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Highly biodegradable fluoroquinolone derivatives designed using the 3D-QSAR model and biodegradation pathways analysis
Ecotoxicology and Environmental Safety 191 (2020) 110186
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Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv
Highly biodegradable fluoroquinolone derivatives designed using the 3DQSAR model and biodegradation pathways analysis
T
Yilin Houa,b, Yuanyuan Zhaoa,b, Qing Lia,b, Yu Lia,b,∗ a b
College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing, 102206, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Fluoroquinolones QSAR Molecular modification Biodegradation Molecular docking Molecular dynamics
A three-dimensional quantitative structure–activity relationship (3D-QSAR) model was established based on molecular structures and docking scores (representing the biodegradability); the scores were obtained for 23 fluoroquinolones (FQs) and the oxidoreductase (PDB ID: 1YZP) of Phanerochaete chrysosporium in the aerobic process of municipal wastewater treatment plants. In the Comparative Molecular Field Analysis (CoMFA) model, q2 was 0.516 and r2pred was 0.727, which showed that the model was reliable and robust. The modification information obtained by the contour maps showed that introducing electronegative, bulky or electropositive groups at different active sites could increase the biodegradability of fluoroquinolone derivatives. Using levofloxacin (LEV) as a modified molecule, 35 fluoroquinolone derivatives with higher biodegradability than LEV were designed. After the evaluation of genotoxicity, bioconcentration and photodegradation, Derivative-15, with higher biodegradability (increased by 27.85%), higher genotoxicity, higher photodegradation and lower bioconcentration, was identified as the most environmentally friendly fluoroquinolone derivative. The 2D-QSAR model of FQ biodegradability was established through the quantization parameters, and q+ was identified as the main parameter affecting the biodegradability of FQs through sensitivity analysis. In addition, the docking results of LEV and Derivative-15 with the oxidoreductase in P. chrysosporium showed that the electrostatic field force between Derivative-15 and the amino acid residues promoted the binding of the donor to the receptor protein, thereby increasing the biodegradability of Derivative-15. Additionally, molecular dynamics simulations revealed that the enhancement of the electrostatic field force with Derivative-15 could promote the binding of the ligand to the receptor, which was basically consistent with the conclusion of molecular docking. Finally, the three microbial degradation pathways of LEV and Derivative-15 were also proposed. The total energy barrier value of the pathway with the lowest total energy barrier of biodegradation was reduced by 32.07%, which was basically consistent with the enhancement of biodegradability of Derivative-15.
1. Introduction Due to the widespread use of antibiotics, the abuse of antibiotics has increased dramatically in recent years (Gong, 2016). However, more than 70% of quinolones are not completely metabolized in the body after taking them, and they are excreted in urine and faeces and are biologically active (Van Doorslaer et al., 2014). Therefore, the large amount of quinolone antibiotics used will cause harm to human health and the environment (Sarmah et al., 2006; Zhu et al., 2019). There are currently four generations of quinolone, of which more than 60 fluoroquinolones are in use (Feng et al., 2017). Fluoroquinolone (FQ) is an important synthetic antibiotic and broad-spectrum antibacterial drug. It is active against both gram-positive and gram-negative bacteria; therefore, FQs are widely used in the medical field (Naeem et al., 2016; ∗
Zhang et al., 2017; Emmerson, 2003). FQs are the most widely used antibiotics, and the total annual consumption is approximately 4.4 × 107 kg throughout the world (Simoens et al., 2011). FQs remain in the soil or flow into the groundwater through rainwater runoff and farmland irrigation when they enter the environment; otherwise, they enter municipal wastewater treatment plants. FQs are frequently detected in water environments, soil sediments and organisms (Chen et al., 2019). Xie et al. detected FQs of 27.8–1537.4 μg/kg in the topsoil of Guangdong, China (Xie et al., 2012); Chen et al. detected norfloxacin, ofloxacin and enrofloxacin in groundwater in Beijing and Changzhou, China, and the detection rate reached 100% with a maximum concentration of 96.8 ng/L (Chen et al., 2016). Studies have shown that a variety of FQs have emerged in municipal wastewater treatment plants in many countries, and only some of the common
Corresponding author. College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China. E-mail addresses: [email protected] (Y. Hou), [email protected] (Y. Zhao), [email protected] (Q. Li), [email protected] (Y. Li).
https://doi.org/10.1016/j.ecoenv.2020.110186 Received 26 October 2019; Received in revised form 6 January 2020; Accepted 8 January 2020 0147-6513/ © 2020 Elsevier Inc. All rights reserved.
Ecotoxicology and Environmental Safety 191 (2020) 110186
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2. Materials and methods
compounds can be eliminated in conventional sewage treatment systems (Liu et al., 2012). The ofloxacin concentration in a municipal wastewater treatment plant in Italy is 463 ng/L, its effluent content is 191 ng/L (Zuccato et al., 2010), and the removal rate is only 58.75%; the influent content of enrofloxacin in a Portuguese municipal wastewater treatment plant is 121.8–447.1 ng/L, its effluent content is 53.7–211.5 ng/L (Seifrtova et al., 2008), and the removal rate is only 52.70%–55.91%. Therefore, the removal rate of FQs in municipal sewage treatment plants is low, and a great quantity of quinolones are still discharged into the environment after treatment. FQs are difficult to biodegrade (Santos et al., 2015) and are mainly eliminated by adsorption in sewage sludge in sewage treatment plants (Prieto et al., 2011). When sewage sludge enters the environment, there is still a risk of harming human health and the environment (Pedersen et al., 2005). Biodegradation is a promising method to remove FQs, and biotransformation of microorganisms is an effective way to degrade and remove FQs (Xia et al., 2019). Since the current municipal sewage treatment plants are not designed to simply treat a certain drug or pollutant (Dorival-García et al., 2013), the degradation of FQs in municipal sewage treatment plants is not ideal, and most sewage treatment plants need to start tertiary sewage treatment. Studies have shown that the average removal rate of levofloxacin (LEV) in municipal sewage treatment plants is less than 10% (Xiong et al., 2017). Considering that the drug molecules of FQs are difficult to biodegrade, the number of FQs entering the environment and municipal sewage treatment plants is increasing. The use of FQs has been threatened by the increasing number of drug-resistant bacteria and clinical pathogens, which has caused negative impacts on the natural environment and public health (Li, 2005). Therefore, the design of novel FQs is particularly important, aiming to improve the biodegradability and pharmacological toxicity of FQs towards environmentally friendly molecular succession. 3D-QSAR focuses on the physical-chemical properties of ligands that may have a causal relationship with biological or chemical reactions (Cramer, 2015). Based on the use of three-dimensional structural information and biological activity of compounds to establish a reasonable mathematical model, the established model is used to determine the relationship between the structures and bioactivities (Zhang et al., 2019a). Therefore, the optimized 3D-QSAR model can be used to improve the environmental and toxicological assessment of chemicals (Linden et al., 2017). CoMFA is one of the most commonly used methods in 3D-QSAR, and has very good prediction results (De Simone et al., 2017; Uddin et al., 2013; Zhang et al., 2019b; Cheng et al., 2018). Zhou et al. (2016) used the CoMFA model to study the binding mechanism of 6-aminonicotinate-based antagonists and P2Y12; Yu et al. (2018) used the CoMFA method to study the affinity of azo dye molecules and cellulose fibers. They all obtained satisfactory results. White rot fungi can degrade or reduce various environmental pollutants (Zeng et al., 2015, 2019). We use CoMFA method to study the degradation of FQs by manganese peroxidase (classification: oxidoreductase) produced by P. chrysosporium belonging to the white rot fungus (Wesenberg et al., 2003; Zhao et al., 2018a). In the present work, the docking scores of FQs and oxidoreductases produced by P. chrysosporium that can effectively degrade FQs were calculated by molecular docking technique, and the scoring values were used as data to establish a model using 3DQSAR. Through CoMFA force field analysis and contour map analysis to find the modification site, LEV was used as the modified molecule, and 35 kinds of novel FQs derivatives were designed by substitution reactions, and the biodegradability and environmental friendliness (genotoxicity, bioconcentration, and photodegradability) of the novel FQ derivatives were evaluated. Finally, Derivative-15, with high biodegradability and other environmentally friendly characteristics, was screened as a novel FQ. In addition, the microbial degradation pathways of Derivative-15 were deduced and the biodegradation mechanism of Derivative-15 was analysed.
