Inhibition of the SARS-CoV-2 Main Protease by Isoquercitrin <i>γ</i>-Cyclodextrin Inclusion Complex Formulations: A Biochemical and <i>In Silico</i> Study

Wurtele, Martin; Coelho, Camila; Gallo, Gloria; Campos, Claudia; Hardy, Leon; Perez Castillo, Yunierkis; Moriwaki, Masamitsu; Kapoor, Mahendra P.; de Sousa, Damião Pergentino

doi:https://doi.org/10.1155/2023/4586423

Journal of Chemistry

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Abstract Introduction Materials and Methods Results and Discussion Conclusions Data Availability Conflicts of Interest Acknowledgments Supplementary Materials References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 4586423 | https://doi.org/10.1155/2023/4586423

Inhibition of the SARS-CoV-2 Main Protease by Isoquercitrin γ-Cyclodextrin Inclusion Complex Formulations: A Biochemical and In Silico Study

Martin Wurtele,¹Camila Coelho,¹Gloria Gallo,¹Claudia Campos,¹Leon Hardy,²Yunierkis Perez Castillo,³Masamitsu Moriwaki,⁴Mahendra P. Kapoor,⁴and Damião Pergentino de Sousa⁵

Academic Editor: Josefina Pons

Received17 Nov 2022

Revised07 Mar 2023

Accepted27 Mar 2023

Published24 Apr 2023

Abstract

The main protease (M^pro) of SARS-CoV-2 is a well-established drug target for rational drug design of COVID-19 inhibitors. To address the serious challenge of COVID-19, we have performed biochemical inhibition screens with recombinantly expressed SARS-CoV-2 main protease (M^pro). A fluorescent assay was used to identify the flavonoid isoquercitrin as an M^pro inhibitor. Both isoquercitrin encapsulated in γ-cyclodextrin (inclusion complex formulations) and alone inhibited SARS-CoV-2 M^pro. For isoquercitrin, a Ki value of 32 μM (IC₅₀ = 63 μM) was obtained. Isoquercitrin γ-cyclodextrin inclusion complex formulations additionally inhibited Zika virus NS2B-NS3^pro leading to an IC₅₀ value of 98 μM. Formulations containing the other flavonoid compounds diosmetin-7-O-glucoside, hesperetin-7-O-glucoside, and naringenin-7-O-glucoside did not inhibit SARS-CoV-2 M^pro. Steady-state kinetics indicate that the inhibition mechanism of M^pro by isoquercitrin is potentially competitive. Molecular modeling studies carried out with MM/PBSA confirm the likely modes of isoquercitrin binding to both proteases. These modeling results can be used in the development of structural analogs of isoquercitrin with better inhibitory profiles and potential candidates for anti-coronavirus drugs. Since the targeted proteases are essential for viral activity, the delivery isoquercitrin-cyclodextrin inclusion complex formulations could be of great interest for the development of future antiviral drugs to target intracellular virus proteins or other components.

1. Introduction

Viral proteases, together with polymerases, are the most prominent targets for the antiviral drug design [1]. In the case of SARS-CoV-2, effective polymerase and main protease (M^pro; also referred to as the 3C-like protease, i.e., 3CL^pro) inhibitor drugs have been developed, like the polymerase inhibitor Remdesivir [2] and the M^pro inhibitor Nirmatrelvir (coadministered with Ritonavir in Paxlovid) [3]. However, due to several issues, including the emergence of new mutant variants of SARS-CoV-2, global efforts for drug discovery remain very relevant, and COVID-19 continues to be of great concern to global public health [1, 4]. SARS-CoV-2 is a single-stranded RNA virus with a genome size of about 29.9 kb in length. About two-thirds of this genome encodes a transcript (Orf1ab) containing the nonstructural proteins. Orf1ab is translated into two polyproteins, which are processed by the virus’s main protease M^pro and a second papain-like protease (PLpro) [5]. M^pro main protease, whose 3D structure has been recently solved [4, 6], is a cysteine protease with essential functions for viral replication and thus is a well-established target for the development of anti-SARS-CoV-2 drugs [7–12]. In addition, it is also an interesting marker for diagnostic purposes [13], and its off-target activity has already been linked to SARS-CoV-2 pathogenicity [14]. Considering all these reasons, we have carried out initial biochemical inhibition screens with purified recombinant SARS-CoV-2 M^pro [15] and Zika virus NS2B-NS3^pro [16] to define potential of pharmacological ingredients of the flavonoid family, which could possibly serve as lead components for the design of future antiviral drugs. These compounds are promising antiviral candidates and are widely found in fruits, wine, vegetables, seeds, and tea [17–19]. The bioactive flavonoid compounds are already known to boost the individual immune system and could effectively inhibit the infection by SARS-CoV-2 as the efficient shield against the onset of COVID-19 [20].

