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. 2023 May 18;13(6):199. doi: 10.1007/s13205-023-03626-8

Evaluation of phytochemicals of Poria cocos against tyrosinase protein: a virtual screening, pharmacoinformatics and molecular dynamics study

Fahad Hassan Shah 1, Young Seok Eom 1, Song Ja Kim 1,✉
PMCID: PMC10195939  PMID: 37215373

Abstract

Tyrosinase inhibitors are commonly used in the pharmaceutical and cosmetic industries for skin lightening and hypopigmentation. The current inhibitors of tyrosinase induce strong safety concerns which necessitate the discovery of new inhibitors. Natural compounds are a promising solution to discover potential candidate for anti-melanogenic activity as they possess less safety concerns and high therapeutic effect. The current study aimed to screen and identify potential phytochemicals from Poria cocos for tyrosinase inhibition. The phytochemicals were obtained from the Traditional Chinese Medicine System Pharmacology Database and screened for druglikeness score and toxicity class and then subjected to in-silico virtual screening and molecular dynamics. 7,9-(11)-Dehydropachymic acid established hydrogen interaction with the tyrosinase protein and was found to be highly stable as validated with MD simulations. The pharmacokinetic results showed that this compound has adequate toxicity and ADME profile that can be exploited for anti-melanogenic effects. Our study identified 7,9-(11)-dehydropachymic acid as an efficient candidate for tyrosinase inhibition.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03626-8.

Keywords: Poria cocos, Medicinal mushroom, Phytochemicals, Virtual screening, Molecular dynamics, Pharmacokinetics

Introduction

Melanin is a biological pigment obtained from the conversion of dopamine, L-tyrosine, and L-DOPA, facilitated by enzymatic and non-enzymatic agents. These are three different types of melanin found in human tissue (Pavan et al. 2020; ElObeid et al. 2017), i.e., neuromelanin which is found in brain tissue, especially in the substantia nigra, or locus coeruleus; pheomelanin and eumelanin which is prevalent in the skin epidermis (Haining and Achat-Mendes 2017). Pheomelanin and eumelanin are primarily formed in specialized organelles known as melanosomes (Micillo et al. 2016). The role of melanosomes is to manufacture, store, and package melanin in melanocytes where tyrosinase (a rate limiting enzyme) comes into play by facilitating the conversion of L-DOPA and L-tyrosine into either pheomelanin or eumelanin (Ohbayashi and Fukuda 2020). Here, the melanin is packaged and then transported to the nearby keratinocytes through dendritic pipelines from melanocytes. The production of melanin regulates skin, hair, or eye color, and its amount is influenced by UV exposure, genetics, exposure to sunlight, hormones, autoimmune diseases, and skin damage (Maranduca et al. 2019).

Some genetic, environmental, or physical factors instigate the overproduction of melanin, resulting in hyperpigmentation (Speeckaert et al. 2014). These include solar lentigines, freckles, café-au-lait macules, melasma, acanthosis nigricans, and many others. Many pharmaceutical and skin industries utilize tyrosinase inhibitors that prevent the production of melanin, resulting in skin lightening and hypopigmentation. Currently, the major challenge faced by these industries is the safety and efficacy of these inhibitors. For example, kojic acid and hydroquinone have been found to be carcinogenic, while others not only influence tyrosinase enzyme activity but also interrupt essential biological processing, resulting in serious side effects (Pillaiyar et al. 2017). Therefore, safe, and effective tyrosinase inhibitor development is a critical hurdle in the ongoing competition. In this study, we have explored natural alternatives and utilized them to obtain potential candidates as tyrosinase inhibitors. As natural extracts offer more safety in terms of side effects than synthetic ones. However, natural extracts also have some drawbacks, such as instability and difficulty obtaining standardized and consistent extracts, and variable efficacy (Karimi et al. 2015). Therefore, in our research, we analyzed the phytochemicals contained in our medicinal mushroom (Poria cocos) to screen and identify potential phytochemical as an efficient candidate for tyrosinase inhibition.

