Mushroom Tyrosinase-Based Enzyme Inhibition Assays Are Not

Article pubs.acs.org/jnp

Cite This: J. Nat. Prod. 2019, 82, 136−147

Mushroom Tyrosinase-Based Enzyme Inhibition Assays Are Not Suitable for Bioactivity-Guided Fractionation of Extracts Fabian Mayr,†,‡ Sonja Sturm,† Markus Ganzera,† Birgit Waltenberger,† Stefan Martens,§ Stefan Schwaiger,*,† Daniela Schuster,*,‡,⊥ and Hermann Stuppner†

J. Nat. Prod. 2019.82:136-147. Downloaded from pubs.acs.org by EASTERN KENTUCKY UNIV on 01/25/19. For personal use only.

†

Institute of Pharmacy/Pharmacognosy, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria ‡ Institute of Pharmacy/Pharmaceutical Chemistry, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria § Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all’Adige (Trentino), Italy ⊥ Institute of Pharmacy, Department of Pharmaceutical and Medicinal Chemistry, Paracelsus Medical University Salzburg, Strubergasse 21, 5020 Salzburg, Austria S Supporting Information *

ABSTRACT: Tyrosinase (Tyr) catalyzes the rate-limiting step of melanogenesis in human skin and is thus the main target for treating pigmentation disorders today. This has led to an increased research interest in Tyr inhibitors during the last decades, with a frequent focus on polyphenols. In the early stages of drug discovery, it is typical to avoid the high costs of human Tyr by using the more economic mushroom tyrosinase (mh-Tyr). Since some polyphenols are accepted as substrates by mh-Tyr, the present study aimed to more generally investigate this enzyme’s specificity toward polyphenols and to discuss its significance in the context of bioactivity-guided fractionation. Mh-Tyr substrates can change the sample color during an inhibition assay, leading to unreliable inhibition constants or to the discontinuation of a bioactivity-guided fractionation campaign. A data set of 56 natural products was investigated and classified into assay interferers (AIs) and noninterferers, using a spectrophotometric and an LC-ESIHRMS assay. Based on these experimental findings, structure−activity relationships defining AIs were deduced and implemented into an in silico tool that will allow for rapid prescreening in the future. We anticipate that these results will aid in the search for new Tyr inhibitors and contribute to the understanding of this enzyme, as well as its optimal use in pharmacological research.

T

publications related to mh-Tyr has steadily increased over the last 30 years (for a detailed listing see S1 SciFinder query for mh-Tyr, Supporting Information). As the number of publications increased, a wide variety of assay protocols emerged over the last three decades. This in particular has led to heterogeneous data in the literature. Compounds proposed as Tyr inhibitors are actually often mhTyr inhibitors, leading to highly variable Ki constants and IC50 values.8,11,12 In the field of natural products research, bioactivity-guided fractionation (BGF) is widely used to identify active principles in extracts or fractions using the same mh-Tyr-based enzyme inhibition assays. Even though numerous examples in the literature confirm that BGF on mhTyr is a successful technique,13−17 our recent findings show that this strategy may have some pitfalls. Mh-Tyr inhibition is typically measured by assessing the activity of the purified

yrosinase (Tyr) is a widespread family of orthologous enzymes across nature. Tyr catalyzes the first two rate limiting steps in melanin biosynthesis, making it a popular target for pharmacological agents. The Tyr reaction can be subdivided into two steps: the monophenolase and the diphenolase reactions (Figure 1). In fruits and vegetables, Tyr oxidizes polyphenols and is thus involved in unwanted food browning.1 In humans, Tyr is expressed in skin melanocytes,2,3 where its deregulation can cause cosmetic flaws such as post inflammatory melanoderma, solar lentigo, or melasma.4−6 Here, Tyr inhibitors are commonly used as “skin tone lighteners” to counter hyperpigmentation. The current state of Tyr inhibitors has been extensively reviewed recently.7−9 Natural products and particularly polyphenols, as well as plant-derived extracts, are well-recognized as inhibitors of Tyr.10,11 By now, it has become common practice to use the much cheaper mushroom tyrosinase (mh-Tyr)a fungal orthologuefor fairly simple and economic in vitro enzyme inhibition assays in drug discovery. The number of © 2019 American Chemical Society and American Society of Pharmacognosy

Received: October 12, 2018 Published: January 10, 2019 136

DOI: 10.1021/acs.jnatprod.8b00847 J. Nat. Prod. 2019, 82, 136−147

Journal of Natural Products

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Figure 1. Two-step reaction catalyzed by Tyr. Tyrmono: monophenolase reaction. Tyrdi: diphenolase reaction.

Table 1. Data Set of Investigated DHCs

compound

2′

4′

6′

3

4

AInta

trilobatin (1) sieboldin (2) phloretin (3) 3-OH-phloretin (4) asebogenin (5) phloridzin (6) 3-OH-phloridzin (7) phlorein 2′-xyloglucoside (8) neohesperidin DHC (9) calomelanen (10) 2′,6′-dihydroxy-4′-methoxy DHC (11)

OH OH OH OH OH O-Glc O-Glc O-Rutb OH OH OH

O-Glc O-Glc OH OH OMe OH OH OH O-Neoc OMe OMe

OH OH OH OH OH OH OH OH OH OH OH

H OH H OH H H OH H OH H H

OH OH OH OH OH OH OH OH OMe OMe H

AI AI AI AI AI AI AI AI AI NI NI

a

Assay interference. bRutinose. cNeohesperidose.

