|Year : 2023 | Volume
| Issue : 1 | Page : 8-17
Biocomputational-mediated screening and molecular docking platforms for discovery of coumarin-derived antimelanogenesis agents
Jing Yu Lim1, Lai Ti Gew1, Yin-Quan Tang2
1 Department of Biological Sciences, School of Medical and Life Sciences, Sunway University, Petaling Jaya, Selangor, Malaysia
2 School of Biosciences; Centre for Drug Discovery and Molecular Pharmacology, Faculty of Health and Medical Sciences, Taylor's University, Subang Jaya, Malaysia
|Date of Submission||26-May-2022|
|Date of Decision||16-Aug-2022|
|Date of Acceptance||02-Nov-2022|
|Date of Web Publication||27-Mar-2023|
Dr. Yin-Quan Tang
School of Biosciences, Faculty of Health and Medical Sciences, Taylor's University, No. 1, Jalan Taylors, Subang Jaya 47500, Selangor
Source of Support: None, Conflict of Interest: None
Background: Hyperpigmentation occurs when excess melanin accumulates in the skin and causes the skin to become darker in color. Pursuing attractive appearance and colorism have promoted the development of the skin whitening market globally. The proteins targeted in this research are tyrosinase-related protein 1, cyclic adenosine monophosphate response element-binding protein, receptor tyrosine kinase, and endothelin receptor type B. Objectives: This study aims to identify the potential of coumarin derivatives as novel effective, safe, and natural antimelanogenesis agents for whitening purposes or therapeutical intention to treat hyperpigmentation disorders. Methods: Four three-dimensional structures of the targeted proteins and 94 ligands were obtained from Protein Data Bank and PubChem, respectively. The ligands were docked against modified targeted proteins to examine the binding affinity and protein-ligand interactions using PyRx and BIOVIA Discovery Studio. The top 13 derivatives were selected for further analysis on the pharmacokinetic properties through SwissADME and pkCSM web servers. A total of eight compounds were further chosen to conduct multiple ligand simultaneous docking (MLSD). Results: Difenacoum is the most potential antimelanogenesis agent due to its strong inhibitory binding affinity in targeted protein models (5M8M, 4TQN, 5X93), but it does not exhibit favorable behavior pharmacokinetic properties. From the in silico pharmacokinetics screening, novobiocin sodium is the most potent derivative due to its relatively appropriate and safer properties. However, none of the ligand pairs investigated in MLSD possesses a synergistic effect on the binding affinity. Conclusion: Our findings identified colladin, farnesiferol C and novobiocin sodium may be promising natural resources for developing antimelanogenesis agents.
Keywords: Colladin, computational screening, farnesiferol C, melanogenesis, novobiocin sodium
|How to cite this article:|
Lim JY, Gew LT, Tang YQ. Biocomputational-mediated screening and molecular docking platforms for discovery of coumarin-derived antimelanogenesis agents. Dermatol Sin 2023;41:8-17
|How to cite this URL:|
Lim JY, Gew LT, Tang YQ. Biocomputational-mediated screening and molecular docking platforms for discovery of coumarin-derived antimelanogenesis agents. Dermatol Sin [serial online] 2023 [cited 2023 Jun 3];41:8-17. Available from: https://www.dermsinica.org/text.asp?2023/41/1/8/372600
| Introduction|| |
Melanin is a biopolymer and ubiquitous natural pigment which can determine the color of human skin, eyes, and hair. Melanin is responsible for protecting the photodamaging of epidermis from long-term exposure to ultraviolet radiation. Besides acting as photoprotective pigmentation, melanin also exhibits antioxidant properties, anticancer against skin cancer cell lines, and broad-spectrum antimicrobial characteristics in melanin-mediated silver nanostructures. Nevertheless, the accumulation of melanin in the skin will lead to hyperpigmentation and cause disorders such as melasma, freckles, postinflammatory hyperpigmentation, and lentigines. Melanin generally can be classified into five types which are eumelanin, pheomelanin, neuromelanin, allomelanin, and pyomelanin. However, the melanin forms usually found in humans are eumelanin, pheomelanin, and neuromelanin. The main melanin types discussed throughout this article are eumelanin and pheomelanin, as both are widely studied. Eumelanin is a brown-black pigment, while pheomelanin exists in yellow to red. Since neuromelanin is generally present in the brain, thus an individual's skin color is usually determined by the relative amount of eumelanin and pheomelanin present in the body.
Melanin is synthesized in melanosomes in melanocytes, while melanoblasts are the precursor for melanocytes. The melanin in melanosomes transferred from melanocytes to keratinocytes can contribute to an individual's skin color. According to the Raper-Mason pathway, melanin is synthesized by converting L-tyrosine to l-3,4-dihydroxyphenylalanine (L-DOPA) with the aid of tyrosinase. In addition, melanin will be produced when the tyrosinase enzyme helps to convert L-DOPA to dopaquinone which is a precursor for melanin. Tyrosinase, a copper-containing enzyme, acts as a rate-limiting enzyme in melanin biosynthesis as it catalyzes multiple processes in the Raper–Mason pathway. Thus, direct inhibition of tyrosinase or indirectly inhibiting tyrosinase activities such as inhibiting mRNA transcription of tyrosinase, aberrant maturation of tyrosinase, the inhibiting catalytic activity of tyrosinase and accelerating tyrosinase degradation are crucial to reducing melanin synthesis.
