Environment-friendly PCN derivatives design and environmental behavior simulation based on a multi-activity 3D-QSAR model and molecular dynamics

Journal Pre-proof Environment-friendly PCN Derivatives Design and Environmental Behavior Simulation Based on a Multi-activity 3D-QSAR Model and Molecular Dynamics Wenwen Gu, Qing Li, Yu Li

PII:

S0304-3894(20)30327-7

DOI:

https://doi.org/10.1016/j.jhazmat.2020.122339

Reference:

HAZMAT 122339

To appear in:

Journal of Hazardous Materials

Received Date:

24 November 2019

Revised Date:

2 February 2020

Accepted Date:

15 February 2020

Please cite this article as: Gu W, Li Q, Li Y, Environment-friendly PCN Derivatives Design and Environmental Behavior Simulation Based on a Multi-activity 3D-QSAR Model and Molecular Dynamics, Journal of Hazardous Materials (2020), doi: https://doi.org/10.1016/j.jhazmat.2020.122339

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Environment-friendly PCN Derivatives Design and Environmental Behavior Simulation Based on a Multi-activity 3D-QSAR Model and Molecular Dynamics Wenwen Gu a, Qing Li a, Yu Li a 

Corresponding author: Yu Li; Tel: 86-10-61772836; Fax: +86-10-6177-2836;

E-mail: [email protected] a

MOE Key Laboratory of Resources and Environmental Systems Optimization,

*

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North China Electric Power University, Beijing 102206, China

Corresponding author: Yu Li

College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China

E-mail: [email protected] The e-mail addresses of other authors:

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Graphical Abstract

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Qing Li; E-mail: [email protected]

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Wenwen Gu; E-mail: [email protected]

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Fax: +86 010 61773886

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Highlights: 

A multi-activity 3D-QSAR model which considers multiple effects of PCNs are founded.



2 derivatives with significantly reduced CEI are considered environment-friendly.



Derivatives’ predicted results accord well with that of environmental behavior simulation.



Optimal external stimulation conditions for promoting PCNs’ degradation are obtained.



Reduction mechanism of PCNs’ CEI values is unlike that of POP characteristics.

Abstract

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A multi-activity three-dimensional quantitative structure-activity relationship (3DQSAR) model was established based on the comprehensive evaluation index (CEI) of

polychlorinated naphthalenes (PCNs). The CEI values were calculated using the vector

analysis method in combination with the following parameters: biological toxicity

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(predicted by logEC50), bioconcentration (predicted by logKow), long-distance migration (predicted by logPL), and biodegradation (predicted by total-score).

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Additionally, sixty-four CN-70 derivatives with lower CEI values were designed, among which three derivatives with reduced CEI values were selected for verification

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based on an evaluation of their persistent organic pollutant properties and practicability. Finally, an environmental behavior simulation was conducted via molecular dynamics simulation aided by the Taguchi experimental design by considering the degradation

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characteristics of the three aforementioned CN-70 derivatives as an example. Only two of the selected CN-70 derivatives were observed to be more easily degraded when compared with the CN-70 molecule (ascending range: 11.57%-13.57%) in a real-world

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setting, which was consistent with the biodegradability prediction results (ascending range: 14.94%-22.49%) obtained through the molecular docking studies. The multi-

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activity 3D-QSAR model established in this study overcame the limitations of generating molecular designs based on single-effect models from the source because it focused on the multiple effects of the pollutants.

Keywords:

Polychlorinated

Comprehensive

evaluation

naphthalenes; index;

Multi-activity

Molecular

experimental design 2

dynamics

3D-QSAR simulation;

model; Taguchi

1 Introduction Polychlorinated naphthalenes (PCNs), which constitute a class of chlorinated polycyclic aromatic hydrocarbons (Cl-PAHs), are a type of persistent organic pollutants (POPs) known to exhibit properties related to biological toxicity, bioconcentration, environmental persistence, and long-distance migration [1-4]. PCNs were mainly used in insulating oils and flame retardants before the 1980s; however, products containing PCNs are still being used [5, 6]. Some alternatives of POPs have been designed to ensure the utilization of their specific functional features. Major examples of such alternatives include the new

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brominated flame retardants and organic phosphorus flame retardants (OPFRs), which are extensively used as alternatives to commercial pentabromodiphenyl ether mixtures (PBDEs) [7, 8]. However, OPFRs have been identified as pollutants with strong neurological, reproductive, and developmental toxicities; unfortunately, no efficient

degradation or adsorption method has been found for their complete elimination [9].

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And most OPFR products have been detected in marine and freshwater animals, poultry, insects, and even in human samples, particularly in the past decade [10]. Thus, it is

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necessary to design environment-friendly PCN substitutes because of their specific flame retardancy and insulation properties.

