synthesis of 2,5-dichlorophenylhydrazine and 2-oxo-pentanedioic acid followed by saponification. FPPS was expressed in Escherichia coli BL21 (DE3)-pLysS as a fusion protein (67−419 residues) with a N-terminal polyhistidine tail and a mutation (threonin with serine) on residue 266. The p11 plasmid was obtained from SGC-Oxford and contained the T7/Lac promoter and ampicillin resistance. The plasmid was transformed into E. coli BL21(DE3)-pLysS cells. Cell growth was monitored spectrophotometrically by measuring OD600 nm periodically. For expression in E. coli, bacterial clones were grown in 1 L of LB (Luria−Bertani) medium containing 50 μg/mL ampicillin. When growth was performed to an OD600 of 0.8 at 37 °C, the 1 mM isopropyl-D-thiogalactoside (IPTG) was added. After 6 h, cells were pelleted by centrifugation and resuspended in lysis buffer (50 mL of 5% glycerol, 5 mM imidazole, 500 mM NaCl, 50 mM PBS (pH 7.5)) and sonicated. Protein was purified with His-Trap HP column at 1 mL/min using an AKTA purifier system, the soluble extract was applied to a nickel-chelated agarose affinity column that had been equilibrated with the same buffer. The protein was eluted from the column with elution buffer (5% glycerol, 250 mM imidazole, 500 mM NaCl, 50 mM PBS (pH 7.5)). Affinity chromatography on a nickelchelated agarose column permitted a simple one-step protein purification. Enzyme purity was judged by using SDS-polyacrylamide gel electrophoresis with Coomassie Blue staining (Figure S4, SI). The eluted fraction was transferred into Vivaspin 20 concentrator, cutoff 3 kDa, to exchange the buffer for NMR studies NMR Experiments. STD NMR and WaterLOGSY experiments were recorded at 25 °C on Bruker AV600 MHz spectrometer at a 1H resonance frequency of 600 MHz equipped with a 5 mm tripleresonance 1H(13C/15N), z-axis pulsed-field gradient probe head. For characterization purposes, i6A samples consisted of a ∼5 mM solution in 25 mM d-Tris, pH 7.4, 0.5 mM MgCl2, and 25 mM NaCl, and the spectra were referenced to residual solvent. 1H−1D spectra were acquired at a resolution of 16k complex points in the time domain with 32 accumulations each (sw = 6000 Hz, d1 = 3 s) (Figure S5, SI). The FPPS protein at 8 μM concentration was titrated with i6A to have protein/ligand molar ratios 1:10, 1:20, 1:30, 1:50, 1:70, and 1:100. For each addition of ligand, STD build-up experiment was

■

ASSOCIATED CONTENT

S Supporting Information *

Tables and figures describing computational and experimental details. This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Authors

*For A.M.D.: phone, +39-089-969748; fax, +39-089-969602; Email, [email protected]. *For G.B.: phone, +39-089-969741; fax, +39-089-969602; Email, [email protected]. Author Contributions ∥

These authors (M.S. and G.L.) equally contributed to the work. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS G.L. acknowledges fellowship support from Associazione Italiana Ricerca sul Cancro (AIRC), and grant IG 2012IG_12777 (to G.B.).

■

ABBREVIATIONS USED i6A, N6-isopentenyladenosine; FPPS, farnesyl pyrophosphate synthase; CCR2, chemokine receptor 2; CCL2, chemokine ligand 2; CCR5, CC chemokine receptor 5; COSY, correlated spectroscopy; FEP, free energy perturbation; IPP, isopentenyl pyrophosphate; TI, thermodynamic integration; LIE, linear 7802



Brief Article

(17) Angulo, J.; Nieto, P. M. STD NMR: application to transient interactions between biomoleculesa quantitative approach. Eur. Biophys. J. 2011, 40, 1357−1369. (18) Di Micco, S.; Vitale, R.; Pellecchia, M.; Rega, M. F.; Riva, R.; Basso, A.; Bifulco, G. Identification of lead compounds as antagonists of protein Bcl-xL with a diversity-oriented multidisciplinary approach. J. Med. Chem. 2009, 52, 7856−7867. (19) Wang, Y. S.; Liu, D.; Wyss, D. F. Competition STD NMR for the detection of high-affinity ligands and NMR-based screening. Magn. Reson. Chem. 2004, 42, 485−489. (20) Wang, Y. S.; Liu, D.; Wyss, D. F. Competition STD NMR for the detection of high-affinity ligands and NMR-based screening. Magn. Reson. Chem. 2004, 42, 485−489. (21) Gao, J.; Chu, X.; Qiu, Y.; Wu, L.; Qiao, Y.; Wu, J.; Li, D. Discovery of potent inhibitor for farnesyl pyrophosphate synthase in the mevalonate pathway. Chem. Commun. 2010, 46, 5340−5342. (22) Maestro, version 9.6; Schrödinger LLC: New York, 2013. (23) O’Boyle, N. M.; Banck, M.; James, C. A.; Morley, C.; Vandermeersch, T.; Hutchison, G. R. Open Babel: an open chemical toolbox. J. Cheminf. 2011, 3, 1−14. (24) Trott, O.; Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010, 31, 455− 461. (25) Humphrey, W.; Dalke, A.; Schulten, K. VMD: visual molecular dynamics. J. Mol. Graphics 1996, 14, 33−38. (26) Piotto, M.; Saudek, V.; Sklenár,̌ V. Gradient-tailored excitation for single-quantum NMR spectroscopy of aqueous solutions. J. Biomol. NMR 1992, 2, 661−665.

