in Silico Screening of Naturally

Article pubs.acs.org/jnp

Combined in Vitro Cell-Based/in Silico Screening of Naturally Occurring Flavonoids and Phenolic Compounds as Potential AntiAlzheimer Drugs Alba Espargaró,†,⊥ Tiziana Ginex,‡,⊥ Maria del Mar Vadell,† Maria A. Busquets,† Joan Estelrich,† Diego Muñoz-Torrero,§ F. Javier Luque,*,‡ and Raimon Sabate*,† †

Department of Pharmacy and Pharmaceutical Technology and Physical-Chemistry, School of Pharmacy, and Institute of Nanoscience and Nanotechnology (IN2UB), University of Barcelona, E-08028, Barcelona, Spain ‡ Department of Nutrition, Food Sciences, and Gastronomy, School of Pharmacy and Institute of Biomedicine, Campus Torribera, University of Barcelona, Prat de la Riba 171, E-08921, Santa Coloma de Gramenet, Spain § Laboratory of Pharmaceutical Chemistry (CSIC Associated Unit), School of Pharmacy, and Institute of Biomedicine (IBUB), University of Barcelona, E-08028, Barcelona, Spain S Supporting Information *

ABSTRACT: Alzheimer’s disease (AD) is the main cause of dementia in people over 65 years. One of the major culprits in AD is the self-aggregation of amyloid-β peptide (Aβ), which has stimulated the search for small molecules able to inhibit Aβ aggregation. In this context, we recently reported a simple, but effective in vitro cell-based assay to evaluate the potential antiaggregation activity of putative Aβ aggregation inhibitors. In this work this assay was used together with docking and molecular dynamics simulations to analyze the anti-Aβ aggregation activity of several naturally occurring flavonoids and phenolic compounds. The results showed that rosmarinic acid, melatonin, and o-vanillin displayed zero or low inhibitory capacity, curcumin was found to have an intermediate inhibitory potency, and apigenin and quercetin showed potent antiaggregation activity. Finally, the suitability of the combined in vitro cellbased/in silico approach to distinguish between active and inactive compounds was further assessed for an additional set of flavonols and dihydroflavonols.

A

The aggregation of Aβ peptides from 40 to 42 amino acids (Aβ40 and Aβ42) is widely accepted to be one of the main culprits of AD. Accordingly, the identification of potential inhibitors of amyloid aggregation has recently attracted much interest. These efforts have had limited success, partly due to the lack of high-throughput screening methods for monitoring amyloid aggregation in cells and tissues, where sensitivity can be affected by low protein concentration, slow aggregation, and low reproducibility.6−8 Therefore, most studies rely on the inhibitory activity determined from in vitro aggregation assays, although this may not reflect adequately the conditions of amyloid aggregation in the cellular environment.9 In this context, the development of fast, reproducible in vivo and in vitro cell-based methods could represent a breakthrough not only for gaining insight into the molecular basis of amyloid aggregation but especially in the search for potential antiAlzheimer antiamyloid drugs.10−13

lzheimer’s disease (AD) is the leading cause of dementia in people over 65. In 2010 there were 36 million people affected worldwide, and the number is expected to triple in 2050.1,2 AD is a chronic, progressive, and currently incurable illness characterized by the progressive loss of the capacity to create new memories as well as confusion, language difficulties, personality changes, and depression, among others. Two distinctive hallmarks characterize the brain physiopathology of AD patients: (1) the formation of extracellular amyloid plaques originating from the aggregation of amyloid-β peptide (Aβ) and (2) neurofibrillary tangles, which are primarily formed by hyperphosphorylated forms of neuronal tau protein associated in microtubules.3,4 Extracellular amyloid plaques and neurofibrillary tangles lead to nerve cell death and tissue loss throughout the brain. In the brain the cortex shrivels up, initially damaging areas involved in cognition (primarily in the formation of new memories) and language, mainly affecting the Broca’s and Wernicke’s areas in the hippocampus. Even though the precise etiology of AD still remains to be completely understood, age is the major risk factor, often regulated by environmental factors and genetic mutations.5 © 2017 American Chemical Society and American Society of Pharmacognosy

Received: July 11, 2016 Published: January 27, 2017 278

DOI: 10.1021/acs.jnatprod.6b00643 J. Nat. Prod. 2017, 80, 278−289

Journal of Natural Products

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Chart 1. Numbering of the Flavone Skeleton and Chemical Structures of Apigenin, Quercetin, Melatonin, o-Vanillin, Curcumin, and Rosmarinic Acid

activity34−36 and neuroprotective, neuroinflammation, and anti-Aβ aggregation properties34−42 (Chart 1). This study examined the reliability of the in vitro cell-based screening method14 to assess the antiamyloid properties of the six natural compounds mentioned above. The inhibitory activity was characterized and permitted the ranking of the in vitro cell-based antiaggregating activity of these compounds. The IC50 values were determined for the most active compounds. The antiaggregating activity was examined in terms of the reported data. Furthermore, the kinetics of Aβaggregation inhibition were characterized for the most active antiaggregating compounds. Finally, combined molecular docking, molecular dynamics (MD), and molecular mechanics-generalized-Born surface area (MM-GBSA) calculations were applied to examine and rationalize the putative binding of these compounds to Aβ-amyloid fibrils, providing a better understanding of the interaction pattern from a structural and physicochemical point of view. To assess the suitability of the method, additional studies were performed for a set of naturally occurring flavonols (myricetin, kaempferol, and galangin) and dihydroflavonols (dihydrokaempferol and dihydromyricetin). These compounds were screened through in silico computations and subsequently in vitro cell-based assayed for their antiaggregating capacity.

A simple and inexpensive in vitro cell-based method that provides a straightforward assay to evaluate the potential antiaggregation capacity of putative Aβ inhibitors has recently been reported.14 This method relies on the use of bacteria as an in vitro cell-based reservoir to monitor the kinetics of amyloid aggregation as physiologically relevant models. In particular, it exploits amyloid properties of the inclusions bodies (IBs) formed by Aβ in bacteria stained by thioflavin-S (Th-S), a specific amyloid dye, to address the bacterial β-amyloid aggregation and the effect of antiamyloid drugs in this polymerization process.15 The method provides direct information about the inhibitory profile of a given compound along the aggregation process, thus allowing acquisition of kinetic and thermodynamic parameters and affording a distinctive ranking between active and inactive compounds. Indeed, it has been employed elsewhere as a successful way to establish the antiaggregation ability of several classes of multitarget compounds.16−19 Natural products have been the subjects of intense studies aiming at disclosing potential anti-AD agents,20−23 including specifically the inhibition of Aβ aggregation.24−30 These compounds encompass a high chemical diversity, as exemplified by curcumin, melatonin, rosmarinic acid, o-vanillin, apigenin, and quercetin, which have been explored in the design of novel strategies targeting Aβ aggregation. Curcumin [(1E,6E)-1,7bis(4-hydroxy-3-methoxyphenyl)-1,6-heptadiene-3,5-dione)] is a known diarylheptanoid antioxidant inhibitor of inflammation triggered by amyloid aggregation and anti-Aβ aggregation.31 Melatonin {N-[2-(5-methoxy-1H-indol-3-yl)ethyl]acetamide} is an antioxidant with proposed antiamyloid activity.32 Rosmarinic acid {(2R)-3-(3,4-dihydroxyphenyl)-2-[(E)-3-(3,4dihydroxyphenyl)prop-2-enoyl]oxypropanoic acid} is a phenolic compound with antioxidant activity, which has also been examined for preventing oligomer formation and Aβ deposition.33 o-Vanillin (2-hydroxy-3-methoxybenzaldehyde) was proposed to have an inhibitory effect on Aβ aggregation.34 Finally, apigenin [5,7-dihydroxy-2-(4-hydroxyphenyl)chromen4-one] and quercetin [2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one] are compounds with anticholinesterase

