In Silico Prediction of Cytochrome P450-Mediated ... - ACS Publications

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In Silico Prediction of Cytochrome P450-Mediated Biotransformations of Xenobiotics: A Case Study of Epoxidation Jing Zhang, Li Ji,* and Weiping Liu College of Environmental and Resource Sciences, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, China S Supporting Information *

ABSTRACT: Predicting the biotransformation of xenobiotics is important in toxicology; however, as more compounds are synthesized than can be investigated experimentally, powerful computational methods are urgently needed to prescreen potentially useful candidates. Cytochrome P450 enzymes (P450s) are the major enzymes involved in xenobiotic metabolism, and many substances are bioactivated by P450s to form active compounds. An example is the conversion of olefinic substrates to epoxides, which are intermediates in the metabolic activation of many known or suspected carcinogens. We have calculated the activation energies for epoxidation by the active species of P450 enzymes (an ironoxo porphyrin cation radical oxidant, compound I) for a diverse set of 36 olefinic substrates with state-of-the-art density functional theory (DFT) methods. Activation energies can be estimated by the computationally less demanding method of calculating the ionization potentials of the substrates, which provides a useful and simple predictive model based on the reaction mechanism; however, the preclassification of these diverse substrates into weakly polar and strongly polar groups is a prerequisite for the construction of specific predictive models with good predictability for P450 epoxidation. This approach has been supported by both internal and external validations. Furthermore, the relation between the activation energies for the regioselective epoxidation and hydroxylation reactions of P450s and experimental data has been investigated. The results show that the computational method used in this work, single-point energy calculations with the B3LYP functional including zero-point energy and solvation and dispersion corrections based on B3LYP-optimized geometries, performs well in reproducing the experimental trends of the epoxidation and hydroxylation reactions.

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INTRODUCTION Cytochrome P450 enzymes (P450s), which constitute a superfamily of heme-containing enzymes that mediate phase I biotransformations, play a vital role in the biotransformation of many xenobiotics.1 The majority of chemical carcinogens and toxicants do not directly produce detrimental biological effects. In most cases, the activation of parent compounds to more electrophilic forms by P450s is necessary to produce molecules capable of reacting irreversibly with tissue nucleophiles.2−5 For this reason, one of the principal motivations to study P450s is to better understand and predict the metabolism and toxicity of chemicals. Monooxygenase activity is one of the most important catalytic activities available to P450 systems, and it involves the catalysis of a diverse set of chemicals with a lot of types of reactions, such as aliphatic and aromatic hydroxylation, double-bond epoxidation, sulfoxidation, and N- and Odealkylation, to name a few.6,7 Epoxidation is of particular interest because the epoxide formed is unstable and tends to covalently bind to DNA and protein amino groups. Furthermore, epoxidation is relevant to the high-volume industrial use of alkenes.8 The epoxidation mechanism appears to involve stepwise oxygen addition to a πsystem and its subsequent collapse into the product. The © XXXX American Chemical Society

catalytically active oxygen species that enables the transfer of an oxygen atom into the π-system of olefinic substrates is commonly believed to be the iron(IV)-oxo heme cation-radical species of P450s called compound I (Cpd I), as shown in Scheme 1.9 Despite the recent breakthrough in the characterization of Cpd I,10 its short lifetime and unusual versatility Scheme 1. Compound I of P450s

Received: January 21, 2015

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DOI: 10.1021/acs.chemrestox.5b00232 Chem. Res. Toxicol. XXXX, XXX, XXX−XXX

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Chemical Research in Toxicology Table 1. Olefinic Substratesa Used to Calculate the Activation Energies of Epoxidation by Cpd I of P450s

a

Training set: substrates 1, 2, 4−7, 9, 11, 12, 14, 17−20, 22, 23, and 25−28 are weakly polar and 29 and 31−35 are strongly polar. Test set: substrates 3, 8, 10, 13, 15, 16, 21, and 24 are weakly polar and 30 and 36 are strongly polar.