generally suitable for mh-Tyr inhibition, which was also reported for some DHCs (a summary of the target prediction is provided in section S2, in silico target prediction, Supporting Information).21−24 Of the 11 compounds screened, trilobatin (1) and sieboldin (2) were predicted as mh-Tyr inhibitors, an activity that was not previously reported in the literature. While performing the mh-Tyr inhibition assay, we discovered that 1 and 2 were both accepted as substrates by mh-Tyr and changed the sample’s color over time when incubated with mh-Tyr. This color formation interferes with the assay’s detection mode, yielding irreproducible and often negative inhibition values. In the course of this investigation, phloretin (3), 3-OH-phloretin (4), asebogenin (5), phloridzin (6), 3-OH-phloridzin (7), phloretin-2′-xyloglucoside (8), and neohesperidin DHC (9) were classified as assay interferers (AIs) due to oxidation by mh-Tyr, while 3 and 6 were also previously described as mh-Tyr substrates by Ortiz-Ruiz et al. in 2015.20 Calomelanen (10) and 2′,6′-dihydroxy-4′-methoxy DHC (11) did not interfere with the assay. A thorough literature search revealed that this issue was not limited to DHCs, but can generally be observed with polyphenols. We hypothesized that (i) 4-hydroxylated and 3,4-dihydroxylated phenols could be suitable substrates of mh-Tyr, given that the molecules fit sterically into the binding pocket; (ii) the presence of such substrates, depending on the absorption maximum, and their oxidation products could alter the OD475 and thus interfere with the assay’s detection mode; and (iii) phenols with masked (methoxylated, acetylated, etc.) or completely non-hydroxylated phenyl moieties would not be accepted as substrates by mh-Tyr. These compounds would not interfere with the assay as described above. To validate these hypotheses, the data set based on our DHC collection was used and extended by 45 additional polyphenols. This data set was created to contain polyphenols that are commonly present in plant extracts and to cover hydroxylation patterns that were thought to be defining for AIs

enzyme after preincubation with a test compound. First, the putative inhibitor is incubated with mh-Tyr. Subsequently, a physiological substrate (L-DOPA or L-tyrosine) is added and the amount of colored product that can still be formed by mhTyr is measured. The enzymatic product has a strong absorption maximum at 475 nm and can thus readily be quantified using a spectrophotometer. The decrease in optical density (OD475) toward an uninhibited enzyme is inversely proportional to the potency of the inhibitor.17 This detection mode is basically trivial, yet prone to interference. Polyphenols are known to be substrates of polyphenol oxidases (PPOs), the plant orthologues of mh-Tyr. Moreover, many common polyphenols such as catechins,18 caffeic acid,19 and tyrosol20 are known to be so-called “alternative substrates” of mh-Tyr. If such compounds are present in an extract or fraction during BGF using mh-Tyr, the assay’s detection mode is likely to interfere with the latter. Alternative substrates can undergo the same oxidation catalyzed by mh-Tyr as the natural substrates. In case the resulting products absorb light at wavelengths similar to those of the products of L-tyrosine and L-DOPA, the OD475 of the sample will be shifted in an unforeseeable direction. During BGF, true inhibitors present in an extract or fraction can thus easily be overlooked by a misleading OD475, indicating inactivity of the sample. Such false negatives (FNs) might have led to numerous ambiguous cases where isolation was discontinued because parent fractions appeared inactive. It is therefore highly likely that BGF using mh-Tyr assays has failed to detect mh-Tyr inhibitors during the past few decades.

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RESULTS AND DISCUSSION Data Set. The underlying objective of this study was to verify virtual hits obtained beforehand in an in silico target prediction for 11 commercially available dihydrochalcones (DHCs). Those DHCs appear in various species of the genus Malus and in Thonningia sanguinea (shown in Table 1). The predictions of these 11 DHCs suggested that the scaffold was 137



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Figure 2. Common phenolic natural products to complete the subset, including classification as assay interferers and noninterferers.

or noninterferers (NIs), respectively. For greater simplicity, this 56-compound data set (“full data set”) was shortened into a representative, structurally diverse subset (“subset”) of 25 compounds as presented in this article. However, for molecular modeling purposes, the full data set (1−56) was used, which is available in Table S3, Supporting Information. The subset is composed of the 11 DHCs that were subjected to the in silico target prediction at the outset of the present study (1−11) and 14 additional polyphenols that are commonly found in natural extracts (12−25), depicted in Figure 2. Mh-Tyr Substrate Assay. To assess the assay interference of the chosen compounds, an mh-Tyr substrate assay was developed. A previously reported mh-Tyr inhibition assay17 was modified from a single-point measurement to a continuous absorbance measurement over 60 min. To exclude any ambiguity, each compound was incubated with mh-Tyr in conditions equal to a typical inhibition assay (phosphate buffer (PB), pH 6.8, room temperature), and the OD475 was measured. This allowed us to investigate if the compound gave rise to any reaction product that absorbs light of 475 nm when brought into contact with mh-Tyr. If so, the compound interferes with the assay’s detection mode and reliable IC50 values cannot be determined. In parallel to these reaction probes, blank probes that lacked mh-Tyr were analyzed for each compound of interest. The hypothesis formulated above can be confirmed by looking at the time-dependent absorption plots shown in Figure 3. Compounds with catechol moieties such as 2, 4, 7, 3OH-tyrosol (12), rosmarinic acid (16), chlorogenic acid (17), butein (24), and 2R,3S-catechin (25), as well as phydroxylated compounds such as 1, 3, 5, 6, 8, arbutin (14), resveratrol (15), ferulic acid (22), and isoliquiritigenin (23),