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RESULTS AND DISCUSSION In Vitro Cell-Based Inhibitory Activity Determination. Since the Aβ42 variant is more prone to form aggregates and thereby more toxic than Aβ40, the overexpression of Aβ42 in bacteria was chosen for the experimental assays of Aβ aggregation. To monitor the formation of Aβ IBs, induced and noninduced bacterial cells (expressing and nonexpressing Aβ, respectively) were growth overnight. As shown in Figure 1, large differences in the Th-S spectra recorded in expressing and nonexpressing Aβ42 cells were observed. Whereas the Th-S staining of noninduced cells displayed a maximum at ∼520 nm, the Th-S spectrum was clearly different in the presence of induced cells (containing Aβ42 IBs). Thus, the formation of a new peak at ∼495 nm due to amyloid staining was observed. Interestingly, when the Th-S spectrum of the noninduced cells 279



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bacterial cells were grown overnight in the presence or absence of 200 μM of each potential inhibitor (Figure 2), and the amyloid band was determined by subtraction of noninduced Th-S spectra in the presence of each inhibitor from induced cultures in the presence of inhibitor (Figure 3A). Whereas rosmarinic acid, melatonin, and o-vanillin displayed practically zero or low inhibitory activity in the test system at 200 μM (inhibitory potency of 5.5%, 7.5%, and 18.5%, respectively), apigenin and quercetin showed potent antiaggregation activity at the same concentration (77.3% and 81.3%, respectively) (Figure 3B). Interestingly, these compounds displayed remarkable higher inhibitory activity of Aβ42 aggregation than curcumin (37.8%), a known anti-Aβ aggregation drug (Figure 3B). In this context, the IC50 values of apigenin and quercetin (Chart 1) were assessed as 176.2 and 124.6 μM, respectively, suggesting that the planar, rigid aromatic scaffold of these compounds or the presence of OH groups could play an important role in assisting the antiaggregating activity, presumably through the binding to Aβ42 fibrils (Figure 4). Importantly, in vitro studies of the inhibitory effect of flavonoids in Aβ42 amyloid aggregation supported the functional role of the OH groups.43 Thus, Sato et al. showed

Figure 1. Th-S fluorescence spectra of bacterial cells overexpressing Aβ42. In blue are induced bacterial cells (overexpressing Aβ42); in green, noninduced bacterial cells (nonexpressing recombinant Aβ42); in red, amyloid band obtained by subtraction of noninduced cells spectrum from the induced cells spectrum.

was subtracted from the Th-S spectrum of the induced cells, the presence of the amyloid band at ∼485 nm could be unequivocally detected and quantified (Figure 1). In order to test the effect of the putative inhibitors (viz., curcumin, melatonin, rosmarinic acid, o-vanillin, apigenin, and quercetin) on amyloid aggregation, induced and noninduced

Figure 2. Th-S staining of induced and noninduced bacterial cells grown in the presence of 200 μM apigenin, curcumin, melatonin, quercetin, rosmarinic acid, and o-vanillin. Green lines are induced cells in the presence of each inhibitor; blue lines, noninduced cells in the presence of each inhibitor; dashed red lines, induced cells without inhibitor; dashed black lines, noninduced cells without inhibitor. 280



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as morin, kaempferol, and datiscetin, displaying IC50 values of 30.3, 75.1, and 55.4 μM respectively, showed moderate in vitro antipolymerization activity.43 In order to gain deeper insight into the Aβ42 antiaggregation activity of the most effective compounds apigenin and quercetin, kinetic studies of the inhibition capacity of both compounds were carried out. As shown in Figure 5 and Table

Figure 5. Aβ42 amyloid aggregation along the time-course kinetics tracked by Th-S staining. In black, red, and green: without inhibitor (control) and the presence of 200 μM apigenin and quercetin, respectively. Th-S relative fluorescence was measured in triplicate, and the standard errors were less than 5%.

1, at 200 μM the aggregation was inhibited by 52.7% and 75.4% for apigenin and quercetin, respectively. In the presence of these compounds the nucleation constant (kn) was reduced by 15−25%, showing an increment of ∼20 min in the lag time (Table 1). In the same sense, the apparent elongation constant (keapp) was increased 2- and 4-fold in the presence of apigenin and quercetin, respectively. Overall, the obtained kinetic data suggested that both compounds act by delaying the fibril elongation and hindering nuclei formation. Molecular Modeling and Binding Free Energy Calculations. Reference Models. Docking and MD simulations were performed to explore the structural basis of Aβ42 antiaggregation activity detected for apigenin, quercetin, and

Figure 3. Aβ42 inclusion bodies staining by Th-S. (A) Amyloid band in the presence or absence of 200 μM of each inhibitor. (B) Inhibition percentage of Aβ42 aggregation in the absence or presence of 200 μM of each inhibitor.

that the in vitro polymerization of Aβ42 was strongly suppressed by myricetin and quercetin (three and two vicinal phenolic B-rings, respectively), whereas galangin, devoid of a Bring phenolic group, did not show any inhibitory activity. IC50 values of 15.1, 15.3, 25.3, and >500 μM were reported for myricetin, quercetin, dihydromyricetin, and galangin, respectively. Interestingly, flavonoids without a catechol B-ring as well

Figure 4. Half-maximal inhibitory concentration (IC50) for apigenin and quercetin: (A and B) apigenin, (C and D) quercetin IC50 determinations. 281



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Kuang and co-workers recently examined the binding of ADZ2184 and Th-T, two compounds used for in vivo imaging, to Aβ42 fibrils (PDB ID 2BEG) through docking and MD calculations.46 They reported the identification of up to four putative ligand-binding sites (three internal and one external to the β-sheet) after a full exploration of all the structural models deposited for 2BEG. For the purposes of this study, these findings were used as reference data to calibrate the interaction of the active compounds to the Aβ42 (2BEG) fibril structure due to their similar size to Th-T and AZD2184, as well as to the fact that AZD2184 and Th-T were found to bind Aβ42 with low and high binding affinity, respectively (see below). In this context, it is worth noting that the binding poses reported for ADZ2184 and Th-T in the original work (Figure S1, Supporting Information) were reproduced and used for comparison with the docking solutions obtained for the compounds included in this study. Regarding the 2MXU structure, binding on an internal but solvent-exposed site was selected after a preliminary blind docking performed using the first structural model of the NMR ensemble (Figure 6). Molecular Docking. A preliminary unbiased docking exploration of the 10 available conformations available for 2BEG was performed. Unlike Th-T and AZD2184, the docking results revealed that apigenin, quercetin, and curcumin might bind only sites 1 and 2 (Figure 6). Site 1 is delimited by residues Leu17, Phe19, Gly38, and Val40, and site 2 is shaped by residues Phe19, Ala21, Val36, and Gly38. Among the 10 available models in 2BEG, the best-ranked poses were found for models 6 (site 1) and 8 (site 2), respectively. Accordingly, these models together with the docked poses of the ligands were

Table 1. Kinetic Parameters of Aβ42 Amyloid Aggregation inhibitor ‑1

kn (10 ·min ) keapp (M‑1·min‑1)b,c t0 (min) t1/2 (min) t1 (min) inhibition (%) 6

without

apigenina

quercetina

580.7 1657.7 64.4 284.7 504.9 0.0

426.4 3697.7 81.3 306.2 531.1 52.7

496.5 6829.6 82.7 293.0 503.4 75.4

a Inhibition parameters at 200 μM of inhibitor. bSince Aβ42 concentration is not constant along the aggregation process, the ke values are apparent. cIn order to calculate the keapp, the final Aβ42 amyloid amount of each aggregation kinetics was considered. The final Aβ42 amounts for each kinetics were determined from the Aβ42 amount obtained without inhibitor14 and taking into account the final inhibition percentage for each inhibitor.

curcumin in the in vitro cell-based screening assay and to estimate their binding free energies. The two recently solved NMR-based high-resolution structures of Aβ42 fibers, viz., 2BEG and 2MXU, were selected as structural templates to examine the Aβ42−ligand interaction, as they have different self-assembled fibrillar architectures.44,45 In 2BEG, the fibril structure showed two intermolecular, parallel β-sheets, formed by residues 18−26 (β1) and 31−42 (β2), which appeared to be stabilized through intermolecular salt bridges (K28−D23). In 2MXU, a triple-β-motif formed by residues 12−18 (β1), 24−33 (β2), and 36−40 (β3) was found, the assembly being stabilized by the salt bridge formed between residue K28 and the terminal carboxylate group of A42 (Figure 6).

