This article appeared in a journal published by Elsevier. The attachedcopy is furnished to the author for internal non-commercial researchand education use, including for instruction at the authors institution

and sharing with colleagues.

Other uses, including reproduction and distribution, or selling orlicensing copies, or posting to personal, institutional or third party

websites are prohibited.

In most cases authors are permitted to post their version of thearticle (e.g. in Word or Tex form) to their personal website orinstitutional repository. Authors requiring further information

regarding Elsevier’s archiving and manuscript policies areencouraged to visit:

http://www.elsevier.com/copyright

Author's personal copy

Enzyme and Microbial Technology 52 (2013) 68– 76

Contents lists available at SciVerse ScienceDirect

Enzyme and Microbial Technology

j our na l ho me p age: www.elsev ier .com/ locate /emt

Prediction model based on decision tree analysis for laccase mediators

Fabiola Medinaa, Sergio Aguilab, Maria Camilla Barattoc, Andrea Martoranac,Riccardo Basosi c, Joel B. Alderetea,∗, Rafael Vazquez-Duhaltb,∗

a Departamento de Química Orgánica, Grupo de Química Biológica y Computacional, Universidad de Concepción, Edmundo Larenas 129, Concepción, Chileb Instituto de Biotecnología, Universidad Nacional Autónoma de México, Apartado Postal 150-3, Cuernavaca, Morelos 62210, Mexicoc Department of Chemistry, University of Siena, Italy, Via A. De Gasperi, 2, 53100 Siena, Italy

a r t i c l e i n f o

Article history:Received 29 June 2012Received in revised form24 September 2012Accepted 24 October 2012

Keywords:Laccase mediatorsPesticide transformationPrediction modelQuantum-chemistrySARElectron paramagnetic resonanceRadical intermediates

a b s t r a c t

A Structure Activity Relationship (SAR) study for laccase mediator systems was performed in order tocorrectly classify different natural phenolic mediators. Decision tree (DT) classification models with aset of five quantum-chemical calculated molecular descriptors were used. These descriptors includedredox potential (ε◦), ionization energy (Ei), pKa, enthalpy of formation of radical (�fH), and O H bonddissociation energy (DO–H). The rationale for selecting these descriptors is derived from the laccase-mediator mechanism. To validate the DT predictions, the kinetic constants of different compounds aslaccase substrates, their ability for pesticide transformation as laccase-mediators, and radical stabilitywere experimentally determined using Coriolopsis gallica laccase and the pesticide dichlorophen. Theprediction capability of the DT model based on three proposed descriptors showed a complete agreementwith the obtained experimental results.

© 2012 Elsevier Inc. All rights reserved.

1. Introduction

Laccases are a family of multi-copper oxidase that catalyzethe oxidation of a wide range of substrates, in particular pheno-lic derivatives [1,2]. Laccases are widely distributed in fungi [3],plant, bacteria [4], insects [5] and recently their existence has beenconfirmed in algae [6].

The reactions catalyzed by laccases perform the mono electronicoxidation of the substrate to the corresponding radical species withthe concomitant reduction of molecular oxygen to water [7]. Theactive site of the enzyme is constituted by four copper atoms asso-ciated in three types of sites, designed as T1, T2, and T3 sites. Theredox potential of the T1 site is characteristic for each particularlaccase. In addition, in the T1-site the substrate oxidation occurs,whereas in the T2 and T3-sites the reduction of molecular oxygento water occurs [2,7,8]. However, in numerous cases laccases arenot able to directly oxidize the substrate of interest, due to the sub-strate inaccessibility to the enzyme or because this possesses a highredox potential (>1.5 V) [9]. These difficulties can be overcome by

∗ Corresponding author at: Instituto de Biotecnología, UNAM, Av. Universidad2001, Cuernavaca, Morelos 62250, Mexico. Tel.: +52 7773291655;fax: +52 7773172388.

E-mail address: vazqduh@ibt.unam.mx (R. Vazquez-Duhalt).

using a low molecular weight compound denominated mediator[10].

Laccase-mediator systems are being systematically studied bytheir wide application range in biotechnological issues [9,11].As laccase-mediator systems can oxidize a broad range of sub-strates, this capacity has been exploited in several applicationssuch as: dyes degradation [12–15], halogenated pesticides transfor-mation [16], polycyclic aromatic hydrocarbons oxidation [17–20],endocrine disrupting chemicals [21–23] and lignin degradation[24,25]. Several synthetic compounds such as ABTS, TEMPO, VIOL,and HBT have been used as laccase mediators in these biocatalyticprocesses. However, the use of these compounds is strongly limitedby their high cost, the toxicity of some by-products, and the largemediator/substrate molar ratio [14,16,17,26,27,19]. In fact, mostof the mediators assayed so far have shown to be false mediatorsbecause they become fully oxidized and depleted from the cycle[28], and they have been used in a mediator/substrate molar ratioup to 40 to 1 [19]. It has been proposed that natural mediators canbe more efficient, cheaper, and more environmentally friendly.

Despite that the mechanism for substrate oxidation has beenstudied for several years, it has not been fully elucidated and it is stillcontroversial [29]. Two mechanisms for the mediated-oxidation ofsubstrates have been proposed. In the electron transfer mechanism(ET route), the mediator abstracts an electron from the substrate,being the redox difference between the mediator and the sub-strate the driving force. On the other hand, the radical H-abstraction

0141-0229/$ – see front matter © 2012 Elsevier Inc. All rights reserved.http://dx.doi.org/10.1016/j.enzmictec.2012.10.009

Author's personal copy

F. Medina et al. / Enzyme and Microbial Technology 52 (2013) 68– 76 69

mechanism (HAT route) is where the laccase oxidizes the mediatorto an oxyl radical that abstracts one hydrogen from the substrate[10]. Spectroscopic studies on the catalytic mechanism of laccasein the presence of mediators have been carried out through EPRspectroscopy, and the radical intermediates formed on mediatorsand precursors have been characterized [30,31].

Structure Activity Relationship (SAR) studies on laccase-mediator systems are necessary to understand the behavior of thesesystems and from this knowledge subsequently find or design newlaccase mediators. Although Quantitative Structure Activity Rela-tionship (QSAR) studies have been extensively used in biologicaland medicinal chemistry [32], QSAR studies on laccase-mediatorsystems are scarce [33]. This fact is due to a series of limita-tions associated with QSAR studies in laccase-mediator systems,which are principally related with the heterogeneity of availableexperimental data. In literature, different reaction conditions wereemployed, such as mediator/substrate molar ratio, enzyme source,nature of substrate, and reaction time. In addition, different activ-ity rate measurements have been used; as well a limited numberof mediators have been studied. This heterogeneity of the availabledata is the main limitation for comparison purpose.