2.1. Data source The PDB (Protein Data Bank) database was used to retrieve the protein structure (PDB ID: 1YZP) of P. chrysosporium, which was used as the receptor, and 23 FQs were used as donors in this study. Software Discovery Studio 4.0 (DS) (Zhao et al., 2018b; Gu et al., 2018; Ren et al., 2019) was used to dock the FQs and protein receptor, and the docking score function value was used to represent the biodegradability of FQs. After taking the logarithm of the docking scoring function (logLDS), the logLDS of 18 molecules were randomly selected as the training set, and the logLDS of the remaining 6 molecules were selected as the test set (both the training set and the test set contained modified molecules) according to a ratio of 3:1. Then, Sybyl-X 2.0 (Rao et al., 2013) was used to establish the biodegradable 3D-QSAR model of FQs. 2.2. Establishment of FQ biodegradation 3D-QSAR model Sybyl-X2.0 (Yang and Liu, 2019) software was used for molecular drawing and optimization. The Ciprofloxacin (CIP) molecule Figure figs1 Alignment the common framework of the template compoundwith the maximum log value of the molecular docking score was selected as the template molecule, and the same structure of 23 molecules was selected as the common framework for the alignment of the training set. The CoMFA model in the QSAR module was used to analyse the electrostatic field and static fields of the molecules. Partial least squares (PLS) analysis was used to establish the relationship between the structure and biological activity of the target compound. When PLS analysis was used, the leave-one-out method was first used to crossvalidate the compound of the training set, and the cross-validation coefficient q2 and the optimal principal component number N were obtained. Then, regression analysis was carried out with No Validation to obtain the non-cross-validation coefficient, R2, standard error of estimate (SEE) and test value, F, and, finally, the establishment of the CoMFA model was completed. 2.3. Calculation method of quantization parameters of the FQs Density functional theory (DFT) is a quantum chemical method to study the electronic structure of multi-electron systems. By means of a variational method or numerical method, the kinetic energy and potential energy of electrons are averaged to obtain the approximate solution of the molecular wave function Schrodinger equation. DFT integrates the idea of statistical mathematics and only calculates the total electron density, not the behaviour of each electron. Currently, it is widely used in the environmental field (Qu et al., 2012). The Gaussian 09 software was used to optimize the geometry of the studied FQs at the B3LYP/6-31G(d) theoretical level (Wu, 2016; Cheng et al., 2020), and the molecular quantization parameters were calculated, including the energy of the highest occupied molecular orbital (EHOMO, a.u.), the energy of the lowest unoccupied molecular orbital (ELUMO, a.u.), the most positive Mulliken charge (q+, e), the most negative Mulliken charge (q-, e), the most positive Mulliken hydrogen charge (q+H, e), positive frequency (F, cm−1) and the quadrupole moment (Qxx, Qyy, Qzz). Finally, the GaussView 5.0 program was used for visual analysis. 2.4. Establishment of the 2D-QSAR model for biodegradability Statistical Product and Service Solutions (SPSS) (Hou et al., 2017) software was used to perform multiple linear regression analysis on the quantization parameters of FQs calculated by DFT, which is the correlation analysis between two or more independent variables and one dependent variable. The biodegradability of FQs was taken as the dependent variable and the quantization parameters as the independent variable. Then, the least square method was used to calculate the model 2
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Fig. 1. Structure of the modified molecule (LEV) and contour maps of the CoMFA model.
constant-temperature) system was adopted in this paper, the number of particles in the control system was constant, the control pressure and temperature were near the specified value, and the result was analysed in the form of energy through server simulation.
parameters to establish a 2D-QSAR model of the biodegradability of the FQs, and the model was statistically tested. 2.5. Mechanism analysis of the high biodegradability of the FQs based on molecular docking
3. Results and analysis Molecular docking is widely applied to the interaction between small molecules and receptor proteins, which requires both space shape matching and energy matching (Cornell et al., 1995). Molecular docking can determine the affinity of small molecules and receptor proteins, and the binding effect of ligands and receptors can be considered as a whole. The docking method of LibDock in DS software was used to dock the oxidoreductase and FQs. After the docking was completed, the score value (LibDockScore) was generated, and the docking effect was judged from the level of the score value. In addition, the twodimensional diagram of ligand-protein interaction can directly reflect the interaction between FQs and oxidoreductase and the changes of key amino acid residues.
3.1. Evaluation of the CoMFA model for FQs biodegradability The cross-validation q2 of the CoMFA model of the FQ biodegradability was 0.516 (> 0.5), the optimal principal component, N, was 6, the non-cross-validation coefficient, R2, was 0.999, the standard error of estimate (SEE) was 0.006 and the test value (F) was 1601.635. Therefore, the model had reliable prediction ability, good fitting ability and good robustness. In the CoMFA model, the contribution rates of the electrostatic field and steric factors were 13.9% and 86.1% respectively, indicating that, compared with the electrical distribution, the effect of the space group had a significant influence on the biodegradability of FQs. In addition, the interactive validation coefficient, r2pred, of the external test set of the model was 0.727 (> 0.6), indicating that the model had external prediction ability and could predict the biodegradability of FQs derivatives.
2.6. Mechanism analysis of biodegradation based on molecular dynamics Molecular dynamics is a molecular simulation method based on force fields and is widely used in the research and analysis of intermolecular interactions in the fields of drug design and life sciences (Ogrizek et al., 2015). DS software was used to simulate the oxidoreductase and FQs molecular complex in the CHARMM field. The CHARMM force field can obtain good results for various simulation systems, such as small molecules and solvated large biological systems. Moreover, the protein-ligand complex was added to the physiological saline solvent environment to make the system more compatible with organisms. To break the regular arrangement of water molecules and ions when the solvent was added, and more in line with the simulated environment, water molecules and ions needed to be optimized. After the water molecules and ions were optimized, Standard Dynamics Cascade process including Minimization, Minimization 2, Heating, Equilibration and Production was performed, in which the Simulation Time of the Production phase (ps) was 200 and Time Step (fs) was set to 2. Since the control of temperature and pressure had an important impact on the nature of the system, the NPT (Constant-pressure,
3.2. Molecular modification of high biodegradable FQs based on the CoMFA model In 2016, LEV ranked first in the frequency of antimicrobial drugs in the HIS system of the first people's hospital of Zhengzhou, China (Liu et al., 2018). LEV was used frequently in quinolone antibiotics. Therefore, highly biodegradable FQs were modified with LEV as the target molecule. In the contour maps of the CoMFA model, the green region indicates that the introduction of bulky groups can improve the biodegradation activity of the molecule, while the yellow region indicates that the introduction of bulky groups can reduce the biodegradation activity of the molecule. The red region in the electrostatic field shows that the introduction of negatively charged groups enhances the biodegradation activity of the molecule, and the blue region shows that the introduction of positively charged groups enhances the biodegradation activity 3
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ensure a remarkable improvement in biodegradability of the derivatives, derivatives with a biodegradability increase of more than 10% were selected for further research. Therefore, 24 derivatives, such as Derivative 1, Derivative 2, and Derivative 3, were selected after screening.
of the molecule (Zhao and Li, 2019). The contour map (Fig. 1) shows that green and yellow contours appeared around site 1. Therefore, increasing or decreasing the volume of groups at site 1 may change the biodegradation activity of the molecule to different degrees, thus changing the value of the docking scoring function. Green contours appeared around sites 3, 4, 5, and 6, so introducing the bulky groups around these four sites could improve the biodegradation of the molecule. The electrostatic field shows that sites 1 and 2 appear around the displayed red region, indicating that the introduction of electronegative groups could improve the molecular biological degradation activity. In contrast, sites 5 and 6 were surrounded by blue contours, and the introduction of groups with positive electrical groups could also improve the biodegradation activity of the molecule. To this end, 11 groups were selected for molecular modification, namely, –CH3, –CH2CH3, –F, –CHO、-CN, –OCH3, –OH, –COOH, –NH2, –CH]CH2 and –SH groups. Finally, a total of 35 LEV derivatives were designed.
3.3.2. Evaluation of the environmental friendliness of derivatives The environmental friendliness (genotoxicity, bioconcentration and photodegradation) of FQs was evaluated. Among these factors, the genotoxicity of the FQs was functional and represented the bactericidal ability of quinolones. Zhao et al. (2019a) used the reliable Holographical Quantitative Structure-Activity Relationship (HQSAR) model to predict the toxicity of FQs. HQSAR is a new QSAR technique that establishes a correlation between the biological activity of a compound and the molecular structure composition described by the sub-structural fragment type of the molecule (Sun et al., 2013). The bioconcentration of FQs means that organisms in the environment may accumulate FQs through the surrounding environment (water, soil, atmosphere) by means of non-ingestion, resulting in an excessive amount of this drug in organisms and damaging them. FQs can be eliminated by non-biodegradable means in the environment, thereby reducing its threat to the environment. Zhao (Zhao et al., 2018a) (Zhao et al., 2019b) et al. have established a 3D-QSAR with good predictability and predicted the bioconcentration and photodegradation of the FQs. The model was robust, and the prediction was reliable. To ensure that the FQs had increased genotoxicity, decreased bioaccumulation and improved photodegradability under the condition of improved biodegradability, the three characteristics were predicted by the QSAR
3.3. Prediction and evaluation of biodegradability and environmental friendliness 3.3.1. Prediction and evaluation of biodegradability Thirty-five LEV derivatives were designed with different sites and different groups for substitution modification. The relative differences between the predicted values of the biodegradability and biodegradability of LEV are shown in Table 1. The biodegradability of the 35 derivatives was higher than that of LEV, and the biodegradability was increased by 0.40%–29.40%. To
Table 1 Prediction of biodegradability of FQ antibiotic derivatives based on the CoMFA model. No.