In this study, we examined the proprietary formulation of flavonoid inclusion complexes with cyclodextrin, wherein their glucoside derivatives were encapsulated in the hydrophobic cavities of cyclodextrin through controlled enzymatic hydrolysis [21]. The spectroscopic evaluation of studied flavonoid-cyclodextrin inclusion complexes revealed the successful formulation of these compositions [22, 23]. Acute, subchronic, Ames, micronucleus, and comet assays support the safe use of these flavonoid-cyclodextrin inclusion complexes as food, food additives, and natural pharmaceutical and nutraceutical ingredients [24–26]. Also, the bioavailability studies in animals and humans revealed that these flavonoid-cyclodextrin inclusion complexes are highly bioavailable and could be effective functional flavonoid ingredients with potential health benefits in humans [27, 28]. It is hypostatized that the biological actions of these flavonoids-cyclodextrin formulations may counteract SARS-CoV-2 infections and modulates the immune system response to the disease prevention.

2. Materials and Methods

2.1. Chemical Compounds

The proprietary formulation of patented compositions, diosmetin-7-O-glucoside (SunActive DCD™, 12% active component content, 51% γ-cyclodextrin, 31% saccharides et al. content and 6% moisture), hesperetin-7-O-glucoside (SunActive HCD™, 14.2–14.5% active component content, 57.5% γ-cyclodextrin, 24% saccharides et al. content, and 6% moisture), naringenin-7-O-glucoside (SunActive NCD™, 22% active component content, 60% β-cyclodextrin, 12% saccharides and other content, and 6% moisture), and quercetin-3-O-glucoside (isoquercitrin, SunActive QCD™, 20% active component content, 65% γ-cyclodextrin, 9% saccharides et al. content, and 6% moisture) were supplied by Taiyo Kagaku Co. (Yokkaichi, Japan).

2.2. Biochemical Assays

SARS-CoV-2 M^pro was expressed recombinantly in E. coli BL21(DE3) and purified as reported [15]. Inhibition assays were performed in a biochemical buffer containing 50 mM Tris-HCl pH 7.3, 20% glycerol, 1 mM EDTA pH 7.3, and 0.01% triton-X. 1 μM M^pro, 500 μM compounds (Taiyo Kagaku Co., Japan), and a 10 μM FRET substrate-peptide (MCA-AVLQSGFR-K(Dnp)-K-NH2, Biomatik Corporation, Cambridge, Canada) [6, 16] was used. The assay was performed at 30°C after an incubation period of 15 minutes. Tannic acid, a wide-range protease enzyme inhibitor, was used as a positive control [15]. Similarly, compounds were tested with recombinantly expressed Zika virus protease [16] in assays containing 50 mM Tris-HCl pH 8.5, 20% glycerol, and 1 mM CHAPS using 5 nM NS2B-NS3 Zika protease, 500 μM compounds (Taiyo Kagaku Co., Japan), and 20 μM substrate-peptide Bz-Nle-Lys-Lys-Arg-AMC [29] at 37°C after an incubation period of 15 minutes. The biochemical reaction was monitored by fluorescence emission of the cleaved peptide substrate. An excitation wavelength of 330 nm and an emission wavelength of 400 nm for the SARS-CoV-2 M^pro probe [15] were used. A 360 nm excitation and a 460 nm emission wavelength were used in the case of Zika virus NS2B-NS3^pro protease assay [16]. All assays were performed on an Infinite M200 plate reader (Tecan Group Ltd.). IC₅₀ values for SARS-CoV-2 M^pro were determined using concentrations from 122 nM to 2 mM of compounds and 1 μM M^pro with 10 μM substrate-peptide. In the case of the Zika virus, 5 nM protease, 20 μM substrate-peptide, and concentrations from 244 nM to 2 mM of compounds were used. IC₅₀ was analyzed by nonlinear regression using a four-parameter dosage-response variable slope model with the GraphPad Prism software (GraphPad Software, USA). Errors were estimated as standard errors of the mean (SEM). Enzyme kinetics experiments were performed using fluorescent peptide concentrations ranging from 0.31 μM to 80 μM and at two different inhibitor concentrations (125 and 62.5 μM of quercetin-3-O-glucoside or isoquercitrin, Fujifilm, Japan). The activity assay was performed using 50 mM Tris-HCl pH 7.3, 20% glycerol, 1 mM EDTA pH 7.3, and 0.01% triton-X. Final concentrations of 2 μM M^pro were used. The inner filter effect (IFE) was included in this analysis as described [30]. Data were analyzed using Dynafit [31]. This software uses ranking methods like the summed squared deviation between the experimental data and the theoretical model (SSQ), the second-order Akaike information criterion (ΔAIC), and the Bayesian information criterion (ΔBIC) as decision criteria for the selection of the most probable enzymatic mechanisms.