Thus, in this work a SAR study for laccase mediator systems wasperformed in order to correctly classify different natural pheno-lic mediators as belonging to a low or high activity class. The SARstudy is based on decision trees (DT) classification model [32]. Inthe classification process a set of five quantum mechanics descrip-tors were used. The activity data for DT classification was first takenfrom literature, and then new experimental data were obtained tovalidate the proposed model. The proposed model is able to predict,based on the molecular descriptors, the capacity to act as laccasemediator of phenolic molecules.

2. Experimental

2.1. SAR model and computational aspects

A set of five molecular descriptors, including redox potential (ε◦), ionizationenergy (Ei), pKa, radical heat of formation (�fH), and O H bond dissociation energy(DO–H), were calculated for several phenol derivatives using Density Functional The-ory (DFT) methods at B3LYP level [34].

B3LYP functional density with the standard 6-311++G(2df,2p) basis set wasemployed for the geometry optimizations and frequency calculations [35]. All theoptimized structures were minima on potential energy surface as was confirmedfrom the calculated vibrational frequencies. The B3LYP/6-311++G(2df,2p) theorylevel was selected to calculate the ionization energy because this level correlatesvery well with the experimental data for a significant number of organic molecules[36–38].

The ionization energy was evaluated according to the following reaction in thegas phase:

Ar − OH(g) → Ar − OH•+(g) + e−

(g)

The redox potentials for one-electron oxidation of natural phenolic mediatorswere calculated from the following half reactions:

Ar − OH(aq) → AR − O•(aq) + H+

(aq) + e−(g)

Ph − O•(aq) + H+

aq + e−(g)

→ Ph − OH(aq)

The free energy of the net redox reaction is related with the difference of redoxpotential between both half reactions as shown in the following expression:

�E◦ = �aqG◦

F

where F is the Faraday constant (23.06 kcal mol−1 V−1), Ar OH is a phenolic medi-ator, Ph OH is the phenol molecule (reference compound) [44] and �aqG◦ can beevaluated using appropriate thermodynamic cycles through the following expres-sion:

�aqG◦ = �gG◦ + �solvGArO• + �solvGPhOH − �solvGArOH − �solvGPhO•

where �gG◦ is the free energy change in gas phase for the net reaction, and �solvGi

is the solvation free energy for the different species involved in the reaction [36].�solvGi was calculated by means of the continuum solvation model C-PCM [39] at

B3LYP/6-311++G(2df,2p) level and using the Bondi radii to construct the solvent-inaccessible cavity. Thus, by using the experimental redox potential for phenol(1.03 V respect to NHE) [40] it was possible to calculate the redox potentials forthe redox mediators by means of the following relation:

E0(ArOH) = �aqG◦

F− 1.03

In order to assess the performance of this procedure we have compared thecalculated redox potential for eleven phenolic derivatives with the experimentalreported values.

To calculate the pKa of phenolic mediators the following proton transfer reactionwas considered [41].

AR − OH(aq) + OH−(aq)

�solG−→ Ar − O−(aq) + H2O(aq)

The free energy for this reaction can be expressed by the following relation:

�solG = �gG + �solvGARO• + �solvGH2O − �solvGOH− − �solvGArOH

where �gG is the gas phase free energy of the reaction and �solvGi are the solvationfree energy of each species involved in the reaction. Considering the molar concen-tration of liquid water at 298 K, and the self-ionization equilibrium for water thefinal expression for the pKa is given by:

pKa = �solG

RT ln 10+ 15.74

The performance of this protocol was evaluated by comparison with the experimen-tal pKa data of fourteen phenols. The B3LYP and B1B95 functional together with theC-PCM and IEF-PCM solvation models using different basis sets were evaluated. Alsoa methodology based on an empirical group contribution named SPARC (System Per-forms Automated Reasoning in Chemistry) [42], which has been widely used for thepKa estimation of organic molecules, was tested.

The O H bond dissociation energy was calculated at a B3LYP/6-311++G(2df,2p)level of theory. This level of theory has been successfully used to calculate the disso-ciation energy [38]. The dissociation energy was evaluated as the enthalpy changefor the following reaction in the gas phase at 298 K:

Ar − OH(g) → Ar − O•(g) + H

•(g)

Heat of formation of radicals was calculated according to standard procedure[43]. The heat of formation for species with general composition of AxByHz can beestimated from quantum chemical calculations and experimental atomic heat offormation by means of the following relation:

�fH0(AxByHz) = x�fH(A) + y�fH(B) + z�fH(H) − ˙D0(AxByHz)

where �fH(B) is the experimental atomic heat of formation at 0 K, taken from JANAFthermochemical tabulated values [39], and ˙D0(AxByHz) is the atomization energycalculated at B3LYP/6-311++G(2df,2p) level considering the zero point energy cor-rections.

In order to obtain a SAR model for the laccase natural mediator system a DTclassification protocol [32,45,46] was implemented based on the five descriptorscalculated by means of DFT at B3LYP level [51]. Briefly, a DT model consists of atree-like structure, which possesses nodes and links. Usually, in each node a testusing a single descriptor is made. Based on the result of the test the algorithm isdirected to other nodes emerging from the parent node. The final decision is relatedwith the activity class associated to the nodes. Hence, the whole decision process isbased on a series of simple tests, which define the path between the different nodes.The tests applied in each node were based on the descriptors values. Di is the value ofthe ith descriptor associated with the laccase mediators. The threshold values havebeen defined in function of the descriptor values of syringaldehyde molecule, sincethis laccase mediator has systematically shown high activity in several applications.Several assays were performed in order to find the root node and also to define thebest architecture of the DT.

2.2. In vitro enzymatic experiments

2.2.1. Chemicals and enzymeSyringaldazine, acetosyringone, sinapic acid, 3-methoxycatechol,

2-ethoxyphenol, 4-hydroxy-3-methoxybenzoic acid, syringaldehyde, 2,6-dichloro-4-nitrophenol and dichlorophen were purchased from Sigma–Aldrich (St. Louis,MI). Salt buffers were obtained from J.T. Baker (Ohillipsburg, NJ). Acetonitrile (ACN)and other organic solvents (HPLC grade) were obtained from Fisher Scientific(Fairlawn, NJ). Laccase was obtained and purified as described previously [47].

2.2.2. Enzymatic reactionsThe catalytic constants of phenolic substrates were estimated by measuring the

transformation rate in a reaction mixture (1 mL) containing 0.22 nM of purified lac-case from Coriolopsis gallica and phenolic substrates ranging from 0.01 to 1.0 mM in50 mM succinate buffer (pH 4.5) containing 20% ACN. The reactions were performedat 25 ◦C (±2 ◦C). Each reaction was started by adding the laccase and stopped after5 min adding 1 mL of ACN.