Figure 6. Crystallographic structure of the Aβ42 fibers in 2BEG and 2MXU. The two putative binding sites (A, site 1; B, site 2) for 2BEG and one internal for 2MXU have also been represented. 282



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Figure 7. Representation of the binding modes for apigenin (in blue), curcumin (in orange), and quercetin (in green) within site 2 (2BEG model 8) obtained after docking and MD simulation and RMSD of fibril backbone (A) and ligand (B) for each of the three complexes.

were considered. In this regard, an opposite orientation of the B-ring can be noted for apigenin and quercetin in site 2 (Figure 7). This different binding mode might be related to the two additional OH groups of quercetin, which in turn might explain its higher antiaggregating effect on Aβ(42) fibrils. For 2MXU, the best-scored complex with apigenin was selected. Using the method of Kuang et al.,46 all the complexes were subjected to 10 ns of MD simulation. Since the main aim of MD simulations was to facilitate the accommodation of the ligand in the binding site while avoiding large conformational changes, a Cartesian restraint of 10 kcal/mol Å2 was applied to the first and the last chains of the two 5-mer structural models (chains A and E for 2BEG and chains F and J for 2MXU) during the simulation. The final binding modes and the time evolution of the structural stability, measured as the root-mean-square deviation (RMSD) of ligand and protein backbone for site 2, are shown in Figure 7. At the end of the trajectories, apigenin, quercetin, and curcumin were all able to form hydrogen-bonding interactions with the backbone of the chains A−C at site 2, particularly with Phe20A, Ala21A, Gly37C, Phe20B, Gly37C, Gly38B, and Gly73C. The RMSD profiles of the ligands bound to this site revealed the presence of a structural rearrangement of quercetin at the beginning of the trajectory (at around the second nanosecond) and the presence of more frequent structural changes for curcumin, whereas the binding of apigenin was stable along the whole trajectory. The RMSD profile of the fibril backbone revealed a comparable conformational stability for all three complexes. Regarding site 1, after a slight rearrangement at the beginning of the simulation, apigenin remained stably bound to site 1 due to hydrogen bonds with Leu17B, Val18D, Leu17E, and Val39E (Figure S2A, Supporting Information). In contrast, binding of curcumin was less stable (Figure S2B, Supporting Information), especially in the last part of the simulation, which revealed the loss of the hydrogen-bonding interactions and subsequent release of curcumin from the binding site (hydrogen bond interactions

chosen as starting systems for MD simulations. Similarly, the blind docking performed for 2MXU revealed a putative, highly hydrophobic binding site internal to the triple-β-motif, delimited by residues Leu17, Ile32, Gly33, and Leu34 (Figure 6), which was also considered for MD simulations. For comparative purposes with the 5-mer structure of 2BEG, the whole structure of 2MXU (composed of 12 chains) was truncated after docking, so that only the five monomeric moieties interacting with the ligand were retained. In contrast, no suitable binding modes were found for melatonin, rosmarinic acid, and o-vanillin, in agreement with the low or zero inhibitory effect found in the in vivo assay (data not shown). Binding affinity data obtained from docking simulations and log P values for apigenin, curcumin, and quercetin are shown in Table S1, Supporting Infomation, which also includes the results obtained for Th-T and AZD2184 as reference compounds for comparative purposes. The results showed that binding to the 2BEG fibril was more favorable than to 2MXU. Considering the results obtained for 2BEG, apigenin and quercetin appeared to have a preference for binding to site 1, as also found for Th-T, whereas curcumin, and to a lesser extent AZD2184, exhibited the opposite trend. Nevertheless, keeping in mind the uncertainty of docking calculations, caution is required to not exaggerate the significance of the small differences in predicted binding affinities. On the other hand, while apigenin, quercetin, and AZD2184 were predicted to have similar log P values, Th-T and curcumin were more hydrophilic and hydrophobic, respectively, which might affect the inhibitory potency measured from the in vitro and in vitro cell-based assays. MD Simulations. MD simulations were subsequently performed to refine the binding mode of the ligands and to examine the structural stability of the ligand−Aβ42 complexes. For 2BEG, the best solutions for site 1 (apigenin and curcumin) and site 2 (apigenin, curcumin, and quercetin) 283



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Table 2. Contributions to Binding Free Energy from MM-GBSA Calculations (kcal/mol)a ΔEvdW 2BEG Models Site 1 apigenin curcumin thioflavin Tb AZD2184b Site 2 apigenin curcumin quercetin thioflavin Tb AZD2184b 2MXU Model apigenin a

ΔEelec

ΔGGB

ΔGSASA

−TΔS

ΔGBind

−33.3 −25.0 −43.9 −37.1

± ± ± ±

1.7 2.6 0.2 0.1

−10.0 −7.7 −126.9 −4.5

± ± ± ±

4.2 5.7 0.2 0.2

28.5 21.9 142.5 14.9

± ± ± ±

4.7 5.4 0.2 0.1

−3.8 −3.5 −4.5 −4.2

± ± ± ±

0.1 0.3 0.1 0.1

14.8 23.0 19.5 13.2

± ± ± ±

5.1 3.6 0.6 0.9

−3.8 8.7 −13.2 −17.7

± ± ± ±

6.0 3.8 0.7 0.9

−33.7 −39.7 −37.7 −36.1 −40.4

± ± ± ± ±

2.3 2.8 2.5 0.2 0.1

−22.9 −13.1 −17.0 −121.9 −8.8

± ± ± ± ±

5.8 5.0 2.7 0.3 0.2

33.1 24.7 28.8 136.8 21.8

± ± ± ± ±

4.2 4.3 2.0 0.3 0.2

−3.9 −5.5 −4.6 −3.8 −4.2

± ± ± ± ±

0.1 0.3 0.1 0.1 0.1

15.7 21.4 17.4 20.3 14.8

± ± ± ± ±

4.6 5.3 6.6 1.0 0.7

−11.8 −12.3 −13.0 −4.6 −16.8

± ± ± ± ±

5.1 5.8 6.8 1.1 0.8

−26.5 ± 1.8

−3.3 ± 1.5

15.5 ± 2.5

−3.1 ± 0.2

22.5 ± 3.0

5.1 ± 3.4

Average and standard deviation values for each contribution are reported as kcal/mol. bData taken from ref 46.

