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Review

Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling

by
Amir Taldaev
1,2,*,
Roman Terekhov
2,
Ilya Nikitin
2,
Anastasiya Zhevlakova
2 and
Irina Selivanova
2
1
Laboratoty of Nanobiotechnology, Institute of Biomedical Chemistry, Pogodinskaya Str. 10/8, 119121 Moscow, Russia
2
Department of Chemistry, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2022, 23(11), 6023; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms23116023
Submission received: 28 April 2022 / Revised: 21 May 2022 / Accepted: 23 May 2022 / Published: 27 May 2022
(This article belongs to the Special Issue Flavonoids and Other Polyphenols in Health and Disease)
Browse Figures
Versions Notes

Abstract

:
Computer modeling is a method that is widely used in scientific investigations to predict the biological activity, toxicity, pharmacokinetics, and synthesis strategy of compounds based on the structure of the molecule. This work is a systematic review of articles performed in accordance with the recommendations of PRISMA and contains information on computer modeling of the interaction of classical flavonoids with different biological targets. The review of used computational approaches is presented. Furthermore, the affinities of flavonoids to different targets that are associated with the infection, cardiovascular, and oncological diseases are discussed. Additionally, the methodology of bias risks in molecular docking research based on principles of evidentiary medicine was suggested and discussed. Based on this data, the most active groups of flavonoids and lead compounds for different targets were determined. It was concluded that flavonoids are a promising object for drug development and further research of pharmacology by in vitro, ex vivo, and in vivo models is required.

1. Introduction

Computer modeling is a rapidly developing scientific method that allows us to predict the courses and results of various processes by the use of modern intelligent technologies [1,2]. Molecular modeling is one of the areas of computer modeling, and it is widely used in drug design [3,4,5]. Several methods such as quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) [6,7], molecular docking [8], and molecular dynamics simulations [9,10,11] give an opportunity to identify the potential biologically active compounds and establish the mechanisms of target-ligand interaction. Molecular modeling is used for the prediction of chemical reactivity and synthesis strategy optimization by, for example, density functional theory (DFT) calculations [12,13]. Moreover, this method has several benefits in ADMET prediction for small molecules that have potential as drugs [14]. Of course, the successfully completed computer-performed experiment (in silico) cannot guarantee the future implementation of a biologically active molecule in clinical practice [15,16]. Nevertheless, using molecular modeling, it is possible to identify the potential area of compounds’ medical applications and to assess the necessity of further in vitro and in vivo assays [17,18].
Flavonoids are widely occurring secondary metabolites of plants. Currently, over 8000 compounds are classified as flavonoids [19]. Quercetin is a textbook example of flavonols that decrease total sick days and severity of upper respiratory tract infection in randomized, double-blinded, placebo-controlled trials [20]. Flavanonol, which belongs to the structural type of quercetin, named taxifolin, demonstrated wound-healing activity [21]. Luteolin, apigenin, and wogonin, which can be classified as flavones, show induced neutrophil apoptosis and have potential as neutrophil apoptosis-inducing anti-inflammatory, proresolution agents [22]. Flavanone hesperetin, in combination with sodium cyclic lysophosphatidic acid, showed significant antiaging effects on skin hydration and elasticity in a single-center clinical trial [23]. In general, data from several studies suggest that higher dietary intake of flavonoids may be associated with better cognitive health [24,25], improved prognosis for cardiovascular diseases [26,27], and decreased body weight [28]. Taken together, due to antioxidant activity, selective affinity to several biological targets, and high safety profiles, flavonoids are a prospective class of natural compounds for drug development [29,30,31,32]. Flavonoids are usually divided into 12 groups based on the structure of the molecule (Figure 1).
The aim of this study is to identify the trends of in silico methods applied as a primary screening tool for drug design on a flavonoid basis.

2. Methods

2.1. Search Strategy

The systematic review adhered to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) [33] guidelines, including search strategy, selection criteria, data extraction, and data analysis. The papers included in this analysis were selected from the Google Scholar database. Studies were restricted to those published from January 2016 to October 2021 to ensure the literature was relevant. The following keywords were used in the search strategy: “flavonoid”, “molecular modeling”, “chemoinformatics”, “computational chemistry”, “computer modeling”, “docking”, “molecular dynamics”. An example search strategy as applied to the scientific database is shown below in English: (“in silico” OR “molecular modeling” OR cheminformatics OR “computational chemistry” OR “molecular simulation” OR “computer simulation” OR docking OR “molecular dynamics”) AND flavonoid; and in Russian: (“мoлекулярнoе мoделирoвание” OR хемoинфoрматика OR дoкинг OR “мoлекулярная динамика”) AND флавoнoид. Only studies published in the English and the Russian languages were considered for inclusion due to a lack of translation resources.

2.2. Review Protocol and Data Extraction

Two independent review authors (I.N. and A.T.) conducted the literature search in the specified scientific databases. Results of the search were collected in Google Drive. Duplicate publications were excluded. The same review authors independently screened publications using the criteria for inclusion (Table 1).
Resolution of any disagreements occurred through discussion and required consultation with the three other authors (R.T., A.Z., and I.S.) for consensus. As a result of the annotations analysis, 66 articles were selected for in-depth study that met the objectives of this research (Figure 2). Qualitative and quantitative content analysis was performed. Data synthesis and required statistical analyses were conducted by I.N. and A.T. Findings are presented as a narrative synthesis, as well as tables and figures.

2.3. Assessment of Risk of Bias

Two review authors (R.T. and A.Z.) made systematic and independent assessments of the risk of bias in each research using the methodological domains presented in Table 2.
Disagreements in judgments about the risk of bias were resolved by discussion or, when necessary, arbitrated by an independent third review author (I.S.).

3. Results and Discussion

3.1. General View on Articles Sample

An increasing number of publications on molecular modeling with flavonoids was observed. For example, in 2016, the 4790 articles were published, while in 2021, this parameter was approximately four times higher and was 16,900 (Figure 3).

3.2. Molecular Modeling Methods

Specialists in the field of medicinal chemistry show great interest in computer modeling of flavonoid pharmacological activities, which is confirmed by the annual increase of publications on this topic.
Molecular docking has the greatest prevalence, which was used in 92.4% of the analyzed articles [34,35,36,37,38,39]. Molecular dynamics simulations were revealed in 31.8% of the papers [40,41,42,43]. Pharmacophore modeling, QSAR/QSPR models, quantum chemical, and hybrid quantum mechanical/molecular mechanical methods were found in 21.1% of publications [44,45,46,47,48].
Interestingly, some articles presented the research that was realized in translational design, including experiments in vitro, ex vivo, and in vivo. For example, Sun et al. confirmed the affinity of taxifolin to 11β-hydroxysteroid dehydrogenase 1 in rats and using human HSD11B1, with IC50 values of 37,833 and 4981 nM, respectively [49]. During another translational study, it was shown that taxifolin inhibits aryl hydrocarbon receptors and some proteins of the cytochrome P450 system that may be promising for breast cancer treatment [50]. The publication of such articles is a rather remarkable outcome.

3.3. Software and Databases for Molecular Docking

Considering that molecular docking is based on protein-ligand interaction, it was interesting to analyze the software needed to create virtual supramolecular structures. Based on the frequency data of different compactional methods, it was suggested that the majority of researchers prefer to optimize the study design by following the criteria:
  • User-friendly software;
  • Intuitive graphical interface;
  • Lack of need for high-performance computing resources;
  • Good predictive ability.
The majority (89%) of target protein structures were obtained from the RCSB Protein Data Bank (PDB) [51,52,53]. In cases when the compounds of interest were not contained in the database (11%), homology modeling was used to construct the necessary structure [45,54,55,56].
Three-dimensional structures of ligands were obtained in two ways: by extracting information from various libraries (65%) [57,58,59,60,61] or by constructing a molecule using special software (35%) [62,63,64,65,66]. PubChem (62%) was the most frequently used database, while ZINC (15.0%) and ChemSpider (12.0%) were less common. PubChem belongs to the US National Library of Medicine, and its leadership, despite the smaller number of compounds (about 109 million) compared to ZINC (230 million), is due to having the richest information about each molecule. ChemSpider has a smaller amount of information on each compound and a smaller number of records (about 101 million). Ligand constructors are used only when the molecular structure is not available in the computer library as the most time-consuming method of virtual model generation. The most common software for flavonoid molecular docking is AutoDock (47%), Glide (23%), and GOLD (9%).