Author's personal copy

70 F. Medina et al. / Enzyme and Microbial Technology 52 (2013) 68– 76

The reaction progress was monitored by an HPLC system (Perkin-Elmer 200series) equipped with a reverse phase C18 column (250 mm × 4 mm) Nucleosil 5 �m(Macherey-Nagel) and coupled to a diode array detector (Perkin-Elmer 235C). Iso-cratic elution with acetonitrile:water mixture was used in proportions dependingof substrate at a constant flow of 0.5 mL/min. The decrease in the concentration ofsubstrates was determined by measuring the decrease in peak area at 280 nm. Allexperiments were done in triplicate.

The capacity as laccase mediator of the different phenolic substrates was esti-mated by using the pesticide dichlorophen as a model compound. This pesticide isnot transformed by laccase in the absence of a mediator [16]. The pesticide transfor-mation was monitored by HPLC at 280 nm, and estimated by the decrease of peakarea using a standard curve. The mediator-pesticide ratio was increased until thehighest mediator activity at fixed enzyme concentration and dichlorophen (0.2 mM)was found.

2.2.3. EPR experimentsCW X-band (9.4 GHz) EPR spectra were recorded at room temperature with a

Bruker E500 Elexsys Series using the Bruker ER4122SHQE cavity. Since EPR sig-nal intensity is directly correlated to the amount of radical spin formed during thereaction, time-resolved EPR spectra (TR-EPR) were collected and processed throughthe Bruker software package X-EPR. Double integrals of each experimental EPRspectra were calculated in order to obtain the corresponding values of area andthen they have been plotted versus time in order to calculate the half-life time foreach radical. Decay curve experiments versus time were performed obtaining thetime-dependent changes in the radical species intensity.

The enzymatic reaction was carried out in 200 �L of aqueous solution contain-ing 15 �M of purified laccase from C. gallica and 30 mM of phenolic mediator in0.1 M acetate buffer pH 4.5, containing ACN ranging from 20 to 40% (v/v) dependingon the mediator solubility. After adding laccase to the reaction mixture, the sam-ples were quickly placed into 1 mm ID quartz capillary tubes and located insidestandard suprasil EPR tubes. The radical signal trend was followed during time untilthe intensity was close to zero. Depending on the half-life time, different parame-ters have been used to record the spectra for each mediator. For syringaldazine, thatshowed the shortest half-life time, each spectrum was recorded automatically for40 s and a delay time of 1 s between a spectrum and the next one. Acetosyringoneand syringaldehyde spectra were recorded for 1.30 min per spectrum using 5 s ofdelay time, while for the sinapic acid each spectrum was recorded for 7 min using adelay time ranging from 15 to 30 min.

3. Results and discussion

3.1. Computational analyses

Five quantum chemistry descriptors were studied to predictthe performance of phenolic laccase-mediator candidates. Thesedescriptors included redox potential (ε◦), ionization energy (Ei),pKa, enthalpy of formation of radical (�fH), and O H bond disso-ciation energy (DO–H). The rational for selecting these descriptorsis derived from the laccase-mediator mechanism. Fabbrini et al.[48] proposed two possible mechanisms by which the mediatorcould oxidize the final substrate: the Electron Transfer (ET) routeand the Hydrogen Abstraction Transfer (HAT) route. In the ET route,the driving force is the redox potential of the substrate or media-tor, while in the HAT route it would be the hydrogen bond energyof the substrate. Thus, the redox potential and ionization energy,which are clearly correlated, seems to be the best descriptors todescribe the substrate capacity to lose an electron, and then theywere considered for the ET route. On the other hand, the O H bonddissociation energy (parameter for the homolytic cleavage of pro-ton) was considered for HAT route. On the other hand, it has beenproposed the radical stability as an important property of a goodmediator [16]. The enzymatic reaction should produce free radi-cals, stable enough, to diffuse away from the active site and oxidizethe final target compound. The chosen descriptor associated to thisproperty is the enthalpy of formation of radical (�fH). The pKa wasalso considered because this property showed to be correlated withthe KM in the laccase transformations of methoxylated phenoliccompounds [49].

Our approach should permit the search or synthesis de newmediators without the use the expansive and time consumingexperimental determinations. From a theoretical point of view,descriptors for SAR studies should be preferably based on ab initio

quantum mechanics tools, in order to employ methodologies whichdirectly correlate the molecular structure with physicochemi-cal properties avoiding empirical approximations. Calculations ofphysicochemical properties in aqueous solution should be carefullyselected and evaluated.

First, a training set of laccase-mediators were selected fromliterature (Fig. 1) [26] and extensive studies of their redox poten-tials and their pKa employing several protocols at DFT level wereperformed. In order to assess the performance of these proce-dures, the calculated redox potential for eleven phenols derivativeswas compared with the reported experimental values [45]. At C-PCM/B3LYP/6-311++G(2df,2p) level, the correlation coefficient (R2)was 0.948 and the mean absolute deviation (MAD) was 0.278 V. Dueto the high MAD obtained at this level of theory, another protocolusing the B1B95 functional [48] and IEF-PCM solvation model [52]was also evaluated, showing lower accuracy. Although, the corre-lation coefficient between the experimental and calculated redoxpotential is good, this result must be taken cautiously since the pre-dictions are not in agreement with the expected chemical effectsexerted by substituent groups. Thus, modeling the solvent as a con-tinuum characterized by its dielectric constant seems to be a severelimitation for redox potential calculations.

The ionization energy seems to be a better descriptor than redoxpotential for the SAR studies on laccase-mediators. This descriptoris directly related to energy required to remove one electron from acompound and it shows values in accordance with the substituenteffects and also reflects the laccase capacity to oxidize phenolic sub-strates. As expected, phenol derivatives with electron-withdrawingsubstituent showed high ionization energy, whereas phenol deriva-tives with electron–donor groups showed low ionization energies(Table 1). The results also indicate that the presence of methoxylgroups significantly reduces the ionization energy of phenols.

The values of the other calculated descriptors for all twenty-four laccase-mediators are shown in Table 1. A general tendencycan be observed for heat of formation of radicals; compounds withmethoxy substituents and carbonyl group in para position showedhigher stabilities (�fH < −800 kcal mol−1), while mono-substitutedphenols showed less stable radical species (�fH > −700 kcal mol−1).In addition, phenols with methoxy groups at the position 2 and 6along with a moderate electron-withdrawing substituent at posi-tion 4 should form radical species with high stability.