mol for sites 1 and 2, respectively). This trend was also found for the binding at site 2, as the ΔEvdW term ranged from −33.7 to −39.7 kcal/mol, thus reflecting the hydrophobicity of the binding cavity. Nevertheless, the electrostatic term was more favorable for the phenolic compounds relative to AZD2184, especially for apigenin and quercetin (with contributions of −22.9 and −17.0 kcal/mol versus −8.8 kcal/mol for AZD2182), which revealed the importance of the hydrogenbonding interactions. In conjunction with the van der Waals component, this term justified the preference of phenolic compounds for binding to site 2. The similar size of the compounds was reflected in the ΔGSASA term, which was comparable in all cases. Finally, the entropy contribution, which is the computationally most expensive term in MM-GBSA calculations,47 was unfavorable for binding given the constraints imposed upon binding to the fibrils. The statistical uncertainty raised from MM-GBSA calculations could be explained by the intrinsic instability of the Aβ42 fibrils, although the use of 40 snapshots attenuates this effect, which was homogeneous for the whole set of compounds.48 The binding affinities of AZD2184 and Th-T were determined to be 8.4 ± 1.0 (Kd) and 0.890 ± 0.092 μM (against Aβ(1−40); Ki), respectively.49,50 There was qualitative agreement between the experimental binding affinities and the computational estimates by Kuang et al.,46 which also reflected the larger binding of AZD2184. The experimental antiaggregating effect of quercetin was estimated to be 15.3 μM (IC50) in in vitro assays,43 while another in vitro study reported an effective concentration of 0.1−0.8 μM for inhibiting the formation and extension of Aβ(1−42).51 In a distinct study, the concentration of apigenin required to reduce the polymerization of Aβ(1−42) by 50% was found to be 23−39 μM.52 On the other hand, under in vitro aggregating conditions, curcumin was reported to inhibit aggregation of Aβ(1−40) with an IC50 value of 0.8 μM,53 whereas an IC50 value of 10 μM was reported in another study.54 While these results reflected the uncertainty arising from the experimental assays, they suggested that the binding affinity profiles of apigenin, quercetin, and curcumin were qualitatively similar to that of Th-T, which was also in agreement with the results obtained from MM-GBSA calculations. In contrast to the preceding findings, the in vitro cell-based antiaggregating activities of the active compounds ranged from 37.8% inhibition of curcumin to 81% for quercetin, which

were found only with Leu17D or Val18D and Val39E). Finally, for the apigenin−2MXU complex (Figure S3, Supporting Information), the binding mode of the ligand was generally stable, although some structural fluctuations were observed along the trajectory, likely reflecting the exposure of the binding site to the aqueous solvent and the lack of hydrogen-bonding interactions between the ligand and the protein residues, which were highly hydrophobic. Binding Free Energy Calculations. In order to evaluate the binding affinities of the ligands, MM-GBSA calculations were performed for the ensemble of 40 snapshots collected along the last 2 ns of the trajectories. According to the MM-GBSA method, the binding free energy (ΔGbind) was expressed as the sum of (i) nonpolar (ΔEvdW) and polar (ΔEelect) energetic contributions in the gas phase, (ii) nonpolar (ΔGSASA) and polar (ΔGGB) contributions to the solvation free energy, and finally (iii) the entropy (ΔS) term (Table 2). Data for Th-T and AZD2184 retrieved from the reference work by Kuang and co-workers46 were also reported in Table 2 for the sake of comparison. As expected from the structural features of the binding of apigenin to 2MXU, the binding affinity was estimated to be 5.1 kcal/mol (Table 2), which was less favored than the binding to sites 1 and 2 in the Aβ42 fibril models taken from the 2BEG structure, which generally ranged from −11 to −18 kcal/mol. The affinity of apigenin, quercetin, and curcumin for site 2 was found to be highly similar, ranging from −11.8 to −13.0 kcal/ mol. Furthermore, the results showed that binding to site 2 was more favorable than site 1, especially for curcumin, as expected from the release of this compound toward the bulk solvent (see above), but even for apigenin, as the binding affinity was estimated as −3.8 kcal/mol. Overall, site 2 in 2BEG appeared to be the most favorable binding cavity for apigenin, quercetin, and curcumin. This finding was in contrast with the preferential binding of Th-T for site 1 (−13.2 kcal/mol) reported by Kuang and co-workers,46 whereas AZD2184 showed similar binding affinities for sites 1 and 2 (ca. −17 kcal/mol). Since Th-T was the only charged compound, its binding affinity energy was dominated by the electrostatic components of the interaction energy (ΔEelec) and desolvation (ΔGGB), which nearly canceled out. Hence, the van der Waals contribution (ΔEvdW) was found to be the major stabilizing term to the binding free energy (−43.9 kcal/mol), similar to the contribution found for AZD2184 (−37.1 and −40.4 kcal/ 284



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translated into effective IC50 values of 176.2 and 124.6 μM for apigenin and quercetin, respectively. Hence, curcumin was less effective than these compounds. This reflected the fact that the antiaggregating activity of a compound stemmed from a complex, often not yet fully understood, network of cellular processes. Membrane transport is the first obstacle for the final activity of a given compound. This property is typically discussed in terms of the molecular hydrophobicity as measured from the n-octanol/water partition coefficient. For apigenin and quercetin, the estimated log P values are 2.2 and 2.7, respectively, which compared with the value obtained for AZD2184 (log P of 2.7), while Th-T was predicted to be much more hydrophilic (log P of 0.4) and curcumin much more hydrophobic (log P of 4.1). Hence, this could be a factor that explains the lower inhibitory activity measured from the in vitro cell-based assays for curcumin relative to apigenin and quercetin (Table S1, Supporting Information). Screening of Other Natural Compounds. In order to validate the suitability of the reported in vitro cell-based/in silico protocol, an additional set of compounds was evaluated for their capacity to inhibit Aβ(1−42) aggregation. In particular, three compounds with strong/medium (dihydromyricetin, myricetin, and kaempferol) and weak (dihydrokaempferol and galangin) inhibitory potencies were screened using a published protocol (Chart 2).43

MD simulations revealed the ability of the novel compounds to form hydrogen-bonding interactions with the backbone of Aβ(1−42) fibrils, similar to the interaction pattern reported for the previous set of compounds, although there were differences regarding the degree of hydroxylation of the B-ring and the presence of the C-ring double bond. Binding free energies from MM-GBSA calculations for 2BEG model 1 and model 2 were also calculated (Table 3). Among the two proposed binding sites for 2BEG, the results suggested that model 2 seemed to better describe the correlation between binding free energy and Aβ(1−42) antiaggregating potential of this class of compounds. In this regard, dyhydromyricetin (−11.1 kcal/mol), myricetin (−10.5 kcal/mol), and kaempferol (−11.9 kcal/mol), pertaining to the strong/medium class inhibitors, showed the best binding affinities. Conversely, lower binding affinities were determined for the weak inhibitors, dihydrokaempferol (4.4 kcal/mol; −1.7 kcal/mol in site 1) and galangin (−5.0 kcal/mol). Considering the single contributions, the nonpolar part (ΔEvdW and ΔESASA) generally dominated the binding affinity of the strong/medium class inhibitors. With regard to dihydrokaempferol and galangin, the favorable contributions of both ΔEvdW and ΔESASA were more attenuated. Moreover, the electrostatic component (ΔEelec) was less favorable for dihydrokaempferol and galangin (−11.3 and −7.0 kcal/mol, respectively) compared to dihydromyricetin (−21.9 kcal/mol), myricetin (−21.2 kcal/mol), and kaempferol (−22.1 kcal/mol). Collectively, the results supported a lower binding affinity for galangin and dihydrokaempferol, which could be related to the lower degree of hydroxylation typically observed for poorly active inhibitors.43 As a final confirmation of the trends emerging from in silico studies, in vitro cell-based assays were performed for the active compounds, including also the weak inhibitor galangin for the sake of comparison. In this regard, IC50 values of 93.8, 182.7, and 384.6 μM were obtained for myricetin, kaempferol, and galangin, respectively (Figure S4, Supporting Information), which compared with the values determined for apigenin and quercetin (176.2 and 124.6 μM; see above). Interestingly, an IC50 value of 442.9 μM was found for dihydromyricetin, which was slightly less potent than galangin (384.6 μM). This was in contrast with the predicted binding affinity (−11.1 kcal/mol; see Table 3) and the experimental potency derived from in vitro antiaggregation studies (25.3 μM),43 which compare well

Chart 2. Chemical Structures of Galangin, Kaempferol, Myricetin, Dihydrokaempferol, and Dihydromyricetin

Table 3. Contributions to Binding Free Energy from MM-GBSA Calculations (kcal/mol) for Dihydromyricetin, Myricetin, Kaempferol, Dihydrokaempferol, and Galangin in the 2BEG Modela ΔEvdW 2BEG Models Site 1 dihydromyricetin myricetin kaempferol dihydrokaempferol galangin Site 2 dihydromyricetin myricetin kaempferol dihydrokaempferol galangin a

