Understanding Resistance Mechanism of Protoporphyrinogen

Jul 1, 2014 - Understanding Resistance Mechanism of Protoporphyrinogen. Oxidase-Inhibiting Herbicides: Insights from Computational. Mutation Scanning ...
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Understanding Resistance Mechanism of Protoporphyrinogen Oxidase-Inhibiting Herbicides: Insights from Computational Mutation Scanning and Site-Directed Mutagenesis Ge-Fei Hao,† Ying Tan,† Wei-Fang Xu,† Run-Jie Cao,† Zhen Xi,*,‡ and Guang-Fu Yang*,†,§ †

Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, P. R. China ‡ State Key Laboratory of Elemento-Organic Chemistry, Nankai University, Tianjin 300071, P. R. China § Collaborative Innovation Center of Chemical Science and Engineering, Tianjin 300072, P.R. China ABSTRACT: The potential of protoporphyrinogen oxidase (PPO) to develop resistance against five PPO-inhibiting herbicides has been studied using computational mutation scanning (CMS) protocol, leading to valuable insights into the resistance mechanisms and structure-resistance relationship of the PPO inhibitors. The calculated shifts in the binding free energies caused by the mutations correlated very well with those derived from the corresponding experimental data obtained from site-directed mutagenesis of PPO, leading to valuable insights into the resistance mechanisms of PPO inhibitors. The calculated entropy change was related to the conformational flexibility of the inhibitor, which demonstrated that inhibitors with appropriate conformational flexibility may inhibit both the wild type and mutants simultaneously. The reasonable correlation between the computational and experimental data further validate that CMS protocol is valuable for predicting resistance associated with amino acid mutations on target proteins. KEYWORDS: PPO, resistance mechanism, computational mutation scanning, herbicide, mutagenesis

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herbicides.9 Therefore, mitochondria PPO is undoubtedly useful for the resistance prediction of PPO-inhibiting herbicides, which are widely used to control weeds in a variety of crops.10 PPO inhibition does more than merely block the production of heme and chlorophyll; the substrate protoporphyrinogen IX will accumulate in the cytoplasm and be slowly oxidized by oxygen to produce protoporphyrin IX.8 In the presence of light, the photosensitive protoporphyrin IX generates singlet oxygen that causes lipid peroxidation and cell death. PPO-inhibiting herbicides are known as light-dependent bleaching herbicides.11 Hence, great interest for PPO inhibitors, such as diphenylethers, phenylpyrazoles, oxadiazoles, triazolinones, thiadiazoles, pyrimidindiones, oxazolidinedione, and Nphenylphthalimides,12−20 has risen from their special mechanism of action and the significance for agriculture. The three-dimensional structure of PPO provides the basis for understanding the inhibitor−PPO interaction mechanism.21 Until now, four crystal structures of PPO from human, tobacco, Myxococcus xanthus, and Bacillus subtilis have been reported.22−25 Fortunately, reports of the crystal structures of PPO show that PPOs from different families have very similar structures and conservative binding envelope. Therefore, human PPO is an undoubtly useful model system for the understanding of interaction mechanism of herbicides. However, the discovery and development of PPO-inhibiting herbicides is now hampered due to rapid emergence of

INTRODUCTION Herbicides have become one of the key components in agriculture and account for around 70% of all agrochemical use.1 Despite their great success in the market, a major concern with the use of herbicides for plant protection is the rapid development of weed resistance. To date, over 400 unique cases of herbicide-resistant weeds have been identified worldwide, with 220 species (130 dicots and 90 monocots). Resistant weeds involved 21 of the 25 known herbicide action sites, which influenced 148 commercial herbicides. Herbicideresistant weeds have been reported in 61 countries.2 Numerous mechanisms influence the likelihood of herbicide resistance evolution in a weed population. Among the known resistance mechanisms,3 in particular, target mutation that alters the interactions between the herbicide molecule and its enzyme target is the most severe situation because herbicides are rendered ineffective once such a mutation occurs. For example, populations of six weed species have been reported with resistance to herbicides that inhibit protoporphyrinogen oxidase (PPO),2 even though these herbicides were designed to mimic the structure of the substrate, which was considered to have low resistance risk.4,5 PPO is an essential enzyme that catalyzes the last common step in the pathway leading to heme and chlorophyll biosynthesis.6 In mammals, there is only one isoform of PPO located on the cytosolic side of the inner mitochondrial membrane,7 whereas in plants there are two isoforms, that is, the chloroplast PPO and the mitochondrial PPO.8 PPOinhibiting herbicide can induce an accumulation of protoporphyrin IX in plant mitochondria, which demonstrate that the mitochondrial PPO of plant is a molecular target of these © 2014 American Chemical Society