Various whitening ingredients such as hydroquinone, kojic acid, and arbutin have been proven effective in the cosmetic market globally. Recently, the practice of using whitening products has become a leading trend globally. According to a questionnaire conducted by a study, researchers discovered that most respondents believe a fair complexion is more attractive and can contribute to a better marital status. Discrimination caused by colorism or skin tone bias may further intensify the popularity in the skin brightening market. Besides intending to have lighter skin color, hyperpigmentation disorder is another reason for an individual to use anti-pigmentation products. However, several depigmenting agents cause adverse effects such as irritation, burning sensation, tingling, and dermatitis. Furthermore, individuals who used the hydroquinone for a prolonged duration have reported a rare adverse effect of exogenous ochronosis. In addition, another commonly used whitening ingredient, arbutin is unstable and carcinogenic. Hence, it is vital to identify novel effective, safe, and natural antimelanogenesis agents for cosmetic or pharmaceutical purposes.
Coumarin, with an IUPAC name of chromen-2-one is a natural compound found in high concentrations in tonka beans (Dipteryx odorata). Coumarin is also known as α-benzopyrone as it consists of a benzene ring and a pyrone [Figure 1]. Cassia cinnamon (Cinnamomum cassia), sweet clover (genus Melilotus), vanilla grass (Anthoxanthum odoratum), apricots, cherry blossom strawberries, and propolis also consist of the naturally occurring coumarin. One of the most well-known coumarin derivatives is warfarin. This anticoagulant drug is usually prescribed to patients who suffer from stroke or deep vein thrombosis to reduce the ability for blood clotting. Numerous studies have documented that coumarin and its derivatives have a wide range of therapeutic properties such as anti-inflammatory, anticancer, antiviral, antibacterial, antifungal, antidiabetic, anti-Alzheimer, antihypertensive, antitubercular, and antioxidant. Although various promising characteristics of coumarin and its derivatives have been reported, only few of the research have focused on the antimelanogenesis properties of coumarin.
|Figure 1: The chemical structure of coumarin consists of a benzene ring and an α-pyrone|
Click here to view
As there are a variety of coumarin and its derivatives that can be found in different databases, the computation approaches will be utilized to ease the drug discovery procedure. The computer-aided drug discovery can be categorized into two types which are structure-based or ligand-based drug discoveries. This project will emphasize molecular docking which is part of the structure-based approach. The molecular docking results can be evaluated by analyzing protein-ligand interactions, optimal compounds orientation, and binding affinity. Molecular docking plays an essential role in the pharmaceutical industry because it is high throughput, time-saving, and cost-effective. In addition, absorption, distribution, metabolism, excretion, and toxicology (ADMET) prediction is carried out to ensure the safety profile of the newly discovered compounds. ADMET prediction is critical as many potential compounds identified through high-throughput approaches were terminated due to the limited ADMET properties.
Since melanogenesis is a complicated process involving multiple signaling pathways, we have targeted four proteins in this study. Tyrosinase-related protein 1 (TRP-1), cyclic adenosine monophosphate (cAMP) response element-binding protein (CREB), receptor tyrosine kinase (c-kit) and endothelin receptor type B (ETBR) were included in this research as the targeted macromolecules in the computational approaches. We aimed to discover safer and natural compounds as potential antimelanogenesis agents for either whitening purposes or therapeutical intention to treat hyperpigmentation disorders.
| Methods|| |
Preparation of targeted macromolecules and ligands
The X-ray diffraction proteins complexes include TRP-1 (5M8M), CREB (4TQN), c-kit (6XV9) and ETBR (5X93), derived from homo sapiens were downloaded in the protein data bank (PDB) format from RCSB PDB (https://www.rcsb.org/). Since coumarin is the main chemical of interest in this project, coumarin and its appropriate derivatives were searched and determined on the Sigma-Aldrich website (https://www.sigmaaldrich.com/MY/en). Sigma-Aldrich is preferred as we need to ensure the availability of the potential antimelanogenesis agents identified through this study. This website can ease the searching and purchasing process for researchers who require the compounds in the future for in vitro or in vivo research. As a result, 94 related compounds were selected. Three-dimensional (3D) chemical structures for coumarin, its derivatives and positive controls were primarily downloaded from PubChem (https://pubchem.ncbi.nlm.nih.gov/) in structure-data file format. If the 3D chemical structures for selected chemicals are inaccessible in PubChem, the ChemSpider database (http://www.chemspider.com/) will be utilized. Once the 3D chemical structures are unavailable from both databases, the two-dimensional chemical structure will be downloaded from PubChem and converted to 3D through Avogadro (v 1.2.0), a molecule editor application.