and cosolvent for pesticides and fumigants,28 1,3-butadiene was used in the production of synthetic rubber,29 acrylonitrile was used in the production of plastics,30 and vinyl halides, such as vinyl chloride and vinyl fluoride, have been used extensively to manufacture plastics.31,32 To date, no definitive predictive model exists for epoxidation by Cpd I of P450s based on a diverse set of alkenes widely used in industry and commonly encountered in the environment. Therefore, there is a pressing need to develop such a state-of-the-art model. Such a model would enable the fast and reliable prediction of P450-mediated epoxidation reactions and thus help to enhance our understanding of the epoxidation mechanism. On the basis of the observed relationship between the reactivity of epoxidation by Cpd I of P450s and the ionization potentials for a selected number of hydrocarbon olefins,16 the present work uses density functional theory (DFT) to construct larger models for predicting the reactivity of P450-mediated epoxidation. A set of 36 diverse substrates, involving both aliphatic and aromatic groups as well as halogenated alkenes, has been considered, as shown in Table 1. This approach aims to address the issue of whether the established relationship between the barriers for epoxidation and the ionization potentials for a small set of hydrocarbon olefins remains valid for a more diverse set of substrates in P450-mediated epoxidation reactions. In this article, we report the development of a strategy that involves an initial classification of these diverse substrates based on the strength of their polarity to construct individual models for weakly polar and strongly polar groups. We find that this classification is a necessary condition

invite the use of theoretical methods, and several quantum chemical studies have previously been reported. Indeed, several theoretical studies have examined the hydroxylation of C−H bonds, 11,12 the epoxidation of CC bonds, 13−16 the hydroxylation of aromatics,17−20 the oxidation of heteroatoms,21−23 and the reduction of halogenated substrates.24 However, quantum chemical calculations of transition states (TS) for isolated substrates are time-consuming and especially difficult for complicated systems. There is considerable evidence that the rates of P450-mediated metabolism affect the abundance of certain potentially toxic or pharmacologically active metabolites, and most new drugs or xenobiotics serve as substrates for P450s. Thus, a predictive model for the fast and reliable prediction of the reactions catalyzed by P450s would be useful. The bond strengths of substrates for hydroxylation have been shown to be valuable metrics for predicting P450 reactivity.25−27,12 In particular, the intrinsic reactivity of a hydrogenatom-abstraction reaction is related to the energy required to break the C−H bond (BDECH). In the case of epoxidation, the first such attempt reported by de Visser, Kumar, and coworkers reveals a key characteristic of epoxidation by Cpd I of P450s; specifically, the activation energies correlate well with the ionization potentials.16 However, this study used only a few hydrocarbon olefins without external validation. Many olefinic substrates with more complex molecular structures were demonstrated to be carcinogenic or toxic in laboratory animal studies following their inevitable release into the environment. For example, ethyl carbamate was widely applied as a solubilizer B


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Chemical Research in Toxicology

Figure 1. Energy profile of vinyl chloride (VC) epoxidation by Cpd I of P450s, along with the optimized geometries of the key reaction species in the quartet- and doublet-spin states. Relative energies (kcal/mol) calculated by the UB3LYP/BSII//BSI level of theory were calculated in a polar environment with ZPE and D3 dispersion corrections (ΔE⧧ + Esolv + Edisp + ZPE⧧, no parentheses), in a polar environment with ZPE corrections (ΔE⧧ + Esolv + ZPE⧧, in parentheses), and in the gas phase with ZPE corrections (ΔE⧧ + ZPE⧧, square brackets). Geometric parameters (lengths in Å and angles in degrees) are given as the quartet state [doublet state]. basis set39,40 on iron and the TZVP basis set41 on the remaining atoms (denoted BSII). Bulk polarity effects were evaluated with the PCM continuum-solvation model42 (ε = 5.6, chlorobenzene) at the UB3LYP/BSI level of theory. Dispersion interactions were included by performing single-point energy calculations with the UB3LYP-D3/ BSI level of theory because B3LYP lacks such interactions.43 We also performed direct B3LYP-D3/BSI geometry optimizations using vinyl chloride epoxidation by Cpd I as a reference. These results showed that the optimized geometries are very similar to the corresponding B3LYP/BSI-optimized geometries with differences in the interatomic distances of less than 0.002 Å. Furthermore, the B3LYP-PCM/BSI single-point energy calculations based on the B3LYP-D3/BSIoptimized structures resulted in a difference for ΔE⧧ + Esolv + Edisp + ZPE⧧ of approximately 0.2 kcal/mol compared to the UB3LYP-D3/ BSI//BSI energy with zero-point energy and solvation corrections included (for details, see Table S1,e Supporting Information). Thus, the more efficient B3LYP-D3/BSI single-point energy calculations based on B3LYP/BSI-optimized structures have been used to study dispersion effects on the entire system. All calculations in this work were performed with the Gaussian 09 program package.44 DFT calculations on P450 catalytic mechanisms have contributed a great deal to understanding the electronic structure features governing reactivity and reaction pathways, and they provide a good balance between computational cost and accuracy.45,7 Nevertheless, the DFT method is limited to the study of relatively small clusters in the gas phase. However, it has been recognized that some of the versatility of P450 may be attributed to the protein environment around the active species of P450. In such cases, the hybrid QM/MM (quantum mechanical/molecular mechanical) method is able to treat the active species within its native protein environment.46,47 However, the main goal of the present study is to model the trends of the intrinsic reactivity by focusing on P450 epoxidation, and it was found previously that the effect of the protein environment on the relative energy barriers is small for small substrates.36,48 Additionally, in vivo metabolic rates have been previously obtained for certain alkenes metabolized by P450s to their corresponding epoxides under the same experimental condition in rats;49 thus, it is possible to benchmark the correlation between the available experimental data and the DFTcalculated epoxidation barriers to calibrate the computational method used in this work. A plot of the experimental rates versus the calculated