Poria cocos (P. cocos) is a fungal species belonging to the family Polyporaceae, and it is an edible mushroom known to have a wide range of medicinal properties. The use of this mushroom dates back > 2000 years, when it was exploited to induce a calming effect, promote diuresis, and improve the function of the spleen (Ríos 2011). This type of fungal species grows in the form of tubers on various pine tree species. The phytochemicals contained in P. cocos are comprised of triterpenoids, polysaccharides, steroids, amino acids, fatty acids, and many others (Ríos 2011). These fungal species are found in subtropical and humid climates, especially Vietnam, Thailand, and China. The pharmaceutical properties of P. cocos range from antioxidant, antitumor, antimicrobial, anti-inflammatory, immunoprotective, and organoprotective effects, as prescribed earlier in Traditional Chinese Medicine (Sun 2014; Na et al. 2015). In recent years, P. cocos has been utilized to avert tyrosinase activity and exploit its properties for cosmetic purposes. As previously mentioned, we attempted to analyze the phytochemicals of P. cocos to identify potential candidates involved in rendering anti-melanogenic effects. Our study analyzed a series of identified phytochemicals of this fungus against the tyrosinase protein by adopting an in-silico method.

Methodology

Phytochemical acquisition

The phytochemicals of P. cocos were collected from the Traditional Chinese Medicine System Pharmacology Database (TCMSP) (Ru et al. 2014). Poria cocos was searched in the database as a keyword in the search bar. The information related to 34 identified phytochemicals of this mushroom was retrieved and then screened for druglikeness and toxicity class (Pro-Tox-II) (Banerjee et al. 2018).

Pharmacokinetics and toxicity class

The toxicity class of phytochemicals was determined with the Protox-II database by accessing the retrieved mol2 information of a phytochemical, and converting it into canonical SMILES format using OpenBabel (O’Boyle et al. 2011), and submitting it to the database. Whereas the pKCSM database (Pires et al. 2015) was used to determine the pharmacokinetic aspects such as absorption, distribution, metabolism, excretion, and toxicity of successful phytochemicals obtained from the virtual screening process.

Virtual screening of compounds with the molecular docking method

The phytochemicals (ligands) were retrieved in the form of mol2 format files from the TCMSP database and then subjected to refinement and energy minimization procedures, which were achieved with the PRODRG server (Schuttelkopf and Aalten 2004). The protein to be used as a receptor for the virtual screening process was a tyrosinase protein (6EI4) attached to a B5N inhibitor was retrieved from the RCSB protein bank (Ferro et al. 2018). The active residues to which B5N was attached were identified by visualizing their 2D-interaction, facilitated by the Protein Plus database (Stierand and Rarey 2007). Then the protein structure of the tyrosinase protein was cleaned and energy was minimized with Modrefiner (Xu and Zhang 2011). The refined structure was used in iGEMDOCK (Hsu et al. 2011; Li et al. 2011; Mr et al. 2014; Sepehri and Ghavami 2018) (a molecular docking software), in which the binding site of B5N was specified and refined ligands were added to the library of the software for the virtual screening procedure.

Molecular dynamics

Molecular dynamics of successful docked complexes in which a ligand establishes interaction with the active residues of the tyrosinase protein was used to determine their stability by using the following parameters, i.e., root mean square deviation (RMSD), root mean square fluctuation (RMSF), the radius of gyration (Rg) and solvent accessibility surface (SAS). These analyses were achieved with the WebGRO server (https://simlab.uams.edu) (Tumskiy and Tumskaia 2021; Ruankham et al. 2021; Kumar et al. 2013; Vennila and Elango 2022). The docked complex of protein–ligand interaction obtained from the iGEMDOCK was uploaded onto the WebGRO database. Ligand topology files of a ligand generated with the PRODRG server were also uploaded to the database. The parameters applied for the protein–ligand complex were as follows: Forcefield: GROMOS96-43al, Water Model: TIP4P, Box Type: Cubic and the system was neutralized by adding sodium (Na +) or chloride ions (Cl – ). Prior to the MD simulations, the steepest descent (50,000 steps) algorithm was applied for energy minimization. The temperature and pressure optimized for the system were 300 K and 1 Bar with 5000 frames per simulation, which were achieved with the leap-frog integrator algorithm. The simulation time set for protein–ligand complex stability was 50 ns.