Single-ligand molecular docking
The protein complexes mentioned above were imported into BIOVIA Discovery Studio Visualizer (v18.104.22.16898) to analyze the bond formation and identify the active sites for each targeted protein. The removal of ligands and water molecules attached to protein complexes and the addition of polar hydrogens were performed through BIOVIA Discovery Studio software. The altered protein file was saved in.pdb format. The modified protein structure files and downloaded chemical structures were used as inputs in PyRx 0.8 program for single-ligand molecular docking simulation. All chemical structures imported undergo energy minimization in the universal force field and are converted to AutoDock ligand in the .pdbqt format. The virtual screening was conducted using Vina Wizard in PyRx, while exhaustiveness was fixed at eight, and other docking parameters were set as default. The grid box sizes were adjusted based on each protein complex to enclose the entire active sites. The binding affinity (kcal/mol) gleaned in PyRx was recorded and exported as comma-separated values files. The visualization diagram for both two and three dimensions was constructed through BIOVIA Discovery Studio to analyze protein-ligand complexes interactions.
Prediction of physiochemical properties and absorption, distribution, metabolism, excretion, and toxicology
Thirteen compounds with the lowest binding affinity were further predicted for their pharmacokinetic properties. The in silico ADMET screening was done through SwissADME (http://www swissadme.ch/) and pkCSM (http://biosig.unimelb.edu.au/pkcsm/) web interfaces. The canonical Simplified Molecular Input Line Entry System (SMILES) for the selected chemicals were derived from the PubChem database. It was being used as an input in SwissADME and pkCSM. As most of the whitening products are designed for topical usage, only six parameters were examined, including lipophilicity, skin permeation (log Kp), total clearance, and Salmonella typhimurium reverse mutation assay (AMES) toxicity, skin sensitization, and hepatoxicity. The first two components aforementioned are available on SwissADME, while the others are gleaned from pkCSM.
Multiple ligand simultaneous docking
After a series of examinations, eight of the top compounds were chosen for the multiple ligand simultaneous docking (MLSD) procedure. The eight chemicals are further classified into 28 pairs, where each pair consists of two chemicals without any repetition. For MLSD, the altered protein files (.pdb) prepared in single-ligand molecular docking section were imported into the AutoDock Tools (v1.5.6). The input protein structures were modified by removing the water molecules, adding polar hydrogens and Kollman charges, and saving it in the. pdbqt format. The grid box dimensions for 5M8M are set as 54 × 58 × 50, while its centers are − 22.791 × 22.795 × −4.550. For 4TQN, the grid box dimensions are 48 × 40 × 36 and have centers values of − 11.509 × 3.643 × 2.146. The grid box dimensions for 6XV9 and 5X93 are 52 × 64 × 84 and 70 × 60 × 68, respectively. On the other hand, the grid box centers for 6XV9 and 5X93 are 190.884 × 30.971 × 71.722 and 1.255 × 46.441 × −0.131. The grid space for every targeted protein was set as 0.375 Å, and the exhaustiveness was eight. These values gleaned were used as a command input in the AutoDock Vina (v1.2.3),, and the binding affinities for MLSD were recorded.
| Results|| |
In this study, four macromolecules obtained from PDB were utilized as targeted proteins, and their active sites were analyzed through BIOVIA Discovery Studio [Figure 2]. The first targeted protein, TRP-1 (5M8M), establishes a hydrogen bonding with Arg 37 in chain A while forming Van der Waals forces with Leu36, Ser 38, Gly 39, Phe 94, Asn 96 in chain A and Val 48 in chain C. The amino acid residues associated with forming Van der Waals force in the second targeted protein, CREB (4TQN), are Phe 1111, Ile1122, Tyr 1125, Ala 1164, Tyr 1167, and Phe 1177. Moreover, the hydrogen bonds formed with Ala 621, Val 668, Thr 670, Glu 671, His 790 in c-kit (6XV9), while Tyr 568, Val 622, Val 643, Leu 647, Val 654, Cys 674, Gly 676, Leu 783, Cys 788, Ile 808 establish Van der Waals force. There have no hydrogen bonds formed in ETBR (5X93), but Van der Waals force was established in Ile 155, Asn 158, Lys 161, Trp 167, Val 177, Glu 236, Cys 255, Ala 375, Ser 376.
|Figure 2: 3D (Left) and 2D (right) illustrations of the binding sites for all targeted proteins. 3D: Three-dimensional|
Click here to view
Single-ligand molecular docking was performed after assessing all of the active sites for each targeted protein. As a result [Table 1], coumermycin A1 has the strongest binding affinity in 5M8M with −8.9 kcal/mol. Difenacoum is the compound with the lowest binding affinity value in 4TQN. It has a binding affinity of −9.4 kcal/mol. The most excellent binding affinity in the 6XV9 structure was −11.4 kcal/mol. This binding affinity was established by the molecule 6-bromo-3-(2,5-dichlorophenyl carbamoyl)-coumarin. Colladin possesses the highest binding affinity of − 10.4 kcal/mol in the 5X93 model. The 6XV9 model exhibits the strongest binding affinity for compounds of interest, followed by 5X93, 4TQN, and 5M8M.