were accepted as substrates by mh-Tyr and are AIs. For compounds with masked hydroxy moieties such as calomelanen (10) and isoferulic acid (21), or with entirely missing hydroxy moieties such as 11, chrysin (19), and cinnamic acid (20), no change in absorbance can be observed over 60 min. Such compounds can hence be considered as NIs of typical mh-Tyr inhibition assays. p-Hydroxy moieties are typical for AIs, which can be confirmed by comparison of the two position isomers 21 (4-methoxylated) and 22 (4-hydroxylated). Compound 22 is an AI, while 21 is not. Interestingly, neohesperidin DHC (9), whose aglycon carries the same hydroxylation pattern as 21, is an AI. Moreover, the kinetics of the diphenolase reaction is faster than the monophenolase reaction: The absorbance curves of the 4-hydroxylated DHCs 1, 3, and 23 rise considerably slower than those of their 3,4dihydroxylated analogues 2, 4, and 24, respectively. Compound 2 is converted faster than 1, while 7 is converted as fast as 6. On the other hand, 1 and 2 are converted faster than 6 and 7. The comparison between 4-hydroxylated and 3,4dihydroxylated phenols indicates that monophenolase activity is in most cases lower than diphenolase activity (consistent with literature data25) or equally fast as found for 6 and 7. The comparison between 4′ (1 and 2) and 2′ (6 and 7) glycosides indicates that the Glc moiety itself, depending on the kind of glycosylation, has an influence on the enzyme kinetics. However, this could be due to the steric hindrance rather than exerted by the respective Glc moiety. Glycosylation at position 4′ allows for a linear, stick-like arrangement of the whole molecule in mh-Tyr’s binding pocket, leading to a facilitated access. Glycosylation at the 2′ position, on the other hand, may lead to a sterically more challenging, water-drop-like 138



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Figure 3. Time-dependent absorption plots of 1−25. Shown is the mean of triplicate measurements for each compound (n = 3) with 95% confidence intervals. Tested compounds’ plots are illustrated in light blue, positive control (19) in red, and negative control in green.

docking resultsso-called posesby exploring the compound’s conformational space inside the binding pocket. Favorably orientated fragments of the compound are “inherited” in the next iteration, until an energetically minimized protein−ligand complex is obtained (final docking pose). After each iteration, a scoring function is applied to calculate the system’s overall energy.

arrangement, ultimately decreasing the molecule’s ability to optimally orient itself within the binding pocket. Molecular Docking. The different effects of those two glycosylation patterns were investigated by molecular docking (Figure 4). We used the GOLD software package, which uses an optimized genetic algorithm (GA). GAs are thereby particularly accurate in predicting meaningful poses, as previously proven in several studies.26,27 GAs generate the 139



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Figure 4. Top three predicted binding poses of 2 in magenta (A, B), 7 in green (C, D), and 9 in yellow (E, F) to mh-Tyr. The binding site is depicted as a blue, semitransparent surface. Binding site amino acids involved in fixing the catalytically active Cu2+ ions are shown in ball-and-stick style.

Figure 5. Stacked view of LC-ESIHRMS profiles of 1 (A), 2 (B), 23 (C), and 24 (D). Blank probes (no mh-Tyr) are shown in gray; reaction probes are shown in black. Molecular structures of the reaction products are hypothetical and solely based on m/z, in-source fragmentation, predicted molecular formulas, and, if available, literature data.

This scoring function considers intramolecular tensions of the sampled compound, as well as steric and electrostatic interactions in the binding site. Since GAs always start from a random conformation and iteratively work their way down to a local binding energy minimum, a consensus evaluation can be applied by running the process several times: The most abundant binding pose from different docking runs is then considered as the most probable one. Ten poses per

compound were generated, while only the top three scored poses were considered. The top three scored poses of 2 (Figure 4, A and B) and 7 (Figure 4, C and D) are almost congruent to each other, respectively (Figure 4, A and B). However, the energetically most favorable binding modes for 2 and 7 seem to be distinctly different from one another: The Glc moieties are reaching out of the binding pocket but are oppositely placed. This undermines our assumption that the 140



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Table 2. LC-ESIHRMS Analysis of the Reaction Probes 1, 2, 23, and 24 Including the Corresponding Blank Probesa compound

tR [min]

trilobatin (1) B

12.20

trilobatin (1) R

9.40

10.75

12.20

sieboldin (2) B

sieboldin (2) R

11.30

10.75

isoliquiritigenin (23) B

15.4

isoliquiritigenin (23) R

13.0 15.4

butein (24) B

14.3

butein (24) R

13.0

measured m/z in ESI negative ion mode (% relative intensity) 435.1305 297.0761 273.0773 449.1092 287.0554 151.0043 449.1091 287.0543 151.0043 435.1295 297.0752 273.0757 451.1237

calculated m/z in ESI negative ion mode (from putative adduct)

(67.03) (15.03) (100) (18.27) (100) (8.04) (16.87) (100) (8.15) (25.79) (14.68) (100) (14.74)

435.1297 n.a. 273.0768 449.1089 287.0561 151.0037 449.1089 287.0561 151.0037 435.1297 n.a. 273.0768 451.1246

289.0719 (100)

289.0718

167.0354 (3.5) 449.1067 (14.28)

167.0350 449.1089

287.0551 (100)

287.0561

151.0044 255.0657 135.0088 119.0508 269.0445 255.0661 135.0193 119.0507 271.0608 135.0367 269.0455 133.0239

151.0037 255.0663 135.0088 119.0502 296.0455 255.0663 135.0088 119.0502 271.0612 135.0452 296.0455 133.0295

(8.13) (100) (46.62) (63.46) (100) (100) (52.21) (69.89) (100) (40.61) (100) (9.45)

predicted molecular formula (score)

mass error [ppm]