It is well known that there are two factors leading to the stabi-lization of radicals: (i) the extent of the steric hindrance of the atomformally carrying the unpaired electron, and (ii) the extent of thedelocalization of the unpaired electron [53]. First, the steric hinder-ing of the phenoxyl’s oxygen by bulky alkyl substituents seems to bethe most important factor for the stabilization of phenoxy-radicals.Sterically hindered phenols by bulky alkyl substituents in the ortho-positions, relative to the hydroxyl group of the phenol, change sometypical properties of phenols, such as water and alkalis solubilityand also reactivity [54]. These bulky ortho-substituents obstruct thereaction of the hydrogen of the O H group with other molecules,influencing also the ability to form an intramolecular hydrogenbond and preventing the association with other molecules. In addi-tion, steric hindering of the hydroxyl group of the phenol leads toa sharp decrease in its acidity [55]. This point can be illustratedwhen the O C bond of the methoxy group at the ortho positionlies coplanar with the benzene ring, overlap of the lone pair of�-symmetry is optimized and the maximum electron-donatingeffect occurs [56]. In addition, the radicals that do not contain �-hydrogen atoms in the ortho-substituents have an extremely highstability [53]. The 2,4,6-tri-t-butylphenoxy-radical, which does notcontain �-hydrogen atoms, is exceptionally stable and may exist fora prolonged period in the absence of oxygen. Thus, the stericallyhindered phenols, resulting from the presence of bulky methoxysubstituents in the ortho-positions are responsible for a number

Author's personal copy

F. Medina et al. / Enzyme and Microbial Technology 52 (2013) 68– 76 71

Fig. 1. Structures of the twenty-four laccase mediators used as training set. The relative mediator activities in the transformation of Reactive Black 5 are shown in percentage.

Data from Camarero et al. [26].

of unusual properties of the radicals formed by the removal ofhydrogen from the hydroxyl group, particularly their high stability.

On the other hand, the delocalization of the unpaired electronleads to an increase in the stability of the radical [57,58]. The etheroxygen could participate in the overall conjugation, and this sta-bilizes the radical to a sufficient extent [59]. Thus, the key factorsresponsible for the stability of phenoxy-radicals are the delocal-ization of the unpaired electron and the steric hindrance of theatom showing a large proportion of the density of the unpairedelectron. These two effects could explain the singular behavior ofphenoxy-radicals as laccase mediators.

The pKa values were calculated by SPARC (System PerformsAutomated Reasoning in Chemistry) methodology, since the SPARCmethods showed better results than B3LYP protocols. The per-formance of PCM/B3LYP and SPARC protocols was evaluated bycomparison with the experimental pKa data of fourteen phenols[50]. The results obtained with IEF-PCM/B3LYP/6-311++G(d,p) cal-culations showed to be more accurate, with a MAD of 0.81 pKa units,than the C-PCM/B3LYP/6-311++G(d,p) calculations. This result is in

agreement with other chemical quantum studies on the pKa deter-mination of organic molecules. However, the correlation coefficientR2 is very low (0.69), and thus the pKa of phenolic mediators hasbeen estimated with SPARC methodology that showed a MAD of0.25 pKa units and the R2 of 0.93.

Finally, the values of O H bond dissociation energy for the setof laccase mediators are also shown in Table 1. The calculated O Hbond dissociation energy (DO–H) ranged between 73.3 kcal mol−1

and 87.8 kcal mol−1. The small energy differences among the medi-ators suggest a low influence of this descriptor. As expected, specieswith electron-withdrawing substituent in para position respect toOH group show high DO–H energy, while molecules with electron-donating substituent show low DO–H values.

3.2. Decision tree (DT) classification model

Before the application of DT analysis, the correlation degreebetween the five selected descriptors has been established. If thedescriptors are mutually interrelated then they encode the same

Author's personal copy

72 F. Medina et al. / Enzyme and Microbial Technology 52 (2013) 68– 76

Table 1Calculated value of molecular descriptors for training set.

No. Mediators ε◦ (V) Ei (eV) pKa �fH (kcal mol−1) DO–H (kcal mol−1)

1 2,4,6-Trimethoxyphenol 1.67 7.12 9.5 −802.02 74.92 2,6-Dimethylphenol 1.32 8.07 10.66 −841.64 79.23 3-Methoxyphenol 1.01 8.02 9.67 −504.57 84.74 p-Coumaric acid 1.06 8.14 8.4 −710.81 82.05 Syringic acid 1.33 7.89 7.35 −916.80 80.36 Vanillic acid 1.14 8.26 7.91 −769.24 84.67 Acetosyringone 1.34 7.82 7.49 −855.06 79.98 Acetovanillone 1.15 8.16 8.06 −1074.14 83.79 Coniferyl alcohol 1.47 7.42 9.74 −745.70 79.6

10 2-Hydroxybiphenyl 1.10 7.82 10.61 −639.56 80.911 3-Hydroxybiphenyl 1.01 7.94 9.91 −638.80 83.812 2,6-Dimethoxyphenol 1.50 7.59 9.23 −653.61 78.713 Ethyl vanillin 1.18 8.25 7.84 −705.90 83.914 2-Methoxyphenol 1.33 8.22 9.85 −506.80 73.315 4-Hydroxybenzoic acid 0.81 8.83 7.93 −620.27 86.116 4-Hydroxybenzoic aclohol 1.15 8.11 10.06 −509.78 81.917 4-Hydroxybenzoic aldehyde 0.80 8.89 7.81 −499.61 86.018 Phenol 1.03 8.51 9.91 −358.73 83.619 Syringaldehyde 1.31 7.98 7.22 −796.60 79.920 Vanillyl alcohol 1.40 7.71 10.00 −657.93 81.221 Vanillin 1.15 8.33 7.79 −648.65 84.122 Methyl vanillate 1.15 8.13 7.85 −814.55 84.123 2-Hydroxybenzyl alcohol 1.06 8.04 10.27 −511.27 81.024 3-Hydroxybenzyl alcohol 0.92 8.09 9.86 −508.71 83.2

Table 2Lineal correlation coefficients (R2) between the five descriptors considered in thisstudy.

D1 (ε◦) D2 (pKa) D3 (Ei) D4 (DO–H) D5 (�fH)

D1 (ε◦) 1D2 (pKa) 0.01 1D3 (Ei) 0.68 0.10 1D4 (DO–H) 0.63 0.11 0.42 1D5 (�fH) 0.21 0.23 0.12 0.02 1

information and, therefore one of these descriptors must be elim-inated of SAR analysis in order to avoid redundant information.Table 2 displays the correlation matrix of the five descriptorsconsidered in this study. Redox potential is strongly correlatedwith both ionization energy and O H bond dissociation energy.Moreover, as discussed above, the calculated redox potential atC-PCM/B3LYP/level presents larger error for some phenolic laccase-mediators. Thus, the redox potential descriptor was not consideredin the DT analysis.