ΔEelec

ΔGGB

ΔGSASA

−TΔS

ΔGBind

−32.4 −29.6 −28.5 −33.1 −28.4

± ± ± ± ±

3.2 2.1 2.3 2.1 2.2

−45.7 −52.7 −41.4 −28.5 −36.8

± ± ± ± ±

8.2 4.2 5.3 7.6 5.3

58.4 65.3 51.5 43.5 48.7

± ± ± ± ±

5.5 2.9 3.8 5.8 4.3

−4.2 −4.0 −3.6 −4.0 −3.6

± ± ± ± ±

0.1 0.1 0.1 0.1 0.1

18.1 19.8 10.8 20.4 17.0

± ± ± ± ±

6.1 2.9 4.7 5.7 3.6

−5.8 −1.2 −11.2 −1.7 −3.1

± ± ± ± ±

6.9 2.9 5.0 5.7 4.1

−38.0 −37.6 −36.1 −29.5 −27.3

± ± ± ± ±

1.9 2.7 2.3 1.7 1.6

−21.9 −21.2 −22.1 −11.3 −7.0

± ± ± ± ±

3.9 4.2 3.7 2.1 1.8

36.2 36.5 34.1 27.5 16.8

± ± ± ± ±

3.8 3.3 2.7 1.9 2.1

−4.5 −4.6 −4.1 −3.7 −3.5

± ± ± ± ±

0.1 0.1 0.2 0.2 0.2

17.0 16.4 16.2 21.4 15.9

± ± ± ± ±

4.4 5.7 4.4 5.7 1.4

−11.1 −10.5 −11.9 4.4 −5.0

± ± ± ± ±

4.2 6.7 5.1 4.1 4.5

Average and standard deviation values for each contribution are reported as kcal/mol. 285



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with myricetin (binding affinity of −10.5 kcal/mol and in vitro IC50 value of 15.1 μM).43 This unexpected finding could be related to a higher metabolic degradation rate, as has been described for polyphenols.55−57 Thus, flavonoids are susceptible to enzymatic C-ring fission, which influenced the effective bioactive fraction of unmetabolized compound within the cell. To verify the validity of such a hypothesis, the IC50 values for dihydromyricetin and myricetin were determined from in vitro (cell-free) assays, leading to similar antiaggregation potencies (IC50 values of 12.8 and 14.8 μM for dihydromyricetin and myricetin, respectively), in agreement with the values reported by Sato et al.43 While all these compounds exhibited similar log P values, the discrepancy between IC50 values obtained from in vitro cell-based and in vitro cell-free assays supported the more rapid metabolic degradation of dihydromyricetin. In turn, this might be related to the absence of the Δ2(3) double bond (Chart 1), as dihydrokaempferol also exhibited a drastic reduction in antiaggregating activity compared to kaempferol (IC50 values of >500 and 75.1 μM, respectively),43 thus rendering dihydromyricetin more susceptible to degradation than the flavonol myricetin. In the past decade, dietary phenolic compounds became a major focus for the treatment of several neurodegenerative pathologies such as AD,20−23 where poor bioavailability and selectivity profile are critical issues.58 Furthermore, the efficiency can be determined by other factors, such as chemical instability and oxidative processes for compounds with adjacent OH groups, as noted in the oxidative degradation of quercetin to 3,4-dihydroxyphenylacetic acid and phloroglucinol.58,59 For compounds with a low number of phenolic groups, reductive phenomena seem to also be plausible, as reported for the double bond in the heptadienedione linker of curcumin by NADPH-dependent reductase, CurA in intestinal E. coli,60 which may also explain the lower activity in the cell-based assay. Metabolic degradation processes, such as enzymatic ring fission and hydrolysis, may also affect the bioavailability of natural phenolic compounds in cell-based assays. Rosmarinic acid is a representative case of this phenomenon. In fact, its ester bond is rapidly hydrolyzed by microbial esterases with subsequent release of caffeic acid, which in turn is metabolized to give mcoumaric acid and m-hydroxyphenylpropionic acid.61 The combined in vitro cell-based/in silico approach presented in this study reveals the complementary usage of the information retrieved from the fluorescence-based bacterial assays and molecular simulations to gain a more fundamental insight into the molecular factors that determine the antiaggregating activity. The data obtained show the inhibitory capacity of phenolic compounds in the Aβ42 aggregation, justifying their choice for the development of more selective, stable, and active anti-Alzheimer drug candidates. Detailed analysis of the mechanism of action and binding sites from in silico simulations suggests that the 2-phenyl-3,4-dihydro-2H-1benzopyran scaffold can interact with the Aβ42 fibers, thus contributing to block their elongation and reduce the Aβ42 polymerization progression in bacterial models. Notably, the lower inhibitory potential observed in the in vitro cell-based assay should be considered of considerable importance, as this assay provides information about the antiaggregating effect in a cellular context. Overall, this strategy may be useful for the design of new functionalized synthetic phenols with enhanced antiaggregating effect against Aβ peptide.

Article

EXPERIMENTAL SECTION

In Vitro Cell-Based Assays. Chemicals and Bacterial Media. Apigenin, quercetin, curcumin, melatonin, rosmarinic acid, o-vanillin, and all other general chemicals were purchased from Sigma-Aldrich (Madrid, Spain). Compounds for bacterial media were purchased from Pronadisa (Sevilla, Spain). M9 minimal medium: for 100 mL, 10 mL of salts 10× (0.68 g of Na2HPO4, 0.30 g of KH2PO4, 0.05 g of NaCl, 0.10 g of NH4Cl), 0.2 mL of 1 M MgSO4, 0.2 mL of 50 mM CaCl2, 2.5 mL of 20% glucose, and 87.1 mL of H2O. Aβ42 Aggregation Inhibition Assay in Bacterial Cells. Escherichia coli competent cells BL21 (DE3) were transformed with the pET28a vector from Novagen carrying the DNA sequence of Aβ42. Because of the addition of the initiation codon ATG, the overexpressed peptide contains an additional methionine residue at its N-terminus (MDAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVVIA). For overnight culture preparation, 10 mL of M9 minimal medium containing 50 μg·mL−1 of kanamycin was inoculated with a single colony of BL21 (DE3) bearing the plasmid to be expressed at 37 °C. For expression of the Aβ42 peptide, 100 μL of overnight culture was transferred into Eppendorf tubes of 1.5 mL with 880 μL of fresh M9 minimal medium containing kanamycin and Th-S for a final concentration of 50 μg·mL−1 and 25 μM, respectively. Then, 10 μL of each compound to be tested in DMSO and 10 μL of isopropyl 1-thio-β-D-galactopyranoside (IPTG) at 100 mM (for induced cultures) were added. The final concentration of the natural compound was fixed at 200 μM. The samples were grown overnight at 37 °C and 1400 rpm using a Thermomixer (Eppendorf, Hamburg, Germany). As negative control (maximal amyloid formation), an additional sample was prepared by adding the same amount of DMSO in the absence of any natural compound. In parallel, noninduced samples (wherein IPTG was substituted for 10 μL of H2O) were also prepared and used as positive controls (non-amyloid presence). In addition, the absorbance at 600 nm (OD600) of these samples was assessed to confirm the correct bacterial growth, discarding potential intrinsic toxicity of the compounds. The same protocol was followed to determine the IC50 values of the natural compounds, except for modifying the initial concentration of each compound. In Vitro Aβ42 Aggregation Assays. Preparation of AggregateFree Amyloid-Beta Peptide. Aβ42 peptide was obtained from Bachem (Bubendorf, Switzerland). Aβ42 (1 mg) was solubilized in 500 μL of 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP) under vigorous stirring at room temperature for 1 h. The resulting solution was sonicated for 30 min and subsequently stirred at room temperature for an hour. The solution was maintained at 4 °C for 30 min to avoid solvent evaporation during aliquot collection. To eliminate possible insoluble materials, the samples were filtered over 0.22 μm filters. Lastly, aliquots of soluble Aβ(1−40) were collected and the HFIP was evaporated under a gentle stream of nitrogen. The samples were stored at −20 °C. Aβ42 Aggregation Assays. The samples were resuspended in 50 μL of DMSO, and the monomers were solubilized through sonication for 10 min. PBS 1× (940 μL) was added, and the samples were divided in four parts (247.5 μL). Finally, 2.5 μL of each compound at 2 mM in DMSO (obtaining a final concentration of 20 μM) or DMSO without compound (positive control) was added. The final concentration of Aβ42 was 20 μM. The samples were placed in a thermomixer (Eppendorf, Germany) at 37 °C and stirred at 1400 rpm. At 48 h, 135 μL of sample was mixed with 15 μL of Th-T at 250 μM, obtaining a final Th-T concentration of 25 μM. Finally, the aggregation was tracked by detecting Th-T fluorescence (λexc = 445 nm; λem = 480 nm) using an Aminco Bowman Series 2 luminescence spectrophotometer (Aminco-Bowman AB2, SLM Aminco, Rochester, NY, USA). Thioflavin-S Steady-State Fluorescence. Th-S fluorescence was detected using an Aminco Bowman Series 2 luminescence spectrophotometer with an excitation wavelength of 440 nm and emission range from 460 to 600 nm. Excitation and emission slit widths of 4 nm were used, and spectra were acquired at 1 nm intervals, 500 nm·min−1 rates, and 875 V. The emission at 485 nm (Th-S amyloid peak) was obtained after subtraction of noninduced samples 286