Target molecule Derivative-1 Derivative-2 Derivative-3 Derivative-4 Derivative-5 Derivative-6 Derivative-7 Derivative-8 Derivative-9 Derivative-10 Derivative-11 Derivative-12 Derivative-13 Derivative-14 Derivative-15 Derivative-16 Derivative-17 Derivative-18 Derivative-19 Derivative-20 Derivative-21 Derivative-22 Derivative-23 Derivative-24 Derivative-25 Derivative-26 Derivative-27 Derivative-28 Derivative-29 Derivative-30 Derivative-31 Derivative-32 Derivative-33 Derivative-34 Derivative-35
Compounds
Levofloxacin 2-Fluorine 2-Fluorine, 5-Ethylene 2-Hydroxyl 2-Sulfhydryl 2-Amino, 5-Fluorine 2-Methoxy, 5- Aldehyde 3-Ethyl 3-Ethyl, 5-Fluorine 2-Hydroxyl, 5-Ethylene 4-Ethyl 2-Fluorine, 5-Cyanide 2-Ethylene, 5-Hydroxyl 6-Fluorine 2-Fluorine, 5-Ethyl 2-Fluorine, 5-Aldehyde 2-Fluorine, 3-Ethylene 2-Fluorine, 3-Aldehyde 2-Aldehyde, 5- Fluorine 2-Fluorine, 3-Amino 2-Fluorine, 3-Methoxy 2-Ethylene, 5- Hydroxyl 2-Fluorine, 4-Aldehyde 2-Fluorine, 4-Fluorine 2-Fluorine, 4-Methoxy 2-Fluorine, 5-Hydroxyl 2-Amino, 5-Methoxy 2-Amino, 5-Hydroxyl 3-Amino, 6- Fluorine 3-Aldehyde, 6- Fluorine 2-Fluorine, 3-Ethyl 2-Aldehyde, 5-Methoxy 2-Fluorine, 3-Fluorine 2-Fluorine, 5-Amino 2-Fluorine, 4-Ethyl 2-Ethylene, 5-Fluorine
For CoMFA model Predicted
Equivalent docking value
Relative error (%)
1.749 1.861 1.793 1.804 1.798 1.804 1.802 1.822 1.771 1.801 1.811 1.793 1.797 1.840 1.832 1.855 1.773 1.853 1.774 1.800 1.825 1.770 1.794 1.791 1.830 1.786 1.770 1.771 1.804 1.783 1.839 1.751 1.792 1.777 1.860 1.757
56.094 72.585 62.035 63.553 62.789 63.553 63.297 66.431 59.109 63.297 64.585 62.035 62.537 69.161 67.782 71.714 59.347 71.138 59.347 63.043 66.699 58.871 62.286 61.786 67.510 61.044 58.871 59.109 63.553 60.555 68.883 56.320 62.035 59.827 72.293 57.235
29.40 10.59 13.30 11.94 13.30 12.84 18.43 5.37 12.84 15.14 10.59 11.49 23.29 20.84 27.85 5.80 26.82 5.80 12.39 18.91 4.95 11.04 10.15 20.35 8.82 4.95 5.37 13.30 7.95 22.80 0.40 10.59 6.65 28.88 2.03
Annotation: Novel derivatives and their biodegradability predictions and relative errors. 4
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Table 2 Prediction of biotoxicity, bioconcentration and photodegradability of FQ antibiotic derivatives through QSAR models. No.
LEV Derivative-1 Derivative-2 Derivative-3 Derivative-4 Derivative-5 Derivative-6 Derivative-7 Derivative-9 Derivative-10 Derivative-11 Derivative-12 Derivative-13 Derivative-14 Derivative-15 Derivative-17 Derivative-19 Derivative-20 Derivative-22 Derivative-23 Derivative-24 Derivative-28 Derivative-30 Derivative-32 Derivative-34
Biotoxicity
Bioaccumulation
Photodegradability
Pred.
Pred.
Pred.
8.224 8.539 8.338 9.024 8.938 8.969 8.504 8.202 8.825 8.129 8.266 8.999 8.162 8.555 8.309 8.752 9.653 8.884 8.516 8.391 8.949 9.755 8.905 8.507 8.702
Relative error (%)
3.83 1.39 9.73 8.68 9.06 3.40 −0.27 7.31 −1.16 0.51 9.42 −0.75 4.02 1.03 6.42 17.38 8.03 3.55 2.03 8.82 18.62 8.28 3.44 5.81
1.399 1.483 1.813 1.712 1.071 1.564 0.755 1.283 1.629 1.803 1.568 1.492 1.411 1.605 1.113 1.448 2.265 2.165 1.745 1.459 1.738 1.783 1.835 1.521 2.096
Relative error (%)
6.00 29.59 22.37 −23.45 11.79 −46.03 −8.29 16.44 28.88 12.08 6.65 0.86 14.72 −20.44 3.50 61.90 54.75 24.73 4.29 24.23 27.45 31.17 8.72 49.82
1.934 1.984 1.993 1.899 1.980 2.084 1.724 2.018 1.909 2.024 2.011 1.834 1.943 2.007 1.727 1.967 2.011 1.969 1.970 1.903 1.986 2.005 2.011 1.910 2.001
Table 3 Sensitivity coefficient's calculation for independent variables in the 2D-QSAR model. Independent parameters
Relative error (%) F ELUMO EHOMO q+ qq+H Qxx Qyy Qzz
2.59 3.05 −1.81 2.38 7.76 −10.86 4.34 −1.29 4.65 3.98 −5.17 0.47 3.77 −10.70 1.71 3.98 1.81 1.86 −1.60 2.69 3.67 3.98 −1.24 3.46
Sensitivity 10%
30%
50%
70%
90%
Average
−0.042 0.103 −0.358 −4.835 −0.052 0.169 −0.194 −0.296 −0.480
−0.049 0.120 −0.452 −47.227 −0.061 0.194 −0.238 −0.370 −0.622
−0.057 0.136 −0.560 8.697 −0.072 0.218 −0.285 −0.453 −0.793
−0.066 0.151 −0.686 4.564 −0.082 0.240 −0.336 −0.546 −1.005
−0.074 0.166 −0.835 3.319 −0.092 0.261 −0.391 −0.652 −1.274
−0.058 0.135 −0.578 −7.096 −0.072 0.216 −0.289 −0.464 −0.835
significance test. The coefficients of the parameters F, ELUMO and q+ in the model were negative, indicating that they were negatively correlated with the molecular biodegradability of the FQs. In contrast, the coefficients of the parameters EHOMO, q-, q+H, Qxx, Qyy and Qzz were positive, indicating that they were positively correlated with the molecular biodegradability of the FQs. The parameters in the above equation were analysed by the sensitivity analysis method to determine the parameters that have the greatest impact on the biodegradability of the FQs (Du et al., 2019). Based on the original value, each specified parameter was increased by 10%, 30%, 50%, 70% and 90%, and other parameters remained unchanged. Then, FQ biodegradability values under different increases of specified parameters were calculated by the above equation (Table 3). A sensitivity coefficient formula was used to calculate the sensitivity degree of FQ biodegradability to changes of each parameter, and the final sensitivity degree, the influence degree of each parameter on FQ biodegradability, was expressed by the sensitivity coefficient, SCi. The calculation formula is as follows:
Annotation: Screened derivatives and their environmental characteristics predictions and relative errors.
model (Table 2). Among the 24 derivatives, the genotoxicity of 21 derivatives, such as Derivative-1, Derivative-2, and Derivative-3, was higher than that of LEV (increased by 0.51%–18.62%). The bioconcentrations of Derivative-4, Derivative-6, Derivative-7 and Derivative-15 decreased by 8.29%–46.03% compared to that of LEV. The photodegradability of derivatives, such as Derivative-3, Derivative-6, and Derivative-9, was higher than that of LEV (increased by 1.24%–10.86%). Derivative-6 and Derivative-15 simultaneously met the requirements of increased genotoxicity and photodegradability and decreased bioconcentration; the biodegradability of Derivative 15 was increased more significantly.
SCi = (ΔYi / Yi )/(ΔXi / Xi )
(2)
SCi——Sensitivity coefficient of parameter i; ΔXi/Xi——The rate of change of the specified parameter; ΔYi/Yi——The rate of change in biodegradability of FQs. Through the comparison of the average absolute value of the sensitivity coefficient of nine parameters, the influence of the FQ molecular biological degradability sensitivity degree is q+ > Qzz > EHOMO > Qyy > Qxx > q+H > ELUMO > q- > F, and the parameter of the highest sensitivity coefficient, the absolute value of q+, reached 7.662; other parameters’ sensitivity coefficient absolute values were less than 1. Therefore, the result showed that q+ was the main parameter affecting the biodegradability of the FQs.