Results

Druglike-able and non-toxic phytochemicals in Poria cocos

The TCMSP database contains information on 34 compounds that are present in this mushroom. These compounds were first screened for druglikeness properties, which were achieved by setting the configuration in this system by selecting compounds’ druglikeness values beyond > 0.7. By setting such configurations, we have obtained 23 compounds. These compounds were further analyzed for oral toxicity using the Pro-Tox II database. In this case, we set the toxicity parameters by selecting those compounds that are either classified as Class 4 or Class 5 chemicals by the database. By applying these parameters, we have procured 21 compounds that were used for the virtual screening process to check their interaction with the tyrosinase protein (Table 1).

Table 1.

List of selected compounds for virtual screening experiment

No Compounds Druglikeness LD50 value Oral toxicity
1 Dehydroeburicoic acid 0.83 1000 mg/kg Class 4 chemical
2 (2R)-2-[(5R,10S,13R,14R,16R,17R)-16-hydroxy-3-keto-4,4,10,13,14-pentamethyl-1,2,5,6,12,15,16,17-octahydrocyclopenta[a]phenanthren-17-yl]-5-isopropyl-hex-5-enoic acid 0.82 5000 mg/kg Chemical 5 Chemical
3 (2R)-2-[(3S,5R,10S,13R,14R,16R,17R)-3,16-dihydroxy-4,4,10,13,14-pentamethyl-2,3,5,6,12,15,16,17-octahydro-1H-cyclopenta[a]phenanthren-17-yl]-5-isopropyl-hex-5-enoic acid 0.82 5000 mg/kg Class 5 chemical
4 Tumulosic acid 0.81 2000 mg/kg Class 4 chemical
5 (2R)-2-[(3S,5R,10S,13R,14R,16R,17R)-3,16-dihydroxy-4,4,10,13,14-pentamethyl-2,3,5,6,12,15,16,17-octahydro-1H-cyclopenta[a]phenanthren-17-yl]-6-methylhept-5-enoic acid 0.81 5000 mg/kg Class 5 chemical
6 Ergosterol peroxide 0.81 2340 mg/kg Class 5 chemical
7 Tumulosic acid 0.81 2000 mg/kg Class 4 chemical
8 7,9(11)-dehydropachymic acid 0.81 5000 mg/kg Class 5 chemical
9 Pachymic acid 0.81 3300 mg/kg Class 5 cchemical
10 3 β -Hydroxy-24-methylene-8-lanostene-21-oic acid 0.80 1000 mg/kg Class 4 chemical
11 3β-Hydroxylanosta-7,9(11),24-trien-21-oic acid 0.80 1000 mg/kg Class 4 chemical
12 Trametenolic acid 0.80 1000 mg/kg Class 4 chemical
13 Poricoic acid DM 0.78 5000 mg/kg Class 5 chemical
14 Poricoic acid D 0.78 5000 mg/kg Class 5 chemical
15 Cerevisterol 0.77 2340 mg/kg Class 5 chemical
16 Poricoic acid A 0.76 5000 mg/kg Class 5 chemical
17 Hederagenin 0.75 890 mg/kg Class 4 chemical
18 Poricoic acid C 0.75 5000 mg/kg Class 5 chemical
19 Poricoic acid B 0.75 5000 mg/kg Class 5 chemical
20 β-Amyrin acetate 0.74 3460 mg/kg Class 5 cchemical
21 Ergosta-7,22E-dien-3beta-ol 0.72 2000 mg/kg Class 4 cchemical
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Virtual screening results

Virtual screening was conducted with the iGEMDOCK software by setting the docking configuration to the standard docking algorithm. According to their algorithm, the number of docked solutions is 3, the generations are 70 and the population size is 200. The active binding site for the ligand’s interaction with the target protein (receptor) was specified to the software by choosing the B5N as a binding site center. Before commencing the screening process, the molecular docking protocol was validated by redocking the B5N ligand to its original receptor active site by applying the above-mentioned protocol. Upon completion, the results were analyzed and showed that iGEMDOCK successfully redocked the B5N ligand to its similar position (Supplementary File 1) which paved the way to conduct the screening studies. In this center, these residues, PHE197, ASN205, HIS208, ARG209 and VAL218 of the tyrosinase protein, participate in establishing hydrogen interaction with [4-[(4-fluorophenyl)methyl]piperazin-1-yl]-(2-methylphenyl)methanone (B5N). Such residues were selected to screen the compounds of P. cocos that establish hydrogen connections with them. By screening these 23 drug-likable compounds against tyrosinase protein, we have found that only a single phytochemical from this mushroom establishes hydrogen interaction with these residues, while the rest showed interaction with residues other than those specified, which were ignored from further analysis. Upon examination, it was observed that 7,9(11)-dehydropachymic acid established a hydrogen bond with ARG208 via the carboxy-terminal group, while MET184, PHE197, MET61, HIS204, HIS60, HIS42, ALA221, and VAL218 formed pi-alkyl bonds and HIS208 participated in establishing a pi-donor hydrogen bond with this ligand. The rest of the residues of the tyrosinase protein contributed to forming hydrophobic Van der Waals interactions (Fig. 1) with adequate binding energy, as summarized in Table 2. As compared to the B5N ligand, 7,9(11)-dehydropachymic acid established only a single hydrogen bond, showing that this ligand does have a weak affinity to inhibit the functional activity of tyrosinase protein.