Article

from induced ones for each inhibitor. In order to normalize the Th-S fluorescence as a function of the bacterial concentration, optical density at 600 nm (OD600) was obtained using a Shimadzu UV-2401 PC UV−vis spectrophotometer. Note that Th-S fluorescence normalization was carried out considering 100% Th-S fluorescence of the bacterial cells expressing Aβ42 in the absence of drug and 0% Th-S fluorescence of the bacterial cells not expressing Aβ42. Aggregation Assay Analysis. The amyloid aggregation in bacteria may be studied as an autocatalytic reaction using eq 1: f=

was applied for temperature regulation during the heating. Finally, 10 ns of MD simulation at constant volume and temperature (300 K) using the weak-coupling algorithm with a time constant of 10.0 enabled stabilization of the temperature during the simulation. The time step for saving the trajectory was set to 2 ps. A Cartesian restraint of 10 kcal/mol Å was applied to the first (chains A in 2BEG and F in 2MXU) and the last (chains E in 2BEG and J in 2MXU) of the 5-mer β-sheet used as models of the Aβ(42) fibrils during both equilibration and MD simulation in order to avoid drastic conformational changes of the intrinsically more unstable fibril backbone. MM-GBSA Binding Free Energy Calculations. The Python version MMPBSA.py54 implemented in Amber12 was used to compute the contributions to the binding free energy of the compounds to the Aβ(42) fibrils according to the GB method. The MM-GBSA method determines the free energy (G) of each species (ligand, protein, and complex) into enthalpic (gas-phase) (Egas), solvation (Gsolv), and entropy (S) terms, according to eq 2.

ρ{exp[(1 + ρ)kt ] − 1} 1 + ρ exp[(1 + ρ)kt ]

(1)

where f is the fraction of Aβ peptide in fibrillar form, the rate constant k includes the kinetic contributions arising from the formation of the nucleus from monomeric Aβ and the elongation of the fibril, which are described by rate constants kn and ke, respectively, and ρ is a dimensionless parameter that describes the ratio of kn to k. Equation 1 is obtained under the boundary conditions of t = 0 and f = 0, where k = kea (a is the protein concentration). By nonlinear regression of f against t, values of ρ and k were obtained, and from them the rate constants, ke (elongation constant), and kn (nucleation constant).14 Note that because the concentration of Aβ is not constant in the bacterial cells during the protein overexpression, the Aβ42 concentration of the final time-course obtained without inhibitor was considered as a,14 obtaining apparent values for ke, which allowed quantitative comparison of kinetics. The extrapolation of the linear portion of the sigmoid curve to the abscissa ( f = 0) and to the highest ordinate value of the fitted plot afforded two values of time (t0 and t1), which corresponded to the lag time and to the end-time reaction. The time at which half of the protein was aggregated (i.e., when f = 0.5) was considered the time of half-aggregation (t1/2).14 Computational Methods. Molecular Docking. The software AutoDock Vina62 was used to perform a blind docking of all the molecules into the 10 protein models of the high-resolution NMR structure of the Aβ42 fiber, 2BEG. A protein-centered grid box, defined with a size of 52 × 37 × 39 Å3 and a regular space of 0.375 Å, able to cover the whole protein, was considered for docking. Accordingly, a search space volume of >27 000 Å3 was explored. Nine poses (docking solutions) for each putative ligand in each protein model were predicted and energetically scored. A total of 54 × 10 docking solutions were predicted and analyzed. After docking, all the results for the 10 models were analyzed in order to find the best model for each binding site. The same protocol was also applied to the 2MXU template. In the latter case, only the first NMR model deposited in PDB for 2MXU was taken into account for docking analysis. Molecular Dynamics Simulations. Amber1263 was used to perform MD simulations on the selected ligand−Aβ42 complexes generated by docking analysis. In this context, the general Amber force field (GAFF) was used to parametrize the ligand, and the partial charges were derived at the B3LYP/6-31G(d) level, after preliminary optimization of the molecular structure, by using the restrained electrostatic potential (RESP) fitting method implemented in Gaussian0964 and Antechamber. All the ligand−Aβ42 complexes were solvated with a truncated octahedral (TIP3P) water box with a layer of 12 Å and neutralized by adding Na+ ions. The complexes were subjected to three stages of energy minimization that involved first all hydrogen atoms, then water molecules, and finally the whole simulated system (protein, ligand, water molecules, and counterions) with a maximum of 10 000 minimization cycles for the latter stage. Before MD simulation, a preliminary heating of the system from 0 to 300 K was accomplished in six steps, the first being performed at constant volume and the rest at constant pressure. The SHAKE algorithm was applied to constrain bonds involving hydrogen atoms. Periodic boundary conditions at constant volume were imposed on the system during the MD simulations. Cutoff for the nonbonded interactions was set to 10 Å. The electrostatic interactions beyond the cutoff within the periodic box were computed by applying the Particle Mesh Ewald (PME) method. Langevin dynamics with a collision frequency of 1.0

G = Egas + Gsolv − TS = E int + Eelect + EvdW + Gsolv,pol + Gsolv,n ‐ pol − TS

(2)

where Eint, Eelect, and EvdW are the internal, Coulomb, and van der Waals energy terms and account respectively for bonded, polar, and nonpolar energy contributions in the gas phase, Gsolv,pol is the polar contribution to solvation free energy, which was evaluated by using the generalized-Born solvation method, and Gsolv,n‑pol accounts for nonpolar contributions to solvation free energy and was computed linearly from the solvent-accessible surface area (SASA; eq 3). Gsolv,n ‐ pol = γ SASA + b

(3)

where γ is the surface tension (set to 0.0072 kcal mol−1 Å−2) and b is a correction term (assumed to be zero in the present calculations). All these contributions were calculated for complex, receptor, and ligand, and the binding free energy (ΔGbind) was evaluated as the difference for the three species (eq 4). ΔG bind = ⟨Gcomplex ⟩traj − ⟨Greceptor ⟩traj − ⟨G ligand⟩traj

(4)

where ⟨Gx⟩traj accounts for the average value for each species x (complex, receptor, ligand) determined for an ensemble of snapshots taken from the MD trajectory of the complex within the framework of the single-trajectory approach. A sampling frequency of 50 ps was applied to the last 2 ns of MD trajectory, leading to an ensemble of 40 snapshots for each trajectory. The vibrational entropy term (S) was determined from separate normal-mode analysis calculations using the same set of snapshots.