3.4. Structure—Biological Activity Relationship: Qualitative Analysis

The parent structure of flavonoids is 1,3-diphenylpropane, and the aromatic fragments are designated as ring A and ring B [67]. The majority of flavonoid groups are characterized by the heterocycle (ring C) containing oxygen. This ring may be aromatic (flavones, flavonols, etc.) or not (flavanones, flavanonols, etc.). As the rule, carbonyl and several hydroxyl functional groups are present in the molecular structure of flavonoids that can act as a pharmacophores.
The phenolic hydroxyl groups of the studied natural compounds serve as H-bond donors. In cases when the hydrophobic interactions play a key role, the presence of the methoxy group leads to an increase of affinity to the target compared with the hydroxyl group [68]. Due to aromatic rings, the π,π-interactions with the side residues of heterocyclic and aromatic α-amino acids (tryptophan, histidine, phenylalanine, and tyrosine) are possible [69]. Figure 4 demonstrates all types of interactions.
It was found that the antiangiogenic potential of the flavonoid depends on the presence of a C2-C3 double bond [70]; the hydroxyl group in the position 3′ of the ring C contributes to an increase in antioxidant, anti-inflammatory, and antitumor activity [71]. If, along with the multiple C2-C3 bonds, a catechol group is present in the ring B, then such a molecule demonstrates a high affinity for the angiotensin-converting enzyme [72]. Substituents 3-OH, 5-OH, 6-OMe, 6-OH, 7-OH, 3′-OH, and 4′-OMe were identified as key fragments of the molecules when interacting with multidrug resistance-associated protein 2 (MRP2) [55].
It was also interesting to determine the specificity of the interaction of flavonoid groups. Thus, flavones (6-hydroxyluteolin, scutellarein), flavonols (kaempferol), and flavanones (naringenin, eridioctyol) exhibit a high affinity to the estrogen receptor α (ERα), which has been proven in both AutoDock and Glide software. Representatives of these groups of flavonoids can be recommended in the development of antitumor drugs for the treatment of breast cancer [71,73]. Interaction with this protein target results in several types of patient management, such as estrogen hormone replacement therapy and preventive care for breast cancer [74]. Flavones (baicalein, ladanein), flavonols (quercetin), and their glycosylated forms (baicalin) interact with the E protein of various strains of the dengue virus causing fever with a similar name [54]. Such ligands may be used in the treatment of this disease [75]. It is worth noting that the width of the confidence interval of the scoring function calculated for flavones is quite large. This indicates a different degree of protein-ligand binding within this group. Flavones (5-hydroxyflavone) and flavonols (quercetin) have a high affinity for the potassium channel Kir6.1, acting on which some cardiovascular diseases can be treated [56]. Flavones (luteolin, apigenin) can serve as the basis of drugs that control the pathogenicity of Helicobacter pylori due to their ability to bind to one of the main virulence factors of bacteria of this species—vacuolating cytotoxin protein (VacA) [76]. Flavonols (quercetin), their glycosides (avicularin, hyperoside), and flavanonols (taxifolin) with comparable effects function as arginase inhibitors, which is a potential target for the development of new approaches to the treatment of leishmaniasis [77]. Flavan-3-ols (catechin, epicatechin) are characterized by the best values of the scoring function when binding to the CA II-F complex in comparison with flavones, flavanones, and flavanonols and are of interest for the treatment of fluorosis [78]. According to the silico results, flavanones (eriodictyol) and flavanonols (taxifolin) are able to inhibit transcription factors Tec1 and Rfg1 because they can be used in the treatment of infection caused by Candida albicans fungus [79].

3.5. Structure—Biological Activity Relationship: Quantitative Analysis

Meta-analyses of scoring functions calculated during molecular docking was studied in [54,56,71,73,76,77,78,79]. General information about the average affinities of each flavonoid group to the biological targets is presented in Table 3 and Table 4 for AutoDock and Glide software, respectively.
Table 3 provides the experimental data on docking flavonoids of different groups to several biological targets, as performed by AutoDock. The extracted affinity values demonstrate the potential ability of small molecules to form supramolecular complexes with selected proteins in their active sites resulting in multiple pharmacological effects. Apparently, flavanonols form significantly more stable complexes with ERα compared with flavonols, which was approved by in vitro experiment [57]. This may be explained by the non-plane structure of the carbon skeleton, which can be quite similar to estrogen. Furthermore, the affinity value for flavones with protein VacA seems high compared to other targets, so it may be interesting to perform similar calculations for other flavonoid groups.
The results obtained from the meta-analysis of affinity values of flavonoid groups to target proteins calculated via Glide are summarized in Table 4. Compared with the previous table, the heterogeneity of values is obvious. The scoring functions for flavonoids docking with complex CA II-F and RFG1 were notably lower than for other biological targets. Surprisingly, the results of Glide calculations confirmed the previous in silico data obtained by AutoDock: Flavonones demonstrate a significantly higher affinity with ERα and RFG1 than other flavonoid groups. At the same time, flavan-3-ols show significantly higher scoring functions with complex CA II-F.
In general, the data of Glide calculations seemed more appropriate for decision support in further study design due to significant differences in affinity values compared with AutoDock.

3.6. Limitation Section

The study was performed in agreement with the PRISMA guidelines to prevent reviewer biases in the review process, including independent, duplicate inclusion and exclusion of identified studies, risk of bias assessments, and data extraction.
Nevertheless, it is important to notice that systematic reviews summarize the limitations of primary studies [80]. Thus, to measure the reliability of current research, it was necessary to assess the bias risk in the studies included in the review. There are many guidelines for considering bias in papers for randomized controlled clinical trials. However, to our data, there is no such instrument for in silico studies. Because of this, we developed average criteria that can serve this goal based on the literature information and considered discussion with professional society (see Table 2).
The domains included in our research primarily cover all types of bias that are currently understood to affect the results of molecular docking. These are:
  • Bias arising from ligands selection process;
  • Bias due to ligands structure optimization;
  • Bias arising from protein target selection process;
  • Bias due to protein target processing;
  • Bias arising from molecular docking process;
  • Bias in results assessment.
Ligand filtering results in optimization of calculation processes and gives opportunity to exclude the molecules with huge volumes, low safety profiles, and inappropriate pharmacokinetic behavior from the ligand set [81,82]. Ionization is a critical parameter of ligand structure because it impacts the formation of a supramolecular complex with the target. Because of this, the ionization assessment should be performed correctly with reference to the pKa value of small molecules and the pH value of media [83,84]. Another important stage of ligand structure optimization is the generation of all possible conformers that would be characterized by the lowest potential energy and optimal bond lengths, angles, and dihedrals. The affinity of conformers may differ significantly, so it influences the bias risks [85,86]. The resolution is one of the most obvious characteristics of the protein target models. In case it is higher than 2.5 Å, the position of atoms cannot be identified unequivocally, and such structures should not be used for molecular docking [87]. Furthermore, the method of target structure generation makes sense because only the NMR spectroscopy gives an opportunity to get the 3D model at the conditions that would be near to the biological ones [88]. However, it was shown that the protonation of amino acid residues, as well as the addition of missing residues and side chains, that was performed by a special toolkit, decreased the bias risks during the target structure generation by X-ray crystallography and cryogenic electron microscopy [89,90]. Checking the histidine charge and addition of metals are the critical stages that should be performed during the protein target processing to make the model biochemically correct [91,92]. The choice of an appropriate tool for molecular docking is a main issue of the bias domain. The software differs by using algorithm methods and scoring functions. The Monte-Carlo algorithm and empirical scoring functions demonstrate better results compared with other calculation methods and are confirmed by in vitro methods [87,93]. Finally, to assess the results of molecular docking correctly, we should be sure that the parameters of modeling are appropriate for the analyzed system, and the re-docking serves this goal [94]. The visual control of docking results is an essential part of in silico research that helps exclude the presence of structural artifacts that are formed by computer calculation [95]. Of course, the verification of the molecular docking results by in vitro study should be performed. Only chemical experiments may confirm the results of theoretical modeling [96]. Additionally, the risk of bias by a conflict of interest of authors was assessed using the information regarding funding source.
The developed methodology was implemented in the articles (Figure 5) and used for the meta-analysis [54,56,71,73,76,77,78,79]. Whilst this study included a total of eight papers, there is very poor information about the processing of protein targets and the assessment of results of molecular docking. Apparently, it is a result of a gap in clear guidelines of reporting for in silico studies that would sign the critical issues of such research. Moreover, half of the studies or even more reported the use of methods during protein target selection and molecular docking characterized by a confirmed high risk of bias. It may be associated with the absence of suitable target models and free software. Based on the data, it can be considered that there are limitations in the applicability of the evidence. Of course, these results of bias risk assessment in our meta-analysis cannot be transferred to all studies that conducted the molecular docking of flavonoids. However, the tendency reflects the necessity of paying more attention to the quality of such papers. Nevertheless, the conflict of interests was not identified in the analyzed studies.
We could not assess publication bias with a Begg funnel plot or an Egger test because the included studies were quite heterogeneous. In general, we may have missed some potentially eligible studies in languages other than English or Russian and studies with negative results.
We have not identified any other systematic reviews or meta-analyses that evaluated the pharmacological effects of flavonoids based on in silico studies. Meanwhile, we cannot draw any conclusions about agreements and disagreements with other reviews.