to retain good predictability of the models for P450 epoxidation based on the ionization potentials. In addition, the competition between CC epoxidation and C−H hydroxylation reactions is a common phenomenon in P450 oxygenation and leads to a high degree of uncertainty in accurate predictions. Thus, these reactions represent a unique challenge to computational methods for predicting the xenobiotic metabolism of epoxidation by P450s. Therefore, we have investigated how the barriers of the epoxidation and hydroxylation reactions catalyzed by P450s relate to the experimental product distributions to evaluate the computational methods used in this work.

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COMPUTATIONAL METHODOLOGY

We selected the six-coordinate triradicaloid oxo-ferryl complex, Fe4+O2−(C20N4H12)−1(SH)−1, as a model for the active site of Cpd I of P450s. Thirty-six olefinic compounds were used as substrates, and the reaction system was investigated in the lowest-lying doublet-spin (low-spin, LS) and quartet-spin (high-spin, HS) states. All geometry optimizations and frequency calculations were performed using the unrestricted hybrid B3LYP density functional33,34 combined with the double-ζ LANL2DZ(Fe)/6-31G** basis set35 (LACVP**, denoted BSI). In this work, the 292.5 ps snapshot of the conformation of Cpd I−propene for epoxidation from a molecular dynamics trajectory in an enzymatic environment reported in the literature36 was used as the starting geometries for sampling the conformations of the enzyme− substrate complex for epoxidation. The main reason for using the B3LYP exchange-correlation functional is that it has been previously shown to accurately reproduce experimentally measured kinetic isotope effects, electron paramagnetic resonance parameters, and vibrational spectra,16 yielding geometries in good agreement with experimental crystal structures.37,38 The computed vibrational frequencies were further used to quantify the zero-point energy correction (ZPE) at 298.15 K and 101.325 kPa. All ground states were confirmed by the presence of only real frequencies, whereas the transition states had one imaginary frequency. More accurate energies were determined by single-point calculations on the optimized geometries with the Wachters+f all-electron C


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Table 2. Structural Characteristics of the Transition States (Quartet [Doublet]), Average Activation Energiesa of Epoxidation between the Quartet- and Doublet-Spin States Relative to Separated Reactants (4,2Cpd I + Substrates), Ionization Potentials (eV), and Dipole Moments (Debye) of the Substrates no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 a

rC−O (Å) 1.928 1.928 1.946 1.935 1.947 1.921 1.919 1.931 1.949 1.948 1.953 1.942 1.940 2.045 2.010 1.969 1.924 1.919 1.954 1.922 1.932 2.009 2.009 1.989 1.994 2.002 2.001 2.017 1.983 1.981 1.924 1.923 1.941 1.879 1.848 1.995

[1.923] [1.940] [1.934] [1.965] [1.958] [1.924] [1.924] [1.935] [1.943] [2.059] [2.101] [2.003] [1.944] [2.060] [2.060] [2.152] [2.055] [1.926] [1.942] [1.943] [1.840] [2.007] [2.047] [2.027] [1.991] [1.999] [1.997] [2.016] [1.972] [1.968] [1.907] [1.919] [1.945] [1.877] [1.834] [1.995]

rFe−O (Å) 1.704 1.708 1.696 1.697 1.703 1.704 1.704 1.702 1.703 1.703 1.705 1.700 1.702 1.683 1.691 1.705 1.710 1.703 1.693 1.706 1.701 1.690 1.690 1.690 1.691 1.690 1.690 1.689 1.690 1.688 1.691 1.696 1.707 1.702 1.688 1.692