2.3. Molecular Modeling

Otherwise noted, calculation parameters for the software employed in this research were kept to their default values. The three-dimensional (3D) structures of the SARS-CoV-2 M^pro and Zika virus NS2B-NS3^pro proteases were retrieved from the Protein Data Bank (PDB) database. The PDB codes of the selected receptor structures were 7JKV [32] and 7ZYS for M^pro and NS2B-NS3^pro, respectively. Any cocrystallized ligand and solvent molecules were removed from the receptor structures before molecular docking calculations. One initial three-dimensional conformation was generated for isoquercitrin with the Omega software [33]. Partial atomic charges of type am1bcc were added to the compound with OpenEye’s Molcharge utility [34].

Molecular docking calculations were carried out with the Gold program [35]. For each target, 30 different docking solutions were generated. The search efficiency parameter of Gold was set to 200%. Primary scoring was performed with the GoldScore scoring function [36] and all docking solutions were rescored with the ChemScore function [37]. The later scoring values were selected for ranking the predicted ligand-receptor complexes. The top 10 scored complexes per enzyme were selected for molecular dynamics (MD) simulations, and the free energies of binding for them were predicted from the MD simulations that were performed with Amber 22 [38] following the previously described protocol [39].

For MD simulations, proteins and isoquercitrin were parameterized with the ff19SB and gaff2 force fields, respectively [40, 41]. Complexes were enclosed in truncated octahedron boxes and solvated with OPC water molecules in such a way that any solute atom was at a minimum distance of 10 Å from the edges of the box. Excess charges were neutralized by adding Na⁺ and Cl⁻ counterions at a concentration of 150 mM according to the methodology described by Machado and Pantano [42]. Energy minimization was performed at constant volume in two stages, the first of which consisted in 500 cycles of the steepest descent algorithm followed by 500 cycles of conjugate gradient. Solute was restrained with a force constant equal to 500 kcal·mol⁻¹·Å⁻² during the first energy minimization stage. For the second energy minimization step, all restraints were released and it consisted in 1,000 cycles of the steepest descent algorithm followed by 1,500 cycles of conjugate gradient. Long-range electrostatic interactions were treated with the Particle-Mesh Ewald (PME) method using a 10 Å cut-off during all MD simulation stages.

Next, the systems were heated from 0 K to 300 K for 20 ps at constant volume and with everything except the solvent restrained with a force constant of 10 kcal·mol⁻¹·Å⁻². Starting from the heating step, the SHAKE algorithm was used to constrain bonds involving hydrogen atoms and the calculation of forces for them was omitted. Likewise, temperature was controlled with a Langevin thermostat whose collision frequency was set to 1 ps⁻¹ and integration took place with a time step of 2 fs. Afterward, the complexes were equilibrated for 100 ps in the NTP ensemble at 1 bar and 300 K. Pressure relaxation time was set to 2 ps.

Each equilibrated system was used as input to five different production runs of 4 ns, leading to a total simulation time of 20 ns per complex. Initial atomic velocities were randomly initialized in each production run. The free energies of binding were estimated with the MM-PBSA method as implemented in Amber 22 [38]. The solute dielectric factor was set to 2 for MM-PBSA calculations that considered 100 MD snapshots evenly drawn from all system’s production runs. In total, 20 snapshots were evenly selected from the 1 ns–4 ns time interval of each production run. The ionic strength for estimating the free energies of binding was set to 150 mM.