3.4. Mechanism analysis of the biodegradability of FQ derivatives 3.4.1. High biodegradability analysis of FQs based on the 2D-QSAR model The 2D-QSAR model of FQ biodegradability was established with the biodegradability parameter (Total score) as the dependent variable and the quantification parameters as the independent variable (Zeng et al., 2016). Using Gaussian software, the energy of the highest occupied molecular orbital (EHOMO, a.u.) and the lowest unoccupied molecular orbital (ELUMO, a.u.) of 23 FQs used in 2D-QSAR modelling were calculated by the DFT method, which were the key parameters determining the molecular reaction. The most positive Mulliken charge (q+, e), the most negative Mulliken charge (q-, e), the most positive Mulliken hydrogen charge (q+H, e), the positive frequency (F, cm−1) and the quadrupole moment (Qxx, Qyy, Qzz) were also selected for analysis in this paper. Finally, the data were subjected to regression linear analysis by SPSS software to establish a 2D-QSAR model.
3.4.2. Mechanism analysis of high biodegradability based on molecular docking and molecular dynamics Aiming at clarifying the modified molecule and Derivative-15 with oxidoreductase molecular docking results, amino acid residues surrounding the two molecules were analysed. The blue circles represent the water molecules around the FQs, while the green circles and the pink circles represent the amino acid residues that acted on the FQs with van der Waals forces and electrostatic field forces, respectively (Fig. 2). By comparing the molecular docking results of LEV and Derivative15 with oxidoreductase, it was found that all the amino acid residues around the FQs contributed to the molecular docking process and were considered favourable amino acids (Chu and Li, 2019). Compared with the amino acid residues around LEV, the amount of amino acid residues around Derivative-15 that interacted with Derivative-15 with the electrostatic field force was increased. The amino acids PRO A:303,
log LDS = 8.615 − 0.003F − 3.388ELUMO + 2.563EHOMO − 9.816q+ + 0.130q− + 0.637q+H + 0.002Qxx + 0.003Q yy + 0.005Qzz (1) In the model, R was 0.854 (n = 23, p = 0.001, rmin = 0.854 > r: 0.6524), and the Sig value was 0.014 (< 0.05), so the model passed the 5
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Fig. 2. Molecular docking maps of LEV and Derivative-15 with oxidoreductase.
Fig. 3. The potential energy, electrostatic energy and van der Waals line diagrams of the interaction between oxidoreductase and LEV and Derivative-15.
process was simulated by DS software under the CHARMM force field. To make the molecular docking environment more suitable for the living body, the solvent environment in the simulation process was a salt solution. Since water molecules and ions were arranged in a regular way when adding solvent, the protein (oxidoreductase)-ligand (FQs) complex system needs to be bound in the kinetic simulation to optimize water molecules and ions. Finally, the potential energy (PE), electrostatic energy (EE) and van der Waals energy (VE) of the interaction between protein and FQs were simulated in the process of molecular dynamics (Fig. 3). According to the potential energy line graph, LEV and Derivative 15 reached the most stable energy conformations at 55,000 steps (energy value: −96723.2 kcal/mol) and 87,000 steps (energy value:
GLN A:200 and GLN A:300 acted with the electrostatic field force around LEV. In addition to these, there was an additional amino acid, MET A:305, interacting with the electrostatic field around Derivative15, which was the favourable amino acid binding to the protein. Therefore, it was concluded that the electrostatic field force was the main influencing factor enhancing the affinity between the modified molecules and receptor proteins (oxidoreductase) and improving the biodegradation of FQs with proteins during molecular docking. Molecular dynamics is a set of molecular simulation methods for calculating molecular motion in a system under Newtonian mechanics. The calculation method can most intuitively reflect the microscopic changes through the form of energy and has become an important method for drug discovery (Naqvi et al., 2018). The molecular docking 6
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Fig. 4. Proposed pathways for the degradation of LEV and Derivative-15 by the white rot fungus.
−97059.4 kcal/mol), respectively. In the process of the molecular dynamics simulation, the fluctuation ranges of potential energy between LEV and Derivative-15 and the protein were −96723.2 kcal/mol - −95956.1 kcal/mol and −97059.4 kcal/mole- −96353.2 kcal/mol, respectively. Therefore, the overall energy conformational form of Derivative-15 and protein was lower, and the binding was more stable. The van der Waals broken line showed that the trends of change of LEV and Derivative-15 were similar to that of protein binding, and the energy ranges were 3552.29 kcal/mole-4219.91 kcal/mol and 3247.15 kcal/mole-4011.1 kcal/mol, respectively. Compared with LEV (energy range: −85068 kcal/mole- −83685.5 kcal/mol), the electrostatic energy of Derivative-15 was decreased when it was binding to protein, and its energy range was −86100.7 kcal/mole- −84956.7 kcal/mol. Therefore, in the simulation process of molecular dynamics, it was found that electrostatic energy played a major role in the binding of Derivative 15 and oxidoreductase, which was basically consistent with the conclusion of molecular docking. In addition, the q+ of FQ was related to its electrostatic force (Yang and Li, 2015). According to the
sensitivity analysis of the 2D-QSAR model, q+ was the main parameter affecting the biodegradability of the FQs. Therefore, the molecular dynamics simulation results also verified the sensitivity analysis conclusion of the 2D-QSAR model. 3.5. Biodegradation pathways inference of LEV and derivative 15 Microbial degradation is a significant method of antibiotic degradation in the environment. FQs entering sewage treatment plants can be degraded by microorganisms to reduce their presence in water. The white rot fungi constitutes an extracellular degradation system through the enzyme system secreted by cells, creating a biodegradable environment for refractory pollutants such as FQs. This paper inferred the pathways of the degradation of LEV and Derivative-15 by P. chrysosporium. The main degradation pathways were decarboxylation, defluorination and piperazine ring cleavage (Wetzstein et al., 1998; Zhang et al., 2019c; Wei et al., 2019). Pathway A was the defluorination reaction. After defluorination of the reactant, hydroxy was formed at the 7
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original position. After that, the hydroxylation reaction was carried out in the ortho-position to generate the final product of pathway A. Pathway B showed that, after the decarboxylation of FQs, hydroxy was also generated at the original site, which led to ring opening through a further reaction, thus producing the final product of pathway B. Pathway C was the typical reaction of FQs, namely, piperazine ring cleavage. The final reaction completely broke the piperazine ring, and the NH2 group was only retained at the original site (Fig. 4). The Gaussian 09 software was used to calculate the energy barrier of the degradation reaction of LEV and Derivative-15. Compared with LEV, the energy barrier values (ΔE) of Derivative-15 in the three biodegradation pathways were lower, indicating that Derivative-15 had a smaller energy barrier to break when it was biodegraded, so it was easier to generate degradation products. The calculation of the sum of energy barriers before and after FQ molecular modification shows that the total energy barrier of LEV pathway A was 30.995 kJ/mol and that of Derivative-15 pathway A was 21.056 kJ/mol, which was 32.07% lower than that of LEV. Relative to LEV, the total energy barrier values of Derivative-15 in the pathways B and C decreased by 3.84% and 3.44%, respectively. The sum of the energy barrier of LEV was 1820.374 kJ/mol, while the energy barrier of Derivative-15 was 1744.19 kJ/mol, which was 4.19% lower than that of LEV. Hence, pathway A was the easiest and most likely for degradation. Compared with LEV, the biodegradability of Derivative-15 was increased by 27.85%, and the decreased degree of the total energy barrier value of pathway A was extremely close to the improvement of biodegradability. Therefore, it was proven that Derivative-15 was more biodegradable.