|Table 1: Top five compounds with the strongest binding affinity for each targeted protein (tyrosinase-related protein 1, cyclic adenosine monophosphate response element-binding protein, receptor tyrosine kinase, and endothelin receptor type B)|
Click here to view
Thirteen of the most potent compounds to act as antimelanogenesis agents were chosen [Table 2]. Difenacoum has exhibited the highest lipophilicity of 6.27, while 0.18 was recorded for lipophilicity in novobiocin sodium. In addition, −3.47 cm/s in difenacoum and −7.81 cm/s in novobiocin sodium were reported as the highest and lowest skin permeation (log Kp), respectively. For total clearance, novobiocin sodium has the most insufficient total clearance of 0.170. For qualitative results, most compounds except 3,3′-methylene-bis (4-hydroxycoumarin), coumarin 7 and 7-diethylamino-3-(4-maleimidophenyl)-4-methylcoumarin show AMES toxicity. According to the findings obtained, none of the compounds exhibits skin sensitization. However, difenacoum, 3,3′-methylene-bis (4-hydroxycoumarin), EM1, atto 425 NHS ester, coumarin 6 and 4-methyl-7-(phenylacetamido) coumarin may result in hepatotoxicity.
|Table 2: Physiochemical properties and absorption, distribution, metabolism, excretion and toxicology for the top thirteen chosen compounds, where arbutin and hydroquinone were used as positive controls|
Click here to view
Based on the findings gleaned from the single-ligand molecular docking and virtual pharmacokinetics screening, thirteen compounds exhibit the potential for being used as antimelanogenesis agents. Eight of the top compounds chosen from single-ligand molecular docking are difenacoum, 3,3′-methylene-bis (4-hydroxycoumarin), colladin, EMI 1, farnesiferol C, and novobiocin sodium. After analyzing the qualitative parameters in pharmacokinetics screening, five compounds including colladin, farnesiferol C, novobiocin sodium, coumarin 30, and 6-bromo-3-(2,5-dichlorophenylcarbamoyl)-coumarin were selected. However, colladin, farnesiferol C, and novobiocin sodium are the top compounds for both single-ligand molecular docking and pharmacokinetics screening. Hence, only eight chemicals will be selected to conduct MLSD and each of them will be paired into 28 pairs. As a result [Table 3], most ligands paired with difenacoum yielded a stronger binding affinity in four targeted proteins. However, difenacoum has the potential to cause hepatotoxicity based on the results obtained from pkCSM. It is noteworthy that farnesiferol C and 6-bromo-3-(2,5-dichlorophenylcarbamoyl)-coumarin exhibits the strongest binding affinity of −17.86 kcal/mol in 6XV9. This finding may be significant because these chemicals show the strongest binding affinity and establish relatively safer pharmacokinetic properties.
|Table 3: Binding affinity of the different pairs of ligands against targeted proteins (5M8M, 4TQN, 6XV9, 5X93)|
Click here to view
| Discussion|| |
As most of the whitening products available in the market are applied topically, several pharmacokinetic parameters were interpreted. The lipophilicity in SwissADME is typically defined by the partition coefficient between n-octanol and water (log Po/w). Five existing predictors are used in Swiss ADME, including iLOGP, XLOGP3, WLOGP, MLOGP, and SILICOS-IT. In this case, the consensus log Po/w is being considered as it is the average value gleaned from the above-stated predictors. Log Po/w is measured by interpreting the ratio of the concentration in n-octanol relative to the water. Since water is a polar substance and n-octanol is a nonpolar compound, lower log Po/w indicates the compound is more hydrophilic and vice versa. It has previously been observed that lipophilicity positively correlates with the drug promiscuity, which may lead to side effects and toxicity., This happens because a drug with higher lipophilicity has a higher probability of binding with other proteins. Moreover, excessively lipid-soluble drugs permeate the skin weakly. Recently researchers have examined the effects of lipophilicity and hepatoxicity. Interestingly, they discovered that drugs with a log Po/w value larger than three would increase the chance of an individual to suffer from drug-induced liver injury aside from the dosage. Based on the results [Table 2], arbutin and hydroquinone which act as positive controls showed lipophilicity of − 0.88 and 0.87. Among the coumarin derivatives, novobiocin sodium has possessed the lowest log Po/w of the 0.18; hence, it is the most potent compound based on the evaluation of lipophilicity characteristics. In addition, 3,3′-Methylene-bis (4-hydroxycoumarin) shows a lipophilicity of 2.62 is also preferable as it is relatively safer than other derivatives. Lipophilicity is a critical parameter for assessing the skin permeability of the drug. On the other hand, skin permeability is also being evaluated to support the aforementioned findings as lipophilicity has been proven to correlate with skin permeability.