Received: Revised: Accepted: Published: 7209

April 17, 2014 June 27, 2014 July 1, 2014 July 1, 2014 dx.doi.org/10.1021/jf5018115 | J. Agric. Food Chem. 2014, 62, 7209−7215

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resistance.26 Hence, it is of great importance to perform resistance prediction and understand the corresponding mechanism at the molecular level.27 In the present study, we have aimed to make a broad prediction of the resistance associated with target mutations in PPO and have acquired a detailed understanding of the resistance mechanisms of typical PPO-inhibiting herbicides by a newly developed computational protocol, computational mutation scanning (CMS).3 We have computationally studied the resistance mechanism of five most frequently applied PPOinhibiting herbicides, including acifluorfen (ACJ), fomesafen (FOS), oxadiazon (OXZ), sulfentrazone (SUT), and Chlorphthalim (CHP) (Scheme 1). The computationally determined

were minimized; the backbone atoms of the protein were then fixed with the ligand, side chains, and other atoms free to move; finally, the entire system was fully minimized without any constraint. In each step, energy minimization was first performed by using the steepest descent algorithm for 2000 steps and then the conjugated gradient algorithm for another 3000 steps. The MD simulation was performed under periodic boundary conditions by using the Sander module of the AMBER9 program, as we have done for the same protein before.27 First, the system was fixed to make the heating only for waters and counterions for 10 ps to make sure the solute was fully solvated; second, the whole system was gradually heated from 10 to 298 K by a weak-coupling method34 and equilibrated for 100 ps with the protein backbone fixed; last, the system was switched to a constant pressure equilibration for 3 ns. During the MD simulation, the particle mesh Ewald (PME) algorithm35,36 was used to deal with long-range electrostatic interactions with a cutoff distance of 10 Å, which was also used for the van der Waals (vdW) energy terms. All of the angles and bonds involving hydrogen atoms were constrained by using the SHAKE algorithm.37 The time step used for the MD simulations was 2.0 fs and the coordinates were collected every 1 ps. Computational Mutation Scanning. A free energy perturbation approach, computational alanine scanning (CAS), can be used to estimate the free energy shift caused by mutation to alanine, which has been widely used to study protein−protein/ligand interactions. The CMS method, as an extension of the CAS method, has been validated in our previous study and can be applied to all kinds of mutations with the entropy effects taken into account.3 We saved a total of 100 snapshots from a stable MD trajectory of the last 1000 ps, that is, one structure in every 10 ps, for each MD-simulated complex. We performed mutation automatically on each snapshot with a modified MMTSB Tool Set package.38 All force field parameters in the topology files for the residue to be mutated were replaced with the parameters of the corresponding new residue. Before the calculation of the binding free energy, the positions of side chain atoms of all residues were energy-minimized by using the Sander module of AMBER9 program via a combined use of the steepest descent/conjugate gradient algorithms, with a convergence criterion of 0.2 kcal mol−1 Å−1. Then the Gibbs binding free energies between the protein and ligand for each snapshot were calculated by carrying out a modified molecular mechanics/Poisson− Boltzmann surface area (MM/PBSA) energy analysis.27 The final binding free energy is the average of the calculated values associated with the 100 snapshots. Kinetic Assays for PPO and Its Mutants. The recombinant plasmid (pHPPO-X) was a generous gift from Dr. Harry A. Dailey (University of Georgia, Athens, GA). hPPO (human PPO) mutations were generated from the recombinant plasmid (pHPPO-X) using the DpnI-mediated site-directed mutagenesis kit (Biocrest Manufacturing LP, Cedar Creek, TX), and mutations were confirmed by sequence analysis. Expression and purification methods of hPPO were the same as those used in our previous publication.39,40 The hPPO activity was assayed by measuring the velocity of the formation of protoporphyrin from protoporphyrinogen on a 96-well plate using the continuous fluorometric method.39 The kinetic parameters were evaluated by Sigma Plot software 10.0 (SPSS, Chicago). IC50 was determined by