C21H23O10 (100) C17H13O5 (100) C15H13O5 (100) C21H21O11 (100) C15H11O6 (98.39) C7H3O4 (100) C21H21O11 (100) C15H11O6 (88.80) C7H3O4 (100) C21H23O10 (100) C17H13O5 (92.30) C15H13O5 (100) C21H23O11 (87.02) C15H13O6 (100.00) C8H7O4 (100) C21H21O11 (100.00) C15H11O6 (100.00) C7H3O4 (100.00) C15H11O4 (100) C7H3O3 (100) C8H7O (100) C15H9O5 (100) C15H11O4 (100) C7H5O4 (100) C8H7O (100) C15H11O5 (100) C7H5O4 (100) C15H9O5 (100) C8H5O2 (100)

−1.9 +2.6 −1.5 −0.5 +2.6 −4.4 −0.4 +2.9 −4.0 +0.4 +5.4 −2.9 +1.9

[M − n.a. [M − [M − [M − A [M − [M − A [M − n.a. [M − [M −

−0.6

[M − Glc − H]−

−2.3 +4.3

F [M − H]−

+3.6

[M − Glc − H]−

−4.4 +2.1 −0.3 −4.8 +4.0 +3.8 0.7 −3.9 +1.6 +3.9 +1.4 +1.3

A [M B D [M [M B D [M

putative adducts/ fragmentsb H]− Glc − H]− H]− Glc-H]−

refs 31, 32 n.a.

H]− Glc − H]−

n.a.

H]−

31, 32

Glc − H]− H]−

31

n.a.

− H]−

33

− H]− − H]−

n.a. 33

− H]−

34

[M − H]− E

30

c

a

Putative adducts were assigned based on m/z, in-source fragmentation, predicted molecular formulas, and, if available, literature data. B: blank probes. R: reaction probes. n.a.: not assigned. bLetters A to F in column “Putative adducts/fragments” refer to fragments depicted in Figure 6. cInsource fragment was assigned to C, based on its exact mass and in comparison to comparable in-source fragments. The predicted molecular formula was in this case neglected due to ambiguity.

Figure 6. Putative fragments obtained in LC-ESIHRMS experiments.

discussed glycosylation patterns have a significant impact on

two best scored docking poses of 9 were congruent, while the third one was deviated (Figure 4, E and F). The reason for this might be that the energetically favorable placement of a disaccharide moiety is more challenging than a monosaccharide one. The GA also struggled to give a

the compound’s overall flexibility. A disaccharide substitution in position 2′ like in 8 or 9 further impedes this access to the active site, resulting in a prolonged lag period (Figure 3). The 141



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Figure 7. Pharmacophore models 1 (A−D) and 2 (E−H). Shown are the pharmacophore model’s features with (A, E) and without (B, F) the respective exclusion volumes coat, a typical substructure mapped into the pharmacophore models (C, G), and in two-dimensional representation (D, H). Green sphere: Hydrogen-bond donor (HBD). Red sphere: Hydrogen-bond acceptor. Yellow sphere: Hydrophobic feature. Blue plane: Aromatic feature. The dotted green sphere of model 1 encodes an optional HBD.

convincing prediction of a final binding mode. This is consistent with the initial hypothesis that the fitting of a DHC-diglycoside could be more challenging from a steric point of view, resulting in a yet prolonged lag period. Gallic acid (18) is also an AI, leading to the assumption that galloyl moieties are also accepted as substrates by mh-Tyr. This observation is strengthened by the time-dependent absorbance data of epigallocatechin gallate (54) (S4, time-dependent absorption plots, Supporting Information) and the literature. As early as in 1981,28 Passi and Nazzaro-Porro previously described 18 as a substrate of mh-Tyr. Time-dependent absorption plots of compounds 26 to 55 are shown in Figures S2−S5. LC-ESIHRMS Assay. To investigate the enzymatic products responsible for the color formation of the incubated mixture, LC-ESIHRMS analyses were performed. Reaction mixtures and blank probes were prepared as described in LC-ESIHRMS Sample Preparation and analyzed using the LC-ESIHRMSOptimized Analytical Method (both outlined in the Experimental Section). In Figure 5, the LC-ESIHRMS profiles of four selected compounds from the subset are shown. Retention times, m/z including mass errors, predicted molecular formula, and in-source fragments are summarized in Table 2 and Figure 6, respectively. For each compound, a blank probe without mhTyr addition was analyzed in parallel. The blank probes thus represent the reaction at time T = 0 min (no oxidation occurred), while the reaction probes represent the reaction state at T = 20 min (occurred oxidation, visible color formation). All blanks contained only one component (substrate peak) that in all cases matched the respective standard in terms of retention time (tR), m/z, and in-source fragments. All reaction probes showed an altered profile in comparison to the blank probes. For p-hydroxylated compounds (1 and 23, Figure 5, A and C), the substrate peak was reproducible and matched tR, m/z, and in-source fragments of the blank probes. Further, new “product peaks” with lower tR emerged whose m/z rose by 14 mass units, which

corresponds to a typical mh-Tyr monophenolase reaction (plus one oxygen, minus two hydrogens), analogous to the reaction depicted in Figure 1. For compounds with catechol moieties (2 and 24, Figure 5, B and D), the substrate peak depleted completely 20 min after mh-Tyr addition. In both 2 and 24, one single product peak emerged, with m/z decreased by two mass units. This number corresponds to a typical mh-Tyr diphenolase reaction (minus two hydrogens, analogous to the reaction shown in Figure 1). Like those observed within the time-dependent absorption plots, these results indicate that diphenolase activity of mh-Tyr is considerably higher than monophenolase activity. This observation is consistent with the current scientific understanding that monophenolase activity is characterized by a later reaction onset (prolonged lag period).25 Furthermore, the incubation of 1 or 2 with mhTyr also led to the same product. In both 1 and 2, the newly formed product peak at tR = 10.75 min showed equal m/z and the same in-source fragments. Interestingly, 1 showed an additional product peak at 9.40 min. This product peak had equal m/z and in-source fragments to those of the product peak at 10.75 min. This could be due to the formation of a pquinone methide caused by tautomerization as exemplified in Figure 5A. Formation of such p-quinone methides was previously described for L-DOPA after oxidation by mhTyr.29 Parent ion and in-source fragments of 1, 2, 23, and 24 were tentatively characterized using the molecular formula predicted by data analysis software (all data shown in Table 2 and Figure 6, respectively). Unfortunately, application of this procedure was only partially possible for the emerging product peaks of the investigated compounds since no mass spectra were available for the putative quinones. The only exception is the o-quinone formed from 24, which was previously described by Khatib et al. in 2005.30 Pharmacophore Modeling. After deducing the preliminary structure−activity relationships (SARs) of mh-Tyr substrates, the goal was to develop an in silico tool for rapid screening of potential AIs. Ideally, this in silico tool can be 142