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4. Conclusion A method for the molecular design and screening of FQ antibiotics with high biodegradability and environmental friendliness based on the 3D-QSAR model was established in this paper. Through molecular docking, molecular dynamics simulation and inference of the biodegradation pathway, it is concluded that the designed environmentally friendly FQ antibiotic has a significant improvement in biodegradability, slightly increased pharmacological toxicity and obvious environmental friendliness, which is expected to be used as a new antibiotic drug. CRediT authorship contribution statement Yilin Hou: Conceptualization, Data curation, Methodology, Software, Writing - original draft. Yuanyuan Zhao: Formal analysis, Resources. Qing Li: Visualization. Yu Li: Validation, Writing - review & editing. Declaration of competing interest None. Acknowledgements We thank American Journal Experts (AJE) for English language editing. This manuscript was edited for English language by American Journal Experts (AJE). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecoenv.2020.110186. References Chen, G., Liu, X., Tartakevosky, D., et al., 2016. Risk assessment of three fluoroquinolone antibiotics in the groundwater recharge system [J]. Ecotoxicol. Environ. Saf. 133,
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Glossary CoMFA: Comparative Molecular Field Analysis FQs: Fluoroquinolones LEV: Levofloxacin PDB: Protein Data Bank DS: Discovery Studio LDS: LibDockScore PLS: Partial least squares SEE: Standard error of estimate DFT: Density functional theory NPT: Constant-pressure, constant-temperature HIS: Hospital Information System HQSAR: Holographic quantitative structure-activity relationship SPSS: Statistical Product and Service Solutions PE: Potential energy EE: Electrostatic energy VE: Van der Waals energy
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Highly biodegradable fluoroquinolone derivatives designed using the 3DQSAR model and biodegradation pathways analysis
T
Yilin Houa,b, Yuanyuan Zhaoa,b, Qing Lia,b, Yu Lia,b,∗ a b
College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing, 102206, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Fluoroquinolones QSAR Molecular modification Biodegradation Molecular docking Molecular dynamics
A three-dimensional quantitative structure–activity relationship (3D-QSAR) model was established based on molecular structures and docking scores (representing the biodegradability); the scores were obtained for 23 fluoroquinolones (FQs) and the oxidoreductase (PDB ID: 1YZP) of Phanerochaete chrysosporium in the aerobic process of municipal wastewater treatment plants. In the Comparative Molecular Field Analysis (CoMFA) model, q2 was 0.516 and r2pred was 0.727, which showed that the model was reliable and robust. The modification information obtained by the contour maps showed that introducing electronegative, bulky or electropositive groups at different active sites could increase the biodegradability of fluoroquinolone derivatives. Using levofloxacin (LEV) as a modified molecule, 35 fluoroquinolone derivatives with higher biodegradability than LEV were designed. After the evaluation of genotoxicity, bioconcentration and photodegradation, Derivative-15, with higher biodegradability (increased by 27.85%), higher genotoxicity, higher photodegradation and lower bioconcentration, was identified as the most environmentally friendly fluoroquinolone derivative. The 2D-QSAR model of FQ biodegradability was established through the quantization parameters, and q+ was identified as the main parameter affecting the biodegradability of FQs through sensitivity analysis. In addition, the docking results of LEV and Derivative-15 with the oxidoreductase in P. chrysosporium showed that the electrostatic field force between Derivative-15 and the amino acid residues promoted the binding of the donor to the receptor protein, thereby increasing the biodegradability of Derivative-15. Additionally, molecular dynamics simulations revealed that the enhancement of the electrostatic field force with Derivative-15 could promote the binding of the ligand to the receptor, which was basically consistent with the conclusion of molecular docking. Finally, the three microbial degradation pathways of LEV and Derivative-15 were also proposed. The total energy barrier value of the pathway with the lowest total energy barrier of biodegradation was reduced by 32.07%, which was basically consistent with the enhancement of biodegradability of Derivative-15.
1. Introduction Due to the widespread use of antibiotics, the abuse of antibiotics has increased dramatically in recent years (Gong, 2016). However, more than 70% of quinolones are not completely metabolized in the body after taking them, and they are excreted in urine and faeces and are biologically active (Van Doorslaer et al., 2014). Therefore, the large amount of quinolone antibiotics used will cause harm to human health and the environment (Sarmah et al., 2006; Zhu et al., 2019). There are currently four generations of quinolone, of which more than 60 fluoroquinolones are in use (Feng et al., 2017). Fluoroquinolone (FQ) is an important synthetic antibiotic and broad-spectrum antibacterial drug. It is active against both gram-positive and gram-negative bacteria; therefore, FQs are widely used in the medical field (Naeem et al., 2016; ∗
Zhang et al., 2017; Emmerson, 2003). FQs are the most widely used antibiotics, and the total annual consumption is approximately 4.4 × 107 kg throughout the world (Simoens et al., 2011). FQs remain in the soil or flow into the groundwater through rainwater runoff and farmland irrigation when they enter the environment; otherwise, they enter municipal wastewater treatment plants. FQs are frequently detected in water environments, soil sediments and organisms (Chen et al., 2019). Xie et al. detected FQs of 27.8–1537.4 μg/kg in the topsoil of Guangdong, China (Xie et al., 2012); Chen et al. detected norfloxacin, ofloxacin and enrofloxacin in groundwater in Beijing and Changzhou, China, and the detection rate reached 100% with a maximum concentration of 96.8 ng/L (Chen et al., 2016). Studies have shown that a variety of FQs have emerged in municipal wastewater treatment plants in many countries, and only some of the common
Corresponding author. College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China. E-mail addresses: [email protected] (Y. Hou), [email protected] (Y. Zhao), [email protected] (Q. Li), [email protected] (Y. Li).
https://doi.org/10.1016/j.ecoenv.2020.110186 Received 26 October 2019; Received in revised form 6 January 2020; Accepted 8 January 2020 0147-6513/ © 2020 Elsevier Inc. All rights reserved.
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2. Materials and methods
compounds can be eliminated in conventional sewage treatment systems (Liu et al., 2012). The ofloxacin concentration in a municipal wastewater treatment plant in Italy is 463 ng/L, its effluent content is 191 ng/L (Zuccato et al., 2010), and the removal rate is only 58.75%; the influent content of enrofloxacin in a Portuguese municipal wastewater treatment plant is 121.8–447.1 ng/L, its effluent content is 53.7–211.5 ng/L (Seifrtova et al., 2008), and the removal rate is only 52.70%–55.91%. Therefore, the removal rate of FQs in municipal sewage treatment plants is low, and a great quantity of quinolones are still discharged into the environment after treatment. FQs are difficult to biodegrade (Santos et al., 2015) and are mainly eliminated by adsorption in sewage sludge in sewage treatment plants (Prieto et al., 2011). When sewage sludge enters the environment, there is still a risk of harming human health and the environment (Pedersen et al., 2005). Biodegradation is a promising method to remove FQs, and biotransformation of microorganisms is an effective way to degrade and remove FQs (Xia et al., 2019). Since the current municipal sewage treatment plants are not designed to simply treat a certain drug or pollutant (Dorival-García et al., 2013), the degradation of FQs in municipal sewage treatment plants is not ideal, and most sewage treatment plants need to start tertiary sewage treatment. Studies have shown that the average removal rate of levofloxacin (LEV) in municipal sewage treatment plants is less than 10% (Xiong et al., 2017). Considering that the drug molecules of FQs are difficult to biodegrade, the number of FQs entering the environment and municipal sewage treatment plants is increasing. The use of FQs has been threatened by the increasing number of drug-resistant bacteria and clinical pathogens, which has caused negative impacts on the natural environment and public health (Li, 2005). Therefore, the design of novel FQs is particularly important, aiming to improve the biodegradability and pharmacological toxicity of FQs towards environmentally friendly molecular succession. 3D-QSAR focuses on the physical-chemical properties of ligands that may have a causal relationship with biological or chemical reactions (Cramer, 2015). Based on the use of three-dimensional structural information and biological activity of compounds to establish a reasonable mathematical model, the established model is used to determine the relationship between the structures and bioactivities (Zhang et al., 2019a). Therefore, the optimized 3D-QSAR model can be used to improve the environmental and toxicological assessment of chemicals (Linden et al., 2017). CoMFA is one of the most commonly used methods in 3D-QSAR, and has very good prediction results (De Simone et al., 2017; Uddin et al., 2013; Zhang et al., 2019b; Cheng et al., 2018). Zhou et al. (2016) used the CoMFA model to study the binding mechanism of 6-aminonicotinate-based antagonists and P2Y12; Yu et al. (2018) used the CoMFA method to study the affinity of azo dye molecules and cellulose fibers. They all obtained satisfactory results. White rot fungi can degrade or reduce various environmental pollutants (Zeng et al., 2015, 2019). We use CoMFA method to study the degradation of FQs by manganese peroxidase (classification: oxidoreductase) produced by P. chrysosporium belonging to the white rot fungus (Wesenberg et al., 2003; Zhao et al., 2018a). In the present work, the docking scores of FQs and oxidoreductases produced by P. chrysosporium that can effectively degrade FQs were calculated by molecular docking technique, and the scoring values were used as data to establish a model using 3DQSAR. Through CoMFA force field analysis and contour map analysis to find the modification site, LEV was used as the modified molecule, and 35 kinds of novel FQs derivatives were designed by substitution reactions, and the biodegradability and environmental friendliness (genotoxicity, bioconcentration, and photodegradability) of the novel FQ derivatives were evaluated. Finally, Derivative-15, with high biodegradability and other environmentally friendly characteristics, was screened as a novel FQ. In addition, the microbial degradation pathways of Derivative-15 were deduced and the biodegradation mechanism of Derivative-15 was analysed.