Scheme 1. Chemical Structures of PPO Inhibitors

shifts of the binding free energies caused by mutations correlated well with the corresponding experimental data, leading to valuable insights into the potential resistance mechanisms of PPO inhibitors, which will help to reduce the resistance risk of new inhibitors discovered by a structure-based design approach.

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MATERIALS AND METHODS Molecular Docking and Molecular Dynamics (MD) Simulation. The X-ray crystal structure of human PPO used for computation was downloaded from RCSB.25 Docking calculations were performed on ACJ, FOS, OXZ, SUT, and CHP with AutoDock4.0.28 Most of the parameters for the docking calculation were set to the default values. Each docked structure was scored by the built-in scoring function and was clustered by 0.8 Å of RMSD criterions. Finally, the enzymeligand complex structures were selected according to the criteria for Autodock Score, which were used as initial complex structures for subsequent MD simulations. Ab initio quantum mechanics (QM) calculations were performed for inhibitor and FAD optimization at the HF/631+G* level to determine the electrostatic potential by using the restrained electrostatic potential (RESP) method according to the Merz−Singh−Kollman scheme.29,30 The RESP charges of ligands (inhibitors and FAD) was produced by using the standard RESP protocol31,32 implemented in the Antechamber module of the AMBER9 program.33 To carry out the MD simulations, the topology and coordinate files of the complexes were built with the Leap module of the AMBER9 package. All molecules were solvated by a rectangular box of TIP3P waters extended at least 10 Å in each direction from the solute. Before the MD simulation, some energy minimization steps were applied to the system. First, the solute was kept fixed with a constraint of 500 kcal mol−1 Å−2 and waters and counterions 7210

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measuring hPPO activity over a range of inhibitor concentrations at a single substrate concentration. IC50 values were calculated by fitting v versus [I] data to a single binding site model described by eq 1 y = min +

max − min 1 + 10 logIC50− x

(1)

where y is the percentage of maximal rate, max and min are the y values at which the curve levels off, x is the logarithm of inhibitor concentration, and IC50 is the inhibitor concentration that elicits 50% of the total inhibition. Calculated Ki value is obtained by applying the following relationship, which exists for competitive inhibition among Ki, Km, and IC50 at any saturating substrate concentration (S). Ki =

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IC50 S /K m + 1

(2)