[1.711] [1.702] [1.699] [1.684] [1.706] [1.713] [1.712] [1.709] [1.703] [1.671] [1.675] [1.683] [1.702] [1.669] [1.667] [1.670] [1.681] [1.691] [1.701] [1.698] [1.708] [1.689] [1.666] [1.667] [1.692] [1.690] [1.691] [1.686] [1.691] [1.690] [1.694] [1.699] [1.712] [1.712] [1.696] [1.688]

rFe−S (Å) 2.421 2.415 2.434 2.430 2.409 2.423 2.429 2.432 2.418 2.414 2.411 2.423 2.415 2.438 2.425 2.402 2.387 2.431 2.439 2.426 2.435 2.424 2.423 2.427 2.428 2.426 2.426 2.421 2.437 2.436 2.447 2.444 2.436 2.454 2.431 2.429

ΔE⧧ + Esolv + Edisp + ZPE⧧ (kcal/mol)

ionization potential (eV)

dipole moment (D)

9.39 8.96 8.86 7.25 6.05 7.50 7.44 6.58 6.50 5.51 5.21 7.22 6.36 3.86 1.40 3.45 3.76 9.61 6.98 7.18 5.23 1.71 0.86 2.11 2.05 1.75 1.83 0.84 2.45 2.18 4.62 4.41 3.08 8.10 1.89 2.42

8.10 7.96 7.72 7.59 7.46 7.88 7.76 7.51 7.42 7.34 7.36 7.60 7.34 6.83 6.72 6.94 6.94 7.96 7.63 7.49 7.38 6.55 6.54 6.71 6.73 6.54 6.52 6.32 6.93 7.12 7.96 7.67 7.46 8.83 6.51 6.83

0.00 1.60 1.56 1.54 0.42 2.15 2.21 2.20 0.42 0.44 0.37 1.67 1.57 0.00 0.28 0.26 0.00 1.50 1.47 1.96 0.90 0.21 1.52 1.68 1.85 1.70 1.72 0.74 5.02 5.42 3.47 3.44 4.00 2.72 4.25 3.74

[2.474] [2.411] [2.506] [2.450] [2.456] [2.484] [2.489] [2.490] [2.471] [2.429] [2.422] [2.434] [2.474] [2.456] [2.448] [2.421] [2.423] [2.531] [2.505] [2.407] [2.427] [2.474] [2.453] [2.455] [2.478] [2.476] [2.477] [2.469] [2.499] [2.450] [2.501] [2.494] [2.489] [2.500] [2.472] [2.484]

Activation engergies are given in kcal/mol, calculated with UB3LYP/BSII//BSI in a polar environment with ZPE and D3 dispersion corrections.

epoxidation barriers for seven alkenes is depicted in Figure S1, Supporting Information. The data show a good linear correlation coefficient of 0.952, which further supports the idea that the protein environment does not have a large influence on the reactivity trend of the Cpd I−substrate system. For these reasons, we have used a small cluster model for the Cpd I species rather than a detailed atomistic representation of the entire enzyme.

subsequent ring-closure barrier (TSrc) separates the intermediate from the product on the HS surface, whereas the LS route is essentially barrierless for ring closure. Considering the fact that the LS ring-closure process has no reaction barrier, the intermediate in the LS state is very short-lived and produces the epoxide product instantaneously. We note that this LS intermediate could not be located with the level of theory used in this work. A significant ring-closure barrier on the HS pathway provides an intermediate with a finite lifetime, which may then undergo side reactions to form inactivating (suicide) complexes or aldehyde products. The source of this ringclosure difference between the HS and LS routes arises from the different electron-transfer processes,7 whereby an electron occupies the low-lying π*xz orbital on the low-spin surface, whereas one electron must be promoted to a higher-lying σ*z2 orbital in the high-spin state. This latter condition leads to a significant barrier for ring closure. The TSE geometries reveal that the LS species occurs slightly later than the HS species and has a shorter C−O distance (LS, 1.934 A; HS, 1.946 A). The gas-phase activation energies for these TSs are 13.9 and 14.3

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RESULTS AND DISCUSSION Substrate Epoxidation by Cpd I of P450s. Figure 1 shows the potential energy profiles for the vinyl chloride epoxidation by Cpd I of P450s on both the high-spin (HS, quartet) and low-spin (LS, doublet) surfaces. Vinyl chloride is a typical substrate for this enzyme. Indeed, in vitro and in vivo studies have shown that the oxidation of vinyl chloride by P450 2E1 gives rise to chloroethylene oxide,50 a highly electrophilic and short-lived epoxide, which is an actual genotoxic carcinogen.51 The initial and rate-determining step involves C−O bond formation via the transition state 4,2TSE, leading to a radical intermediate in the HS state, followed by the direct formation of a ferric-epoxide product in the LS state. Thus, a D