2.1. Data source The PDB (Protein Data Bank) database was used to retrieve the protein structure (PDB ID: 1YZP) of P. chrysosporium, which was used as the receptor, and 23 FQs were used as donors in this study. Software Discovery Studio 4.0 (DS) (Zhao et al., 2018b; Gu et al., 2018; Ren et al., 2019) was used to dock the FQs and protein receptor, and the docking score function value was used to represent the biodegradability of FQs. After taking the logarithm of the docking scoring function (logLDS), the logLDS of 18 molecules were randomly selected as the training set, and the logLDS of the remaining 6 molecules were selected as the test set (both the training set and the test set contained modified molecules) according to a ratio of 3:1. Then, Sybyl-X 2.0 (Rao et al., 2013) was used to establish the biodegradable 3D-QSAR model of FQs. 2.2. Establishment of FQ biodegradation 3D-QSAR model Sybyl-X2.0 (Yang and Liu, 2019) software was used for molecular drawing and optimization. The Ciprofloxacin (CIP) molecule Figure figs1 Alignment the common framework of the template compoundwith the maximum log value of the molecular docking score was selected as the template molecule, and the same structure of 23 molecules was selected as the common framework for the alignment of the training set. The CoMFA model in the QSAR module was used to analyse the electrostatic field and static fields of the molecules. Partial least squares (PLS) analysis was used to establish the relationship between the structure and biological activity of the target compound. When PLS analysis was used, the leave-one-out method was first used to crossvalidate the compound of the training set, and the cross-validation coefficient q2 and the optimal principal component number N were obtained. Then, regression analysis was carried out with No Validation to obtain the non-cross-validation coefficient, R2, standard error of estimate (SEE) and test value, F, and, finally, the establishment of the CoMFA model was completed. 2.3. Calculation method of quantization parameters of the FQs Density functional theory (DFT) is a quantum chemical method to study the electronic structure of multi-electron systems. By means of a variational method or numerical method, the kinetic energy and potential energy of electrons are averaged to obtain the approximate solution of the molecular wave function Schrodinger equation. DFT integrates the idea of statistical mathematics and only calculates the total electron density, not the behaviour of each electron. Currently, it is widely used in the environmental field (Qu et al., 2012). The Gaussian 09 software was used to optimize the geometry of the studied FQs at the B3LYP/6-31G(d) theoretical level (Wu, 2016; Cheng et al., 2020), and the molecular quantization parameters were calculated, including the energy of the highest occupied molecular orbital (EHOMO, a.u.), the energy of the lowest unoccupied molecular orbital (ELUMO, a.u.), the most positive Mulliken charge (q+, e), the most negative Mulliken charge (q-, e), the most positive Mulliken hydrogen charge (q+H, e), positive frequency (F, cm−1) and the quadrupole moment (Qxx, Qyy, Qzz). Finally, the GaussView 5.0 program was used for visual analysis. 2.4. Establishment of the 2D-QSAR model for biodegradability Statistical Product and Service Solutions (SPSS) (Hou et al., 2017) software was used to perform multiple linear regression analysis on the quantization parameters of FQs calculated by DFT, which is the correlation analysis between two or more independent variables and one dependent variable. The biodegradability of FQs was taken as the dependent variable and the quantization parameters as the independent variable. Then, the least square method was used to calculate the model 2
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Fig. 1. Structure of the modified molecule (LEV) and contour maps of the CoMFA model.
constant-temperature) system was adopted in this paper, the number of particles in the control system was constant, the control pressure and temperature were near the specified value, and the result was analysed in the form of energy through server simulation.
parameters to establish a 2D-QSAR model of the biodegradability of the FQs, and the model was statistically tested. 2.5. Mechanism analysis of the high biodegradability of the FQs based on molecular docking
3. Results and analysis Molecular docking is widely applied to the interaction between small molecules and receptor proteins, which requires both space shape matching and energy matching (Cornell et al., 1995). Molecular docking can determine the affinity of small molecules and receptor proteins, and the binding effect of ligands and receptors can be considered as a whole. The docking method of LibDock in DS software was used to dock the oxidoreductase and FQs. After the docking was completed, the score value (LibDockScore) was generated, and the docking effect was judged from the level of the score value. In addition, the twodimensional diagram of ligand-protein interaction can directly reflect the interaction between FQs and oxidoreductase and the changes of key amino acid residues.
3.1. Evaluation of the CoMFA model for FQs biodegradability The cross-validation q2 of the CoMFA model of the FQ biodegradability was 0.516 (> 0.5), the optimal principal component, N, was 6, the non-cross-validation coefficient, R2, was 0.999, the standard error of estimate (SEE) was 0.006 and the test value (F) was 1601.635. Therefore, the model had reliable prediction ability, good fitting ability and good robustness. In the CoMFA model, the contribution rates of the electrostatic field and steric factors were 13.9% and 86.1% respectively, indicating that, compared with the electrical distribution, the effect of the space group had a significant influence on the biodegradability of FQs. In addition, the interactive validation coefficient, r2pred, of the external test set of the model was 0.727 (> 0.6), indicating that the model had external prediction ability and could predict the biodegradability of FQs derivatives.
2.6. Mechanism analysis of biodegradation based on molecular dynamics Molecular dynamics is a molecular simulation method based on force fields and is widely used in the research and analysis of intermolecular interactions in the fields of drug design and life sciences (Ogrizek et al., 2015). DS software was used to simulate the oxidoreductase and FQs molecular complex in the CHARMM field. The CHARMM force field can obtain good results for various simulation systems, such as small molecules and solvated large biological systems. Moreover, the protein-ligand complex was added to the physiological saline solvent environment to make the system more compatible with organisms. To break the regular arrangement of water molecules and ions when the solvent was added, and more in line with the simulated environment, water molecules and ions needed to be optimized. After the water molecules and ions were optimized, Standard Dynamics Cascade process including Minimization, Minimization 2, Heating, Equilibration and Production was performed, in which the Simulation Time of the Production phase (ps) was 200 and Time Step (fs) was set to 2. Since the control of temperature and pressure had an important impact on the nature of the system, the NPT (Constant-pressure,
3.2. Molecular modification of high biodegradable FQs based on the CoMFA model In 2016, LEV ranked first in the frequency of antimicrobial drugs in the HIS system of the first people's hospital of Zhengzhou, China (Liu et al., 2018). LEV was used frequently in quinolone antibiotics. Therefore, highly biodegradable FQs were modified with LEV as the target molecule. In the contour maps of the CoMFA model, the green region indicates that the introduction of bulky groups can improve the biodegradation activity of the molecule, while the yellow region indicates that the introduction of bulky groups can reduce the biodegradation activity of the molecule. The red region in the electrostatic field shows that the introduction of negatively charged groups enhances the biodegradation activity of the molecule, and the blue region shows that the introduction of positively charged groups enhances the biodegradation activity 3
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ensure a remarkable improvement in biodegradability of the derivatives, derivatives with a biodegradability increase of more than 10% were selected for further research. Therefore, 24 derivatives, such as Derivative 1, Derivative 2, and Derivative 3, were selected after screening.
of the molecule (Zhao and Li, 2019). The contour map (Fig. 1) shows that green and yellow contours appeared around site 1. Therefore, increasing or decreasing the volume of groups at site 1 may change the biodegradation activity of the molecule to different degrees, thus changing the value of the docking scoring function. Green contours appeared around sites 3, 4, 5, and 6, so introducing the bulky groups around these four sites could improve the biodegradation of the molecule. The electrostatic field shows that sites 1 and 2 appear around the displayed red region, indicating that the introduction of electronegative groups could improve the molecular biological degradation activity. In contrast, sites 5 and 6 were surrounded by blue contours, and the introduction of groups with positive electrical groups could also improve the biodegradation activity of the molecule. To this end, 11 groups were selected for molecular modification, namely, –CH3, –CH2CH3, –F, –CHO、-CN, –OCH3, –OH, –COOH, –NH2, –CH]CH2 and –SH groups. Finally, a total of 35 LEV derivatives were designed.
3.3.2. Evaluation of the environmental friendliness of derivatives The environmental friendliness (genotoxicity, bioconcentration and photodegradation) of FQs was evaluated. Among these factors, the genotoxicity of the FQs was functional and represented the bactericidal ability of quinolones. Zhao et al. (2019a) used the reliable Holographical Quantitative Structure-Activity Relationship (HQSAR) model to predict the toxicity of FQs. HQSAR is a new QSAR technique that establishes a correlation between the biological activity of a compound and the molecular structure composition described by the sub-structural fragment type of the molecule (Sun et al., 2013). The bioconcentration of FQs means that organisms in the environment may accumulate FQs through the surrounding environment (water, soil, atmosphere) by means of non-ingestion, resulting in an excessive amount of this drug in organisms and damaging them. FQs can be eliminated by non-biodegradable means in the environment, thereby reducing its threat to the environment. Zhao (Zhao et al., 2018a) (Zhao et al., 2019b) et al. have established a 3D-QSAR with good predictability and predicted the bioconcentration and photodegradation of the FQs. The model was robust, and the prediction was reliable. To ensure that the FQs had increased genotoxicity, decreased bioaccumulation and improved photodegradability under the condition of improved biodegradability, the three characteristics were predicted by the QSAR
3.3. Prediction and evaluation of biodegradability and environmental friendliness 3.3.1. Prediction and evaluation of biodegradability Thirty-five LEV derivatives were designed with different sites and different groups for substitution modification. The relative differences between the predicted values of the biodegradability and biodegradability of LEV are shown in Table 1. The biodegradability of the 35 derivatives was higher than that of LEV, and the biodegradability was increased by 0.40%–29.40%. To
Table 1 Prediction of biodegradability of FQ antibiotic derivatives based on the CoMFA model. No.