3. Results and Discussion

To identify potential SARS-CoV-2 M^pro main protease inhibitors, biochemical screens with recombinant M^pro were initially carried out using several SunActive™ inclusion complex formulations (DCD, HCD, NCD, and QCD) with 500 µM active compound concentrations. The inhibitory activity of each compound was assayed by their ability to inhibit the reaction rate (V₀) of SARS-CoV-2 main protease (Table 1) and, as a control, Zika virus protease (Table 2).

The screens of the provided formulations led to interesting hits. In this work, it was possible to detect the inhibitory activity of the SunActive QCD™ formulation on SARS-CoV-2 protease. Its active compound isoquercitrin (quercetin-3-O-glucoside) showed an IC₅₀ of 87.9 μM for this enzyme when tested in the SunActive QCD™ formulation (Figure 1). This same active compound in formulation presented an IC₅₀ of 97.8 μM for Zika virus NS2B-NS3^pro (Figure 2(a)). The active compound of SunActive DCD™ showed an IC₅₀ value of 98.1 μM with regards to this protease (Figure 2(b)). SunActive HCD™ and SunActive NCD™ formulations, however, did not show significant inhibitory activity on either of both the proteases.

(a)

(b)

In subsequent experiments, Isoquercitrin, the active compound of the most promising formulation (SunActive QCD™) was tested in purified form. Isoquercitrin (quercitin-3-O-glucoside) (Figure 3(a)) showed a similar IC₅₀ value (63.22 μM) as in the formulation, thus confirming its inhibitory activity on SARS-CoV-2 main protease M^pro (Figure 3(b)). Most interestingly, an inhibitor constant (Ki) of 32 μM could be determined for the compound in steady-state kinetics enzyme assays using this protease (Figure 3(c)). Regarding potential inhibitory mechanisms, these assays indicate an underlying competitive mechanism (Table 3).

(a)

(b)

(c)

Modeling studies were performed as described in the Materials and Methods section. The presented modeling approach has the objective of identifying the most probable binding mode of isoquercitrin to both the proteases. Molecular docking was employed to generate initial ligand-receptor binding hypotheses. Since docking algorithms are limited in the exploration of the complexes’ conformational space and in the evaluation of the binding energy, the free energies of binding of the docking-predicted complexes were computed from MD simulations. The postprocessing of molecular docking predictions with MD-based energy calculation methods has been shown to improve the discrimination between correct and incorrect ligand binding poses [43].

In our approach, 20 complexes were subject to MD-based free energy of binding calculations. Considering that a simulation time of 20 ns was performed per complex, the total MD simulations time required to perform all experiments was 400 ns. Different literature reports propose that the MD simulation times less than 5 ns are sufficient for estimating free energies of binding with the MM/PBSA method [44, 45]. Considering this, we opted for five productions runs of 4 ns length each one per complex. One advantage of the multiple trajectories approach over the single trajectory strategy is that with the first it can be achieved a better exploration of the complex’s conformational space because initial atomic velocities are randomly initialized prior to each production run.

The docking scores for the 30 docking solutions explored per target as well as the predicted MM/PBSA free energies of binding for the 10 top ranked docking solutions are provided as Supporting Information in Tables S1 and S2, respectively. Since MM/PBSA calculations should be performed over MD snapshots extracted from equilibrium simulations, the total energy of the systems was analyzed for all production runs. The plots of total energy vs. simulation time are provided as Supporting Information in Figures S1–S10 and S11–S20 for the SARS-CoV-2 M^pro and NS2B-NS3^pro proteases, respectively. The conformational stability of the simulated complexes was also analyzed as the RMSD values relative to the starting docking complex. The results of the RMSD measurements are given separately for the ligand and the receptors’ backbone as Supporting Information in Figures S21–S60 for both proteases. As observed in the total energy and RMSD plots, the systems remain energetically and conformationally stable during the production runs. According to the RMSD analysis, in all cases, the deviation relative to the starting docking poses remains below or close to the 2 Å thresholds. More importantly, the ligand RMSD plots for the five production runs suggest that diverse conformational subspaces are explored in each of them.