Fig. 1.

Fig. 1

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A Binding of 7,9-(11) dehydropachymic acid in the active pocket of the tyrosinase protein A In the 3D illustration, the functional groups of 7,9-(11) dehydropachymic acid are interacting with the residues of tyrosinase by creating a hydrogen bond cloud pocket around the protein. In this cloud pocket, the pink color represents a hydrogen bond donor, green implies a hydrogen bond acceptor, and white shows no bonding. B Simplified illustration of chemical interactions formed by 7,9-(11) dehydropachymic acid with tyrosinase protein. In this illustration, pink interactions show Pi-alkyl interactions, green interactions indicate hydrogen bonds, and light green interactions show Van der Waals and Pi-hydrogen bond interactions between the compound and the protein residues

Table 2.

The interaction profile of 7,9(11)-dehydropachymic acid with tyrosinase protein

No Ligand Receptor Active site residues Type of interaction Interaction residues Binding energy (KJ/mol)
1 7,9(11)-dehydropachymic acid Tyrosinase (6EI4) PHE197, ASN205, HIS208, ARG209, VAL218 Hydrogen Bond ARG209  – 115.35
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Molecular dynamics results

The docked position of 7,9(11)-dehydropachymic acid interaction with the tyrosinase protein was procured and analyzed for protein–ligand stability using molecular dynamics modules. The MD modules analyzed for the evaluation of protein–ligand stability were RMSD, which is used to observe the system’s stability; RMSF, for conformation changes in the structure of protein upon ligand binding; the radius of gyration for the evaluation of protein size and compactness; solvent accessibility surface area for the inspection of protein interaction with the nearby solvent molecules. Upon MD analysis, the RMS deviation of unbound protein was noted around 0.4 Å but when this ligand (7,9(11)-dehydropachymic acid) interacts with the tyrosinase protein, there is a minor deviation pattern (0.2–0.6 Å) between 0 and 10 ns time. Beyond this time, the complex becomes unstable between 10 and 35 ns, where deviation can be observed at 0.5–0.95 Å but after 40 ns, the protein–ligand complex re-establishes stability, which nearly overlaps with the RMS deviation of unbound protein. It has been reported in the literature that if the RMSD of the ligand bound to the protein backbone is within 2–4 Å is considered safe and stable enough to render its activity (Ramírez and Caballero 2018). The RMSF value of unbound protein had a fluctuation observed around the 150–270th amino acid. Upon ligand binding, the fluctuation was considerably reduced. Whereas the Rg of unbound protein was situated at approximately 1.90 nm, ligand binding reduced the gyration radius to 1.88 nm. Protein–ligand complex and unbound protein showed similar solvent accessibility surface values, as both peaks almost overlapped with one another. These extensive fluctuations indicate that weak binding of 7,9(11)-dehydropachymic acid with the backbone of tyrosinase protein which can be improved by introducing structural or algorithmic adjustment with different synthetic and in-silico methods (Singh and Purohit 2023; Kumar et al. 2023; Shah et al. 2020). Hence, these results indicate that 7,9(11)-dehydropachymic acid bound to the tyrosinase is moderately stable, as evident from the module results obtained via MD analysis Fig. 2.

Fig. 2.