Target molecule Derivative-1 Derivative-2 Derivative-3 Derivative-4 Derivative-5 Derivative-6 Derivative-7 Derivative-8 Derivative-9 Derivative-10 Derivative-11 Derivative-12 Derivative-13 Derivative-14 Derivative-15 Derivative-16 Derivative-17 Derivative-18 Derivative-19 Derivative-20 Derivative-21 Derivative-22 Derivative-23 Derivative-24 Derivative-25 Derivative-26 Derivative-27 Derivative-28 Derivative-29 Derivative-30 Derivative-31 Derivative-32 Derivative-33 Derivative-34 Derivative-35
Compounds
Levofloxacin 2-Fluorine 2-Fluorine, 5-Ethylene 2-Hydroxyl 2-Sulfhydryl 2-Amino, 5-Fluorine 2-Methoxy, 5- Aldehyde 3-Ethyl 3-Ethyl, 5-Fluorine 2-Hydroxyl, 5-Ethylene 4-Ethyl 2-Fluorine, 5-Cyanide 2-Ethylene, 5-Hydroxyl 6-Fluorine 2-Fluorine, 5-Ethyl 2-Fluorine, 5-Aldehyde 2-Fluorine, 3-Ethylene 2-Fluorine, 3-Aldehyde 2-Aldehyde, 5- Fluorine 2-Fluorine, 3-Amino 2-Fluorine, 3-Methoxy 2-Ethylene, 5- Hydroxyl 2-Fluorine, 4-Aldehyde 2-Fluorine, 4-Fluorine 2-Fluorine, 4-Methoxy 2-Fluorine, 5-Hydroxyl 2-Amino, 5-Methoxy 2-Amino, 5-Hydroxyl 3-Amino, 6- Fluorine 3-Aldehyde, 6- Fluorine 2-Fluorine, 3-Ethyl 2-Aldehyde, 5-Methoxy 2-Fluorine, 3-Fluorine 2-Fluorine, 5-Amino 2-Fluorine, 4-Ethyl 2-Ethylene, 5-Fluorine
For CoMFA model Predicted
Equivalent docking value
Relative error (%)
1.749 1.861 1.793 1.804 1.798 1.804 1.802 1.822 1.771 1.801 1.811 1.793 1.797 1.840 1.832 1.855 1.773 1.853 1.774 1.800 1.825 1.770 1.794 1.791 1.830 1.786 1.770 1.771 1.804 1.783 1.839 1.751 1.792 1.777 1.860 1.757
56.094 72.585 62.035 63.553 62.789 63.553 63.297 66.431 59.109 63.297 64.585 62.035 62.537 69.161 67.782 71.714 59.347 71.138 59.347 63.043 66.699 58.871 62.286 61.786 67.510 61.044 58.871 59.109 63.553 60.555 68.883 56.320 62.035 59.827 72.293 57.235
29.40 10.59 13.30 11.94 13.30 12.84 18.43 5.37 12.84 15.14 10.59 11.49 23.29 20.84 27.85 5.80 26.82 5.80 12.39 18.91 4.95 11.04 10.15 20.35 8.82 4.95 5.37 13.30 7.95 22.80 0.40 10.59 6.65 28.88 2.03
Annotation: Novel derivatives and their biodegradability predictions and relative errors. 4
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Table 2 Prediction of biotoxicity, bioconcentration and photodegradability of FQ antibiotic derivatives through QSAR models. No.
LEV Derivative-1 Derivative-2 Derivative-3 Derivative-4 Derivative-5 Derivative-6 Derivative-7 Derivative-9 Derivative-10 Derivative-11 Derivative-12 Derivative-13 Derivative-14 Derivative-15 Derivative-17 Derivative-19 Derivative-20 Derivative-22 Derivative-23 Derivative-24 Derivative-28 Derivative-30 Derivative-32 Derivative-34
Biotoxicity
Bioaccumulation
Photodegradability
Pred.
Pred.
Pred.
8.224 8.539 8.338 9.024 8.938 8.969 8.504 8.202 8.825 8.129 8.266 8.999 8.162 8.555 8.309 8.752 9.653 8.884 8.516 8.391 8.949 9.755 8.905 8.507 8.702
Relative error (%)
3.83 1.39 9.73 8.68 9.06 3.40 −0.27 7.31 −1.16 0.51 9.42 −0.75 4.02 1.03 6.42 17.38 8.03 3.55 2.03 8.82 18.62 8.28 3.44 5.81
1.399 1.483 1.813 1.712 1.071 1.564 0.755 1.283 1.629 1.803 1.568 1.492 1.411 1.605 1.113 1.448 2.265 2.165 1.745 1.459 1.738 1.783 1.835 1.521 2.096
Relative error (%)
6.00 29.59 22.37 −23.45 11.79 −46.03 −8.29 16.44 28.88 12.08 6.65 0.86 14.72 −20.44 3.50 61.90 54.75 24.73 4.29 24.23 27.45 31.17 8.72 49.82
1.934 1.984 1.993 1.899 1.980 2.084 1.724 2.018 1.909 2.024 2.011 1.834 1.943 2.007 1.727 1.967 2.011 1.969 1.970 1.903 1.986 2.005 2.011 1.910 2.001
Table 3 Sensitivity coefficient's calculation for independent variables in the 2D-QSAR model. Independent parameters
Relative error (%) F ELUMO EHOMO q+ qq+H Qxx Qyy Qzz
2.59 3.05 −1.81 2.38 7.76 −10.86 4.34 −1.29 4.65 3.98 −5.17 0.47 3.77 −10.70 1.71 3.98 1.81 1.86 −1.60 2.69 3.67 3.98 −1.24 3.46
Sensitivity 10%
30%
50%
70%
90%
Average
−0.042 0.103 −0.358 −4.835 −0.052 0.169 −0.194 −0.296 −0.480
−0.049 0.120 −0.452 −47.227 −0.061 0.194 −0.238 −0.370 −0.622
−0.057 0.136 −0.560 8.697 −0.072 0.218 −0.285 −0.453 −0.793
−0.066 0.151 −0.686 4.564 −0.082 0.240 −0.336 −0.546 −1.005
−0.074 0.166 −0.835 3.319 −0.092 0.261 −0.391 −0.652 −1.274
−0.058 0.135 −0.578 −7.096 −0.072 0.216 −0.289 −0.464 −0.835
significance test. The coefficients of the parameters F, ELUMO and q+ in the model were negative, indicating that they were negatively correlated with the molecular biodegradability of the FQs. In contrast, the coefficients of the parameters EHOMO, q-, q+H, Qxx, Qyy and Qzz were positive, indicating that they were positively correlated with the molecular biodegradability of the FQs. The parameters in the above equation were analysed by the sensitivity analysis method to determine the parameters that have the greatest impact on the biodegradability of the FQs (Du et al., 2019). Based on the original value, each specified parameter was increased by 10%, 30%, 50%, 70% and 90%, and other parameters remained unchanged. Then, FQ biodegradability values under different increases of specified parameters were calculated by the above equation (Table 3). A sensitivity coefficient formula was used to calculate the sensitivity degree of FQ biodegradability to changes of each parameter, and the final sensitivity degree, the influence degree of each parameter on FQ biodegradability, was expressed by the sensitivity coefficient, SCi. The calculation formula is as follows:
Annotation: Screened derivatives and their environmental characteristics predictions and relative errors.
model (Table 2). Among the 24 derivatives, the genotoxicity of 21 derivatives, such as Derivative-1, Derivative-2, and Derivative-3, was higher than that of LEV (increased by 0.51%–18.62%). The bioconcentrations of Derivative-4, Derivative-6, Derivative-7 and Derivative-15 decreased by 8.29%–46.03% compared to that of LEV. The photodegradability of derivatives, such as Derivative-3, Derivative-6, and Derivative-9, was higher than that of LEV (increased by 1.24%–10.86%). Derivative-6 and Derivative-15 simultaneously met the requirements of increased genotoxicity and photodegradability and decreased bioconcentration; the biodegradability of Derivative 15 was increased more significantly.
SCi = (ΔYi / Yi )/(ΔXi / Xi )
(2)
SCi——Sensitivity coefficient of parameter i; ΔXi/Xi——The rate of change of the specified parameter; ΔYi/Yi——The rate of change in biodegradability of FQs. Through the comparison of the average absolute value of the sensitivity coefficient of nine parameters, the influence of the FQ molecular biological degradability sensitivity degree is q+ > Qzz > EHOMO > Qyy > Qxx > q+H > ELUMO > q- > F, and the parameter of the highest sensitivity coefficient, the absolute value of q+, reached 7.662; other parameters’ sensitivity coefficient absolute values were less than 1. Therefore, the result showed that q+ was the main parameter affecting the biodegradability of the FQs.