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whereas the Fe−S and Fe−O distances are approximately 1.7 and 2.4 Å, respectively. Then, we attempted to correlate the geometric features of the transition states (i.e., C−O, Fe−O, and Fe−S bond lengths) with the epoxidation barrier heights; however, no such correlations were found (see Figure S2, Supporting Information). Vinyl halides have been studied extensively because of their widespread industrial use coupled with concerns about carcinogenicity. All of the epoxides generated from vinyl halides by P450s are highly unstable and reactive in experiments.8 For this reason, quantum chemical calculations may provide new insight on the molecular level. Notably, when the halide-substituent changes from F to Cl to Br (with decreasing substituent electronegativity), the activation barriers decrease gradually for aliphatic vinyl halides (substrates 2−4 and 6−8). In contrast, the activation barriers rise substantially for para-substituted styrene (substrates 23, 26, and 27). These completely different phenomena can be rationalized by two opposing effects characteristic of halogen atoms: electron donation by conjugation and electron withdrawal by induction. When a halogen atom substitutes the para position of styrene, the effect of conjugation is greater than the inductive effect for the most electronegative atom F, and conjugation decreases from F to Br. Thus, fluorine donates the most electron density to a double bond, which favors C−O bond formation during P450 oxidation. In the case of aliphatic alkenes, fluorine withdrawals the most electron density from the double bond and destabilizes the transition state for P450 epoxidation to a maximum extent relative to other halogen substituents. To more quantitatively understand the substitution effects including halogens on the reactivity, we explored the relationship between the epoxidation barriers for parasubstituted styrene and Hammett parameters. As shown in Figure 2a,b, increasing the electron-withdrawing power of the styrene substituent, as quantified through the Hammett constants σp (from −0.170 for CH3 to 0.778 for NO2) and σ+ (from −0.306 for CH3 to 0.790 for NO2), gradually increases the epoxidation barrier. A good correlation is found for the model based on σp and σ+, with R2 values of 0.715 and 0.772, respectively. Geometrically, as the para substituent changes from CH3 to NO2, the corresponding transition state TSE lies later and later along the O-addition coordinate (shown in Table 2). As is known, the inductive effects of substituents are directly correlated with electronegativity; however, the epoxidation barriers of para-substituted styrene calculated here have no correlation with the Pauling electronegativities (Figure S3, Supporting Information). These results suggest that both the inductive and resonance effects of the substituent are operative and that the physical phenomena that determine the Hammett parameter σ play a key role in the epoxidation reaction. Developing a Predictive Model for Epoxidation. To date, all calculations have involved the optimization of TS geometries, which is a time-consuming process that is not always successful. It would be very useful if a correlation between the activation energies and the properties of the isolated substrates could be found because geometry optimizations of ground states tend to be more straightforward than TS geometry optimizations. One important aim of this study is to determine whether we can predict the epoxidation barrier with faster and simpler methods than those currently used. As is known, a predictive model based on a few fundamental properties of the molecules not only provides a