The training set of laccase-mediators selected from literature[26] were used for the DT analysis. According to DT analysis,

various assays on different DT architectures were performed tochoose the number, type and sequence of nodes more adequateto correctly classify the activity of laccase-mediators. The mostadequate architecture and nodes sequence is shown in Fig. 2. TheDT is formed by three nodes, which are associated to the follow-ing descriptors: ionization energy, heat of formation of radical andO H bond dissociation energy. The selection of expected valueswas based on the descriptor values of the syringaldehyde molecule,since several studies have shown that this compound is a goodlaccase mediator [9,16,26,60]. Thus, the training set of laccase-mediators that not reach expected values in each node wereclassified as low activity mediators, while the laccase-mediatorsthat reach the expected values were classified as high activity medi-ators.

First, it was compared the ionization energy descriptor (D1)for each laccase-mediator from the training set with an expectedvalue of 8.00 (eV). Those compounds that posses ionization ener-gies greater than 8.00 (eV) were classified as low activity mediators.Then, to the group of compounds that fulfill the rule (Ei < 8.00 eV)in the first node, another selection criterion is applied in the sec-ond node. In this second node, the heat of formation of radical

Fig. 2. Classification scheme for training set mediators using a decision tree based on three descriptors, where D1 is the ionization energy (eV), D2 is heat of formation ofradical (kcal mol−1) and D3 is the dissociation energy O H bond (kcal mol−1).

Author's personal copy

F. Medina et al. / Enzyme and Microbial Technology 52 (2013) 68– 76 73

O

OH

R1

CH3

G

OR2

OH

R3

A)

B)

R1= -OCH3 ; G= -CH=N-NH=CH-C6H2(OCH3)2OH, Syringaldazin e

R1= -OCH3 ; G= -COCH3, ace tosyringone

R1= -OCH3 ; G= -CHO, syringaldehyde

R1= -OCH3 ; G= -CH=CHCOO H, sin apic acid

R1= -H ; G= -COO H, 4-hydroxy-3-meth oxybenzoic acid

R2= -OH ; R3= -CH3 , 3-meth oxicatec hol

R2= -H ; R3= -CH2CH3 , 2-eth oxyphenol

Fig. 3. Proposed structures for the validation set, where G is a slightly electron withdrawing group. The different compounds, according to their structural features shouldhave different laccase-mediator activity. The general structure (A) represents a typical active mediator, and the structure (B) should be a typical inactive or low activitymediator.

(D2) of the compounds were compared with the reference value−797.0 kcal mol−1 as in the first case, those that not fulfill the rule(�fH < −797.0 kcal mol−1) were classified as low activity mediators.Following the same logic, for those compounds that meet this sec-ond rule a third node was considered. In this last node the O H bonddissociation energy (D3) was used as descriptor and an expectedvalue of 80.0 (kcal mol−1) was applied, which allowed to achievethe most appropriate classification for the activity of each mediatorconsidered in the training set.

As is shown in Fig. 2, the most important descriptor for theclassification process is the ionization energy. This descriptor wasable to correctly classify a subset of 15-laccase-mediators of thetraining set as low activity species. Subsequently, the heat offormation of radical discriminated other five low activity medi-ators (coniferyl alcohol, 2-hydroxybiphenyl, 3-hydroxybiphenyl,2,5-dimetoxyphenol and vanillyl alcohol). Finally, the O H bonddissociation energy correctly classifies other laccase-mediator,syringic acid, as a low activity compound. Thus, the use of the DTdisplayed in Fig. 2 permitted to obtain a classification model with a96% certainty, and only one compound was misclassified, the 2,4,6-trimethoxyphenol. This mediator was classified as highly active, butits experimentally determined activity is low [27]. Leave-one-outcross-validation method was applied to the training data set and asexpected a 96% of performance was obtained.

3.3. Prediction accuracy of the DT model

In order to evaluate the general use of the DT classificationmodel, and also to overcome possible limitations, we have defineda validation set that consider both inactive and active mediators.This validation set was built from the DT results and a detailedanalysis of substituent effects on the ionization energy and radicalheat of formation. Subsequently, in order to check the predictionsbased on DT analysis, the experimental activity as laccase mediatorof these phenolic derivatives was evaluated in the degradation ofthe pesticide dichlorophen, employing C. gallica purified laccase.

Two general structures were included in the validation set(Fig. 3): structures “A” containing 2,6-dimethoxy electron-donatingsubstituent groups and an electron withdrawing group in paraposition respect to OH group of the phenol derivative. In general,compounds with ionization energies lower than the reference value(8.00 eV) were considered. The oxidation of these derivatives by

laccases should be favored. In addition, the radical heat of formationof this class of compounds also should be lower than the refer-ence value (−797.0 kcal mol−1), accounting for the stability of theirradical species. On the other hand, structures “B” including pheno-lic derivatives ortho substituted by an alkoxy or hydroxy group. Inthis case, it can be expected that the oxidation of these compoundsby laccases should not be favored, since their ionization energiesshould be greater than the reference values. In addition, their rad-ical heat of formation is expected to be also high, and hence thestability of radical species should be lower.

Based on this analysis, seven compounds were selected to ini-tially predict their mediator capacities. The calculated values ofthree descriptors considered in the DT analysis are shown in Table 3.Compounds that posses 2,6-dimethoxy groups and carbonyl or

Fig. 4. EPR spectra of sinapic acid reported versus time. A double integral of eachspectra has been calculated and the kinetic trend of the signal has been reported inthe decay curves reported in Fig. 5.

Author's personal copy

74 F. Medina et al. / Enzyme and Microbial Technology 52 (2013) 68– 76

Table 3Calculated value of molecular descriptors for the validation set and predicted activity associated to each compound. All the calculations were performed at B3LYP/6-311++G(2df,2p) level of theory.

Mediators Ei (eV) �fH (kcal mol−1) DO–H(kcal mol−1) Predicted activity

Syringaldazine 6.45 −1729.9 76.2 HighAcetosyringone 7.82 −855.1 79.9 HighSyringaldehyde 7.98 −796.6 79.9 HighSinapic acid 7.53 −1396.6 68.0 High4-Hydroxy-3-methoxybenzoic acid 8.26 −796.2 84.5 Low or inactive2-Ethoxyphenol 7.82 −563.9 82.5 Low or inactive3-Methoxycatechol 7.95 −617.0 74.8 Low or inactive

Table 4Kinetic constants of different compounds as laccase substrates, pesticide transformation activity as laccase-mediators, and radical stability determined by EPR.