3.7. Lead Compounds

Molecular docking makes it possible to evaluate the affinity of the ligand with the target. Based on this criterion, it is possible to compile a list of the most active compounds for each specific interaction of a flavonoid with a macromolecule. The structures of the leader compounds are shown in Figure 6. Thus, taxifolin (dihydroquercetin) has a high potential for tuberculosis therapy, as it has demonstrated the ability to interact with DNA gyrase and aminoacyl-tRNA synthetase—two enzymes involved in the translation, transcription, and replication of bacterial DNA [97]. The pathogenic effect of the Ebola virus can be disrupted by affecting the vital protein structures VP40, VP35, VP30, and VP24 with flavonoids such as gossypetin or taxifolin. These compounds proved to be leaders in the study of more than 4500 flavonoids [98]. It is interesting to notice that despite the near structures of the molecules, they have similar interaction patterns with only two proteins among four targets. Both taxifolin and gossypetin can form H-bonds with His124, Gly126, and Gln170 in VP40. Furthermore, they interact with Gln103, Ser123, Asp124, and Asn185 or Gln184 in the active site of VP24. Key amino acid residues in VP35 are Gln244(2) and Asp302 for taxifolin, while for gossypetin, it is only the Gln241(2). Finally, gossypetin interacts with Asp158(2), Arg196, and Gln233(2) in VP30. However, taxifolin forms H-bonds with Arg196, Gly200(3), Gln233, Ser234, and Phe238 residues of the VP30. Differences in the affinity of these flavonoids are apparently associated with the spatial structure of the heterocyclic ring. Gossypetin has a plane aromatic structure, and the taxifolin molecule is characterized by two chiral centers. Thus, taxifolin can exist as four stereoisomers. Saltillin, taxifolin, and 6-methoxyflavone have a high affinity for N-myristoyltransferase (NMT), a target for the treatment of candidiasis [99]. There is also information indicating the feasibility of studying a number of flavonoids in the following diseases: lung cancer [100], breast cancer [50,71], metabolic syndrome [49], pathological conditions caused by Pseudomonas aeruginosa [101], hypoestrogenism [102], and depressive disorders [103].
Taxifolin is one of the most promising lead compounds, mentioned in Figure 6, due to its wide range of pharmacological activity, rich raw material base, and high safety profile. Prospective taxifolin use in medicine was discussed in a number of articles and reviews [104,105,106]. Moreover, in Russia, this compound is registered as an active pharmaceutical ingredient, and in Europe, it is a supplement (Regulation EU 2017/2470). (2R,3R)-taxifolin is the most investigated isomer.
It is known that the (2R,3R)-taxifolin obtained from natural sources is safe [107]. The low toxic potential of taxifolin was predicted by different methods, such as the ORISIS DataWarrior program [108]. Furthermore, it is quite important to note that not only the major trans-stereoisomer is characterized by a high safety profile. The VirtualToxLab platform determined (2S,3S)-taxifolin as one of the lowest toxic molecules from the set of 29 molecules that demonstrated affinity to the SARS-CoV-2 main protease [109]. This software is based on calculating individual binding affinities to 16 validated off-targets, including intracellular receptors, metabolic enzymes, and the hERG potassium channel. Taxifolin is the safest flavonoid in comparison with eriodictyol, luteolin, isoscutellarein, and quercetin. It has shown in silico potential as the main protease of SARS-CoV-2 [108].
Despite the great pharmacological potential, this flavonoid is characterized by low bioavailability [110,111]. To explain the observed efficacy, research on taxifolin metabolism was performed. More than 190 structures of different compounds were found as the result of taxifolin biotransformation by the HPLC-MS/MS method [112]. There was research that tested most of them as COX-2 inhibitors by in silico study [39]. To evaluate the affinity of metabolites to target, molecular docking was performed. A significant number of these compounds were absent in databases. For this reason, the ligand set of 214 3D-structures of all taxifolin stereoisomers and their metabolites was generated manually using ChemBioDraw Ultra (v. 13.0, PerkinElmer, Waltham, MA, USA). Then, the GOLD software (v. 5.4, CCDC, Cambridge, UK) was used to conduct the modeling of intramolecular interaction. During this research, all metabolites were classified into three groups. Compounds that contain all three rings of the initial structure (A, B, and C—see Figure 1) can interact with the three most important amino acid residues in the active site of the target: SER353, SER530, and ARG513. Metabolites that contain two rings (A, C or B, C) do not interact with ARG513 and, for this reason, are less selective. The third group consists of molecules with only one ring. These compounds have a low affinity to COX-2. Thus, research in the field of the design of anti-inflammatory drugs based on taxifolin should be continued.
Crystal engineering is another area of taxifolin research. Microtubes are one of the most significant achievements in the development of new crystal forms in the last 5 years [113]. They were synthesized by precipitation with water from an ethanol solution of taxifolin in the presence of urea. This modification showed notable differences in comparison with the initial active pharmaceutical ingredient. To characterize the properties of the new taxifolin forms in silico, the 3D-models of nanoparticles were generated based on the different crystal unit structures. The simulation of the nanoparticle deformation was carried out by molecular dynamics modeling to evaluate the physical characteristics of taxifolin modifications. This investigation was performed using Materials Science Suite 2018-2 (Schrodinger, New York, NY, USA). A cross-shaped pore was observed at the core of the taxifolin nanoparticle (Figure 7). The diameter of this pore varied from 4 nm at the narrow part to 11 nm at the widest part. These findings make it possible to suggest that the taxifolin microtubes are a mesoporous material. Taxifolin microtubes may have an application in drug delivery.
A wide range of pharmacological activity of flavonoids is a potential for the design of drugs on these bases. After the in silico stage, preclinical and clinical trials are required to verify the safety and efficacy of the candidate molecule. At this moment, on the website https://clinicaltrials.gov (accessed on 25 January 2021) 253 clinical trials of drugs containing flavonoids have been registered. It is worth noting that the number of trials currently being conducted or planned for the near future is 45, and these include pathologies such as post-thrombotic syndrome, sickle cell anemia, chronic kidney disease, psoriasis, glaucoma, type 2 diabetes, and others. It is interesting to note that the possibility of flavonoids being used for the COVID-19 treatment is being studied in 11 trials.

3.8. COVID-19 Treatment

Undoubtedly, the Coronavirus disease 2019 (COVID-19) pandemic is one of the most socially important problems nowadays. There have been approximately 448 million confirmed cases of COVID-19, including more than 6 million deaths, reported by the World Health Organization (WHO) [114]. According to these epidemiological data, it is extremely important to find effective remedies against COVID-19. The flavonoid group is a promising object for this purpose. Indeed, thousands of investigations have been carried out. Obviously, most of them were conducted by in silico approaches.
For instance, one study aimed to establish flavonoid’s affinity to the spike protein of SARS-CoV-2. The ligand set included apigenin, chrysin, fisetin, galangin, hesperetin, luteolin, morin, naringin, quercetin, and rutin. Docking studies showed that all flavonoids demonstrate considerably high binding affinity, but naringin is characterized by the highest one. This compound shared hydrophobic interactions with the following residues: Asn290, Ile291, His374, Leu370, Leu410, Ala413, Pro415, Phe438, and Gln442. Furthermore, hydrogen bonds were formed with Asp367, Thr371, Lys441, Glu406, and Ser409 amino acid residues [115].
Six polyphenolic compounds, namely leucopelargonidin, morin, myricetin, eriodictyol, taxifolin, and enterodiol, exhibited a significant binding affinity for SARS-CoV-2 papain-like protease (PLpro) and main protease (Mpro) during another investigation via molecular docking [116]. The analysis of free binding energy showed that taxifolin has the highest affinity against Mpro and forms H-bonds with Cys145, Ser144, Gly143, Asn142, Leu141 (hydroxyl groups of ring A), Glu166, Met165, His164 (carbonyl group of ring C), Tyr54, Pro52, and Met49. Morin was determined as a compound with the highest affinity toward Plpro. Hydrogen bonding interactions of morin were observed with the following amino acid residues: Gly266, Asn267 (hydroxyl groups of rings A and B), Thr301 (carbonyl group of ring C), Tyr273, Tyr264, and Tyr268 (phenolic hydroxyl groups of ring B). Moreover, further molecular dynamics simulation showed that the binding of bioactive molecules on the corresponding proteases is characterized by structural changes that can disrupt the functions of enzymes and, thus, enhance their antiviral activity.
Another approach considers angiotensin-converting enzyme 2 (ACE-2) receptors as a target. Molecular docking was used to predict the activity of flavonoid sets, including hesperetin, chrysin, kaempferol, galangyn, myricetin, and rutin [117]. The last molecule showed the best binding affinity to the ACE-2 enzyme. It was found that rutin forms the strongest hydrogen bond with Asn149, Met270, His345, Lys363, Asp368, and Thr445 amino acid residues of the ACE-2 protein. Likewise, this compound has the π-anion interaction with Arg269, π-π stacking interaction with Phe274, π-alkyl interaction with Ala153, and alkyl interaction with Pro346, Met360, and Cys361 residues.
Moreover, in our recent study, 163 flavonoids were screened [118]. ATP-binding domain SP3, main protease NSP 5, RNA-dependent RNA polymerase NCP12, and endoribonuclease NCP15 were considered as targets. Much of the recent COVID-19 treatment research in silico has focused on the identification of lead compounds, while our study pays more attention to the binding affinity of the all-flavonoids groups to biological targets. The median binding energies were −7.4, −7.4, −8.9, and −7.3 kcal/mol for the ADP-binding domain NSP3, main protease NSP5, RNA-dependent RNA polymerase NSP12, and endoribonuclease NSP15, respectively. Therefore, these data suggest that flavonoids can find application in the complex therapy of COVID-19.
The potential benefits of flavonoids in COVID-19 therapy were confirmed by a randomized controlled trial [119]. Groups that obtained quercetin in combination with antiviral drugs demonstrated significantly lower serum levels of critical markers involved in COVID-19 severity and better respiratory rate. However, further clinical trials with other flavonoids and bigger group sizes are required.
These results are very promising and require further research.