RESULTS AND DISCUSSION Structures of PPO−Inhibitors Complexes. Our strategy for the prediction of PPO resistance to herbicides involved determining the binding models of the five inhibitors with PPO. We used molecular docking to examine their possible binding models. Due to docking, the method we used could not fully account for the flexibility of the protein and so the binding models from docking were further studied by molecular dynamics (MD) simulations. We selected the final structures of PPO−inhibitor complexes according to docking scores, the stability of the MD simulation, and geometric matching qualities with the known crystal structures of INH in a complex with tobacco PPO and ACJ in a complex with Myxococcus xanthus PPO and also with human PPO.22,23,39 In addition, obtaining a stable MD trajectory is crucial for subsequent analysis, so the plot of root-mean-square deviation (RMSD) of the protein backbone and inhibitor atoms throughout the whole process of the MD simulation was examined for convergence. According to the RMSD values of the MD trajectories (Figure 1), the simulations have reached equilibrium states. The MD simulations were also repeated with different sets of parameters and force fields to further validate the convergence of the simulations (data not shown). To further verify the reliability of the MD simulations, the binding free energies of the five inhibitors were calculated by using a modified MM/PBSA method.27 The results included the overall binding free energy and each energy term (Table 1). The calculated binding affinities were also compared with the experimental data derived from inhibitions studies performed with the mutated PPO enzymes that we prepared. As listed in Table 1, the binding free energies (ΔGcal) of the five inhibitors with respect to wild-type PPO obtained from the MM/PBSA calculations ranged from −11.7 to −23.80 kcal/mol. The corresponding experimentally derived free energies of binding (ΔGexp) using wild-type PPO varied over the narrow range of −5.54 to −8.82 kcal/mol. These data show that the calculation largely overestimated the absolute values of the binding free energies by ∼2.0−2.7-fold. Nevertheless, the qualitative order of the calculated binding affinities for the inhibitors using computational methods was FOS > SUT > ACJ > OXZ > CHP, which is in accordance with the ranking of the binding determined experimentally. Quantitatively, the calculated binding free energies are precisely correlated with the corresponding experimental data, with a correlation coefficient

Figure 1. Time dependence of the RMSD of protein backbone atoms (color in black) and inhibitors (color in red). (A) PPO−ACJ, (B) PPO−FOS, (C) PPO−OXZ, (D) PPO−SUT, and (E) PPO−CHP.

r2 = 0.88. The good agreement between the computational results and the experimental data further confirmed the computational models constructed and tested in this study are reliable. In addition, the simulated binding models were also compared with available X-ray crystal structures and our previous studies.27 The interaction between the inhibitor and PPO may occur in the binding pocket defined by seven residues. We have shown that most inhibitors interacted with PPO by establishing strong hydrogen bonds with the highly conserved Arg97 residue and a hydrophobic interaction with Met368, which is similar to that in the X-ray crystal structure of ACJ in a complex with M. xanthus PPO and the crystal structure of INH in a complex with tobacco PPO.22,23 To make a clear view of the interacting pattern, MD simulated models of PPO inhibitors binding with residues in the pocket are shown in Figure 2. The five inhibitors have similar binding modes because they are thought to inhibit PPO activity by mimicking the same part of the substrate. So, the simulated threedimensional structure of PPO inhibitor complexes obtained in the present investigation should be reasonable. Selection of Residues for Mutation. The amino acids for mutation in PPO have been the subject of intensive resistance research in agriculture because of the tendency of this enzyme to lose efficacy toward a wide range of pesticides. To make reasonable selection of amino acid residues for mutation, we should compare the key residues interacting with substrate and inhibitors. In light of our earlier studies of both residue mutation experiments and in silico studies, we gained insights 7211

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Table 1. Binding Free Energies Calculated for the WT PPO with Five Inhibitors (kcal/mol) inhibitor ACJ FOS OXZ SUT CHP

ΔEele −24.05 −55.25 −10.72 −27.77 −11.42

(2.06) (9.19) (1.02) (1.36) (0.53)

ΔEvdW −50.24 −54.97 −51.73 −45.07 −42.20

(1.02) (1.29) (0.79) (0.89) (0.46)

ΔEMM −74.29 −110.23 −62.45 −72.84 −53.62

(1.49) (9.79) (1.06) (1.89) (0.71)

ΔGPB 35.82 63.84 25.61 34.68 24.03

(1.26) (8.43) (1.02) (1.56) (1.04)

ΔEbind −38.47 −46.38 −36.84 −38.16 −29.60

(1.08) (1.91) (1.00) (1.16) (1.04)

−TΔS 20.55 22.50 21.94 18.07 18.43

(0.97) (1.24) (0.65) (1.02) (0.56)

ΔGbinda −17.92 −23.89 −14.90 −20.09 −11.17

(1.03) (1.58) (0.83) (1.09) (0.80)