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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.jnatprod.6b00643. Representation of the kinetics of the Aβ42 fraction and the binding mode of AZD2184 and thioflavin T in Aβ42 fibers (PDF)

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AUTHOR INFORMATION

Corresponding Authors

*Tel (F. J. Luque): +34 93 4033788. E-mail: [email protected]. *Tel (R. Sabaté): +34 93 4035986. Fax: +34 93 4035987. Email: [email protected]. ORCID

Diego Muñoz-Torrero: 0000-0002-8140-8555 F. Javier Luque: 0000-0002-8049-3567 Raimon Sabate: 0000-0003-3894-2362 287



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Author Contributions

(22) Silva, T.; Reis, J.; Teixeira, J.; Borges, F. Ageing Res. Rev. 2014, 15, 116−145. (23) Sundaram, R.; Vazhayil, B.; Annapandian, V.; Abhirama, B.; Sudha, M.; Thiyagarajan, T.; KG, K.; Pushpa, S. Br. Med. Bull. 2014, 2, 359−370. (24) Khan, A.; Vaibhav, K.; Javed, H.; Tabassum, R.; Ahmed, M. E.; Khan, M. M.; Khan, M. B.; Shrivastava, P.; Islam, F.; Siddiqui, M. S.; Safhi, M. M. Neurochem. Res. 2014, 39, 344−352. (25) Muthaiyah, B.; Essa, M. M.; Lee, M.; Chauhan, V.; Kaur, K.; Chauhan, A. J. Alzheimers Dis. 2014, 42, 1397−1405. (26) Ono, K.; Hasegawa, K.; Naiki, H.; Yamada, M. Biochim. Biophys. Acta, Mol. Basis Dis. 2004, 1690, 193−202. (27) Sharoar, M. G.; Thapa, A.; Shahnawaz, M.; Ramasamy, V. S.; Woo, E. R.; Shin, S. Y.; Park, I. S. J. Biomed. Sci. 2012, 19, 104. (28) Subash, S.; Essa, M. M.; Al-Adawi, S.; Memon, M. A.; Manivasagam, T.; Akbar, M. Neural Regener. Res. 2014, 9, 1557−1566. (29) Villar-Pique, A.; Espargaro, A.; Sabate, R.; de Groot, N. S.; Ventura, S. Microb. Cell Fact. 2012, 11, 55. (30) Williams, P.; Sorribas, A.; Liang, Z. Curr. Alzheimer Res. 2010, 7, 210−213. (31) Mishra, S.; Palanivelu, K. Ann. Indian. Acad. Neurol. 2008, 11, 13−19. (32) Lin, L.; Huang, Q. X.; Yang, S. S.; Chu, J.; Wang, J. Z.; Tian, Q. Int. J. Mol. Sci. 2013, 14, 14575−14593. (33) Hamaguchi, T.; Ono, K.; Murase, A.; Yamada, M. Am. J. Pathol. 2009, 175, 2557−2565. (34) Necula, M.; Kayed, R.; Milton, S.; Glabe, C. G. J. Biol. Chem. 2007, 282, 10311−10324. (35) Katalinic, M.; Rusak, G.; Domacinovic Barovic, J.; Sinko, G.; Jelic, D.; Antolovic, R.; Kovarik, Z. Eur. J. Med. Chem. 2010, 45, 186− 192. (36) Xie, Y.; Yang, W.; Chen, X.; Xiao, J. Food Funct. 2014, 5, 2582− 2589. (37) Amini, R.; Yazdanparast, R.; Bahramikia, S. Int. J. Biol. Macromol. 2013, 60, 334−340. (38) Ansari, M. A.; Abdul, H. M.; Joshi, G.; Opii, W. O.; Butterfield, D. A. J. Nutr. Biochem. 2009, 20, 269−275. (39) Islam, M. R.; Zaman, A.; Jahan, I.; Chakravorty, R.; Chakraborty, S. J. Young Pharm. 2013, 5, 173−179. (40) Millington, C.; Sonego, S.; Karunaweera, N.; Rangel, A.; Aldrich-Wright, J. R.; Campbell, I. L.; Gyengesi, E.; Munch, G. BioMed Res. Int. 2014, 2014, 309129. (41) Venigalla, M.; Gyengesi, E.; Munch, G. Neural Regener. Res. 2015, 10, 1181−1185. (42) Zhao, L.; Wang, J. L.; Liu, R.; Li, X. X.; Li, J. F.; Zhang, L. Molecules 2013, 18, 9949−9965. (43) Sato, M.; Murakami, K.; Uno, M.; Nakagawa, Y.; Katayama, S.; Akagi, K.; Masuda, Y.; Takegoshi, K.; Irie, K. J. Biol. Chem. 2013, 288, 23212−23224. (44) Luhrs, T.; Ritter, C.; Adrian, M.; Riek-Loher, D.; Bohrmann, B.; Dobeli, H.; Schubert, D.; Riek, R. Proc. Natl. Acad. Sci. U. S. A. 2005, 102, 17342−17347. (45) Xiao, Y.; Ma, B.; McElheny, D.; Parthasarathy, S.; Long, F.; Hoshi, M.; Nussinov, R.; Ishii, Y. Nat. Struct. Mol. Biol. 2015, 22, 499− 505. (46) Kuang, G.; Murugan, N. A.; Tu, Y.; Nordberg, A.; Agren, H. J. Phys. Chem. B 2015, 119, 11560−11567. (47) Genheden, S.; Ryde, U. Expert Opin. Drug Discovery 2015, 10, 449−461. (48) Kollman, P. A.; Massova, I.; Reyes, C.; Kuhn, B.; Huo, S.; Chong, L.; et al. Acc. Chem. Res. 2000, 33, 889−897. (49) Johnson, A. E.; Jeppsson, F.; Sandell, J.; Wensbo, D.; Neelissen, J. A. M.; Jureus, A.; Strom, P.; Norman, H.; Farde, L.; Svensson, S. P. J. Neurochem. 2009, 108, 2217−2227. (50) Klunk, W. E.; Wang, Y. M.; Huang, G. F.; Debnath, M. L.; Holt, D. P.; Mathis, C. A. Life Sci. 2001, 69, 1471−1484. (51) Ono, K.; Yoshiike, Y.; Takashima, A.; Hasegawa, K.; Naiki, H.; Yamada, M. J. Neurochem. 2003, 87, 172−181.

⊥

A. Espargaró and T. Ginex contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the Ministerio de Economiá y Competitividad (MINECO) (SAF2014-57094-R, F.J.L.), startup grant of the Ramón y Cajal program RYC-2011-07987 (R.S.), and Generalitat de Catalunya (GC) (2014SGR938 to R.S. and A.E. and 2014SGR1189 to F.J.L.). A contract from the Ramón y Cajal program of MICINN to R.S. (RYC-201107987) and a contract from the Juan de la Cierva program of MINECO to A.E. (JCI-2012-12193) are gratefully acknowledged. F.J.L. acknowledges the support from Icrea Academia.