Fig. 2

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The structural and dynamic behavior of tyrosinase (receptor) protein-7,9-(11) dehydropachymic acid (ligand) was analyzed from molecular dynamic simulations. A shows the root mean square deviation, which calculates the average displacement of atoms of the protein–ligand complex (Black Lines) and the crystal structure of protein alone (Blue Lines) over a 50 ns time. B represents root mean square fluctuation and measures the average displacement of specific group atoms of protein affected by the binding of ligand. C indicates the protein’s structural compactness upon ligand interaction (radius of gyration), D Solvent Accessibility Surface describes the conformational change in protein–ligand complex upon exposure to a solvent

ADMET properties

The pharmacokinetic features of 7,9(11)-dehydropachymic acid were analyzed with the pKCSM database, which determines the compound’s absorption, distribution, metabolism, excretion, and toxicity attributes. The results showed low water solubility with high intestinal absorption and caco2 permeability. The skin permeability was − 2.7 log Kp and possessed no affinity for p-glycoprotein except for P-glycoprotein, where this compound acts as an inhibitor. The volume of distribution (VDss) was noted at − 0.89 log L/kg with low BBB and CNS permeability. 7,9(11)-dehydropachymic acid shows no interaction with CYP proteins except that it acts as a substrate for the CYP3A4 protein. The total clearance of this compound is 0.23 log ml/min/kg and it possesses no concerning toxicity. The results of the pharmacokinetic features of this compound are summarized in Table 3.

Table 3.

7,9(11)-Dehydropachymic acid pharmacokinetic properties

Property Compound Model name Predicted value Unit
Absorption 7,9(11)-dehydropachymic acid Water solubility  – 4.059 Numeric (log mol/L)
Absorption Caco2 permeability 0.713 Numeric (log Papp in 10–6 cm/s)
Absorption Intestinal absorption (human) 98.007 Numeric (% Absorbed)
Absorption Skin permeability  – 2.735 Numeric (log Kp)
Absorption P-glycoprotein substrate No Categorical (Yes/No)
Absorption P-glycoprotein I inhibitor No Categorical (Yes/No)
Absorption P-glycoprotein II inhibitor Yes Categorical (Yes/No)
Distribution VDss (human)  – 0.899 Numeric (log L/kg)
Distribution Fraction unbound (human) 0 Numeric (Fu)
Distribution BBB permeability  – 0.286 Numeric (log BB)
Distribution CNS permeability  – 1.794 Numeric (log PS)
Metabolism CYP2D6 substrate No Categorical (Yes/No)
Metabolism CYP3A4 substrate Yes Categorical (Yes/No)
Metabolism CYP1A2 inhibitor No Categorical (Yes/No)
Metabolism CYP2C19 inhibitor No Categorical (Yes/No)
Metabolism CYP2C9 inhibitor No Categorical (Yes/No)
Metabolism CYP2D6 inhibitor No Categorical (Yes/No)
Metabolism CYP3A4 inhibitor No Categorical (Yes/No)
Excretion Total clearance 0.234 Numeric (log ml/min/kg)
Excretion Renal OCT2 substrate No Categorical (Yes/No)
Toxicity AMES toxicity No Categorical (Yes/No)
Toxicity Max. tolerated dose (human) 0.019 Numeric (log mg/kg/day)
Toxicity hERG I inhibitor No Categorical (Yes/No)
Toxicity hERG II inhibitor No Categorical (Yes/No)
Toxicity Oral rat acute toxicity (LD50) 2.276 Numeric (mol/kg)
Toxicity Oral rat chronic toxicity (LOAEL)  – 0.232 Numeric (log mg/kg_bw/day)
Toxicity Hepatotoxicity No Categorical (Yes/No)
Toxicity Skin sensitization No Categorical (Yes/No)
Toxicity T. pyriformis toxicity 0.285 Numeric (log ug/L)
Toxicity Minnow toxicity  – 0.798 Numeric (log mM)
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Discussion

Melanin is a critical ingredient for the maintenance of skin homeostasis, and other vital biological functions, however, excessive melanin production in the body can instigate undesirable skin changes, hyperpigmentation, and pre-mature aging (Speeckaert et al. 2014). The tyrosinase enzyme is responsible for the regulation of melanin, and it has been a center of attention among researchers to procure anti-tyrosinase drugs due to their role in the melanogenesis pathway. Despite the existence of anti-tyrosinase inhibitors, it is of immense importance to discover a new range of compounds with these properties. As current inhibitors possess multiple adverse effects, insufficient effectiveness, and a non-specific mode of action (Karimi et al. 2015). Therefore, these effects necessitate the discovery of newer agents to regulate the tyrosinase activity in melanin production, more targeted, potent, and effective alternatives for both pharmaceutical and cosmetic purposes.