ΔGexpb −7.88 −8.82 −7.55 −8.39 −5.54

a

Results determined by the MM−PBSA calculations. The standard deviation (STD) values are included in the parentheses. bExperimental values ΔGexp were derived from the experimental Ki values of hPPO according to ΔGexp = −RT ln Ki.

categories according to thermodynamic parameters: decrease of enthalpy, decrease of entropy, decrease of both the enthalpy and entropy, no significant change in enthalpy and entropy, decrease of enthalpy compensated by the increase of entropy, and decrease of entropy compensated by the increase of enthalpy. Computational mutation scanning (CMS) calculations were performed to predict the resistance for the five inhibitors and to gain further insight into the contributions of the various energy terms to the differences in the binding free energy of R59Q, R97G, and M368Q, including the changes in the van der Waals and electrostatic interaction energies, as well as the electrostatic and nonpolar desolvation free energies upon mutation. The relative binding free energies calculated by CMS and the corresponding experimental values are given in Table 2. The ΔΔG value can be viewed as an indicator of the resistance level, with the negative and positive values of ΔΔGbind (ΔGmutant − ΔGwild‑type) indicating favorable and unfavorable outcomes, respectively. Thus, a positive ΔΔG value indicates resistance, whereas a negative ΔΔG value signifies lack of resistance. Most of the calculated relative binding free energies were positive, illustrating that the three mutants usually induced resistance in PPO with respect to the five inhibitors. To gain further insight into the resistance mechanisms associated with the mutations, contributions of enthalpy and entropy to the binding free energy changes were analyzed by computational methods. As seen in Table 2, all of the mutations, except R97G for OXZ, R59Q for CHP, and R97G for CHP, should result in resistance due to the positive shift in binding free energy. The resistance prediction results of PPO were validated in vivo, using site-directed mutagenesis and enzyme kinetic studies performed on PPO. We constructed mutant PPO genes, isolated the respective mutant proteins, determined their kinetic parameters via continuous fluorometric method, and compared the results with wild-type (WT) PPO. The three mutations could mostly induce resistance (uppermost 11.63fold) for the five inhibitors with the exception of R59Q for CHP, R97G for OXZ, and R97G for CHP (Table 2). These outcomes were consistent with the computational studies. CMS allowed us to further analyze the role of the individual thermodynamic parameters in the mechanism of resistance associated with the mutations. These data are presented as a histogram that compares the effect of the selected mutations in PPO on each parameter affecting the binding of the five inhibitors (Figure 3). R59Q and M368Q appear to function via the same resistance mechanism; they share the common characteristic that the decrease in the enthalpy contribution is mainly responsible for the decrease in binding affinity for the inhibitors. In contrast, R97G led to another mechanism, that the decrease in the enthalpy contribution was compensated by an increase in the entropic contribution. The replacement of

Figure 2. MD simulated models of human PPO in a complex with ACJ, FOS, OXZ, SUT, and CHP. (A) PPO−ACJ, (B) PPO−FOS, (C) PPO−OXZ, (D) PPO−SUT, (E) PPO−CHP, and (F) alignment of the binding conformation of five inhibitors and protoporphyrinogen IX.

into the binding modes of substrate and inhibitors in PPO.40,41 The residues R59, R97, M368, G169, V347, L334, and F331 comprise the active site of PPO. On the basis of our previously determined binding model of the substrate41 and site-directed mutagenesis studies of PPO, we identified three mutations, R59Q, R97G, and M368Q, that had no strong effect on the structure and catalytic activity of PPO. This result was at odds with our original idea that the R97G mutation would decrease the catalytic activity of the enzyme through loss of a key hydrogen bond interaction with the substrate. Indeed, the replacement of R97 with glycine improved the enzyme activity.41 We therefore undertook further studies to understand the mechanism of binding of the five inhibitors to the R59Q, R97G, and M368Q mutants. Hence, site-directed mutagenesis and enzyme kinetic studies were performed on hPPO. Computational Prediction of Resistance. To aid the course of pesticide discovery, the prediction of pesticide resistance by computational methods would be most valuable. Computational methods can provide information about the possible mechanisms of resistance that can be conferred by a target mutation. These mechanisms can be divided into six 7212