Name R2 kcat (min−1) KM (mM) kcat/KM (min−1 mM−1) Pesticide transformationrate (nmol min−1 U−1)

Radical stability (min)

Sinapic acid 0.9968 771 0.03 2570 25,900 (±792) 174Syringaldazine 0.9822 530 0.79 671 1296 (±61) <1Syringaldehyde 0.9866 230 0.12 1917 1016 (±56) 98Acetosyringone 0.8663 50 0.74 67 600 (±28) 34-Hydroxy-3-methoxybenzoic acid 0.9613 12 0.32 38 463 (±105) NSDb

3-Methoxycatechol 0.9519 31 0.16 194 NADa NSD2-Ethoxyphenol NAD NAD NAD NAD NAD NSD

R2 correlation coefficient for Michaelis–Menten equation.a NAD, no activity detected.b NSD, no signal detected either at room or low temperature.

Fig. 5. Kinetic decay curves for acetosyringone (A), syringaldehyde (B), syringaldazine (C) and sinapic acid (D) for the radical intermediate formed during the catalyticmechanism at 298 K. Square points represent the experimental data, red line is the best polynomial fitting. Inset: Lineshape of the radical EPR spectra for each mediator.Experimental conditions: (A) � = 9.87 GHz microwave frequency, 0.63 mW microwave power, 0.01 mT modulation amplitude, 298 K; (B) � = 9.87 GHz microwave frequency,0.63 mW microwave power, 0.1 mT modulation amplitude, 298 K; (C) � = 9.68 GHz microwave frequency, 2 mW microwave power, 0.05 mT modulation amplitude, 298 K;(D) � = 9.67 GHz microwave frequency, 0.63 microwave power, 0.2 mT modulation amplitude, 298 K. (For interpretation of the references to color in this figure legend, thereader is referred to the web version of the article.)

Author's personal copy

F. Medina et al. / Enzyme and Microbial Technology 52 (2013) 68– 76 75

C NR group at the 4-position showed ionization energy valueslower than 8.0 eV. In addition, their radical heats of formationare also lower than −797 kcal mol−1, indicating the high stabilityof their radical species. Consistently, the O H bond dissociationenergy fulfills the DT rule. Therefore, the following compounds:syringaldazine, acetosyringone, syringaldehyde, and sinapic acidwere classified as high activity laccase-mediators. The otherthree compounds considered in this validation set: 4-hydroxy-3-methoxybenzoic acid, 2-ethoxyphenol and 3-methoxycatecholwere classified as low activity or inactive mediators, since thesespecies do not fill the DT rule associate at the root node, i.e. theypossess ionization energy higher than 8.00 eV.

To validate the DT predictions, the laccase-mediator activity ofthese compounds was experimentally evaluated. The assays werecarried out using C. gallica laccase and the pesticide dichlorophen.The catalytic constants of mediators in the laccase reaction alongwith their activities on the transformation of dichlorophen weredetermined (Table 4). In order to prove that laccase-mediator activ-ity is strongly associated with the stability of the radical species, ascan be inferred from the DT models, the t1/2 of radical species weredeterminated by using electron paramagnetic resonance (EPR)spectroscopy (e.g. sinapic acid, Fig. 4). The decrease of EPR radicalsignal versus time for acetosyringone, syringaldehyde and sinapicacid has been reported in Fig. 5. The area (double integral of theexperimental EPR radical signal) is directly proportional to theconcentration of the radical species formed during the catalyticreaction. Furthermore dividing by two the highest y value and solv-ing the function for this value, the corresponding x value has beenobtained and that is the half-life time of each species [61]. Ace-tosyringone and syringaldehyde have shown an exponential decayof signal and the highest intensity is recorded for the first species.This means that the reaction is very fast and both mediators aregood substrates, but on the other hand the half-life time is reachedin a very short time. The decay of acetosyringone is faster thansyringaldehyde showing a half-life time of 3 and 98 min respec-tively (Table 4). Syringaldazine has an even shorter half-life timewith a quicker kinetic. On the contrary sinapic acid has showed aparabolic trend of the signal intensity with a maximum absorptionafter 2 h with a half-life time of 174 min (Fig. 4).

All the considered compounds in this validation set are sub-strates for C. gallica laccase. From this mediator set, sinapic acidis the compound showing the higher affinity to C. gallica lac-case, while 4-hydroxy-3-methoxybenzoic acid showed the lowestaffinity. In general, there is no correlation between the lac-case affinities (KM) and their activity as mediators. Although3-methoxycatechol has higher affinity to laccase than both4-hydroxy-3-methoxybenzoic acid and acetosyringone, this com-pound is not able to perform the degradation of dichlorophenwhereas 4-hydroxy-3-methoxybenzoic acid and acetosyringoneare able to transform the pesticide.

A characteristic feature of four from the active compounds isthat they form relatively stable radical species, since their t1/2 arehigher than 2 min, determined by EPR measurements. In particularthe most active laccase-mediator, sinapic acid has the higher t1/2 of174 min. From the validation set, this compound was the best lac-case mediator. Sinapic acid showed to be the best laccase substrate(higher kcat and lower KM) and also it formed the most stable rad-ical (higher t1/2). Syringaldazine showed to be slightly more activeas mediator for the pesticide transformation than syrinagaldehyde,besides it was twice active as laccase substrate. Nevertheless, thishigher activity is counterbalanced by its low radical stability. Thus,the experimental results confirm the DT prediction which showthat phenolic derivatives with 2,6-dimethoxy electron-donatinggroups give stable phenoxyl radicals. Preliminary experiments withother kind of final targets show that the selected as best media-tor for pesticide laccase transformation remains the best for other

target compounds, i.e. polyaromatic hydrocarbons. Currently, weare exploring different pollutants.

4. Conclusions

The classification model based on DT analysis showed thationization energy and radical heat of formation are the maindescriptors that determine the laccase-mediator activity of phe-nolic derivatives. The DT model permitted to establish that2,6-dimethoxyphenol derivatives with a slightly electron with-drawing substituent at the 4-position of the benzene ring areactive mediators. The prediction capability of the DT model wastested in the degradation of dichlorophen pesticide by C. gallicalaccase, obtaining a complete agreement between the experimen-tal results and the predictions carried out by the DT model. Finally,the experimental data confirm that the ionization potential, thelaccase substrate performance and the radical stability are the keycompound properties to be a good laccase mediator.

Acknowledgements

This research was been funded by the National University ofMexico (UNAM-PAPIIT-IN201611), the Italian Miur Prin 2009 andthe CSGI (Consorzio Interuniversitario per lo Sviluppo dei Sistemi aGrande Interfase). We thank Rosa Roman and Dr. Lucia Perezgasgafor her technical assistance.