4. Conclusions

Currently, there is a trend of article numbers increasing, which focuses on the computer modeling of flavonoid interactions with biological targets. Such studies help to accumulate the data on lead compounds that can find medicinal implementation, including COVID-19. In drug discovery, in silico research plays the role of the first screening stage. Assessment of bias risk showed the necessity of clear guidelines of reporting for in silico studies that would sign the critical issues for such research. The results of computational calculations should be supported by a large-scale study of candidate molecules by in vitro, ex vivo, and in vivo methods. The implementation of this translational research model is a promising way for the creation of new phytomedicines based on flavonoids.

Author Contributions

Conceptualization, I.S. and R.T.; methodology, A.Z. and R.T.; formal analysis, I.N. and A.T.; investigation, I.N. and A.T.; data curation, A.Z. and A.T.; writing—original draft preparation, I.N.; writing—review and editing, I.S., A.Z. and R.T.; visualization, I.N. and A.T.; supervision, I.S.; project administration, A.Z.; funding acquisition, A.T. All authors have read and agreed to the published version of the manuscript.

Funding

The study was performed employing “Avogadro” large-scale research facilities and was financially supported by the Ministry of Education and Science of the Russian Federation, Agreement No. 075-15-2021-993, unique project ID: RF00121X0004.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is contained within the article.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Bossel, H. Modeling and Simulation; CRC Press: Boca Raton, FL, USA, 2018; pp. 3–6. [Google Scholar]
  2. Goh, G.B.; Hodas, N.O.; Vishnu, A. Deep learning for computational chemistry. J. Comput. Chem. 2017, 38, 1291–1307. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Durrant, J.D.; McCammon, J.A. Molecular dynamics simulations and drug discovery. BMC Biol. 2011, 9, 71. [Google Scholar] [CrossRef] [Green Version]
  4. Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E.W. Computational methods in drug discovery. Pharmacol. Rev. 2014, 66, 334–395. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Sabe, V.T.; Ntombela, T.; Jhamba, L.A.; Maguire, G.E.M.; Govender, T.; Naicker, T.; Kruger, H.G. Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review. Eur. J. Med. Chem. 2021, 224, 113705. [Google Scholar] [CrossRef] [PubMed]
  6. Cherkasov, A.; Muratov, E.N.; Fourches, D.; Varnek, A.; Baskin, I.I.; Cronin, M.; Dearden, J.; Gramatica, P.; Martin, Y.C.; Todeschini, R.; et al. QSAR modeling: Where have you been? Where are you going to? J. Med. Chem. 2014, 57, 4977–5010. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 2017, 7, 42717. [Google Scholar] [CrossRef] [Green Version]
  8. Yuriev, E.; Holien, J.; Ramsland, P.A. Improvements, trends, and new ideas in molecular docking: 2012–2013 in review. J. Mol. Recognit. 2015, 28, 581–604. [Google Scholar] [CrossRef]
  9. Hollingsworth, S.A.; Dror, R.O. Molecular dynamics simulation for all. Neuron 2018, 99, 1129–1143. [Google Scholar] [CrossRef] [Green Version]
  10. Polkovnikova, Y.A.; Lenshin, A.S.; Glushko, A.A.; Khishova, O.M. Modeling and analysis of molecular dynamics of silicon-solvent-vinpocetin systems and silicon-solvent-vinpocetin dioxide. Drug Dev. Reg. 2017, 21, 44–49. (In Russian) [Google Scholar]
  11. Polkovnikova, Y.A. Modeling the formation of liposomes with vinpocetine from soy lecithin phospholipids by molecular dynamics. Drug Dev. Reg. 2021, 10, 83–87. (In Russian) [Google Scholar] [CrossRef]
  12. Il’In, M.V.; Sysoeva, A.A.; Bolotin, D.S.; Novikov, A.S.; Suslonov, V.V.; Rogacheva, E.V.; Kraeva, L.A.; Kukushkin, V.Y. Aminonitrones as highly reactive bifunctional synthons. An expedient one-pot route to 5-amino-1,2,4-triazoles and 5-amino-1,2,4-oxadiazoles—Potential antimicrobials targeting multi-drug resistant bacteria. New J. Chem. 2019, 43, 17358–17366. [Google Scholar] [CrossRef]
  13. Novikov, A.S.; Kuznetsov, M.L. Theoretical study of Re(IV) and Ru(II) bis-isocyanide complexes and their reactivity in cycloaddition reactions with nitrones. Inorg. Chim. Acta 2012, 380, 78–89. [Google Scholar] [CrossRef]
  14. Bitew, M.; Desalegn, T.; Demissie, T.B.; Belayneh, A.; Endale, M.; Eswaramoorthy, R. Pharmacokinetics and drug-likeness of antidiabetic flavonoids: Molecular docking and DFT study. PLoS ONE 2021, 16, e0260853. [Google Scholar] [CrossRef] [PubMed]
  15. Kirchmair, J.; Göller, A.H.; Lang, D.; Kunze, J.; Testa, B.; Wilson, I.D.; Glen, R.C.; Schneider, G. Predicting drug metabolism: Experiment and/or computation? Nat. Rev. Drug Discov. 2015, 14, 387–404. [Google Scholar] [CrossRef] [Green Version]
  16. Fröhlich, E.; Salar-Behzadi, S. Toxicological assessment of inhaled nanoparticles: Role of in vivo, ex vivo, in vitro, and in silico studies. Int. J. Mol. Sci. 2014, 15, 4795–4822. [Google Scholar] [CrossRef]
  17. PCC|Insilico Medicine. Available online: https://insilico.com/blog/pcc (accessed on 19 December 2021).
  18. Shaker, B.; Ahmad, S.; Lee, J.; Jung, C.; Na, D. In silico methods and tools for drug discovery. Comput. Biol. Med. 2021, 137, 104851. [Google Scholar] [CrossRef]
  19. Juca, M.M.; Filho, F.M.S.C.; de Almeida, J.C.; da Silva Mesquita, D.; de Moraes Barriga, J.R.; Dias, K.C.F.; Bardosa, T.M.; Vasconcelos, L.C.; Leal, L.K.A.M.; Robeiro, J.E.; et al. Flavonoids: Biological activities and therapeutic potential. Nat. Prod. Res. 2020, 34, 692–705. [Google Scholar] [CrossRef]
  20. Heinz, S.A.; Henson, D.A.; Austin, M.D.; Jin, F.; Nieman, D.C. Quercetin supplementation and upper respiratory tract infection: A randomized community clinical trial. Pharmacol. Res. 2010, 62, 237–242. [Google Scholar] [CrossRef]
  21. Terekhov, R.P.; Selivanova, I.A.; Anurova, M.N.; Zhevlakova, A.K.; Nikitin, I.D.; Cong, Z.; Ma, S.; Yang, F.; Dong, Z.; Liao, Y. Comparative study of wound-healing activity of dihydroquercetin pseudopolymorphic modifications. Bull. Exp. Biol. Med. 2021, 170, 444–447. [Google Scholar] [CrossRef]
  22. Lucas, C.D.; Allen, K.C.; Dorward, D.A.; Hoodless, L.J.; Melrose, L.A.; Marwick, J.A.; Tucker, C.S.; Haslett, C.; Duffin, R.; Rossi, A.G. Flavones induce neutrophil apoptosis by down-regulation of Mcl-1 via a proteasomal-dependent pathway. FASEB J. 2013, 27, 1084–1094. [Google Scholar] [CrossRef] [Green Version]
  23. Sheen, Y.S.; Huang, H.Y.; Liao, Y.H. The efficacy and safety of an antiaging topical serum containing hesperetin and sodium cyclic lysophosphatidic acid: A single-center clinical trial. J. Cosmet. Dermatol. 2021, 20, 3960–3967. [Google Scholar] [CrossRef] [PubMed]
  24. Godos, J.; Caraci, F.; Castellano, S.; Currenti, W.; Galvano, F.; Ferri, R.; Grosso, G. Association between dietary flavonoids intake and cognitive function in an italian cohort. Biomolecules 2020, 10, 1300. [Google Scholar] [CrossRef] [PubMed]
  25. Godos, J.; Caraci, F.; Micek, A.; Castellano, S.; D’amico, E.; Paladino, N.; Ferri, R.; Galvano, F.; Grosso, G. Dietary phenolic acids and their major food sources are associated with cognitive status in older italian adults. Antioxidants 2021, 10, 700. [Google Scholar] [CrossRef]
  26. Grosso, G.; Godos, J.; Currenti, W.; Micek, A.; Falzone, L.; Libra, M.; Giampieri, F.; Forbes-Hernández, T.Y.; Quiles, J.L.; Battino, M.; et al. The Effect of dietary polyphenols on vascular health and hypertension: Current evidence and mechanisms of action. Nutrients 2022, 14, 545. [Google Scholar] [CrossRef] [PubMed]