Skin permeability (log Kp) is also one of the parameters used to predict the pharmacokinetic properties of compounds. The higher the log Kp, the compound has a larger molecular size and stronger lipophilicity. Thus, in this case, compounds with lower log Kp are more favorable as they can maximize the permeability rate into the skin for topical usage. Based on the previous study, compounds with log Kp more than −6.94 × 10 − 4 cm/s have low skin permeability. Total clearance is the sum of the clearance from various body parts, including the liver, kidney, and lungs. The total clearance of a drug is directly proportional to the elimination rate of the drugs from the body. Nevertheless, the higher the total clearance, the lower the elimination half-life and plasma concentration. Hence, choosing a compound with optimum total clearance is vital for enhancing the elimination rate and half-life. Based on the skin permeability obtained from virtual screening [Table 2], arbutin and hydroquinone have log Kp of −8.92 and −6.55 , respectively. Coumarin derivatives such as novobiocin sodium, atto 425 NHS ester, and 3,3′-Methylene-bis (4-hydroxycoumarin) exhibit comparably suitable skin permeability to optimize the penetration of the compound into the skin and maximize the effect. From the total clearance perspectives, arbutin has 0.542 log (ml/min/kg) while hydroquinone has 0.520 log (ml/min/kg). Based on the findings of positive controls, derivatives such as difenacoum and farnesiferol C possess similar total clearance with arbutin and hydroquinone. EMI1 exhibits the highest total clearance of 1.154 log (ml/min/kg) followed by coumarin 7. Based on this parameter, most of the chemicals listed in [Table 2] have favorable total clearance except 6-bromo-3-(2,5-dichlorophenylcarbamoyl)-coumarin, novobiocin sodium, atto 425 NHS ester, coumarin 6 and colladin which exhibit low total clearance in comparison to positive controls.
Besides the quantitative parameters mentioned above, AMES toxicity, skin sensitization, and hepatotoxicity were also being analyzed to ensure the safety profile of the targeted compounds. AMES toxicity indicates the potential for the compounds to have mutagenic effects, while skin sensitization allows the researchers to assess the ability of the compounds to cause skin conditions, including contact dermatitis. Furthermore, the liver's function can be monitored by examining the hepatoxicity of the compounds. As these three properties are qualitative parameters, excluding the compounds with limited pharmacokinetic properties will be eased. As a result, colladin, farnesiferol C, novobiocin sodium, coumarin 30, and 6-bromo-3-(2,5-dichlorophenylcarbamoyl)-coumarin does not have any AMES toxicity, skin sensitization, and hepatotoxicity. Hence, these compounds will be safer and more potent for future studies. On the other hand, arbutin also does not exhibit the parameters mentioned above, while hydroquinone has the likelihood of causing skin sensitization.
Out of the 94 compounds screened, only 18 compounds were chosen as the top compounds from single ligand molecular docking and ADMET screening [Figure 3]. Difenacoum shows as the most potential antimelanogenesis agent due to its strong inhibitory binding affinity in targeted protein models (5M8M, 4TQN, 5X93). However, difenacoum might cause hepatotoxicity based on its virtual pharmacokinetics screening, where it also has relatively higher lipophilicity and log Kp. Besides, colladin, farnesiferol C and novobiocin sodium are demonstrated to have a strong binding affinity to targeted proteins and have relatively safer from pharmacology perspectives [Table 2]. Although the in silico study facilitates eliminating a wide range of undesirable compounds in the drug discovery process, future research, either in vitro or in vivo is required to prove this information. Based on the single-ligand molecular docking and MLSD results, most compounds exhibit the highest binding affinity against receptor tyrosine kinase (6XV9).
|Figure 3: The chemical structures of coumarin derivatives from PubChem and ChemSpider database|
Click here to view
Besides the c-kit receptor, three major proteins (TRP-1, CREB, and ETBR) play essential roles in the melanogenesis pathway. TRP-1 was targeted as direct inhibition of this protein can inhibit melanogenesis. Previous studies have proved that TRP-1 has the capability to oxidize 5,6-dihydroxyindole-2-carboxylic acid (DHICA) to a carboxylated indole-quinone which may result in the eumelanin synthesis., Thus, in this study, TRP-1 is targeted to discover the potential of various coumarin derivatives as tyrosinase inhibitors. Tyrosinase inhibitor plays a significant role in treating hyperpigmentation disorders as its ability to interrupt the tyrosinase activity, prevent the excessive stimulation of tyrosinase, and reduce melanin production. Once there have coumarin derivatives which can bind to TRP-1 with a stronger binding affinity, TRP-1 activity will be downregulated, and thus lesser eumelanin will be synthesized. Recent research that proved hydroxycoumarin as a potent tyrosinase inhibitor has substantiated the statement of its role in preventing hyperpigmentation. Furthermore, the inhibition of CREB can disrupt tyrosinase activity through suppression of the protein kinase A/cyclic adenosine monophosphate response element binding protein (PKA/CREB) pathway, leading to the expression of microphthalmia-associated transcription factor (MITF), tyrosinase, TRP-1 and dopachrome tautomerase (TRP-2). To support the above-stated prediction, there have a variety of chemicals discovered from natural products, which include (10E)-9, 16-dihydroxyoctadeca-10,17-dien-12,14-diynoate (DMW-1), and beauvericin can suppress the melanogenesis using the similar pathway. Recent evidence suggests that decursin, a pyranocoumarins possess an inhibitory effect of melanogenesis through the phosphorylation of CREB when PKA is activated. As an outcome, tyrosinase, TRP-1, and MITF expression were reduced, and this suggests decursin inhibits melanogenesis via PKA/CREB signaling pathway and will decrease the melanin levels.