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Table 2. Inhibition Constants (Ki) of WT and Mutant Human PPO Activities by Inhibitors, the Relative Resistance Fold, and Calculated Binding Free Energy Changes (kcal/mol) inhibitors

types

Ki (μM)

RFa

ΔΔHcalb

−TΔΔS

ΔΔGcal

ACJ

WT R59Q R97G M368Q WT R59Q R97G M368Q WT R59Q R97G M368Q WT R59Q R97G M368Q WT R59Q R97G M368Q

1.71 4.27 9.76 6.21 0.35 0.25 1.15 1.33 2.96 11.52 0.13 11.63 0.72 4.47 8.78 1.79 87.87 63.28 37.33 152.79

1 2.50 5.71 3.63 1 0.70 3.29 3.81 1 3.89 0.04 3.93 1 6.21 12.19 2.49 1 0.72 0.42 1.74

0 4.06 5.87 3.88 0 3.88 8.55 3.91 0 1.00 0.69 3.35 0 1.27 4.09 4.41 0 −0.32 0.75 4.37

0 −0.53 −2.97 −0.07 0 −0.78 −2.78 −0.16 0 −0.06 −2.30 0.10 0 0.04 −3.13 0.32 0 0.19 −3.25 0.13

0 3.53 2.90 3.81 0 3.11 5.78 3.76 0 0.94 −1.61 3.45 0 1.31 0.96 4.73 0 −0.13 −2.50 4.50

FOS

OXZ

SUT

CHP

resultc R R R R R R R N R R R R N N R

a Resistance fold (RF) = Ki,mutant/Ki,WT. RF > 1 means resistance, whereas RF ≤ 1 means no resistance. bΔΔG = ΔGMT − ΔGWT calculated by CMS protocol. cResistance prediction results. R means resistance, whereas N means no resistance.

R97 with glycine could enlarge the mouth of the pocket and could improve the turnover of the inhibitors, which would favor the improvement in the conformational degrees of freedom of the inhibitors in the active site.41 This structural change could cause an increase in conformational entropy, such that the loss of enthalpy would be offset. However, R97G confers resistance for ACJ, FOS, and SUT, but not for OXZ and CHP. To explore why the R97G mutation could have such different effects, we further examined the binding models of the five inhibitors (Figure 2). According to a previous study, R97 was located in the pocket between the active site and the binding pathway of the substrate.41 The inhibitors ACJ, FOS, and SUT can form important hydrogenbonding interactions with R97 in WT PPO, but OXZ and CHP cannot. The replacement of the salt bridge functionality in R97 by a simple glycine caused ACJ, FOS, and SUT to lose more enthalpy contribution than did OXZ and CHP, which could induce resistance for ACJ, FOS, and SUT. But, the enthalpy loss was compensated by an increase in the entropy component of binding, thereby reducing the level of resistance. It is worth noting that R97G experienced a large enthalpy loss for FOS; such an enthalpy loss should result in over 100-fold rise in resistance if not for the entropic compensation. That is to say, the entropy increase due to the improvement of the conformational degrees of freedom can effectively reduce the resistance level. The computational studies using the CMS protocol have led to a detailed understanding of the resistance mechanisms and the structure-resistance relation of PPO and five inhibitors associated with the target mutations. Computational simulations based on our developed 3D model of PPO−inhibitor binding structures, followed by resistance prediction, sitedirected mutagenesis, and PPO activity assays, have led to a detailed understanding of PPO interaction with herbicides. The computationally determined mutation-caused shifts in the binding free energies are reflected by two resistance

Figure 3. Thermodynamic representation of the resistance mechanism. The histogram means changes of enthalpy, entropy, and binding free energy of the five inhibitors in each mutant. Possible mechanisms of resistance were shown. (A) Resistance mechanism of R59Q. (B) Resistance mechanism of R97G. (C) Resistance mechanism of M368Q.