  27. Grosso, G.; Micek, A.; Godos, J.; Pajak, A.; Sciacca, S.; Galvano, F.; Giovannucci, E.L. Dietary flavonoid and lignan intake and mortality in prospective cohort studies: Systematic review and dose-response meta-analysis. Am. J. Epidemiol. 2017, 185, 1304–1316. [Google Scholar] [CrossRef] [PubMed]
  28. Marranzano, M.; Ray, S.; Godos, J.; Galvano, F. Association between dietary flavonoids intake and obesity in a cohort of adults living in the Mediterranean area. Int. J. Food Sci. Nutr. 2018, 69, 1020–1029. [Google Scholar] [CrossRef]
  29. Tyukavkina, N.A.; Selivanova, I.A.; Terekhov, R.P. Modern Trends in the Design of Drugs Based on Flavonoids. In Phenolic Compounds: Properties, Activity, Innovation, Fundamental and Applied Aspects; Zagoskina, N.V., Tyukavkina, N.A., Lapshin, P.V., Nechaev, T.L., Eds.; IFR RAN: Moscow, Russia, 2018; pp. 526–532. (In Russian) [Google Scholar]
  30. Ilyasov, I.R.; Beloborodov, V.L.; Selivanova, I.A. Three ABTS•+ radical cation-based approaches for the evaluation of antioxidant activity: Fast- and slow-reacting antioxidant behavior. Chem. Pap. 2018, 72, 1917–1925. [Google Scholar] [CrossRef]
  31. Markova, E.Y.; Matveev, A.V.; Cox, Y.D.; Kovalevskaya, M.A.; Dergacheva, L.I. Modern antioxidant therapy in pediatric ophthalmology. Eff. Pharmacother. 2011, 29, 46–51. (In Russian) [Google Scholar]
  32. Shikov, A.N.; Pozharitskaya, O.N.; Miroshnyk, I.; Mirza, S.; Urakova, I.N.; Hirsjärvi, S.; Makarova, V.G.; Heinämäki, J.; Yliruusi, J.; Hiltunen, R. Nanodispersions of taxifolin: Impact of solid-state properties on dissolution behavior. Int. J. Pharm. 2009, 377, 148–152. [Google Scholar] [CrossRef] [PubMed]
  33. Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G.; Altman, D.; Antes, G.; Atkins, D.; Barbour, V.; Barrowman, N.; Berlin, J.A.; et al. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009, 6, e1000097. [Google Scholar] [CrossRef] [Green Version]
  34. Chen, H.J.; Chung, Y.L.; Li, C.Y.; Chang, Y.T.; Wang, C.C.N.; Lee, H.Y.; Lin, H.Y.; Hung, C.C. Taxifolin resensitizes multidrug resistance cancer cells via uncompetitive inhibition of P-glycoprotein. Funct. Mol. 2018, 23, 3055. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Liu, J.; Wang, X.; Geng, S.; Liu, B.; Liang, G. Inhibitory mechanism of taxifolin against α-glucosidase based on spectrofluorimetry and molecular docking. Nat. Prod. Commun. 2017, 12, 1725–1728. [Google Scholar] [CrossRef] [PubMed]
  36. Mahdavimehr, M.; Meratan, A.A.; Ghobeh, M.; Ghasemi, A.; Saboury, A.A.; Nemat-Gorgani, M. Inhibition of HEWL fibril formation by taxifolin: Mechanism of action. PLoS ONE 2017, 12, e0187841. [Google Scholar] [CrossRef] [PubMed]
  37. Haque, M.; Mohd Siddique, M.; Bose, P.; Pattanayak, S. Taxifolin possesses anti-cancer activity on the 7,12-Dimethylbenz(a)anthracene-Induced breast cancer in the sprague dawley rats by remodeling nuclear factor Erythroid 2- Kelch-like ECH-associated protein 1-heme oxygenase 1 and anti-oxidant pathways. Pharmacogn. Mag. 2018, 14, 110. [Google Scholar] [CrossRef]
  38. George, T.K.; Joy, A.; Divya, K.; Jisha, M.S. In vitro and in silico docking studies of antibacterial compounds derived from endophytic Penicillium setosum. Microb. Pathog. 2019, 131, 87–97. [Google Scholar] [CrossRef]
  39. Terekhov, R.P.; Selivanova, I.A. Molecular modeling of the interaction of the dihydroquercetin and its metabolites with cyclooxygenase-2. Bull. Exp. Biol. Med. 2019, 18, 101–106. [Google Scholar] [CrossRef]
  40. Mohseni-Shahri, F.S.; Housaindokht, M.R.; Bozorgmehr, M.R.; Moosavi-Movahedi, A.A. Comparative study of the effects of the structurally similar flavonoids quercetin and taxifolin on the therapeutic behavior of alprazolam. Can. J. Chem. 2016, 94, 458–469. [Google Scholar] [CrossRef]
  41. Chilingaryan, G.V. Comparative analysis of quercetin and taxifolin interaction with human telomeric G-quadruplex DNA hybrid form based on molecular dynamic simulations. Biol. J. Armenia 2018, 7, 57–61. [Google Scholar]
  42. Verma, S.; Grover, S.; Tyagi, C.; Goyal, S.; Jamal, S.; Singh, A.; Grover, A. Hydrophobic interactions are a key to MDM2 inhibition by polyphenols as revealed by molecular dynamics simulations and MM/PBSA free energy calculations. PLoS ONE 2016, 11, e0149014. [Google Scholar] [CrossRef]
  43. Mary, V.; Haris, P.; Varghese, M.K.; Aparna, P.; Sudarsanakumar, C. Experimental probing and molecular dynamics simulation of the molecular recognition of DNA duplexes by the flavonoid luteolin. J. Chem. Inf. Model. 2017, 57, 2237–2249. [Google Scholar] [CrossRef]
  44. Ginex, T.; Trius, M.; Luque, F.J. Computational study of the aza-michael addition of the flavonoid (+)-taxifolin in the inhibition of β-amyloid fibril aggregation. Chem. A Eur. J. 2018, 24, 5813–5824. [Google Scholar] [CrossRef] [PubMed]
  45. Proença, C.; Freitas, M.; Ribeiro, D.; Oliveira, E.F.T.; Sousa, J.L.C.; Tomé, S.M.; Ramos, M.J.; Silva, A.M.S.; Fernandes, P.A.; Fernandes, E. α-glucosidase inhibition by flavonoids: An in vitro and in silico structure–activity relationship study. J. Enzyme Inhib. Med. Chem. 2017, 32, 1216–1228. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Meshram, R.J.; Bagul, K.T.; Pawnikar, S.P.; Barage, S.H.; Kolte, B.S.; Gacche, R.N. Known compounds and new lessons: Structural and electronic basis of flavonoid-based bioactivities. J. Biomol. Struct. Dyn. 2019, 38, 1168–1184. [Google Scholar] [CrossRef] [PubMed]
  47. Manjula, S.; Kumaradhas, P. Evaluating the suitability of RNA intervention mechanism exerted by some flavonoid molecules against dengue virus MTase RNA capping site: A molecular docking, molecular dynamics simulation, and binding free energy study. J. Biomol. Struct. Dyn. 2019, 38, 3533–3543. [Google Scholar] [CrossRef] [PubMed]
  48. Hashemi, S.; Sharifi, A.; Zareei, S.; Mohamedi, G.; Biglar, M.; Amanlou, M. Discovery of direct inhibitor of KRAS oncogenic protein by natural products: A combination of pharmacophore search, molecular docking, and molecular dynamic studies. Res. Pharm. Sci. 2020, 15, 226. [Google Scholar] [CrossRef] [PubMed]
  49. Sun, J.; Ge, F.; Wang, Y.; Dong, Y.; Shan, Y.; Zhu, Q.; Wu, X.; Wu, C.; Ge, R.S. Taxifolin is a rat and human 11β-hydroxysteroid dehydrogenase 1 inhibitor as a possible drug to treat the metabolic syndrome. J. Funct. Foods 2018, 49, 181–187. [Google Scholar] [CrossRef]
  50. Haque, M.W.; Pattanayak, S.P. Taxifolin inhibits 7,12-dimethylbenz(a)anthracene-induced breast carcinogenesis by regulating AhR/CYP1A1 signaling pathway. Pharmacogn. Mag. 2017, 13, S749. [Google Scholar] [CrossRef]
  51. Thao, N.P.; Luyen, B.T.T.; Kim, J.H.; Jo, A.R.; Dat, N.T.; van Kiem, P.; van Minh, C.; Kim, Y.H. Identification, characterization, kinetics, and molecular docking of flavonoid constituents from Archidendron clypearia (Jack.) Nielsen leaves and twigs. Bioorg. Med. Chem. 2016, 24, 3125–3132. [Google Scholar] [CrossRef]
  52. Rehman, K.; Chohan, T.A.; Waheed, I.; Gilani, Z.; Akash, M.S.H. Taxifolin prevents postprandial hyperglycemia by regulating the activity of α-amylase: Evidence from an in vivo and in silico studies. J. Cell. Biochem. 2019, 120, 425–438. [Google Scholar] [CrossRef] [Green Version]
  53. Al-Khayyat, M.Z. In silico homology modeling and docking studies of RecA from Campylobacter jejuni. Int. J. Boautoimmun. 2019, 23, 1–12. [Google Scholar] [CrossRef] [Green Version]