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used in the first instance when assessing whether a compound may be an mh-Tyr substrate to save costs and time. Those compounds that are flagged as potential mh-Tyr substrates should be subjected to an in vitro verification. In addition to specific chemical functionalities in a distinct spatial arrangement, a chemical entity that is accepted by mh-Tyr as a substrate has to fulfill certain physicochemical requirements, such as adequate solubility and molecular weight, to be able to access the binding pocket. Moreover, all mh-Tyr substrates contain specific phenolic substructures. According to the results that were obtained thus far, it seemed very likely that the hydroxylation pattern is defining for substrates and nonsubstrates. Hydroxy moieties need to be unsubstituted, and, if more than one hydroxy moiety is present, they need to be ortho relative to each other. These features make the phenol oxidizable and therefore suitable for an mh-Tyr-catalyzed reaction. Furthermore, the oxidizable phenol has to be positioned in a linear arrangement to the remaining molecule. This allows the molecule to enter the rectilinear binding pocket with minimum steric hindrance, which would hamper the energetically favorable binding to the enzyme. These preliminary SARs can be translated into a ligand-based pharmacophore model. Here, all phenolic substructures accepted by mh-Tyr as a substrate can be covered and brought into relation with their steric arrangement. Hydroxy moieties are represented as H-bond donors (HBDs), which will exclude any substituted or completely lacking hydroxy moiety. The binding pocket’s boundaries can also be mimicked by so-called exclusion volumes, which are forbidden areas that would, if occupied, lead to a steric clash with the enzyme. To cover all identified substructures that are prone to oxidation by mh-Tyr, two complementary pharmacophore models were created. These two models accounted for different substructures present in our data set and are meant to work in a collaborative manner (Figure 7). Model 1 was designed to cover all 4-hydroxylated and 3,4-dihydroxylated phenols, while model 2 was designed to cover all 3,4,5-trihydroxylated phenols. Model 1 was generated as a shared (common) feature pharmacophore model of a training set comprising compounds 4, 13, 24, 25, caffeic acid (30), butin (36), and epicatechin (54). Model 2 was generated as a shared feature pharmacophore model of a training set containing compounds 18, myricetin (46), and epigallocatechin gallate (53). Both models were refined manually after generation to increase their cooperative performance. Details are provided in S5 Pharmacophore modeling, Supporting Information. As a test set, the complete data set provided in Table S3, Supporting Information, was used (56 compounds comprising 39 AIs and 17 NIs). To account for the stereochemistry of all compounds in the test set, all possible enantiomers or diastereomers were generated separately if the stereochemistry was unknown. This was the case for eight compounds, resulting in a total size of 64 entries in the final virtual test set (45 AI, 19 NIs). In total, pharmacophore models 1 and 2 used in cooperative mode classified 56 compounds (88%) of the test set correctly, while eight compounds (12%) were incorrectly classified. Five compounds (8%) of the NIs were wrongly classified as AIs (false positive), and three compounds (4%) of the AIs were wrongly classified as NIs (FN). Moreover, it should be noted that this in silico tool is by design more sensitive (0.93) than specific (0.74): It is better at detecting AIs than it is at detecting NIs. This is in good agreement with the overall goal to detect as many AIs as possible.

To estimate the fraction of actual mh-Tyr substrates among compounds reported as mh-Tyr inhibitors in the literature, we queried the ChEMBL database35 for compounds active on “mushroom tyrosinase” (ChEMBL-ID 3318). ChEMBL is a publicly available database provided by the European Molecular Biology Laboratory (EMBL) containing smallmolecule bioactivity data retrieved from peer-reviewed literature.35−38 The data set was queried and filtered using the custom-built Konstanz Information Miner (KNIME)39 workflow described in Figure S6. Finally, this data set of 1254 reported mh-Tyr inhibitors was subjected to our in silico workflow, yielding a total of 466 hits (37.16%). By applying the calculated accuracy of 0.88 to this percentage, it is estimated that about 32% of the mh-Tyr inhibitors listed in ChEMBL are actually substrates of mh-Tyr and thus potential AIs. This high number is consistent with the overall literature situation, considering that a large amount of known mh-Tyr inhibitors are in fact polyphenols, which are mostly substrates.