kcal/mol for the HS and LS states, respectively. Inclusion of polar effects increases the barriers slightly to 16.3 and 16.2 kcal/ mol for the HS and LS states, respectively, whereas dispersion corrections lower the barriers by approximately 7 to 8.9 and 8.9 kcal/mol for the HS and LS states, respectively, which is consistent with previous findings.52,53 Regarding the electronic structures for the epoxidation reaction (Tables S7 and S8, Supporting Information), a decrease in the spin density in the porphyrin for both the HS and LS transition states, as well as only small amounts of spin density remaining on the porphyrin in the HS intermediate, suggests an electron transfers from the substrate into the shell of the porphyrin macrocycle. Collectively, these results suggest the prevalence of an FeIV (ferryl) species over an FeIII (ferric) species in the HS radical intermediate (4I). Because the ring-closure barrier is much lower than the barrier for C−O bond formation via 4,2TSE, we will next focus on the rate-determining step for C−O bond formation. We have studied 36 different epoxidation reactions, which were selected on the basis of their epoxidation barriers, the type of substituent group, and the carbon-chain length. Table 2 summarizes the average barrier for C−O bond formation between the HS and LS states for all substrate epoxidations calculated by the UB3LYP/BSII//BSI level of theory with corrections for the zero-point energy, solvation, and dispersion included (ΔE⧧ + Esolv + Edisp + ZPE⧧). Moreover, geometrical characteristics of all transition states are included in Table 2 as well. The C−O bond formation can involve either the terminal CH2 group or the CH group; however, because the latter gives a higher activation barrier than the former (for further details, see Table S2, Supporting Information), we restricted our attention to C−O bond formation at the terminal CH2. In addition, five substrates in this work (substrates 1, 5, 9, 17, and 22) have been studied previously by de Visser and co-workers using similar methods (i.e., optimization at the B3LYP/LACVP level of theory and single-point energy calculations at the B3LYP/LACV3P+* level of theory).16 The two sets of activation energies differ by about 1.5 kcal/mol, and the optimized geometries of the transition states between the two approaches are very similar, with deviations below 0.008 Å for distances and less than 1° for angles (Table S3, Supporting Information). As shown in Table 2, the calculated epoxidation barriers range from 0.84 kcal/mol for 4-methylstyrene to 9.61 kcal/mol for 1,1-difluoroethylene. Generally, the epoxidation barrier becomes smaller with increasing carbon-chain length. For example, the barrier for epoxidation is 9.39 kcal/mol for ethylene, whereas the barrier is 6.05 kcal/mol for propene and 5.51 kcal/mol for 1-pentene. From the perspective of physical organic chemistry, the electron-donating effect of alkyl groups raises the electron density of a double bond. Consequently, each additional alkyl substituent on an alkene will more effectively stabilize the transition state for C−O bond formation during a P450 oxidation. In addition, the substrates with unsaturated substituents show much lower epoxidation barriers as a result of conjugation. For example, the styrene series of compounds have epoxidation barriers that range from 0.84 to 2.45 kcal/mol (substrates 22−30), and aliphatic olefins substituted by unsaturated groups have barriers that range from 1.40 to 4.62 kcal/mol (substrates 14, 15, 31−33, 35, and 36). Geometrically, Table 2 shows that the transition states exist early with relatively long C−O distances of approximately 2 Å, E


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Figure 3. Average epoxidation barrier height plotted against the ionization potential of the substrates (weakly polar, green; strongly polar, orange).

other trend has a relatively small slope and is encircled by an orange circle. We then analyzed the primary intrinsic factors responsible for these two trends of the substrates. Related to this, the seventh column in Table 2 shows the dipole moments for all olefins. It can be seen that the polarity of the substrate should be a key factor in the determined reactivity because the data in the green circle represents weakly polar substrates, whereas the data in the orange circle represents highly polar substrates. (Note that we used a dipole moment of 2.2 debye to discriminate between weakly and strongly polar olefins.) As is well-known in chemical toxicology, the polarity of toxicants has often been used to differentiate toxic modes of action.58 Thus, in this work, it is necessary to explore separately the quantitative relationship between the P450-mediated epoxidation barriers and the ionization potentials for weakly and strongly polar olefins. Before the development of predictive models, the 28 weakly polar substrates (1−28) and eight strongly polar substrates (29−36) shown in Table 1 were divided into a training set (weakly polar, 20 substrates; strongly polar, 6 substrates) for model development and a test set (weakly polar, 8 substrates; strongly polar, 2 substrates) to assess the predictive behavior of the model. The details of these two data sets can be found in the footnote of Table 1. The substrates with the highest and lowest ionization potentials were included in the training set. The principle for choosing the test set is that it should guarantee that the test set is scattered over all of the chemical groups of the training set, as far as possible. This principle applies well in the group of weakly polar olefins: the test set in this group contains aliphatic alkenes, halogenated alkenes, and styrene species. However, because of the small number of strongly polar olefins, only two substrates were selected randomly as the test set. The models developed with the training sets shown in Figure 4 give good correlations for ΔE⧧ + Esolv + Edisp + ZPE⧧ against the ionization potentials, with R2 values of 0.972 for the predictive model for weakly polar olefins and 0.936 for strongly polar olefins. The corresponding mean absolute errors (MAEs) are 0.391 and 0.428 kcal/mol for the weakly and strongly polar olefins, respectively. For each predictive model, a good linear relationship is observed, which shows clearly that an increased barrier height is associated with an increased ionization potential. The applicability domain of the models was determined by considering the range of ionization potentials

Figure 2. Average barrier heights for styrene epoxidation (ΔE⧧ + Esolv + Edisp + ZPE⧧) plotted against the para substituent (a) σp and (b) σ+ values of the corresponding substrates (22, 23, and 26−30).