  54. Ismail, N.A.; Jusoh, S.A. Molecular docking and molecular dynamics simulation studies to predict flavonoid binding on the surface of DENV2 E protein. Interdiscip. Sci. Comput. Life Sci. 2016, 9, 499–511. [Google Scholar] [CrossRef] [PubMed]
  55. Fang, Y.; Cao, W.; Liang, F.; Xia, M.; Pan, S.; Xu, X. Structure affinity relationship and docking studies of flavonoids as substrates of multidrug-resistant associated protein 2 (MRP2) in MDCK/MRP2 cells. Food Chem. 2019, 291, 101–109. [Google Scholar] [CrossRef] [PubMed]
  56. Trezza, A.; Cicaloni, V.; Porciatti, P.; Langella, A.; Fusi, F.; Saponara, S.; Spiga, O. From in silico to in vitro: A trip to reveal flavonoid binding on the Rattus norvegicus Kir6.1 ATP-sensitive inward rectifier potassium channel. PeerJ 2018, 6, e4680. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Liu, H.; He, S.; Wang, T.; Orang-Ojong, B.; Lu, Q.; Zhang, Z.; Pan, L.; Chai, X.; Wu, H.; Fan, G.; et al. Selected phytoestrogens distinguish roles of ERα transactivation and ligand binding for anti-inflammatory activity. Endocrinology 2018, 159, 3351–3364. [Google Scholar] [CrossRef] [PubMed]
  58. Vijayasri, S.; Hopper, W. Towards the identification of novel phytochemical leads as macrodomain inhibitors of chikungunya virus using molecular docking approach. J. Appl. Pharm. Sci. 2017, 7, 74–082. [Google Scholar] [CrossRef] [Green Version]
  59. Xiong, Z.; Liu, W.; Zhou, L.; Zou, L.; Chen, J. Mushroom (Agaricus bisporus) polyphenoloxidase inhibited by apigenin: Multi-spectroscopic analyses and computational docking simulation. Food Chem. 2016, 203, 430–439. [Google Scholar] [CrossRef] [PubMed]
  60. Maharani, M.G.; Lestari, S.R.; Lukiati, B. Molecular docking studies flavonoid (quercetin, isoquercetin, and kaempferol) of single bulb garlic (Allium sativum) to inhibit lanosterol synthase as anti-hypercholesterol therapeutic strategies. AIP Conf. Proc. 2020, 2231, 040021. [Google Scholar] [CrossRef]
  61. Kurakin, G.F.; Lopina, N.P.; Bordina, G.E. Computational modelling of the interaction between flavonoids and adenosine receptors. Probl. Biol. Med. Pharm. Chem. 2019, 22, 42–47. (In Russian) [Google Scholar] [CrossRef]
  62. Zeng, L.; Zhang, G.; Lin, S.; Gong, D. Inhibitory mechanism of apigenin on α-glucosidase and synergy analysis of flavonoids. J. Agric. Food Chem. 2016, 64, 6939–6949. [Google Scholar] [CrossRef]
  63. Bhuiya, S.; Haque, L.; Dutta, T.; Chowdhury, S.; Das, S. Binding aspects of dietary flavone, luteolin, with polymorphic forms of natural DNA: A spectroscopic and molecular docking approach. New J. Chem. 2018, 43, 249–260. [Google Scholar] [CrossRef]
  64. Geethalakshmi, R.; Sundaramurthi, J.C.; Sarada, D.V.L. Antibacterial activity of flavonoid isolated from Trianthema decandra against Pseudomonas aeruginosa and molecular docking study of FabZ. Microb. Pathog. 2018, 121, 87–92. [Google Scholar] [CrossRef] [PubMed]
  65. Bhuiya, S.; Haque, L.; Pradhan, A.B.; Das, S. Inhibitory effects of the dietary flavonoid quercetin on the enzyme activity of zinc(II)-dependent yeast alcohol dehydrogenase: Spectroscopic and molecular docking studies. Int. J. Biol. Macromol. 2017, 95, 177–184. [Google Scholar] [CrossRef] [PubMed]
  66. Pérez-Abril, M.; Lucas-Abellán, C.; Castillo-Sánchez, J.; Pérez-Sánchez, H.; Cerón-Carrasco, J.P.; Fortea, I.; Gabaldón, J.A.; Núñez-Delicado, E. Systematic investigation and molecular modelling of complexation between several groups of flavonoids and HP-β-cyclodextrins. J. Funct. Foods 2017, 36, 122–131. [Google Scholar] [CrossRef]
  67. Kurkin, V.A. Phenylpropanoids from medicinal plants: Distribution, classification, structural analysis, and biological activity. Chem. Nat. Compd 2003, 39, 123–153. [Google Scholar] [CrossRef]
  68. Shanmugasundaram, J.; Subramanian, V.; Nadipelly, J.; Kathirvelu, P.; Sayeli, V.; Cheriyan, B.V. Anxiolytic–like activity of 5–methoxyflavone in mice with involvement of GABAergic and serotonergic systems—In vivo and in silico evidences. Eur. Neuropsychopharmacol. 2020, 36, 100–110. [Google Scholar] [CrossRef] [PubMed]
  69. Taldaev, A.K.; Terekhov, R.P.; Selivanova, I.A. Interaction of Taxifolin and P-Glycoprotein in silico. In Book of Abstracts XI International Conference on Chemistry for Young Scientists; Izdatelstvo VVM: Moscow, Russia, 2019; p. 436. [Google Scholar]
  70. Gacche, R.N.; Shegokar, H.D.; Gond, D.S.; Yang, Z.; Jadhav, A.D. Evaluation of selected flavonoids as antiangiogenic, anticancer, and radical scavenging agents: An experimental and in silico analysis. Cell Biochem. Biophys. 2011, 61, 651–663. [Google Scholar] [CrossRef]
  71. Yugandhar, P.; Kumar, K.K.; Neeraja, P.; Savithramma, N. Isolation, characterization and in silico docking studies of synergistic estrogen receptor a anticancer polyphenols from Syzygium alternifolium (Wt.) Walp. J. Intercult. Ethnopharmacol. 2017, 6, 296. [Google Scholar] [CrossRef]
  72. Guerrero, L.; Castillo, J.; Quiñones, M.; Garcia-Vallvé, S.; Arola, L.; Pujadas, G.; Muguerza, B. Inhibition of angiotensin-converting enzyme activity by flavonoids: Structure-activity relationship studies. PLoS ONE 2012, 7, e49493. [Google Scholar] [CrossRef] [Green Version]
  73. Ye, H.; Shaw, I.C. Food flavonoid ligand structure/estrogen receptor-α affinity relationships—Toxicity or food functionality? Food Chem. Toxicol. 2019, 129, 328–336. [Google Scholar] [CrossRef]
  74. Katzenellenbogen, B.S.; Choi, I.; Delage-Mourroux, R.; Ediger, T.R.; Martini, P.G.V.; Montano, M.; Sun, J.; Weis, K.; Katzenellenbogen, J.A. Molecular mechanisms of estrogen action: Selective ligands and receptor pharmacology. J. Steroid Biochem. Mol. Biol. 2000, 74, 279–285. [Google Scholar] [CrossRef]
  75. Anasir, M.I.; Ramanathan, B.; Poh, C.L. Structure-based design of antivirals against envelope glycoprotein of dengue virus. Viruses 2020, 12, 367. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Jouimyi, M.R.; Bounder, G.; Essaidi, I.; Boura, H.; Zerouali, K.; Lebrazi, H.; Kettani, A.; Maachi, F. Molecular docking of a set of flavonoid compounds with Helicobacter pylori virulence factors CagA and VacA. J. Herbmed. Pharmacol. 2020, 9, 412–419. [Google Scholar] [CrossRef]
  77. Da Silva, E.R.; Brogi, S.; Lucon-Júnior, J.F.; Campiani, G.; Gemma, S.; Maquiaveli, C.D.C. Dietary polyphenols rutin, taxifolin and quercetin related compounds target Leishmania amazonensis arginase. Food Funct. 2019, 10, 3172–3180. [Google Scholar] [CrossRef] [PubMed]
  78. Validandi, V.; Khandare, A.L. Reduction of fluoride toxicity by tamarind components: An in silico study. Fluoride 2018, 51, 122–136. [Google Scholar]
  79. Mishra, S.; Singh, S.; Misra, K. Restraining pathogenicity in Candida albicans by taxifolin as an inhibitor of Ras1-pka pathway. Mycopathologia 2017, 182, 953–965. [Google Scholar] [CrossRef]
  80. Garg, A.X.; Hackam, D.; Tonelli, M. Systematic review and meta-analysis: When one study is just not enough. Clin. J. Am. Soc. Nephrol. 2008, 3, 253–260. [Google Scholar] [CrossRef] [PubMed]
  81. Kumar, A.; Zhang, K.Y.J. Hierarchical virtual screening approaches in small molecule drug discovery. Methods 2015, 71, 26–37. [Google Scholar] [CrossRef]
  82. Lyne, P.D. Structure-based virtual screening: An overview. Drug Discov. Today 2002, 7, 1047–1055. [Google Scholar] [CrossRef]
  83. Onufriev, A.V.; Alexov, E. Protonation and pK changes in protein–ligand binding. Q. Rev. Biophys. 2013, 46, 181–209. [Google Scholar] [CrossRef] [Green Version]