References

[1] Giardina P, Faraco V, Pezzella C, Piscitelli A, Vanhulle S, Sannia G. Laccases: anever-ending story. Cellular and Molecular Life Sciences 2010;67:369–85.

[2] Baldrian P. Fungal laccases – occurrence and properties. FEMS MicrobiologyReview 2006;30:215–42.

[3] Valderrama B, Oliver P, Medrano-Soto A, Vazquez-Duhalt R. Evolution-ary and structural diversity of fungal laccases. Antonie Van Leeuwenhoek2003;84:289–99.

[4] Claus H. Laccases and their occurrence in prokaryotes. Archives of Microbiology2003;179:145–50.

[5] Dittmer NT, Kanost MR. Insect multicopper oxidases: diversity, proper-ties, and physiological roles. Insect Biochemistry and Molecular Biology2010;40:179–88.

[6] Otto B, Schlosser D, Reisser W. First description of a laccase-like enzyme in soilalgae. Archives of Microbiology 2010;192:759–68.

[7] Dwivedi UN, Singh P, Pandey VP, Kumar A. Structure–function relationshipamong bacterial, fungal and plant laccases. Journal of Molecular Catalysis B:Enzymatic 2011;68:117–28.

[8] Claus H. Laccases: structure, reactions, distribution. Micron 2004;35:93–6.[9] Kunamneni A, Camarero S, García-Burgos C, Plou F, Ballesteros A, Alcalde M.

Engineering and applications of fungal laccases for organic synthesis. MicrobialCell Factories 2008;32:1–17.

[10] Baiocco P, Barreca AM, Fabbrini M, Galli C, Gentili P. Promoting laccase activ-ity towards non-phenolic substrates: a mechanistic investigation with somelaccase-mediator systems. Organic and Biomolecular Chemistry 2003;1:191–7.

[11] Witayakran S, Ragauskas AJ. Synthetic applications of laccase in green chem-istry. Advanced Synthesis and Catalysis 2009;351:1187–209.

[12] Reyes P, Pickard MA, Vazquez-Duhalt R. Hydroxybenzotriazole increases therange of textile dyes decolorized by immobilized laccase. Biotechnology Letters1999;21:875–80.

[13] Abadulla E, Tzanov T, Costa S, Robra KH, Cavaco-Paulo A, Gubitz GM. Decol-orization and detoxification of textile dyes with a laccase from Trametes hirsuta.Applied and Environment Microbiology 2000;66:3357–62.

[14] Camarero S, Canas AI, Nousiainen P, Record E, Lomascolo A, Martinez MJ.p-Hydroxycinnamic acids as natural mediators for laccase oxidation of recalci-trant compounds. Environmental Science and Technology 2008;42:6703–9.

[15] Hsu CA, Wen TN, Su YC, Jiang ZB, Chen CW, Shyur LF. Biological degradation ofanthroquinone and azo dyes by a novel laccase from Lentinus sp. EnvironmentalScience and Technology 2012;46:5109–17.

[16] Torres-Duarte C, Roman R, Tinoco R, Vazquez-Duhalt R. Halogenated pesticidetransformation by a laccase-mediator system. Chemosphere 2009;77:687–92.

[17] Majcherczyk A, Johannes C, Hüttermann A. Oxidation of polycyclic aromatichydrocarbons (PAH) by laccase of Trametes versicolor. Enzyme and MicrobialTechnology 1998;22:335–41.

[18] Pickard AM, Roman R, Tinoco R, Vazquez-Duhalt R. Polycyclic aromatic hydro-carbon metabolism by white rot fungi and oxidation by Coriolopsis gallicaUAMH 8260 laccase. Applied and Environment Microbiology 1999;65:3805–9.

Author's personal copy

76 F. Medina et al. / Enzyme and Microbial Technology 52 (2013) 68– 76

[19] Johannes C, Majcherczyk A. Natural mediators in the oxidation of polycyclicaromatic hydrocarbons by laccase mediator systems. Applied and EnvironmentMicrobiology 2000;66:524–8.

[20] Dai Y, Yin L, Niu J. Laccase-carrying electrospun fibrous membranes for adsorp-tion and degradation of PAHs in shoal soils. Environmental Science andTechnology 2011;45:10611–8.

[21] Songulashvili G, Jimenéz-Tobón GA, Jaspers C, Penninckx MJ. Immobilized lac-case of Cerrena unicolor for elimination of endocrine disruptor micropollutants.Fungal Biology 2012;116:883–9.

[22] Lloret L, Hollmann F, Eibes G, Feijoo G, Moreira MT, Lema JM. Immobilisation oflaccase on Eupergit supports and its application for the removal of endocrinedisrupting chemicals in a packed-bed reactor. Biodegradation 2012;23:373–86.

[23] Torres-Duarte C, Viana MT, Vazquez-Duhalt R. Laccase-Mediated Transforma-tions of endocrine disrupting chemicals abolish binding affinities to estrogenreceptors and their estrogenic activity in zebrafish. Applied Biochemistry andBiotechnology 2012;168:864–76.

[24] Hernández Fernaud JR, Carnicero A, Perestelo F, Hernández Cutuli M, Arias E,Falcón MA. Upgrading of an industrial lignin by using laccase produced byFusarium proliferatum and different laccase-mediator systems. Enzyme andMicrobial Technology 2006;38:40–8.

[25] Camarero S, Ibarra D, Martinez AT, Romero J, Gutierrez A, del Rio JC. Paperpulp delignification using laccase and natural mediators. Enzyme and MicrobialTechnology 2007;40:1264–71.

[26] Camarero S, Ibarra D, Martinez MJ, Martinez AT. Lignin-derived compounds asefficient laccase mediators for decolorization of different types of recalcitrantdyes. Applied and Environment Microbiology 2005;71:1775–84.

[27] Morozova OV, Shumakovich GP, Shleev SV, Yaropolov YI. Laccase-mediator sys-tems and their applications: a review. Applied Biochemistry and Microbiology2007;43:523–35.

[28] d’Acunzo F, Galli C, Masci B. First evidence of catalytic mediation by pheno-lic compounds in the laccase-induced oxidation of lignin models. EuropeanJournal of Biochemistry 2003;270:3634–40.

[29] Torres-Duarte C, Aguila S, Vazquez-Duhalt R. Syringaldehyde a true laccasemediator. Comments on the Letter to the Editor from Jeon, J-R., Kim, E-J. andChang, Y-S. Chemosphere 2011;85:1761–2.