interaction energy; MM-GBSA, molecular mechanics/generalized Born surface area; MM-PBSA, molecular mechanic/ Poisson−Boltzmann surface area; NK, natural killer; N-BP, nitrogen-containing bisphosphonates; NMR, nuclear magnetic resonance; STD, saturation transfer difference; TLC, thin layer chromatography; tRNA, tRNA

■

REFERENCES

(1) Bifulco, M.; Malfitano, A. M.; Proto, M. C.; Santoro, A.; Caruso, M. G.; Laezza, C. Biological and pharmacological roles of N6isopentenyladenosine: an emerging anticancer drug. Anti-Cancer Agents Med. Chem. 2008, 8, 200−204. (2) Kersten, H. On the biological significance of modified nucleosides in tRNA. Prog. Nucleic Acids Res. Mol. Biol. 1984, 31, 59. (3) Laten, H. M.; Zahareas-Doktor, S. Presence and source of free isopentenyladenosine in yeasts. Proc. Natl. Acad. Sci. U. S. A. 1985, 82, 1113−1115. (4) Thurnher, M.; Nussbaumer, O.; Gruenbacher, G. Novel aspects of mevalonate pathway inhibitors as antitumor agents. Clin. Cancer Res. 2012, 18, 3524−3531. (5) Laezza, C.; Notarnicola, M.; Caruso, M. G.; Messa, C.; Macchia, M.; Bertini, S.; Minutolo, F.; Portella, G.; Fiorentino, L.; Stingo, S. N6Isopentenyladenosine arrests tumor cell proliferation by inhibiting farnesyl diphosphate synthase and protein prenylation. FASEB J. 2006, 20, 412−418. (6) Ciaglia, E.; Pisanti, S.; Picardi, P.; Laezza, C.; Malfitano, A. M.; D’Alessandro, A.; Gazzerro, P.; Vitale, M.; Carbone, E.; Bifulco, M. N6-isopentenyladenosine, an endogenous isoprenoid end product, directly affects cytotoxic and regulatory functions of human NK cells through FDPS modulation. J. Leukocyte Biol. 2013, 94, 1207−1219. (7) Jahnke, W.; Rondeau, J.-M.; Cotesta, S.; Marzinzik, A.; Pellé, X.; Geiser, M.; Strauss, A.; Götte, M.; Bitsch, F.; Hemmig, R. Allosteric non-bisphosphonate FPPS inhibitors identified by fragment-based discovery. Nature Chem. Biol. 2010, 6, 660−666. (8) Lauro, G.; Romano, A.; Riccio, R.; Bifulco, G. Inverse virtual screening of antitumor targets: pilot study on a small database of natural bioactive compounds. J. Nat. Prod. 2011, 74, 1401−1407. (9) Wang, J.-C.; Chu, P.-Y.; Chen, C.-M.; Lin, J.-H. idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach. Nucleic Acids Res. 2012, 40, W393−W399. (10) Chen, Y.; Zhi, D. Ligand−protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins: Struct., Funct., Bioinf. 2001, 43, 217−226. (11) Cheruku, P.; Plaza, A.; Lauro, G.; Keffer, J.; Lloyd, J. R.; Bifulco, G.; Bewley, C. A. Discovery and Synthesis of Namalide Reveals a New Anabaenopeptin Scaffold and Peptidase Inhibitor. J. Med. Chem. 2012, 55, 735−742. (12) Lauro, G.; Masullo, M.; Piacente, S.; Riccio, R.; Bifulco, G. Inverse Virtual Screening allows the discovery of the biological activity of natural compounds. Bioorg. Med. Chem. 2012, 20, 3596−3602. (13) Gong, J.; Sun, P.; Jiang, N.; Riccio, R.; Lauro, G.; Bifulco, G.; Li, T.-J.; Gerwick, W. H.; Zhang, W. New Steroids with a Rearranged Skeleton as (h) P300 Inhibitors from the Sponge Theonella swinhoei. Org. Lett. 2014, 16, 2224−2227. (14) Kavanagh, K. L.; Guo, K.; Dunford, J. E.; Wu, X.; Knapp, S.; Ebetino, F. H.; Rogers, M. J.; Russell, R. G. G.; Oppermann, U. The molecular mechanism of nitrogen-containing bisphosphonates as antiosteoporosis drugs. Proc. Natl. Acad. Sci. U. S. A. 2006, 103, 7829−7834. (15) Mayer, M.; Meyer, B. Group epitope mapping by saturation transfer difference NMR to identify segments of a ligand in direct contact with a protein receptor. J. Am. Chem. Soc. 2001, 123, 6108− 6117. (16) Dalvit, C.; Fogliatto, G.; Stewart, A.; Veronesi, M.; Stockman, B. WaterLOGSY as a method for primary NMR screening: practical aspects and range of applicability. J. Biomol. NMR 2001, 21, 349−359. 7803


Structural Evidence of N6-Isopentenyladenosine As a New Ligand of

Recommend Documents