  84. Kalliokoski, T.; Salo, H.S.; Lahtela-Kakkonen, M.; Poso, A. The effect of ligand-based tautomer and protomer prediction on structure-based virtual screening. J. Chem. Inf. Model. 2009, 49, 2742–2748. [Google Scholar] [CrossRef]
  85. Vieth, M.; Hirst, J.D.; Brooks, C.L. Do active site conformations of small ligands correspond to low free-energy solution structures? J. Comput. Mol. Des. 1998, 12, 563–572. [Google Scholar] [CrossRef] [PubMed]
  86. Patrick Walters, W.; Stahl, M.T.; Murcko, M.A. Virtual screening—An overview. Drug Discov. Today 1998, 3, 160–178. [Google Scholar] [CrossRef]
  87. Mannhold, R.; Kubinyi, H.; Timmerman, H. Molecular Modeling Basic Principles and Applications; John Wiley & Sons: New York, NY, USA, 2008. [Google Scholar]
  88. Billeter, M. Comparison of protein structures determined by NMR in solution and by X-ray diffraction in single crystals. Q. Rev. Biophys. 1992, 25, 325–377. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  89. Forli, S.; Huey, R.; Pique, M.E.; Sanner, M.F.; Goodsell, D.S.; Olson, A.J. Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nat. Protoc. 2016, 11, 905–919. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  90. Lohning, A.E.; Levonis, S.M.; Williams-Noonan, B.; Schweiker, S.S. A practical guide to molecular docking and homology modelling for medicinal chemists. Curr. Top. Med. Chem. 2017, 17, 2023–2040. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  91. Kim, M.O.; Nichols, S.E.; Wang, Y.; McCammon, J.A. Effects of histidine protonation and rotameric states on virtual screening of M. tuberculosis RmlC. J. Comput. Aided. Mol. Des. 2013, 27, 235–246. [Google Scholar] [CrossRef] [Green Version]
  92. Riccardi, L.; Genna, V.; de Vivo, M. Metal–ligand interactions in drug design. Nat. Rev. Chem. 2018, 2, 100–112. [Google Scholar] [CrossRef]
  93. Kellenberger, E.; Rodrigo, J.; Muller, P.; Rognan, D. Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins Struct. Funct. Bioinforma. 2004, 57, 225–242. [Google Scholar] [CrossRef]
  94. Chen, Y.C. Beware of docking! Trends Pharmacol. Sci. 2015, 36, 78–95. [Google Scholar] [CrossRef]
  95. Bender, B.J.; Gahbauer, S.; Luttens, A.; Lyu, J.; Webb, C.M.; Stein, R.M.; Fink, E.A.; Balius, T.E.; Carlsson, J.; Irwin, J.J.; et al. A practical guide to large-scale docking. Nat. Protoc. 2021, 16, 4799–4832. [Google Scholar] [CrossRef]
  96. Macip, G.; Garcia-Segura, P.; Mestres-Truyol, J.; Saldivar-Espinoza, B.; Ojeda-Montes, M.J.; Gimeno, A.; Cereto-Massagué, A.; Garcia-Vallvé, S.; Pujadas, G. Haste makes waste: A critical review of docking-based virtual screening in drug repurposing for SARS-CoV-2 main protease (M-pro) inhibition. Med. Res. Rev. 2022, 42, 744–769. [Google Scholar] [CrossRef] [PubMed]
  97. Davis, C.K.; Nasla, K.; Anjana, A.K.; Rajanikant, G.K. Taxifolin as dual inhibitor of Mtb DNA gyrase and isoleucyl-tRNA synthetase: In silico molecular docking, dynamics simulation and in vitro assays. Silico Pharmacol. 2018, 6, 8. [Google Scholar] [CrossRef] [PubMed]
  98. Raj, U.; Varadwaj, P.K. Flavonoids as multi-target inhibitors for proteins associated with ebola virus: In silico discovery using virtual screening and molecular docking studies. Interdiscip. Sci. Comput. Life Sci. 2015, 8, 132–141. [Google Scholar] [CrossRef] [PubMed]
  99. Meenambiga, S.S.; Rajagopal, K. Antibiofilm activity and molecular docking studies of bioactive secondary metabolites from endophytic fungus Aspergillus nidulans on oral Candida albicans. Artic. J. Appl. Pharm. Sci. 2018, 8, 37–45. [Google Scholar] [CrossRef]
  100. Rajendran, P.; Maheshwari, U.; Muthukrishnan, A.; Muthuswamy, R.; Anand, K.; Ravindran, B.; Dhanaraj, P.; Balamuralikrishnan, B.; Chang, S.W.; Chung, W.J. Myricetin: Versatile plant based flavonoid for cancer treatment by inducing cell cycle arrest and ROS–reliant mitochondria-facilitated apoptosis in A549 lung cancer cells and in silico prediction. Mol. Cell. Biochem. 2020, 476, 57–68. [Google Scholar] [CrossRef]
  101. Xu, Z.; Li, K.; Pan, T.; Liu, J.; Li, B.; Li, C.; Wang, S.; Diao, Y.; Liu, X. Lonicerin, an anti-algE flavonoid against Pseudomonas aeruginosa virulence screened from Shuanghuanglian formula by molecule docking based strategy. J. Ethnopharmacol. 2019, 239, 111909. [Google Scholar] [CrossRef]
  102. Lu, J.J.; Zhou, F.M.; Hu, X.J.; Fang, J.J.; Liu, C.X.; Zhu, B.Q.; Ding, Z.S. Molecular docking simulation and in vitro studies on estrogenic activities of flavonoids from leaves of Carya cathayensis Sarg. Steroids 2020, 163, 108726. [Google Scholar] [CrossRef]
  103. Esfahani, A.N.; Mirzaei, M. Flavonoid derivatives for monoamine oxidase–A inhibition. Adv. J. Chem. Sect. B 2019, 1, 17–22. [Google Scholar] [CrossRef]
  104. Orlova, S.V.; Tatarinov, V.V.; Nikitina, E.A.; Sheremeta, A.V.; Ivlev, V.A.; Vasil’ev, V.G.; Paliy, K.V.; Goryainov, S.V. Molecular-biological problems of drug design and mechanism of drug action bioavailability and safety of dihydroquercetin (Review). Pharm. Chem. J. 2021, 55, 3–8. [Google Scholar] [CrossRef]
  105. Weidmann, A.E. Dihydroquercetin: More than just an impurity? Eur. J. Pharmacol. 2012, 684, 19–26. [Google Scholar] [CrossRef]
  106. Das, A.; Baidya, R.; Chakraborty, T.; Samanta, A.K.; Roy, S. Pharmacological basis and new insights of taxifolin: A comprehensive review. Biomed. Pharmacother. 2021, 142, 112004. [Google Scholar] [CrossRef] [PubMed]
  107. Turck, D.; Bresson, J.; Burlingame, B.; Dean, T.; Fairweather-Tait, S.; Heinonen, M.; Hirsch-Ernst, K.I.; Mangelsdorf, I.; McArdle, H.J.; Naska, A.; et al. Statement on the safety of taxifolin-rich extract from Dahurian Larch (Larix gmelinii). EFSA J. 2017, 15, e05059. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  108. Gogoi, N.; Chowdhury, P.; Goswami, A.K.; Das, A.; Chetia, D.; Gogoi, B. Computational guided identification of a citrus flavonoid as potential inhibitor of SARS-CoV-2 main protease. Mol. Divers. 2021, 25, 1745–1759. [Google Scholar] [CrossRef] [PubMed]
  109. Fischer, A.; Sellner, M.; Neranjan, S.; Smieško, M.; Lill, M.A. Potential inhibitors for novel coronavirus protease identified by virtual screening of 606 million compounds. Int. J. Mol. Sci. 2020, 21, 3626. [Google Scholar] [CrossRef]
  110. Yang, C.; Wang, Z.; Mi, Y.; Gao, M.; Lv, J.; Meng, Y.; Yang, B.; Kuang, H. UHPLC-MS/MS determination, pharmacokinetic and bioavailability study of taxifolin in rat plasma after oral administration of its nanodispersion. Molecules 2016, 21, 494. [Google Scholar] [CrossRef]
  111. Zu, Y.; Wu, W.; Zhao, X.; Li, Y.; Wang, W.; Zhong, C. Enhancement of solubility, antioxidant ability and bioavailability of taxifolin nanoparticles by liquid antisolvent precipitation technique. Int. J. Pharm. 2014, 471, 366–376. [Google Scholar] [CrossRef]
  112. Yang, P.; Xu, F.; Li, H.F.; Wang, Y.; Li, F.C.; Shang, M.Y.; Liu, G.X.; Wang, X.; Cai, S.Q. Detection of 191 taxifolin metabolites and their distribution in rats using HPLC-ESI-IT-TOF-MSn. Molecules 2016, 21, 1209. [Google Scholar] [CrossRef] [Green Version]
  113. Terekhov, R.P.; Selivanova, I.A.; Tyukavkina, N.A.; Shylov, G.V.; Utenishev, A.N.; Porozov, Y.B. Taxifolin tubes: Crystal engineering and characteristics. Acta Crystallogr. Sect. B Struct. Sci. Cryst. Eng. Mater. 2019, 75, 175–182. [Google Scholar] [CrossRef]
  114. WHO Coronavirus (COVID-19) Dashboard|WHO Coronavirus (COVID-19) Dashboard with Vaccination Data. Available online: https://covid19.who.int/ (accessed on 9 March 2022).