[30] Martorana A, Bernini C, Valensin D, Sinicropi A, Pogni R, Basosi R, et al. Insightsinto the homocoupling reaction of 4-methylamino benzoic acid mediated byTrametes versicolor laccase. Molecular BioSystems 2011;7:2967–9.

[31] Brogioni B, Biglino D, Sinicropi A, Reijerse EJ, Giardina P, Sannia G, et al.Characterization of radical intermediates in laccase-mediator systems. Amultifrequency EPR, ENDOR and DFT/PCM investigation. Physical ChemistryChemical Physics 2008;10:7284–92.

[32] Dudek AZ, Arodz T, Galvez J. Computational methods in developing quantitativestructure-activity relationships (QSAR): a review. Combinatorial Chemistry andHigh Throughput Screening 2006;9:213–28.

[33] Jiang GX, Niu JF, Zhang SP, Zhang ZY, Xie B. Prediction of biodegradation rateconstants of hydroxylated polychlorinated biphenyls by fungal laccases fromTrametes versicolor and Pleurotus ostreatus. Bulletin of Environment Contami-nation and Toxicology 2008;81:1–6.

[34] Becke AD. Density-functional exchange-energy approximation with correctasymptotic behavior. Physical Review A 1988;38:3098–100.

[35] Foresman JB, Firsch A. Exploring chemistry with electronic structure methods: aguide to using Gaussian. 2nd ed. Wallingford, CT: Gaussian Inc.; 1994. p. 1–303.

[36] Fu Y, Liu L, Yu H-Z, Wang Y-M, Guo Q-X. Quantum-chemical predictionsof absolute standard redox potentials of diverse organic molecules and freeradicals in acetonitrile. Journal of the American Chemical Society 2005;127:7227–34.

[37] Jursic BS. A density functional theory evaluation of the ionization energiesof alkanes, cycloalkanes and their unsaturated analogs. Journal of MolecularStructure: THEOCHEM 1998;452:145–52.

[38] Reynisson J, Steenken S. Hydrogen bonding between histidine and lignin modelcompounds or redox mediators as calculated with the DFT method. Effects onthe ease of oxidation. Organic and Biomolecular Chemistry 2004;2:578–84.

[39] Cossi M, Rega N, Scalmani G, Barone V. Energies, structures, and electronicproperties of molecules in solution with the C-PCM solvation model. Journal ofComputational Chemistry 2003;24:669–81.

[40] Li C, Hoffman MZ. One-electron redox potentials of phenols in aqueous solution.Journal of Physical Chemistry B 1999;103:6653–6.

[41] Pliego JR, Riveros JM. Theoretical calculation of pKa using the cluster-continuummodel. Journal of Physical Chemistry A 2002;106:7434–9.

[42] Karickhoff SW, McDaniel VK, Melton C, Vellino AN, Nute DE, Carreira LA.Predicting chemical-reactivity by computer. Environmental Toxicology andChemistry 1991;10:1405–16.

[43] Curtiss LA, Raghavachari K, Redfern PC, Pople JA. Assessment of Gaussian-2and density functional theories for the computation of enthalpies of formation.Journal of Chemical Physics 1997;106:1063–79.

[44] Chase MW, Davies CA, Downey JR, Frurip DJ, McDonald RA, Syverud AN. JANAFthermochemical tables. Third edition. Journal of Physical and Chemical Refer-ence Data 1985;14:1–926.

[45] Panaye A, Doucet JP, Devillers J, Marchand-Geneste N, Porcher JM. Decisiontrees versus support vector machine for classification of androgen receptorligands. SAR and QSAR in Environmental Research 2008;19:129–51.

[46] Cao D-S, Xu Q-S, Liang Y-Z, Chen X, Li H-D. Automatic feature subset selec-tion for decision tree-based ensemble methods in the prediction of bioactivity.Chemometrics and Intelligent Laboratory Systems 2010;103:129–36.

[47] Tinoco R, Pickard MA, Vazquez-Duhalt R. Kinetic differences of purified lac-cases from six Pleurotus ostreatus strains. Letters in Applied Microbiology2001;32:331–5.

[48] Fabbrini M, Galli C, Gentili P. Comparing the catalytic efficiency of somemediators of laccase. Journal of Molecular Catalysis B: Enzymatic 2002;16:231–40.

[49] Xu F. Oxidation of phenols, anilines, and benzenethiols by fungal laccases: cor-relation between activity and redox potentials as well as halide inhibition.Biochemistry 1996;35:7608–14.

[50] Medina F, Alderete J. Quantum chemical calculations of redox potentials andpKa for phenolic laccase mediators. Journal of the Chilean Chemical Society; inpress.

[51] Becke AD. Density-functional thermochemistry. 4. A new dynamical correlationfunctional and implications for exact-exchange mixing. Journal of ChemicalPhysics 1996;104:1040–6.

[52] Barone V, Cossi M, Tomasi J. A new definition of cavities for the computa-tion of solvation free energies by the polarizable continuum model. Journalof Chemical Physics 1997;107:3210–21.

[53] Pokhodenko VD, Khizhnyi VA, Bidzilya VA. Stable phenoxy-radicals. RussianChemical Reviews 1968;37:435–48.

[54] Denisov E, Khudyakov I. Mechanisms of action and reactivities of the free-radicals of inhibitors. Chemical Reviews 1987;87:1313–57.

[55] Rochester CH, Rossall B. Steric hindrance and acidity Part I. The effect of 2,6-di-t-butyl substitution on the acidity of phenols in methanol. Journal of theChemical Society B: Physical Organic 1967:743–8.

[56] Wright JS, Johnson ER, DiLabio GA. Predicting the activity of phenolic antiox-idants: theoretical method, analysis of substituent effects, and applicationto major families of antioxidants. Journal of the American Chemical Society2001;123:1173–83.

[57] Cao H, Li T-H, Li C, Pan X-L, Song Z-R, Xie X-G. Two phenolic diterpenes asantioxidants. Jiegou Huaxue 2005;24:219–25.

[58] Cheng Z, Ren J, Li Y, Chang W, Chen Z. Study on the multiple mechanismsunderlying the reaction between hydroxyl radical and phenolic compoundsby qualitative structure and activity relationship. Bioorganic and MedicinalChemistry 2002;10:4067–73.

[59] Momose T. A theoretical study on the hyperfine coupling constant of the radicalcations of aliphatic ethers. Theoretica Chimica Acta 1992;81:291–301.

[60] Canas AI, Camarero S. Laccases and their natural mediators: biotechnolo-gical tools for sustainable eco-friendly processes. Biotechnology Advances2010;28:694–705.

[61] Hristova D. Kinetic studies of radical reaction using time-resolved EPR. PhDThesis, University of Basel, Faculty of Science; 2005.