The extract of P. cocos has been exploited for centuries to treat various diseases in traditional Chinese medicine. Recently, Lee and Cha 2018 investigated the extract of P. cocos against a melanin cell model (B16F10) to determine its effect on pigmentation (Lee and Cha 2018). The exposure of P. cocos extract to these cells decreased the expression and production of tyrosinase. In the following year, Kim et al. 2019 also reported that the bark extract of P. cocos reduces melanin production (Kim et al. 2019). In our study, we explored the phytochemicals of P. cocos against tyrosinase protein by employing the in-silico method. The reason behind the exploitation of these phytochemicals was to discover compounds that are involved in rendering anti-tyrosinase activity. This is because plant extracts may lack behind compared to synthetic drugs due to factors such as lack of standardization and quality control, lower potency, limited research and development, and safety concerns.

Upon screening, 21 compounds from this mushroom against the tyrosinase protein, we found that 7,9(11)-dehydropachymic acid was the only compound that established a hydrogen bond with the indicated active residues of this protein. As hydrogen bonding of the ligand with the target protein/receptor distorts their structure and jeopardizes their functional activity (Pandey et al. 2019). These results were further verified with protein–ligand stability analysis through MD modules. Both the RMSD and RMSF modules of MD simulations indicated that the 7,9(11)-dehydropachymic acid interaction with the tyrosinase protein is stable enough to interfere with their activity. Moreover, the pharmacokinetic and toxicity evaluation of this compound is moderate and can be further ameliorated using a nanotherapeutic approach to increase their absorption and reduce their toxicity.

Conclusion

Our in-silico results described that out of 21 compounds, 7,9-(11) dehydropachymic acid found in the P. cocos extract shows interaction with the tyrosinase protein. It is a class 5 chemical, can induce a high toxic response at 5000 mg/kg, and has a high druglikeness score. This compound can be used to dampen melanin production by disrupting the function of the tyrosinase enzyme to treat various hyperpigmentation diseases. Since these results provided a preliminary insight that 7,9-(11) dehydropachymic acid has the capability to inhibit the function of this protein which must be proven with in-vitro and in-vivo studies.

Summary points

  • The Traditional Chinese Medicine System Pharmacology Database (TCMSP) contained the information of 34 compounds that has been identified in the literature pertaining to Poria cocos which were retrieved for further analysis.

  • Among these 34 compounds from Poria cocos mushroom, 23 drug-likeable compounds were selected, out of which 21 were further screened for oral toxicity.

  • Only one phytochemical, 7,9(11)-dehydropachymic acid, was found to interact with active residues in the tyrosinase protein through hydrogen bonding.

  • The molecular dynamics analysis showed that the protein–ligand complex was moderately stable and exhibited minimal fluctuations, indicating a stable interaction between 7,9(11)-dehydropachymic acid and the tyrosinase protein.

  • 7,9(11)-dehydropachymic acid was found to have low water solubility but high intestinal absorption and Caco2 permeability. It also showed no concerning toxicity and no interaction with most CYP proteins.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PNG 416 KB) (416.5KB, png)

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) funded by the Korean Government (MEST) (2020R1I1A306969914).

Data availability

The authors confirm that the data supporting the findings of this study are available within the article.

Declarations

Conflict of interest

All authors declare that they have no conflict of interest.