In a normal situation, c-kit will be activated when stem cell factor (SCF) binds onto it, while endothelin 1 (ET-1) will attach to ETBR. Existing research recognizes the critical role played by both SCF and ET-1 in melanogenesis. Recent evidence suggests that these paracrine melanogenic cytokines synergistically stimulate melanogenesis in human skin xenografts by increasing skin pigmentation, melanin production, and tyrosinase gene expression. Hence, c-kit and ETBR were targeted to discover the potential compounds which will compete with the same binding sites with either SCF or ET-1. For instance, the SCF exhibits a relatively stronger binding affinity of-10.3 kcal/mol in the 6XV9 model, while 6-bromo-3-(2,5-dichlorophenylcarbamoyl)-coumarin and farnesiferol C, which establishes-11.4 kcal/mol and-11.3 kcal/mol respectively. This may indicate that 6-bromo-3-(2,5-dichlorophenylcarbamoyl)-coumarin and farnesiferol C which have stronger binding affinity are more likely to compete for the binding site with SCF. Based on the in silico study, ET-1 shows a binding affinity of 288.8 kcal/mol against 5X93. This result varies from the theoretical perspective; it may be caused by the limitation of the computational approaches to screen for highly complex compounds, as ET-1 has a chemical formula of C109H159N25O32S5. The previous study has suggested that molecular docking may have a limitation on scoring functions and affect the accuracy of the binding affinity especially for ligands which are larger in size. Targeting the c-kit may suppress the MAPK pathway while aiming ETBR can suppress the ET-1/ETBR pathway, where suppressing both pathways can inhibit melanogenesis.
The eight compounds used to conduct MLSD [Table 3] were chosen based on their favorable binding affinities or pharmacokinetics. According to the supplementary materials, six of the top hits compounds identified through single molecular docking are difenacoum, 3,3′-Methylene-bis (4-hydroxycoumarin), colladin, EMI1, farnesiferol C, and novobiocin sodium. In addition, the derivatives such as colladin, farnesiferol C, novobiocin sodium, coumarin 30, and 6-bromo-3-(2,5-dichlorophenylcarbamoyl)-coumarin are the top five compounds selected through in silico pharmacokinetic screening. Since colladin, farnesiferol C, and novobiocin sodium are repeated in both groups, a total of eight compounds will be used for MLSD. MLSD is conducted to examine whether there has a synergistic effect of binding affinity when both ligands bind to the same proteins. Three ligand pairs which are EMI1 and farnesiferol C, difenacoum and 6-bromo-3-(2,5-dichlorophenylcarbamoyl)-coumarin, 3,3′-Methylene-bis (4-hydroxycoumarin), and 6-bromo-3-(2,5-dichlorophenylcarbamoyl)-coumarin possess mild synergistic effect on the binding affinity against 5M8M. They establish an increase of 0.08 kcal/mol, 0.12 kcal/mol, and 0.14 kcal/mol of binding affinity against 5M8M, respectively. In addition, all the ligands investigated show significantly higher binding affinity than the positive controls in every targeted protein models.
| Conclusion|| |
Our results showed that coumarin derivatives, namely, difenacoum, 3,3-methylene-bis (4-hydroxycoumarin), colladin, EMI 1, farnesiferol C, and novobiocin sodium might be the potential antimelanogenesis agents based on their strong binding affinities to targeted proteins. Nevertheless, based on this in silico study, coumarin itself does not exhibit desired antimelanogenesis effect; a variety of coumarin derivatives show a strong binding affinity to the targeted proteins and establish preferred pharmacokinetics properties. The in silico ADMET screening shows that novobiocin sodium exhibits as the most potent compound due to its relatively safer pharmacokinetics property. Novobiocin sodium, colladin, farnesiferol C, coumarin 30, and 6-bromo-3-(2,5-dichlorophenylcarbamoyl)-coumarin also have the potential to be further investigated in future research because these derivatives do not exhibit AMES toxicity, skin sensitization, and hepatotoxicity. Overall, colladin, farnesiferol C, and novobiocin sodium may be the most potent antimelanogenesis agents. Therefore, further in vitro and in vivo studies should be conducted to ensure the application of these coumarin derivatives in cosmetic or pharmaceutical industries in the near future.