useful tool for the fast prediction of substrate metabolic activation energies but also helps to achieve a better understanding of the reaction mechanism. Previous experimental54−56 and theoretical16,57 studies involving a few substrates have demonstrated that the epoxidation rate constants catalyzed by Cpd I correlate well with the ionization potentials. To test whether this relationship remains valid for a much larger data set composed of diverse alkenes, the epoxidation barriers of 36 substrates (shown in Table 1) were used to construct a predictive model based on ionization potentials. The ionization potentials were calculated at the B3LYP/6-311++G** level of theory, and the bulk polarity effects were included using a PCM model with ε = 5.6 at the same level of theory. The ionization potentials are listed in the sixth column of Table 2. As shown, the lowest calculated ionization potential within the series is 6.32 eV (for 4cyanostyrene, substrate 29), and the highest calculated ionization potential is 8.83 eV (for 3,3,3-trifluoroprop-1-ene, substrate 34). Figure 3 shows a plot of the epoxidation barriers versus ionization potentials for all 36 olefins. The correlation between the epoxidation barriers and ionization potentials, with an R2 of only 0.77, is not satisfactory. We note that the analogous correlation determined previously for a few hydrocarbon olefins is R2 > 0.95. Notably, it is clear that there is a branch within the plot that may form two different qualitative trends. One trend has a large slope and is surrounded by a green circle, and the F


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within each group. The ionization potential involves ionizing an electron from the double bond, which is capable of measuring the double bond strength; therefore, a good relationship between epoxidation activation energies and ionization potentials implies that the activation energy of epoxidation by P450s depends strongly on converting the double bond of the olefin to a single bond in the product. Regioselective Reaction Catalyzed by Cpd I of P450s. An interesting regioselectivity issue arises in the reactions catalyzed by P450s when competition occurs in a substrate for epoxidation of a CC bond and hydroxylation of a C−H bond. Clearly, the CC/C−H competition in P450 chemistry is a complex problem that may benefit from theoretical insight. In this work, 2-butene (substrates 16 and 17) was selected as a model substrate to study the regioselectivity on the basis that the experimental P450 regioselective data were available for both the cis and trans isomers.59 Experimentally, despite the fact that both epoxidation and hydroxylation are observed to occur, the oxidation of both cis- and trans-butene by P450s results in the formation of the epoxide products more quickly than the hydroxylated products. Therefore, it is likely that the barrier for epoxidation is lower than the barrier for hydroxylation. As indicated in Table 3, the spin-averaged B3LYP-D3// B3LYP barriers including ZPE and solvation corrections for

Figure 4. Average epoxidation barrier height plotted against the ionization potential of the substrates (training set, p < 0.01).

for the substrates in the training set. Thus, the predictive model for weakly polar olefins is adaptable to weakly polar olefins with ionization potentials between 6.3 and 8.1 eV, whereas the model for strongly polar olefins can be used when predicting unknown strongly polar olefins with ionization potentials between 6.5 and 8.8 eV. As mentioned above, eight weakly polar olefins and two strongly polar olefins were used as the external test sets. The correlation plots of the predicted vs calculated ΔE⧧ + Esolv + Edisp + ZPE⧧ for the training set and the external test set are shown in Figure 5, and the predicted results for all substrates

Table 3. Average Activation Energies (kcal/mol) at Various Levels of Theory Using the B3LYP Exchange-Correlation Functional for the Regioselective Reaction of cis-2-Butene and trans-2-Butene (Substrates 16 and 17) by Cpd I of P450s ΔE⧧ + Esolv + Edisp + ZPE⧧

ΔE⧧ + Esolv + ZPE⧧

ΔE⧧ + ZPE⧧

± 0.6 ± 0.02

3.49 4.79

13.77 12.19

12.23 11.05

± 0.2 ± 0.02

3.81 5.00

15.53 12.36

13.95 11.34

experimental reaction ratea cis-2-Butene TSE 6.4 TSH 0.15 trans-2-Butene TSE 3.8 TSH 0.11 a

Reaction rate, nmol/min per nmol of P450 Δ2B4 enzyme, ref 59.

epoxidation and hydroxylation of cis-2-butene are 3.5 and 4.8 kcal/mol, respectively. Relative to the barriers calculated with B3LYP//B3LYP (13.8 vs 12.2 kcal/mol for epoxidation and hydroxylation, respectively), a significant improvement relative to experiment is observed after including corrections for dispersion. The ZPE and solvation-corrected B3LYP-D3// B3LYP epoxidation and hydroxylation barriers for trans-2butene are 3.8 and 5.0 kcal/mol, respectively. Once again, these are more consistent with experiment than the barriers calculated with B3LYP//B3LYP (with barrier heights of 15.5 and 12.4 kcal/mol for epoxidation and hydroxylation, respectively). The results clearly show that B3LYP-D3 singlepoint energy calculations based on the B3LYP optimized geometries with ZPE and solvation corrections is suitable for reproducing the experimental trends, which also validates the computational methods used in this work. In future calculations, the cost-efficient method B3LYP-D3//B3LYP with ZPE and solvation corrections is recommended to study the P450 regioselective reactions involving epoxidation and hydroxylation.