References

  1. Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2018;46:W257–W263. doi: 10.1093/nar/gky318. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. de Pires V, Blundell TL, Ascher DB. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem. 2015;58:4066–4072. doi: 10.1021/acs.jmedchem.5b00104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. ElObeid AS, Kamal-Eldin A, Abdelhalim MAK, Haseeb AM. Pharmacological properties of melanin and its function in health. Basic Clin Pharmacol Toxicol. 2017;120:515–522. doi: 10.1111/bcpt.12748. [DOI] [PubMed] [Google Scholar]
  4. Ferro S, Deri B, Germanò MP, et al. Targeting tyrosinase: development and structural insights of novel inhibitors bearing arylpiperidine and arylpiperazine fragments. J Med Chem. 2018;61:3908–3917. doi: 10.1021/acs.jmedchem.7b01745. [DOI] [PubMed] [Google Scholar]
  5. Haining RL, Achat-Mendes C. Neuromelanin, one of the most overlooked molecules in modern medicine, is not a spectator. Neural Regen Res. 2017;12:372. doi: 10.4103/1673-5374.202928. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Hsu K-C, Chen Y-F, Lin S-R, Yang J-M. iGEMDOCK: a graphical environment of enhancing GEMDOCK using pharmacological interactions and post-screening analysis. BMC Bioinformatics. 2011;12:1–11. doi: 10.1186/1471-2105-12-S1-S33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Karimi A, Majlesi M, Rafieian-Kopaei M. Herbal versus synthetic drugs; beliefs and facts. J Nephropharmacology. 2015;4:27. [PMC free article] [PubMed] [Google Scholar]
  8. Kim MG, Park SI, An GM, Heo SH, Shin MS. Physiological activity of supercritical Poria cocos back extract and its skin delivery application using epidermal penetrating peptide. J Korean Appl Sci Technol. 2019;36:766–778. [Google Scholar]
  9. Kumar A, Kumar S, Jain S, Kumar P, Goyal R. Study of binding of pyridoacridine alkaloids on topoisomerase II using in silico tools. Med Chem Res. 2013;22:5431–5441. doi: 10.1007/s00044-013-0496-5. [DOI] [Google Scholar]
  10. Kumar S, Bhardwaj VK, Singh R, Purohit R. Structure restoration and aggregate inhibition of V30M mutant transthyretin protein by potential quinoline molecules. Int J Biol Macromol. 2023;231:123318. doi: 10.1016/j.ijbiomac.2023.123318. [DOI] [PubMed] [Google Scholar]
  11. Lee H, Cha HJ. Poria cocos Wolf extracts represses pigmentation in vitro and in vivo. Cell Mol Biol. 2018;64:80–84. doi: 10.14715/cmb/2018.64.5.13. [DOI] [PubMed] [Google Scholar]
  12. Li Y, Frenz CM, Li Z, Chen M, Wang Y, Li F, Luo C, Sun J, Bohlin L, Li Z. Virtual and In vitro bioassay screening of phytochemical inhibitors from flavonoids and isoflavones against Xanthine oxidase and Cyclooxygenase-2 for gout treatment. Chem Biol Drug Des. 2011 doi: 10.1111/j.1747-0285.2011.01248.x. [DOI] [PubMed] [Google Scholar]
  13. Maranduca MA, Branisteanu D, Serban DN, Branisteanu DC, Stoleriu G, Manolache N, Serban IL. Synthesis and physiological implications of melanic pigments. Oncol Lett. 2019;17:4183–4187. doi: 10.3892/ol.2019.10071. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Micillo R, Panzella L, Koike K, Monfrecola G, Napolitano A, d’Ischia M. “Fifty shades” of black and red or how carboxyl groups fine tune eumelanin and pheomelanin properties. Int J Mol Sci. 2016;17:746. doi: 10.3390/ijms17050746. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. LS MR, Jude J, Kannan I, Shankar KA (2014) Molecular docking study for inhibitors of Aggregatibacter actinomycetamcomitans toxins in treatment of aggressive perioodontitis. J Clin diagnostic Res JCDR 8:ZC48 [DOI] [PMC free article] [PubMed]
  16. Na S-S, Chong M-S, Woo J-H, Kwon Y-O, Lee MK, Oh K-W. Poria cocos ethanol extract and its active constituent, pachymic acid, modulate sleep architectures via activation of GABAA-ergic transmission in rats. J Biomed Transl Res. 2015;16:84–92. [Google Scholar]
  17. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. J Cheminform. 2011;3:33. doi: 10.1186/1758-2946-3-33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Ohbayashi N, Fukuda M (2020) Recent advances in understanding the molecular basis of melanogenesis in melanocytes. F1000Research 9:608 [DOI] [PMC free article] [PubMed]