According to the modeling results, the free energies of binding of isoquercitrin to the SARS-CoV-2 M^pro and NS2B-NS3^pro proteases are −7.21 kcal/mol and −5.08 kcal/mol, respectively. These binding energies are consistent with the higher inhibition potency of isoquercitrin against M^pro. The predicted binding modes of isoquercitrin to both proteases are shown in Figure 4. The centroid of the largest cluster resulting from grouping the ligand conformations used for MM-PBSA calculations were selected for representation. The figure was produced with UCSF Chimera [46] and LigPlot+ [47], while the frequencies of interactions were analyzed with Cytoscape [48].

(a)

(b)

As shown in Figure 4, isoquercitrin is predicted to form large networks of interactions with the two studied proteases. For main protease M^pro, the active site has been proposed to be formed by four subcavities S1–S4 [4]. In the most energetically stable predicted binding mode, the compound binding site spans across the S1, S2, and S3 sites, with the dihydroxy phenyl moiety occupying the S1 subpocket. The meta-hydroxyl substituent forms hydrogen bonds with the side chain of H163, while the ring interacts with L141, N142, S144, C145, and E166. The S2 cavity of M^pro accommodates the glucoside group of isoquercitrin that is predicted to form a hydrogen bond to G143 and also interact with the catalytic H41. On the other side, the S3 cavity binds the substituted flavone nucleus predicted to hydrogen bond the side chain of Q192.

For the NS2B-NS3^pro protease, the glucoside group is predicted to bind in a region delimited by D129, Y130, A132, Y150 Y161, and S163. In the predicted conformation, this moiety interacts through hydrogen bonds with the side chain of D129 and the backbone of Y130. Another observed hydrogen bond involves the meta-hydroxyl group in the dihydroxy phenyl ring and the backbone of V154. This ring also interacts with G153, V155, and Y161 at the entrance of the active site. Finally, the flavone ring orients itself to block the access to the catalytic S135 and makes additional contacts with M49, H51, V52, A132, G151, and N152, additionally forming a hydrogen bond with the side chain of D75.

4. Conclusions

This work demonstrates the potential COVID-19 antiviral activity of the SunActive™ isoquercitrin compound against SARS-CoV-2 virus protease both in the SunActive QCD™ formulation and in the isolated form. Interestingly, isoquercitrin has been recently identified as a ZIKV inhibitor in cellular and animal model experiments, apparently exerting its activity via a different molecular mechanism related to viral internalization in cells [19, 49]. Further biochemical as well as cellular experiments are recommendable to provide further evidence of the active compound’s specific binding and inhibitory activity. Efforts should be undertaken to confirm the activity of these compounds and if necessary to improve their affinity and inhibition activity. In this regard, modeling studies combining molecular docking and molecular dynamics simulations lead to the probable binding modes of isoquercitrin to both proteases. These modeling results can aid and guide the optimization of the inhibitory activity of isoquercitrin derivatives in future experimental efforts. As the formulation of this compound has probably good ADME-Tox effects, including cell permeability, it could open up new avenues for the development of effective Anti-COVID-19 drugs. Furthermore, because the targeted enzyme, M^pro, is essential for the replication of the SARS-CoV-2 virus, these highly bioavailable flavonoid-cyclodextrin inclusion complex formualtions could be interesting lead compounds for the development of future COVID-19 therapies, as well as other emerging viral diseases.

Data Availability

All the data used in the study are included within the manuscript.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This research was funded by the Brazilian agencies FINEP grant 04.16.0054.02 (institutional/MW) and National Council for Scientific and Technological Development (CNPq).

Supplementary Materials

Table S1: results of docking isoquercitrin to the SARS-CoV-Mpro and ZIKV NS2B-NS3pro proteases; Table S2 predicted free energies of binding for the top 10 ranked docking solutions; Figures S1–S10: total energy of the SARS-CoV-2 Mpro—isoquercitrin systems during the MD simulations; Figures S11–S20: total energy of the ZIKV NS2B-NS3pro—isoquercitrin systems during the MD simulations; Figures S21–S30: ligand RMSD during the MD simulations of the SARS-CoV-2 Mpro—isoquercitrin complexes; Figures S31–S40: protein backbone RMSD during the MD simulations of the SARS-CoV-2 Mpro—isoquercitrin complexes; Figures S41–S50: ligand RMSD during the MD simulations of the ZIKV NS2B-NS3pro—isoquercitrin complexes; Figures S51–S60: protein backbone RMSD during the MD simulations of the ZIKV NS2B-NS3pro—isoquercitrin complexes. (Supplementary Materials)

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Copyright © 2023 Martin Wurtele et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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