Figure 5. Predicted vs calculated epoxidation activation energies (kcal/ mol) by Cpd I of P450s for both the training and test sets.

are given in Table S9, Supporting Information. The correlation coefficient R2pred is 0.956 for the weakly polar test set, and the corresponding MAE values are 0.577 and 0.506 kcal/mol for the weakly and strongly polar test sets, respectively, indicating that the separate models are promising tools to predict the activation energies for diverse olefinic compounds by P450 enzymes in toxicology. Thus, preclassification of the substrates based on polarity strength dramatically improves the predictability of the model for P450 epoxidation. We show here, for the first time, that a predictive model involving a diverse set of olefinic substrates for the reactivity of P450 enzymes depends strongly on the polarity of the substrates. It appears that the nature of all of these transition states within weakly and strongly polar groups is similar in that a common intrinsic property determines the barrier height of epoxidation G


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Notes

CONCLUSIONS In summary, we have modeled the epoxidation reactions catalyzed by P450s to develop a complete model for the rapid prediction of xenobiotic biotransformations. The epoxidation reactions proceed stepwise via radical intermediates, and C−O bond formation is the rate-determining step. The transition states for C−O bond formation by compound I of P450s for a diverse set of olefinic substrates have been determined by DFT calculations. On the basis of an initial classification of the olefinic substrates into weakly and strongly polar groups, it is found that individual predictive models consisting of each group for P450 epoxidation barriers are superior to a comprehensive model constructed from all of the diverse olefinic substrates in both the weakly and strongly polar groups. This approach enables the fast prediction of epoxidation barriers using ground-state calculations, and it also rationalizes the fundamental mechanism responsible for epoxidation. To the best of our knowledge, this is the first time that a predivision according to the polar strength of a diverse set of substrates has been suggested. We show that this approach is useful to construct predictive models for P450 reactivity. Because the optimization of stable geometries is much faster than the relatively arduous task of searching for and optimizing transition states, quick insight into the toxicological risk of xenobiotics by P450s can be gained, in addition to saving a substantial amount of computational time. Finally, this study further shows that dispersion corrections in the form of B3LYPD3 single-point energy calculations with zero-point energy and solvation corrections based on B3LYP-optimized geometries are able to predict the experimentally observed regioselectivity between P450 epoxidation and hydroxylation.

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The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The China National Supercomputing Center in Shenzhen is acknowledged for providing the Gaussian 09 software package and the high-performance computing clusters.

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DEDICATION We dedicate this work to Prof. Dr. Rudi van Eldik at the University of Erlangen-Nuremberg in Germany on the occasion of his 70th birthday (autumn 2015).

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ABBREVIATIONS P450s, cytochrome P450 enzymes; Cpd I, compound I; LS, low spin; HS, high spin; DFT, density functional theory; PCM, polarized continuum-solvation model; ZPE, zero-point energy; TS, transition state; MAE, mean absolute error

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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.chemrestox.5b00232. Absolute and relative energies at various levels of B3LYP calculation for all P450-mediated reactions of olefinic substrates; Mulliken group spin densities and charges; predicted and calculated activation energies for the training and test sets; plot of experimental metabolic rate constants (kapp) of alkenes versus activation energies; structural characteristics of the transition states as a function of the epoxidation barrier heights; epoxidation barrier heights of para-substituted styrenes plotted against the para-substituent electronegativity values of corresponding substrates; Cartesian coordinates of all molecular structures studied in this work (PDF).

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AUTHOR INFORMATION

Corresponding Author

*Tel: +86 57188982467. Fax: +86 57188982344. E-mail: [email protected]. Funding

This work was supported by the Provincial Education Department of Zhejiang (Grant No.Y201329793), the National Natural Science Foundation of China (Grant No. 21307107), the Fundamental Research Funds for the Central Universities, and the Cultivation Fund of the Key Scientific and Technical Innovation Project from Ministry of Education of China (No.708052). H


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