  19. Pandey SK, Yadav S, Goel Y, Temre MK, Singh VK, Singh SM. Molecular docking of anti-inflammatory drug diclofenac with metabolic targets: potential applications in cancer therapeutics. J Theor Biol. 2019;465:117–125. doi: 10.1016/j.jtbi.2019.01.020. [DOI] [PubMed] [Google Scholar]
  20. Pavan ME, López NI, Pettinari MJ. Melanin biosynthesis in bacteria, regulation and production perspectives. Appl Microbiol Biotechnol. 2020;104:1357–1370. doi: 10.1007/s00253-019-10245-y. [DOI] [PubMed] [Google Scholar]
  21. Pillaiyar T, Manickam M, Namasivayam V. Skin whitening agents: medicinal chemistry perspective of tyrosinase inhibitors. J Enzyme Inhib Med Chem. 2017;32:403–425. doi: 10.1080/14756366.2016.1256882. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Ramírez D, Caballero J. Is It Reliable to Take the Molecular Docking Top Scoring Position as the Best Solution without Considering Available Structural Data? Mol. 2018 doi: 10.3390/molecules23051038. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Ríos J-L. Chemical constituents and pharmacological properties of Poria cocos. Planta Med. 2011;77:681–691. doi: 10.1055/s-0030-1270823. [DOI] [PubMed] [Google Scholar]
  24. Ru J, Li P, Wang J, Zhou W, Li B, Huang C, Li P, Guo Z, Tao W, Yang Y. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6:1–6. doi: 10.1186/1758-2946-6-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Ruankham W, Phopin K, Pingaew R, Prachayasittikul S, Prachayasittikul V, Tantimongcolwat T. In silico and multi-spectroscopic analyses on the interaction of 5-amino-8-hydroxyquinoline and bovine serum albumin as a potential anticancer agent. Sci Rep. 2021;11:1–15. doi: 10.1038/s41598-021-99690-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Schuttelkopf AW, van Aalten DMF. PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr Sect D. 2004;60:1355–1363. doi: 10.1107/S0907444904011679. [DOI] [PubMed] [Google Scholar]
  27. Sepehri B, Ghavami R. The identification of new CD38 inhibitors by combined structure and ligand based virtual screening approaches of ZINC database. Lett Drug Des Discov. 2018;15:654–660. doi: 10.2174/1570180814666170914120848. [DOI] [Google Scholar]
  28. Shah FH, Salman S, Idrees J, Idrees F, Shah STA, Khan AA, Ahmad B. Current Progress of Phytomedicine in Glioblastoma Therapy. Curr Med Sci. 2020;40:1067–1074. doi: 10.1007/s11596-020-2288-8. [DOI] [PubMed] [Google Scholar]
  29. Singh R, Purohit R. Computational analysis of protein-ligand interaction by targeting a cell cycle restrainer. Comput Methods Programs Biomed. 2023;231:107367. doi: 10.1016/j.cmpb.2023.107367. [DOI] [PubMed] [Google Scholar]
  30. Speeckaert R, Van Gele M, Speeckaert MM, Lambert J, van Geel N. The biology of hyperpigmentation syndromes. Pigment Cell Melanoma Res. 2014;27:512–524. doi: 10.1111/pcmr.12235. [DOI] [PubMed] [Google Scholar]
  31. Stierand K, Rarey M. From modeling to medicinal chemistry: automatic generation of two-dimensional complex diagrams. ChemMedChem. 2007;2:853–860. doi: 10.1002/cmdc.200700010. [DOI] [PubMed] [Google Scholar]
  32. Sun Y. Biological activities and potential health benefits of polysaccharides from Poria cocos and their derivatives. Int J Biol Macromol. 2014;68:131–134. doi: 10.1016/j.ijbiomac.2014.04.010. [DOI] [PubMed] [Google Scholar]
  33. Tumskiy RS, Tumskaia AV. Multistep rational molecular design and combined docking for discovery of novel classes of inhibitors of SARS-CoV-2 main protease 3CLpro. Chem Phys Lett. 2021;780:138894. doi: 10.1016/j.cplett.2021.138894. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Vennila KN, Elango KP. Multimodal generative neural networks and molecular dynamics based identification of PDK1 PIF-pocket modulators. Mol Syst Des Eng. 2022;7:1085–1092. doi: 10.1039/D2ME00051B. [DOI] [Google Scholar]
  35. Xu D, Zhang Y. Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys J. 2011;101:2525–2534. doi: 10.1016/j.bpj.2011.10.024. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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Supplementary Materials

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Data Availability Statement

The authors confirm that the data supporting the findings of this study are available within the article.

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