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(7) Deybach, J.-C.; Da Silva, V.; Grandchamp, B.; Nordmann, Y. The mitochondrial location of protoporphyrinogen oxidase. Eur. J. Biochem. 1985, 149, 431−435. (8) Lermontova, I.; Kruse, E.; Mock, H. P.; Grimm, B. Cloning and characterization of a plastidal and a mitochondrial isoform of tobacco protoporphyrinogen IX oxidase. Proc. Natl. Acad. Sci. U. S. A. 1997, 94, 8895−8900. (9) Matringe, M.; Camadro, J. M.; Labbe, P.; Scalla, R. Protoporphyrinogen oxidase as a molecular target for diphenyl ether herbicides. Biochem. J. 1989, 260, 231−235. (10) Poulson, R. The enzymic conversion of protoporphyrinogen IX to protoporphyrin IX in mammalian mitochondria. J. Biol. Chem. 1976, 251, 3730−3733. (11) Sherman, T. D.; Becerril, J. M.; Matsumoto, H.; Duke, M. V.; Jacobs, J. M.; Jacobs, N. J.; Duke, S. O. Physiological basis for differential sensitivities of plant species to protoporphyrinogen oxidase-inhibiting herbicides. Plant Physiol. 1991, 97, 280−287. (12) Li, B.; Lan, Y. M.; Hsu, C. T.; Liu, Z. L.; Song, H. B.; Wu, C.; Yang, H. Z. Three trifluoromethyl-substituted protoporphyrinogen IX oxidase inhibitors. Acta Crystallogr. C 2005, 61, o122−126. (13) Li, H. B.; Zhu, Y. Q.; Song, X. W.; Hu, F. Z.; Liu, B.; Li, Y. H.; Niu, Z. X.; Liu, P.; Wang, Z. H.; Song, H. B.; Zou, X. M.; Yang, H. Z. Novel protoporphyrinogen oxidase inhibitors: 3H-pyrazolo[3,4-d][1,2,3]triazin-4-one derivatives. J. Agric. Food Chem. 2008, 56, 9535− 9542. (14) Zhang, L.; Hao, G. F.; Tan, Y.; Xi, Z.; Huang, M. Z.; Yang, G. F. Bioactive conformation analysis of cyclic imides as protoporphyrinogen oxidase inhibitor by combining DFT calculations, QSAR and molecular dynamic simulations. Bioorg. Med. Chem. 2009, 17, 4935− 4942. (15) Hao, G. F.; Zuo, Y.; Yang, S. G.; Yang, G. F. Protoporphyrinogen oxidase inhibitor: an ideal target for herbicide discovery. Chimia (Aarau) 2011, 65, 961−969. (16) Jiang, L. L.; Tan, Y.; Zhu, X. L.; Wang, Z. F.; Zuo, Y.; Chen, Q.; Xi, Z.; Yang, G. F. Design, synthesis, and 3D-QSAR analysis of novel 1,3,4-oxadiazol-2(3H)-ones as protoporphyrinogen oxidase inhibitors. J. Agric. Food Chem. 2010, 58, 2643−2651. (17) Jiang, L. L.; Zuo, Y.; Wang, Z. F.; Tan, Y.; Wu, Q. Y.; Xi, Z.; Yang, G. F. Design and syntheses of novel N-(benzothiazol-5-yl)4,5,6,7-tetrahydro-1H-isoindole-1,3(2H)-dione and N-(benzothiazol5-yl)isoindoline-1,3-dione as potent protoporphyrinogen oxidase inhibitors. J. Agric. Food Chem. 2011, 59, 6172−6179. (18) Yang, Z.; Sheng-Gang, Y.; Yan-Ping, L.; Ying, T.; Ge-Fei, H.; Qiong-You, W.; Zhen, X.; Guang-Fu, Y. Design and synthesis of 1(benzothiazol-5-yl)-1H-1,2,4-triazol-5-ones as protoporphyrinogen oxidase inhibitors. Bioorg. Med. Chem. 2013, 21, 3245−3255. (19) Zhang, L.; Tan, Y.; Wang, N. X.; Wu, Q. Y.; Xi, Z.; Yang, G. F. Design, syntheses and 3D-QSAR studies of novel N-phenyl pyrrolidin2-ones and N-phenyl-1H-pyrrol-2-ones as protoporphyrinogen oxidase inhibitors. Bioorg. Med. Chem. 2010, 18, 7948−7956. (20) Zuo, Y.; Yang, S. G.; Jiang, L. L.; Hao, G. F.; Wang, Z. F.; Wu, Q. Y.; Xi, Z.; Yang, G. F. Quantitative structure-activity relationships of 1,3,4-thiadiazol-2(3H)-ones and 1,3,4-oxadiazol-2(3H)-ones as human protoporphyrinogen oxidase inhibitors. Bioorg. Med. Chem. 2012, 20, 296−304. (21) Yang, S. G.; Hao, G. F.; Dayan, F. E.; Tranel, P. J.; Yang, G. F. Insight into the Structural Requirements of Protoporphyrinogen Oxidase Inhibitors: Molecular Docking and CoMFA of Diphenyl Ether, Isoxazole Phenyl, and Pyrazole Phenyl Ether. Chin. J. Chem. 2013, 31, 1153−1158. (22) Koch, M.; Breithaupt, C.; Kiefersauer, R.; Freigang, J.; Huber, R.; Messerschmidt, A. Crystal structure of protoporphyrinogen IX oxidase: a key enzyme in haem and chlorophyll biosynthesis. EMBO J. 2004, 23, 1720−1728. (23) Corradi, H. R.; Corrigall, A. V.; Boix, E.; Mohan, C. G.; Sturrock, E. D.; Meissner, P. N.; Acharya, K. R. Crystal structure of protoporphyrinogen oxidase from Myxococcus xanthus and its complex with the inhibitor acifluorfen. J. Biol. Chem. 2006, 281, 38625−38633.