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CONCLUSION The experimental findings together with the modeling results indicate that 4-hydroxylated phenols and 3,4-dihydroxylated phenols are in principle accepted as substrates by mh-Tyr, given that the molecule fits into the binding pocket. Not all mh-Tyr substrates interfere with a typical mh-Tyr inhibition assay’s detection mode to the same extent. Therefore, it is important to point out that regarding this investigated data set, not all mh-Tyr substrates are AIs, but vice versa, all AIs are substrates of mh-Tyr. While substrates of mh-Tyr can easily be recognized in advance (e.g., using the in silico and in vitro workflows presented in the present study), it is very difficult to predict the extent of interference that a substrate will have. This must finally be examined in the wet lab, e.g., by an mhTyr substrate assay similar to that described above. Nevertheless, these findings give rise to some reflective thoughts: First, BGF with mh-Tyr-based assays is not an appropriate approach for finding new bioactive molecules in complex extracts. It is prone to FN results, whenever polyphenols with 4-hydroxy or 3,4-dihydroxy moieties are present. Unfortunately, this can never be fully excluded, since even very small amounts of such phenols could interfere dramatically with the assay. Moreover, the exact composition of the extract or fraction is typically not known during BGF. The scientist fully relies on the iterative enrichment of bioactive principles. Furthermore, half-maximal inhibitory concentration (IC50) values and similar metrics of extracts or fractions, describing their inhibitory potency on mh-Tyr, need to be challenged. Such quantitative metrics could be shifted and are not reliable. The degree of falsification from their true inhibitory power is dependent on three main factors: First, the kinetic constant of the alternative substrate defines how fast the resulting color is formed. Second, the light-absorption capacity of the alternative substrate’s product at 475 nm defines how intense the formed color will be. The third factor is the abundance of the alternative substrate in the extract, and thus the abundance of the formed color. All three factors relate proportionally with the degree of falsification of the assay. Second, IC50 values and Ki constants of 4-hydroxylated or 3,4-dihydroxylated polyphenols should be used with care. These values are very likely of poor validity, since it is usually not clear what was measured. A polyphenol that acts as an mhTyr substrate does admittedly inhibit L-DOPA from entering the binding pocket and thus from being converted. On the 143



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guidance for the use of both in silico and in vitro tools. The pharmacophore models are intended to be used as a computational prescreening to identify a molecular structure that is prone to oxidation or is very similar to confirmed mhTyr substrates. The flagged molecules can be passed on to the wet lab for an in vitro evaluation, in order to save time and resources. The .pmz files of both pharmacophore models are available from the authors. In conclusion, mh-Tyr is an enzyme generally suitable for screening for new skin-whitening agents. However, data from current and past studies on this enzyme regarding the suitability of the assay systems should be critically evaluated before use. Inhibition assays as described above can be flawed, but can still deliver reliable results if used correctly. Regarding BGF, mh-Tyr is not an appropriate enzyme to use. In practice, it can never be completely excluded that extracts contain phenols before they are subjected to any bioactivity test. The very nature of BGF is opposed to this, since the goal is solely to find the bioactive components in a complex matrix regardless of any other constituents. This study shows that many polyphenols are prone to oxidation by mh-Tyr, and since they are found ubiquitously in plant extracts, they represent a constant threat to the success of BGF using mh-Tyr. The search for bioactive compounds in complex biological matrices remains challenging, and a detailed understanding of strengths and limitations of the used techniques is crucial. Besides those drawbacks, there are severe safety concerns arising from this enzymatic conversion, which must be examined closely in the near future. Nevertheless, Tyr inhibition remains an important and valuable principle, not just in cosmetics but also in pharmacology. We hope that our results will contribute to a scientific discussion on mh-Tyr research and help to improve the understanding of Tyr.

other hand, this compound alters the OD475 of the sample even before L-DOPA or L-tyrosine is added. This alteration occurs to an unknown extent; therefore, measured IC50 values and Ki constants do not stand in any relation to the actual degree of inhibition. This theory could explain the noticeable variability of such metrics as previously described by various research groups.8,11,12 Some of these issues could be circumvented by employing a less common, but established technique. When using an autographic assay based on mh-Tyr, the FN results during BGI could be reduced. Here, the various compounds are preseparated on a TLC plate, before being sprayed with mh-Tyr and L-DOPA to assess the inhibitory activity.40,41 Alternative substrates can hereby be detected as colored spots after being sprayed with mh-Tyr and do not overlay other signals. However, autographic assays also have limitations, like the inability to assess IC50 values. Third, a thorough evaluation of the substrate specificity of murine and human orthologues is desperately needed. Many mh-Tyr inhibitors discovered with assays based on mh-Tyr are passed on to cell-based assays. These assays are also conducted in vitro and are mostly based on murine melanoma cell lines. Tyr enzymes across various species are not very similar; for example, Mann et al. recently published that inhibitors of human Tyr and mh-Tyr require different motifs.42 Unfortunately, compounds or extracts with confirmed inhibitory activity on mh-Tyr or murine melanoma cells are marketed in cosmetic products for human use without further evaluation regarding safety or efficacy in humans. To date, it is not clear if the murine and human orthologues accept polyphenols as substrates similar to mh-Tyr. Furthermore, the idea that cosmetic skin tone lightening products may contain alternative substrates of Tyr is concerning. The Tyr reaction remains the same across all species (plants, fungi, and mammals) and is after all an oxidative process, yielding highly reactive Michael systems and reactive oxygen species (ROS) as byproducts.3,43 Both are considered cytotoxic and cancerogenic.44 Moreover, the final products that may arise from further reactions of the reactive o-quinones are unknown. This information gap may represent a serious safety concern in current cosmetic products. This issue should be handled with care, until these ambiguities are completely resolved. Fourth, a discussion about the requirements of an enzyme inhibitor is needed. To our knowledge, a proper inhibitor should be of covalent, competitive, uncompetitive, or similar nature. Moreover, a certain pharmacokinetic stability should be given, allowing the inhibitor to remain intact for a defined time in the enzyme’s direct environment. An alternative substrate will be consumed by the enzyme itself over time, while the inhibitory behavior of the resulting products and their potential toxicity are not known. Furthermore, an enzyme inhibitor should be safe, meaning that neither the inhibitor itself nor eventual metabolic products should be of a reactive nature (e.g., o-quinones). Our presented data suggest that none of the aforementioned requirements are met by an alternative substrate of mh-Tyr. Fifth, the literature contains many examples of compounds known to be alternative substrates of mh-Tyr. A thorough literature search was performed for compounds 1 to 25 and summarized in S7, Literature search for compounds 1 to 25, Supporting Information. In the future, it should become common practice to investigate if novel mh-Tyr inhibitors are actually substrates of mh-Tyr, by literature research and by experimental work. For this task, the present study provides