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REFERENCES

(1) Reitz, C.; Brayne, C.; Mayeux, R. Nat. Rev. Neurol. 2011, 7, 137− 152. (2) Rocca, W. A.; Petersen, R. C.; Knopman, D. S.; Hebert, L. E.; Evans, D. A.; Hall, K. S.; Gao, S.; Unverzagt, F. W.; Langa, K. M.; Larson, E. B.; White, L. R. Alzheimer's Dementia 2011, 7, 80−93. (3) Ballard, C.; Gauthier, S.; Corbett, A.; Brayne, C.; Aarsland, D.; Jones, E. Lancet 2011, 377, 1019−1031. (4) Invernizzi, G.; Papaleo, E.; Sabate, R.; Ventura, S. Int. J. Biochem. Cell Biol. 2012, 44, 1541−1554. (5) Thies, W.; Bleiler, L. Alzheimer's Dementia 2011, 7, 208−244. (6) Aisen, P. S. CNS Drugs 2005, 19, 989−996. (7) Godyn, J.; Jonczyk, J.; Panek, D.; Malawska, B. Pharmacol. Rep. 2016, 68, 127−138. (8) Lannfelt, L.; Möller, C.; Basun, H.; Osswald, G.; Sehlin, D.; Satlin, A.; Logovinsky, V.; Gellerfors, P. Alzheimer's Res. Ther. 2014, 6, 16. (9) Villar-Pique, A.; Espargaro, A.; Ventura, S.; Sabate, R. Biotechnol. J. 2016, 11, 172−177. (10) Xu, X.; Lei, Y.; Luo, J.; Wang, J.; Zhang, S.; Yang, X. J.; Sun, M.; Nuwaysir, E.; Fan, G.; Zhao, J.; Lei, L.; Zhong, Z. Stem Cell Res. 2013, 10, 213−227. (11) Hou, X. Q.; Yan, R.; Yang, C.; Zhang, L.; Su, R. Y.; Liu, S. J.; Zhang, S. J.; He, W. Q.; Fang, S. H.; Cheng, S. Y.; Su, Z. R.; Chen, Y. B.; Wang, Q. Int. J. Mol. Med. 2014, 33, 543−549. (12) Bartolini, M.; Bertucci, C.; Bolognesi, M. L.; Cavalli, A.; Melchiorre, C.; Andrisano, V. ChemBioChem 2007, 8, 2152−2161. (13) Bartolini, M.; Naldi, M.; Fiori, J.; Valle, F.; Biscarini, F.; Nicolau, D. V.; Andrisano, V. Anal. Biochem. 2011, 414, 215−225. (14) Espargaro, A.; Medina, A.; Di Pietro, O.; Munoz-Torrero, D.; Sabate, R. Sci. Rep. 2016, 6, 23349. (15) Pouplana, S.; Espargaro, A.; Galdeano, C.; Viayna, E.; Sola, I.; Ventura, S.; Munoz-Torrero, D.; Sabate, R. Curr. Med. Chem. 2014, 21, 1152−1159. (16) Di Pietro, O.; Perez-Areales, F. J.; Juarez-Jimenez, J.; Espargaro, A.; Clos, M. V.; Perez, B.; Lavilla, R.; Sabate, R.; Luque, F. J.; MunozTorrero, D. Eur. J. Med. Chem. 2014, 84, 107−117. (17) Perez-Areales, F. J.; Di Pietro, O.; Espargaro, A.; VallverduQueralt, A.; Galdeano, C.; Ragusa, I. M.; Viayna, E.; Guillou, C.; Clos, M. V.; Perez, B.; Sabate, R.; Lamuela-Raventos, R. M.; Luque, F. J.; Munoz-Torrero, D. Bioorg. Med. Chem. 2014, 22, 5298−5307. (18) Sola, I.; Aso, E.; Frattini, D.; Lopez-Gonzalez, I.; Espargaro, A.; Sabate, R.; Di Pietro, O.; Luque, F. J.; Clos, M. V.; Ferrer, I.; MunozTorrero, D. J. Med. Chem. 2015, 58, 6018−6032. (19) Viayna, E.; Sola, I.; Bartolini, M.; De Simone, A.; Tapia-Rojas, C.; Serrano, F. G.; Sabate, R.; Juarez-Jimenez, J.; Perez, B.; Luque, F. J.; Andrisano, V.; Clos, M. V.; Inestrosa, N. C.; Munoz-Torrero, D. J. Med. Chem. 2014, 57, 2549−2567. (20) Brahmachari, G. Asia-Pac. BioTech News 2011, 15, 14−34. (21) Chiang, K.; Koo, E. H. Annu. Rev. Pharmacol. Toxicol. 2014, 54, 381−405. 288



Article

(52) Thapa, A.; Woo, E. R.; Chi, E. Y.; Sharoar, M. G.; Jin, H. G.; Shin, S. Y.; Park, I. S. Biochemistry 2011, 50, 2445−2455. (53) Yang, F.; Lim, G. P.; Begum, A. N.; Ubeda, O. J.; Simmons, M. R.; Ambegaokar, S. S.; Chen, P. P.; Kayed, R.; Glabe, C. G.; Frautschy, S. A.; Cole, G. M. J. Biol. Chem. 2005, 280, 5892−5901. (54) Reinke, A. A.; Gestwicki, J. E. Chem. Biol. Drug Des. 2007, 70, 206−215. (55) Aura, A. M. Phytochem. Rev. 2008, 7, 407−429. (56) Del Rio, D.; Rodríguez-Mateos, A.; Spencer, J. P.; Tognolini, M.; Borges, G.; Crozier, A. Antioxid. Redox Signaling 2013, 18, 1818− 1892. (57) Rechner, A. R.; Kuhnle, G.; Bremmer, P.; Hubbard, G. P.; Moore, K. P.; Rice-Evans, C. A. Free Radical Biol. Med. 2002, 33, 220− 235. (58) Gao, S.; Hu, M. Mini-Rev. Med. Chem. 2010, 10, 550−567. (59) Agarry, S. E.; Durojaiye, A. O.; Solomon, B. O. Int. J. Environ. Pollut. 2008, 32, 12−28. (60) Schneider, C.; Gordon, O. N.; Edwards, R. L.; Luis, P. B. J. Agric. Food Chem. 2015, 63, 7606−7614. (61) Konishi, Y.; Kobayashi, S. Biosci., Biotechnol., Biochem. 2005, 69, 583−591. (62) Trott, O.; Olson, A. J. J. Comput. Chem. 2010, 31, 455−461. (63) Case, D. A.; Darden, T. A.; Cheatham, III, T. E.; Simmerling, C. L.; Wang, J.; Duke, R.e.; Luo, R.; Walker, R. C.; Zhang, W.; Merz, K. M.; Roberts, B.; Hayik, S.; Roitberg, A.; Seabra, G.; Swails, J.; Götz, A. W.; Kolossváry, I.; Wong, K. F.; Paesani, F.; Vanicek, J.; Wolf, R. M.; Liu, J.; Wu, X.; Brozell, S. R.; Steinbrecher, T.; Gohlke, H.; Cai, Q.; Ye, X.; Wang, J.; Hsieh, M.-J.; Cui, G.; Roe, D. R.; Mathews, D. H.; Seetin, M. G.; Salomon-Ferrer, R.; Sagui, C.; Babin, V.; Luchko, T.; Gusarov, S.; Kovalenko, A.; Kollman, P. A. AMBER 12; University of California: San Francisco, 2012. (64) Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G. A.; Nakatsuji, H.; Caricato, M.; Li, X.; Hratchian, H. P.; Izmaylov, A. F.; Bloino, J.; Zheng, G.; Sonnenberg, J. L.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Montgomery, J. A., Jr.; Peralta, J. E.; Ogliaro, F.; Bearpark, M.; Heyd, J. J.; Brothers, E.; Kudin, K. N.; Staroverov, V. N.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A.; Burant, J. C.; Iyengar, S. S.; Tomasi, J.; Cossi, M.; Rega, N.; Millam, J. M.; Klene, M.; Knox, J. E.; Cross, J. B.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.; Pomelli, C.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Zakrzewski, V. G.; Voth, G. A.; Salvador, P.; Dannenberg, J. J.; Dapprich, S.; Daniels, A. D.; Farkas, Ö .; Foresman, J. B.; Ortiz, J. V.; Cioslowski, J.; Fox, D. J. Gaussian 09, Revision E.01; Gaussian, Inc.: Wallingford, CT, 2009.

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