3.4. Mechanism analysis of the biodegradability of FQ derivatives 3.4.1. High biodegradability analysis of FQs based on the 2D-QSAR model The 2D-QSAR model of FQ biodegradability was established with the biodegradability parameter (Total score) as the dependent variable and the quantification parameters as the independent variable (Zeng et al., 2016). Using Gaussian software, the energy of the highest occupied molecular orbital (EHOMO, a.u.) and the lowest unoccupied molecular orbital (ELUMO, a.u.) of 23 FQs used in 2D-QSAR modelling were calculated by the DFT method, which were the key parameters determining the molecular reaction. The most positive Mulliken charge (q+, e), the most negative Mulliken charge (q-, e), the most positive Mulliken hydrogen charge (q+H, e), the positive frequency (F, cm−1) and the quadrupole moment (Qxx, Qyy, Qzz) were also selected for analysis in this paper. Finally, the data were subjected to regression linear analysis by SPSS software to establish a 2D-QSAR model.
3.4.2. Mechanism analysis of high biodegradability based on molecular docking and molecular dynamics Aiming at clarifying the modified molecule and Derivative-15 with oxidoreductase molecular docking results, amino acid residues surrounding the two molecules were analysed. The blue circles represent the water molecules around the FQs, while the green circles and the pink circles represent the amino acid residues that acted on the FQs with van der Waals forces and electrostatic field forces, respectively (Fig. 2). By comparing the molecular docking results of LEV and Derivative15 with oxidoreductase, it was found that all the amino acid residues around the FQs contributed to the molecular docking process and were considered favourable amino acids (Chu and Li, 2019). Compared with the amino acid residues around LEV, the amount of amino acid residues around Derivative-15 that interacted with Derivative-15 with the electrostatic field force was increased. The amino acids PRO A:303,
log LDS = 8.615 − 0.003F − 3.388ELUMO + 2.563EHOMO − 9.816q+ + 0.130q− + 0.637q+H + 0.002Qxx + 0.003Q yy + 0.005Qzz (1) In the model, R was 0.854 (n = 23, p = 0.001, rmin = 0.854 > r: 0.6524), and the Sig value was 0.014 (< 0.05), so the model passed the 5
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Fig. 2. Molecular docking maps of LEV and Derivative-15 with oxidoreductase.
Fig. 3. The potential energy, electrostatic energy and van der Waals line diagrams of the interaction between oxidoreductase and LEV and Derivative-15.
process was simulated by DS software under the CHARMM force field. To make the molecular docking environment more suitable for the living body, the solvent environment in the simulation process was a salt solution. Since water molecules and ions were arranged in a regular way when adding solvent, the protein (oxidoreductase)-ligand (FQs) complex system needs to be bound in the kinetic simulation to optimize water molecules and ions. Finally, the potential energy (PE), electrostatic energy (EE) and van der Waals energy (VE) of the interaction between protein and FQs were simulated in the process of molecular dynamics (Fig. 3). According to the potential energy line graph, LEV and Derivative 15 reached the most stable energy conformations at 55,000 steps (energy value: −96723.2 kcal/mol) and 87,000 steps (energy value:
GLN A:200 and GLN A:300 acted with the electrostatic field force around LEV. In addition to these, there was an additional amino acid, MET A:305, interacting with the electrostatic field around Derivative15, which was the favourable amino acid binding to the protein. Therefore, it was concluded that the electrostatic field force was the main influencing factor enhancing the affinity between the modified molecules and receptor proteins (oxidoreductase) and improving the biodegradation of FQs with proteins during molecular docking. Molecular dynamics is a set of molecular simulation methods for calculating molecular motion in a system under Newtonian mechanics. The calculation method can most intuitively reflect the microscopic changes through the form of energy and has become an important method for drug discovery (Naqvi et al., 2018). The molecular docking 6
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Fig. 4. Proposed pathways for the degradation of LEV and Derivative-15 by the white rot fungus.
−97059.4 kcal/mol), respectively. In the process of the molecular dynamics simulation, the fluctuation ranges of potential energy between LEV and Derivative-15 and the protein were −96723.2 kcal/mol - −95956.1 kcal/mol and −97059.4 kcal/mole- −96353.2 kcal/mol, respectively. Therefore, the overall energy conformational form of Derivative-15 and protein was lower, and the binding was more stable. The van der Waals broken line showed that the trends of change of LEV and Derivative-15 were similar to that of protein binding, and the energy ranges were 3552.29 kcal/mole-4219.91 kcal/mol and 3247.15 kcal/mole-4011.1 kcal/mol, respectively. Compared with LEV (energy range: −85068 kcal/mole- −83685.5 kcal/mol), the electrostatic energy of Derivative-15 was decreased when it was binding to protein, and its energy range was −86100.7 kcal/mole- −84956.7 kcal/mol. Therefore, in the simulation process of molecular dynamics, it was found that electrostatic energy played a major role in the binding of Derivative 15 and oxidoreductase, which was basically consistent with the conclusion of molecular docking. In addition, the q+ of FQ was related to its electrostatic force (Yang and Li, 2015). According to the
sensitivity analysis of the 2D-QSAR model, q+ was the main parameter affecting the biodegradability of the FQs. Therefore, the molecular dynamics simulation results also verified the sensitivity analysis conclusion of the 2D-QSAR model. 3.5. Biodegradation pathways inference of LEV and derivative 15 Microbial degradation is a significant method of antibiotic degradation in the environment. FQs entering sewage treatment plants can be degraded by microorganisms to reduce their presence in water. The white rot fungi constitutes an extracellular degradation system through the enzyme system secreted by cells, creating a biodegradable environment for refractory pollutants such as FQs. This paper inferred the pathways of the degradation of LEV and Derivative-15 by P. chrysosporium. The main degradation pathways were decarboxylation, defluorination and piperazine ring cleavage (Wetzstein et al., 1998; Zhang et al., 2019c; Wei et al., 2019). Pathway A was the defluorination reaction. After defluorination of the reactant, hydroxy was formed at the 7
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original position. After that, the hydroxylation reaction was carried out in the ortho-position to generate the final product of pathway A. Pathway B showed that, after the decarboxylation of FQs, hydroxy was also generated at the original site, which led to ring opening through a further reaction, thus producing the final product of pathway B. Pathway C was the typical reaction of FQs, namely, piperazine ring cleavage. The final reaction completely broke the piperazine ring, and the NH2 group was only retained at the original site (Fig. 4). The Gaussian 09 software was used to calculate the energy barrier of the degradation reaction of LEV and Derivative-15. Compared with LEV, the energy barrier values (ΔE) of Derivative-15 in the three biodegradation pathways were lower, indicating that Derivative-15 had a smaller energy barrier to break when it was biodegraded, so it was easier to generate degradation products. The calculation of the sum of energy barriers before and after FQ molecular modification shows that the total energy barrier of LEV pathway A was 30.995 kJ/mol and that of Derivative-15 pathway A was 21.056 kJ/mol, which was 32.07% lower than that of LEV. Relative to LEV, the total energy barrier values of Derivative-15 in the pathways B and C decreased by 3.84% and 3.44%, respectively. The sum of the energy barrier of LEV was 1820.374 kJ/mol, while the energy barrier of Derivative-15 was 1744.19 kJ/mol, which was 4.19% lower than that of LEV. Hence, pathway A was the easiest and most likely for degradation. Compared with LEV, the biodegradability of Derivative-15 was increased by 27.85%, and the decreased degree of the total energy barrier value of pathway A was extremely close to the improvement of biodegradability. Therefore, it was proven that Derivative-15 was more biodegradable.
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4. Conclusion A method for the molecular design and screening of FQ antibiotics with high biodegradability and environmental friendliness based on the 3D-QSAR model was established in this paper. Through molecular docking, molecular dynamics simulation and inference of the biodegradation pathway, it is concluded that the designed environmentally friendly FQ antibiotic has a significant improvement in biodegradability, slightly increased pharmacological toxicity and obvious environmental friendliness, which is expected to be used as a new antibiotic drug. CRediT authorship contribution statement Yilin Hou: Conceptualization, Data curation, Methodology, Software, Writing - original draft. Yuanyuan Zhao: Formal analysis, Resources. Qing Li: Visualization. Yu Li: Validation, Writing - review & editing. Declaration of competing interest None. Acknowledgements We thank American Journal Experts (AJE) for English language editing. This manuscript was edited for English language by American Journal Experts (AJE). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecoenv.2020.110186. References Chen, G., Liu, X., Tartakevosky, D., et al., 2016. Risk assessment of three fluoroquinolone antibiotics in the groundwater recharge system [J]. Ecotoxicol. Environ. Saf. 133,
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Glossary CoMFA: Comparative Molecular Field Analysis FQs: Fluoroquinolones LEV: Levofloxacin PDB: Protein Data Bank DS: Discovery Studio LDS: LibDockScore PLS: Partial least squares SEE: Standard error of estimate DFT: Density functional theory NPT: Constant-pressure, constant-temperature HIS: Hospital Information System HQSAR: Holographic quantitative structure-activity relationship SPSS: Statistical Product and Service Solutions PE: Potential energy EE: Electrostatic energy VE: Van der Waals energy
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