Financial support and sponsorship
This research was funded by Fundamental Research Grant Scheme (FRGS) (Grant no.: FRGS/1/2020/STG01/SYUC/02/1 and FRGS/1/2020/SKK0/TAYLOR/02/2) awarded by the Ministry of Higher Education (MOHE) Malaysia.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Solano F. Photoprotection and skin pigmentation: Melanin-related molecules and some other new agents obtained from natural sources. Molecules.2020:25:1537.
El-Naggar NE, El-Ewasy SM. Bioproduction, characterization, anticancer and antioxidant activities of extracellular melanin pigment produced by newly isolated microbial cell factories Streptomyces glaucescens
NEAE-H. Sci Rep 2017;7:42129.
Tran-Ly AN, Reyes C, Schwarze FW, Ribera J. Microbial production of melanin and its various applications. World J Microbiol Biotechnol 2020;36:170.
Pillaiyar T, Manickam M, Namasivayam V. Skin whitening agents: Medicinal chemistry perspective of tyrosinase inhibitors. J Enzyme Inhib Med Chem 2017;32:403-25.
McCallum NC, Son FA, Clemons TD, Weigand SJ, Gnanasekaran K, Battistella C, et al.
Allomelanin: A biopolymer of intrinsic microporosity. J Am Chem Soc 2021;143:4005-16.
Ketelboeter LM, Bardy SL. Methods to inhibit bacterial pyomelanin production and determine the corresponding increase in sensitivity to oxidative stress. J Vis Exp 2015;102:e53105.
Schlessinger DI, Anoruo M, Schlessinger J. Biochemistry, melanin. In: StatPearls. Treasure Island (FL): StatPearls Publishing Copyright © 2022, StatPearls Publishing LLC.; 2022.
Cao W, Zhou X, McCallum NC, Hu Z, Ni QZ, Kapoor U, et al.
Unraveling the structure and function of melanin through synthesis. J Am Chem Soc 2021;143:2622-37.
Lui F, Stokkermans TJ. Heterochromia. In: StatPearls. Treasure Island (FL): StatPearls Publishing Copyright © 2022, StatPearls Publishing LLC.; 2022.
Maranduca MA, Branisteanu D, Serban DN, Branisteanu DC, Stoleriu G, Manolache N, et al.
Synthesis and physiological implications of melanic pigments. Oncol Lett 2019;17:4183-7.
Sonthalia S, Daulatabad D, Sarkar R. Glutathione as a skin whitening agent: Facts, myths, evidence and controversies. Indian J Dermatol Venereol Leprol 2016;82:262-72.
] [Full text]
Choi MH, Shin HJ. Anti-melanogenesis effect of quercetin. Cosmetics 2016;3:18.
Hamed SH, Tayyem R, Nimer N, Alkhatib HS. Skin-lightening practice among women living in Jordan: Prevalence, determinants, and user's awareness. Int J Dermatol 2010;49:414-20.
Canache D, Hayes M, Mondak J, Seligson M. Determinants of perceived skin-color discrimination in Latin America. J Polit 2014;76:506-20.
Nautiyal A, Wairkar S. Management of hyperpigmentation: Current treatments and emerging therapies. Pigment Cell Melanoma Res 2021;34:1000-14.
Pollock S, Taylor S, Oyerinde O, Nurmohamed S, Dlova N, Sarkar R, et al.
The dark side of skin lightening: An international collaboration and review of a public health issue affecting dermatology. Int J Womens Dermatol 2021;7:158-64.
Ishack S, Lipner SR. Exogenous ochronosis associated with hydroquinone: A systematic review. Int J Dermatol 2022;61:675-84.
Lončar M, Jakovljević M, Šubarić D, Pavlić M, Buzjak Služek V, Cindrić I, et al.
Coumarins in food and methods of their determination. Foods 2020;9:645.
Sharifi-Rad J, Cruz-Martins N, López-Jornet P, Lopez EP, Harun N, Yeskaliyeva B, et al
. Natural coumarins: Exploring the pharmacological complexity and underlying molecular mechanisms. Oxid Med Cell Longev 2021;2021:6492346.
Tutone M, Almerico AM. Computational approaches: Drug discovery and design in medicinal chemistry and bioinformatics. Molecules 2021;26:7500.
Wang G, Zhu W. Molecular docking for drug discovery and development: A widely used approach but far from perfect. Future Med Chem 2016;8:1707-10.
Pires DE, Blundell TL, Ascher DB. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem 2015;58:4066-72.
Lai X, Wichers HJ, Soler-Lopez M, Dijkstra BW. Structure of human tyrosinase related protein 1 reveals a binuclear zinc active site important for melanogenesis. Angew Chem Int Ed Engl 2017;56:9812-5.
Xu M, Unzue A, Dong J, Spiliotopoulos D, Nevado C, Caflisch A. Discovery of CREBBP bromodomain inhibitors by high-throughput docking and hit optimization guided by molecular dynamics. J Med Chem 2016;59:1340-9.
McAulay K, Hoyt EA, Thomas M, Schimpl M, Bodnarchuk MS, Lewis HJ, et al.