  115. Jain, A.S.; Sushma, P.; Dharmashekar, C.; Beelagi, M.S.; Prasad, S.K.; Shivamallu, C.; Prasad, A.; Syed, A.; Marraiki, N.; Prasad, K.S. In silico evaluation of flavonoids as effective antiviral agents on the spike glycoprotein of SARS-CoV-2. Saudi J. Biol. Sci. 2021, 28, 1040–1051. [Google Scholar] [CrossRef]
  116. Rudrapal, M.; Issahaku, A.R.; Agoni, C.; Bendale, A.R.; Nagar, A.; Soliman, M.E.S.; Lokwani, D. In silico screening of phytopolyphenolics for the identification of bioactive compounds as novel protease inhibitors effective against SARS-CoV-2. J. Biomol. Struct. Dynam. 2021, 1–17. [Google Scholar] [CrossRef]
  117. Guler, H.I.; Tatar, G.; Yildiz, O.; Belduz, A.O.; Kolayli, S. Investigation of potential inhibitor properties of ethanolic propolis extracts against ACE-II receptors for COVID-19 treatment by molecular docking study. Arch. Microbiol. 2021, 203, 3557–3564. [Google Scholar] [CrossRef] [PubMed]
  118. Taldaev, A.K.; Terekhov, R.P.; Selivanova, I.A. Flavonoids as potential inhibitors of SARS-CoV-2 infection: In silico study. Bull. Sib. Med. 2022, 21, 103–108. [Google Scholar] [CrossRef]
  119. Shohan, M.; Nashibi, R.; Mahmoudian-Sani, M.R.; Abolnezhadian, F.; Ghafourian, M.; Alavi, S.M.; Sharhani, A.; Khodadadi, A. The therapeutic efficacy of quercetin in combination with antiviral drugs in hospitalized COVID-19 patients: A randomized controlled trial. Eur. J. Pharmacol. 2022, 914, 174615. [Google Scholar] [CrossRef] [PubMed]
Ijms 23 06023 g001 550
Figure 1. Base structures of classical flavonoid groups.
Figure 1. Base structures of classical flavonoid groups.
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Figure 2. PRISMA flowchart of the search and selection process of the articles.
Figure 2. PRISMA flowchart of the search and selection process of the articles.
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Figure 3. Monitoring of scientific information on molecular modeling of flavonoids (Google Scholar data).
Figure 3. Monitoring of scientific information on molecular modeling of flavonoids (Google Scholar data).
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Figure 4. Interaction of taxifolin and P-glycoprotein.
Figure 4. Interaction of taxifolin and P-glycoprotein.
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Figure 5. Risk of bias graph.
Figure 5. Risk of bias graph.
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Figure 6. Lead compounds.
Figure 6. Lead compounds.
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Figure 7. The view of taxifolin nanoparticle (A) and its cross-section (B) [113].
Figure 7. The view of taxifolin nanoparticle (A) and its cross-section (B) [113].
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Table
Table 1. Overall inclusion and exclusion criteria for publication screening.
Table 1. Overall inclusion and exclusion criteria for publication screening.
SectionCriteriaInclude If:
LanguageEnglishYes
RussianYes
DesignIn silico studies, complex translational studies with the molecular modeling stageYes
In vitro and in vivo studies, reviews, editorials, letter to the editorNo
ContentStudies examining the affinity of natural flavonoids aglycons to different biological targetsYes
Studies examining the affinity of synthetic flavonoids aglycons to different biological targetsNo
Studies examining the affinity of flavonoids glycosides or other natural polyphenols to different biological targetsNo
AccessFull-text article accessibleYes
Table
Table 2. Overall inclusion and exclusion criteria for publication screening.
Table 2. Overall inclusion and exclusion criteria for publication screening.
Bias DomainIssueLow Risk of BiasHigh Risk of BiasUnclear Risk of Bias
Ligand selectionLigand filteringShould be performedDid not appliedNo data
Ligands optimizationIonization assessmentThe ligands were ionized according to pKa and pH values of mediaThe research was performed without reference to pKa values of ligands and pH values of mediaNo data
Generation of energetically possible conformationsShould be performedGeneration was performed without reference to potential energy calculationNo data
Target selectionResolution of protein structureNot more than 2.5 ÅMore than 2.5 ÅNo data
Method of protein target structure obtainingNMR spectroscopyX-ray crystallography or cryogenic electron microscopyNo data
Target optimizationControl of histidine protonationShould be performedThe structure of target did not reference biological conditionsNo data
Protonation of amino acids after X-ray crystallography or cryogenic electron microscopyShould be performedThe structure of target did not reference biological conditionsNo data
Addition of missing residues and side chains after X-ray crystallography or cryogenic electron microscopyShould be performedWas performed without special toolsNo data
Addition of metalsShould be performedThe structure of target did not reference biological conditionsNo data
DockingMolecular docking softwareGlide, GOLDAutoDock, DOCK, FlexXNo data
Results assessmentVisual controlShould be performedStructure defects were observedNo data
Re-dockingShould be performedThe RMSD value is too high compared with the initial structureNo data
Verification of docking results by in vitro studyBinding constant should be determined The quantitative calculations were not performedNo data
Table
Table 3. Comparison of the average affinity of flavonoid groups to target proteins in the AutoDock.
Table 3. Comparison of the average affinity of flavonoid groups to target proteins in the AutoDock.
Flavonoid GroupAffinity to the Biological Target, kcal/mol *
ERαE Protein
DENV2-Thai		E Protein
DENV2-My		Potassium Channel Kir6.1Protein VacA
Flavones−8.3 ± 0.6−7.8 ± 1.3−7.5 ± 0.9−6.7 ± n/a−8.5 ± 0.3
Flavonols−7.9 ± 0.5−8.4 ± n/a−8.6 ± n/a−8.1 ± n/a-
Flavonol
glycosides		-−8.1 ± n/a−7.7 ± n/a--
Flavanones−8.5 ± n/a----
Flavanonols−9.0 ± n/a----
* A lower value of the scoring function corresponds to a better binding.
Table
Table 4. Comparison of the average affinity of flavonoid groups to target proteins in Glide.
Table 4. Comparison of the average affinity of flavonoid groups to target proteins in Glide.
Flavonoid GroupAffinity to the Biological Target, kcal/mol *
ERαComplex CA II-FArginaseTec1Rfg1
Flavones−8.5 ± 0.3−3.3 ± 0.0---
Flavonols−8.8 ± n/a-−8.1 ± n/a--
Flavonol
glycosides		--−8.2 ± 0.3--
Flavanones−10.2 ± n/a−2.7 ± 0.2-−7.7 ± n/a−6.7 ± n/a
Flavanonols-−2.9 ± n/a−8.2 ± n/a−7.7 ± n/a−4.9 ± n/a
Flavan-3-ols-−4.7 ± 0.6---
Isoflavones−9.0 ± 0.20----
Dihydrochalcones−8.3 ± n/a----
* A lower value of the scoring function corresponds to a better binding.
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Taldaev, A.; Terekhov, R.; Nikitin, I.; Zhevlakova, A.; Selivanova, I. Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling. Int. J. Mol. Sci. 2022, 23, 6023. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms23116023

AMA Style

Taldaev A, Terekhov R, Nikitin I, Zhevlakova A, Selivanova I. Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling. International Journal of Molecular Sciences. 2022; 23(11):6023. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms23116023

Chicago/Turabian Style

Taldaev, Amir, Roman Terekhov, Ilya Nikitin, Anastasiya Zhevlakova, and Irina Selivanova. 2022. "Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling" International Journal of Molecular Sciences 23, no. 11: 6023. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms23116023

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