mechanisms, which provide essential information for structurebased pesticide design. Our computational results demonstrated that the calculated entropy change seems to relate with the conformational degrees of freedom of the inhibitor in the binding pocket. That is to say, inhibitors designed to have more conformational flexibility in the binding site may inhibit both the WT and mutants of a targeted enzyme. The structural and mechanistic insights obtained from the present study provide a valuable base for rational design of novel PPO inhibitors.

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AUTHOR INFORMATION

Corresponding Authors

*State Key Laboratory of Elemento-Organic Chemistry, Nankai University, Tianjin 300071, P. R. China. Tel: 86-22-23504782. Fax: 86-22-23504782. E-mail: [email protected] (Z.X.). *College of Chemistry, Central China Normal University, 152 Luoyu Road, 430079 Wuhan, Hubei, P.R. China. Tel.: 86-2767867800. Fax: 86-27-67867141. E-mail: [email protected]. edu.cn (G.-F.Y.). Funding

The research was supported in part by the NSFC (No. 21202055), "Specialized Research Fund for the Doctoral Program of Higher Education" (No. 20120144120003), and the "Hong Kong Scholars Program" (No. XJ2012020). This research is also supported in part by the Guangdong Province Key Laboratory of Computational Science and the Guangdong Province Computational Science Innovative Research Team. Notes

The authors declare no competing financial interest.

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ABBREVIATIONS USED PPO, protoporphyrinogen oxidase; CMS, computational mutation scanning; CAS, computational alanine scanning; ACJ, acifluorfen; FOS, fomesafen; OXZ, oxadiazon; SUT, sulfentrazone; CHP, Chlorphthalim; QM, quantum mechanics; RESP, restrained electrostatic potential; MD, molecular dynamics; PME, particle mesh Ewald; vdW, van der Waals; MM/PBSA, molecular mechanics/Poisson−Boltzmann surface area; RMSD, root-mean-square deviation; WT, wild-type

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