Alkynyl benzoxazines and dihydroquinazolines as cysteine targeting covalent warheads and their application in identification of selective irreversible kinase inhibitors. J Am Chem Soc 2020;142:10358-72.
Shihoya W, Nishizawa T, Yamashita K, Inoue A, Hirata K, Kadji FM, et al.
X-ray structures of endothelin ET (B) receptor bound to clinical antagonist bosentan and its analog. Nat Struct Mol Biol 2017;24:758-64.
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al.
The protein data bank. Nucleic Acids Res 2000;28:235-42.
Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al.
PubChem in 2021: New data content and improved web interfaces. Nucleic Acids Res 2021;49:D1388-95.
Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR. Avogadro: An advanced semantic chemical editor, visualization, and analysis platform. J Cheminform 2012;4:17.
Dallakyan S, Olson AJ. Small-molecule library screening by docking with PyRx. In: Hempel JE, Williams CH, Hong CC, editors. Chemical Biology: Methods and Protocols. New York, NY: Springer; 2015. p. 243-50.
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.
Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010;31:455-61.
Eberhardt J, Santos-Martins D, Tillack AF, Forli S. AutoDock vina 1.2.0: New docking methods, expanded force field, and python bindings. J Chem Inf Model 2021;61:3891-8.
Chandrasekaran B, Abed SN, Al-Attraqchi O, Kuche K, Tekade R. Computer-aided prediction of pharmacokinetic (ADMET) properties. In: Tekade RK, editor. Dosage form Design Parameters. Ch. 21. Cambridge, MA: Academic Press; 2018. p. 731-55.
Arnott JA, Planey SL. The influence of lipophilicity in drug discovery and design. Expert Opin Drug Discov 2012;7:863-75.
Armstrong D, Li S, Frieauff W. 2.04 – Predictive toxicology: Latest scientific developments and their application in safety assessment. In: Chackalamannil S, Rotella D, Ward SE, editors. Comprehensive Medicinal Chemistry III. Oxford: Elsevier; 2017. p. 94-115.
Murthy SN. Approaches for delivery of drugs topically. AAPS Pharm Sci Tech 2019;21:30.
Chen M, Borlak J, Tong W. High lipophilicity and high daily dose of oral medications are associated with significant risk for drug-induced liver injury. Hepatology 2013;58:388-96.
Ward RM, Kern SE. 19 – Principles of pharmacokinetics. In: Polin RA, editors. Fetal and Neonatal Physiology. 5th
ed. Oxford: Elsevier; 2017. p. 201-7.
Hinderliter P, Saghir SA. Pharmacokinetics. In: Wexler P, editor. Encyclopedia of Toxicology. 3rd
ed. Oxford: Academic Press; 2014. p. 849-55.
Tuerxuntayi A, Liu YQ, Tulake A, Kabas M, Eblimit A, Aisa HA. Kaliziri extract upregulates tyrosinase, TRP-1, TRP-2 and MITF expression in murine B16 melanoma cells. BMC Complement Altern Med 2014;14:166.
Holcomb NC, Bautista RM, Jarrett SG, Carter KM, Gober MK, D'Orazio JA. cAMP-mediated regulation of melanocyte genomic instability: A melanoma-preventive strategy. In: Donev R, editor. Advances in Protein Chemistry and Structural Biology. Ch. 8. Cambridge, MA: Academic Press; 2019. p. 247-95.
Fais A, Corda M, Era B, Fadda MB, Matos MJ, Quezada E, et al
. Tyrosinase inhibitor activity of coumarin-resveratrol hybrids. Molecules 2009;14:2514-20.
Asthana S, Zucca P, Vargiu AV, Sanjust E, Ruggerone P, Rescigno A. Structure-activity relationship study of hydroxycoumarins and mushroom tyrosinase. J Agric Food Chem 2015;63:7236-44.
Park JU, Yang SY, Guo RH, Li HX, Kim YH, Kim YR. Anti-melanogenic effect of dendropanax morbiferus and its active components via protein kinase A/Cyclic adenosine monophosphate-responsive binding protein-and p38 mitogen-activated protein kinase-mediated microphthalmia-associated transcription factor downregulation. Front Pharmacol 2020;11:507.
Lee SE, Park SH, Oh SW, Yoo JA, Kwon K, Park SJ, et al.
Beauvericin inhibits melanogenesis by regulating cAMP/PKA/CREB and LXR-α/p38 MAPK-mediated pathways. Sci Rep 2018;8:14958.
Choi H, Yoon JH, Youn K, Jun M. Decursin prevents melanogenesis by suppressing MITF expression through the regulation of PKA/CREB, MAPKs, and PI3K/Akt/GSK-3β cascades. Biomed Pharmacother 2022;147:112651.
Sriwiriyanont P, Ohuchi A, Hachiya A, Visscher MO, Boissy RE. Interaction between stem cell factor and endothelin-1: Effects on melanogenesis in human skin xenografts. Lab Invest 2006;86:1115-25.
Sethi A, et al
. Molecular docking in modern drug discovery: Principles and recent applications. London: IntechOpen; 2019.
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3]