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EXPERIMENTAL SECTION

NMR Spectroscopy. 1H NMR experiments were recorded on a Bruker Avance II 600 spectrometer (Bruker) operating at 600.19 MHz (1H) at 300 K (chemical shifts δ in ppm, coupling constants J in Hz). Deuterated methanol (MeOH-d4) was used as solvent and purchased from Euriso-top SAS (Saint-Aubin Cedex, France). LC-ESIHRMS Sample Preparation. For reaction control, a 60 μM substrate solution (amount in three wells of a 96-well plate) was dissolved in 0.06 M PB (pH 6.8) with 1% DMSO and incubated with 12 U of mh-Tyr. For the blank probes, a 60 μM substrate solution (amount in three wells of a 96-well plate) was dissolved in 0.06 M PB (pH 6.8) with 1% DMSO without addition of mh-Tyr. The missing volume of mh-Tyr was equated with the same volume of PB with 1% DMSO. The reaction mixture was incubated for 20 min at room temperature, before being stopped by extraction of all present polyphenols (substrates and/or products). To do this, the aqueous reaction mixture was extracted three times with one volume (v/v) of ethyl acetate, which was dried over nitrogen flow. The dried ethyl acetate fraction was reconstituted in 200 μL of HPLC-grade methanol (MeOH) and used for LC-ESIHRMS. Test compounds were either purchased or isolated from various plants. The details for each compound are listed in Table S3, Supporting Information. LC-ESIHRMS-Optimized Analytical Method. LC-ESIHRMS analysis was performed on an Agilent 1200 series HPLC coupled without split to a Bruker micrOTOF QII high-resolution mass spectrometer. Separation was obtained on a Phenomenex Gemini C18 column (250 × 3 mm, 3 μm, 110 Å), with a 0.5 mL/min flow rate, using a gradient of water fortified with 0.1% formic acid (FA) to acetonitrile (MeCN), from 2% to 98% MeCN in 10 min and held for 10 min. LC-ESIHRMS experiments were performed in negative ESI mode with the following parameters: capillary energy, 3500 V; nebulizer gas, 23.2 psi; dry gas, 6.0 L/min at a temperature of 220 °C; 144



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NM, USA) using “best-setting”. The protein was obtained from the Research Collaboratory for Structural Bioinformatics Protein Data Bank52 (RCSB-PDB) by downloading the PDB entry 2y9x.53 All water molecules were removed from the protein, since none of these were considered as structured water. The binding site was defined in chain “B” with a radius of 6 Å around the cocrystallized ligand B. Furthermore, “Chemscore_kinase” was used as a template docking algorithm in the GOLD software and “CHEMPLP” as the scoring function. Two constraints were introduced, in order to guide the correct placing of the catechol moiety in the binding site. One scaffold constraint with a weight of 5.0 and one similarity constraint with the option “H-bond donor overlap” and a weight of 10.0 were introduced into the docking workflow. The docked ligands were all classified as substrates, meaning that the catechol moiety must be orientated toward the copper ions. In this case, the placement of the other molecular features, especially the Glc moieties, was of particular interest. Both constraints used the cocrystallized ligand from chain B as the template file. Remaining parameters were set to default. To validate our settings for molecular docking, a redocking was performed. The native ligand was redrawn manually in ChemDraw Professional and processed to an .sd file with Pipeline Pilot, and a starting conformation was generated with OMEGA. This minimized ligand was then docked into 2y9x using the before described parameters. The resulting RMSD value was calculated in LigandScout version 3.12, using the “align by reference points” option. Based on this calculated RMSD value of 1.361 Å, the docking workflow is considered as representative. The resulting docking poses were analyzed visually in Maestro54 11.2.013 (Schrödinger, LLC, New York, NY, USA). Pharmacophore Modeling. Pharmacophore modeling was performed in LigandScout 3.1255 (Inte:Ligand, Vienna, AT). Molecules were first drawn manually in ChemDraw Professional (PerkinElmer Inc.) and afterward converted to an .sd file by a custommade PipelinePilot (Dassault Systèmes, Vélizy-Villacoublay, FR) protocol. The .sd file was then imported into LigandScout version 3.12. The ligand-based pharmacophore models were created from favorable starting conformations of the compounds in the respective training set. These starting conformations were generated with OMEGA (OpenEye Scientific Software) incorporated in LigandScout 3.12 using BEST settings (500 conformers per ligand). The pharmacophore models were created as “shared feature pharmacophore models”, meaning that only common features present in all training compounds are considered by the algorithm while creating the pharmacophore model. These kinds of pharmacophore models are often referred to as “common feature pharmacophore models” in the literature. The obtained models were then manually refined as described in Vuorinen et al.56 To assess the quality of the obtained pharmacophore models in a quantitative manner, ROC curves, relative enrichment factor, yield of actives, sensitivity, and specificity were used as performance metrics as described in Akram et al.57 Details are provided in Tables S4 and S5, Supporting Information. Pharmacophore screening was performed using the data sets “AIs” (45 compounds) and “NIs” (19 compounds). Both AI and NI were first drawn manually in ChemDraw Professional and converted in an .sd file with a Pipeline Pilot protocol. From the respective .sd files, multiconformational screening databases were generated in LigandScout version 3.12, using OMEGA BEST settings (maximum of 500 conformers). Assignment to the respective data set (AIs, NIs) was based on the results obtained in the mh-Tyr substrate assay and on LC-ESIHRMS results, as indicated in Table S3, Supporting Information.

scan range, 50−1000 m/z. High-resolution mass calibration was performed by infusing 10 mM NaOH in 2-propanol/water (1:1, v/v) fortified with 0.1% FA at the beginning of each analysis via syringe pump and valve switch for 1 min. The exact mass of the sodium formate clusters was used as an internal calibrant. Post-run analysis as well as prediction of the molecular formula was performed in Bruker’s DataAnalysis software, version 4.0 SP 5 (Bruker, Billerica, MA, USA). Calculated exact mass from putative ions or fragments, respectively, were obtained from ChemDraw Professional (PerkinElmer, Waltham, MA, USA). Materials. All relevant details of the compounds tested in the mhTyr substrate assay or LC-ESIHRMS assay are listed in Table S3, Supporting Information. Acetonitrile (1.00029.2500, I0895429 727, Merck KGaA, Darmstadt) and formic acid (84865.180, OH647426, VWR) were HPLC-MS grade. Methanol (1.06007.2500, I856807 642, Merck KGaA, Darmstadt) and water were HPLC grade. As ingredients for PB, K2HPO4 (6346.1000, 241 A672746, Merck KGaA, Darmstadt) and NaH2PO4 (1.05104.1000, A0187204227, Merck KGaA, Darmstadt) were used. DMSO was purchased from Merck KGaA, Darmstadt (16743.1000 312, K19000143). Ethyl acetate was distilled before use and dried with appropriate drying agents. Mh-Tyr was purchased from Sigma-Aldrich Inc. (T3824, SLBM7158 V). Mh-Tyr Substrate Assay. This assay protocol was mainly inspired by the mh-Tyr enzyme inhibition assay recently published by our group.17 The assay was performed in 96-well plates (200 μL per well) using a Tecan Spark 10 Mplate reader. For each compound, a reaction probe and a blank probe were prepared. Reaction probes consisted of three parallel wells containing the compound of interest in 0.06 M PB (pH = 6.8) and 1% DMSO (yielding a 100 μM compound solution) and 4 U mh-Tyr. The blank probe was analyzed using only one well containing the compound of interest in 0.06 M PB and 1% DMSO (yielding a 100 μM compound solution) without addition of mh-Tyr. Blank probes were introduced to account for eventual self-absorbance of the tested compounds. At each time point, the mean of the absorbance values of the reaction probe triplicates was calculated and the absorbance value of the blank probe subtracted. Thereby, we were able to measure only an increase/ decrease of the compound-specific absorbance profile, independent of any background interference. This analysis was repeated twice (n = 3), and mean and confidence intervals at 95% (CI95) were calculated over these three biological replicates and represented in time-dependent absorption plots (Figure 3). Position and order of the compounds on the 96-well plate were changed for each biological replicate, to exclude bias due to, for example, certain positioning on the 96-well plate. The 96-well plate was incubated at 23 °C under constant shaking. Absorbance of each well was recorded every 60 s over 70 min. UV spectra of each well were recorded at the beginning and the end of the entire analysis (data not shown). Compounds were considered as AIs if OD475 changed in the time-dependent absorption plot (Figure 3) over 60 min. If the time-dependent absorption plot did not show any changes in OD475, the compound was classified as NI. As an example, both a positive control (PC, red curve, Figure 3) and a negative control (NC, green curve, Figure 3) were analyzed, to serve as examples for an AI and NI, respectively. As PC, L-DOPA (13) was used, which is a physiological substrate of mh-Tyr next to L-tyrosine. The NC contained only mh-Tyr in PB with 1% DMSO (reaction probes) or PB with 1% DMSO (blank probes), respectively. L-DOPA was purchased from Sigma, Buchs, Switzerland (details on origin and purity are provided in Table S3, Supporting Information). Raw data were processed in Microsoft Excel and plotted with MATLAB R2017b (9.3.0.713579),45 using the boundedline package.46 Molecular Docking. Molecular docking was carried out using the GOLD software version 5.2 (The Cambridge Crystallographic Data Centre, Cambridge, UK).47 The ligands were prepared for the docking process in ChemDraw Professional48 (PerkinElmer Inc.) and converted into an .sd file using a custom-built Pipeline Pilot49 protocol (version 9.5.0.831, Dassault Systèmes BIOVIA, VélizyVillacoublay, France). Subsequently, ligands were minimized with OMEGA50,51 version 2.3.3 (OpenEye Scientific Software, Santa Fe,

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jnatprod.8b00847. Summary of the in silico target prediction, including quantitative performance assessment; full data set with 145



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classification in AIs or NIs; time-dependent absorption plots from 26−55; validation report of pharmacophore models 1 and 2; KNIME workflow used for retrieving mh-Tyr inhibitors from ChEMBL; 1H NMR spectra of 32, 33, and 34; literature search for 1−25 regarding mhTyr substrate specificity (PDF)

AUTHOR INFORMATION

Corresponding Authors

*Tel: +43-512-507-58409. Fax: +43-512-507-58499. E-mail: [email protected]. *Tel: +43-662-2420-80610. Fax: +43-512-507-58299. E-mail: [email protected]. ORCID

Stefan Schwaiger: 0000-0002-3731-7349 Notes

The authors declare no competing financial interest. The pmz files mentioned in this paper are available from the authors upon request.

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ACKNOWLEDGMENTS The research has been funded by GECT Euregio Tirol− Südtirol−Trentino (IPN55). The authors thank P. Schuster and N. Engels for proofreading the manuscript. F.M. and D.S. thank OpenEye Scientific Software and Inte:Ligand for their academic licenses. D.S. is an Ingeborg Hochmair professor at the University of Innsbruck.

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Mushroom Tyrosinase-Based Enzyme Inhibition Assays Are Not

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