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Traditional three-dimensional quantitative structure-activity relationship (3DQSAR) models, which are built based on the data obtained with respect to the single effect of the compounds, can provide helpful information on the molecular forces at

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play in order to design environment-friendly compounds with changed activity [11, 12]. Gu et al. initially used 3D-QSAR methods to determine the biological toxicity [4], bioconcentration [13], long-range migration [14], and biodegradability [15] of PCNs

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before screening for environment-friendly PCN derivatives via the stepwise application of single-activity 3D-QSAR models. This method was time-consuming and the inability

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to design PCN derivatives using multiple models is a major issue for researchers, moreover, previous 3D-QSAR models only focused on the analysis of individual environmental effects. To date, there is no comprehensive evaluation method to overcome these challenges. In this study, the CEI values that characterize the toxicity, bioconcentration, long-distance migration, and biodegradability of PCNs are calculated based on the vector analysis method followed by the application of a multi-activity 3DQSPR model. Here, all the aforementioned parameters were considered, solving the 3

limitations often observed in the molecular design of the single-effect models for pollutants. Research on PCN pollution control methods has mostly focused on the chemical and microbial degradation processes in the environment [16]. In addition, because the existing POP substitutes, such as OPFRs, exhibit serious degradation problems [10], the design method of using the derivatives can overcome this issue. Herein, a molecular dynamics simulation aided by the Taguchi experimental design was used to verify the evaluation results of the environmental characteristics and explore the external stimulating conditions that promoted the degradation of PCNs. The degradation

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behavior of PCNs and new PCN derivatives was simulated, and PCNs derivatives which were degraded easily in the real environment were determined to be

environment-friendly PCN substitutes, solving the limitation of new derivatives’ biodegradation from the source.

In this study, two types of multi-activity 3D-QSAR models were established, where

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the parameters of the molecular structure of PCNs were considered to be the

independent variables and the CEI values were considered to be the dependent variables.

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Sixty-four CN-70 (No. 70 PCN molecule; 1, 2, 3, 6, 7, 8-HexaCN) derivatives with low CEI values were designed, and the characteristics of the associated POPs were

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evaluated through the 3D-QSAR models, density functional theory (DFT), molecular docking, and the molecular dynamics simulations aided by the Taguchi experimental design. This study intended to create a 3D-QSAR model for multi-index comprehensive

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evaluations that could provide theoretical support for the development and design of environment-friendly POPs substitutes. 2 Materials and Methods

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2.1 Establishment of a comprehensive evaluation system In this study, the evaluation process refers to the determination of the attributes of

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the object system according to a specific purpose; it seeks to establish the changes that can transform this attribute into an objective and quantitatively define the calculations or the observed subjective utility behavior. Comprehensive evaluation refers to the overall evaluation of the object system described via the multi-attribute architecture. A certain method is used to assign an evaluation value to each evaluation object according to the given conditions (comprehensive evaluation index, CEI) [17]. For example, Xie et al. [18] used the fuzzy comprehensive evaluation theory to evaluate the 4

environmental quality of two farms and established an evaluation index system that was used as a part of the environmental control and warning systems for achieving aquaculture environmental management. This was conducted to improve the production and welfare of aquaculture in the region. In this study, the CEI values linked with the biotoxicity (predicted by logEC50) [4], bioconcentration (predicted by logKow) [13], long-distance migration (predicted by logPL) [14], and biodegradability (predicted by total-score) [15] of PCNs were calculated based on vector analysis [19] via the framework of a comprehensive index method to evaluate the environmental quality. Generally, vector analysis focuses on understanding a vector/factor and its related concepts and properties. Here, each pollutant is considered to be a component according

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to the theory of “Hebrew Space,” where N pollutants constitute an N-dimensional space and the overall state of environmental pollution caused by each pollutant in the system

is considered to be a vector, A, in this N-dimensional space comprising N pollutants. In

this study, each characteristic of the PCN molecule was considered to be a component,

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and the state of environmental pollution caused by these PCNs was considered to be a vector, A, in this N-dimensional space comprising N environmental characteristics. The

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environmental characteristic value for each PCN molecule was defined as component Ai and its comprehensive index was the modulus value of vector A. The calculation

PI  A 

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formula can be given as follows: A1  A2  ......  An 2

2

2

,

(1)

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where |Ai| = Ci/Li, I (i = 1, 2, 3, 4) is the sub-index of the ith pollutant with respect to the molecular environmental characteristics of the PCN molecules and Li is the maximum allowable concentration of a certain characteristic.

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2.2 Establishment of the multi-activity 3D-QSAR models In this study, the SYBYL-X2.0 software from Tripos was used for conducting 3D-

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QSAR analysis [20]. To ensure structural diversity and universal distribution [21, 22], fifty-eight PCN compounds (Fig. S1) were randomly selected as the training set, whereas the remaining seventeen PCN compounds were selected as the test set, and the CN-70 parent molecule (possessing the largest CEI value) was used as the template molecule to construct the 3D-QSAR model [21]. CN-70 was superimposed onto the other molecules using the Align Database to create the best 3D-QSAR statistical model. Further, comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) 5

models were selected for conducting the 3D-QSAR analysis. The partial least-squares (PLS) analysis was used to evaluate the predictive ability and reliability. Crossvalidation was performed using the leave-one-out (LOO) method, where one compound was removed and its activity was predicted using the model derived from the remainder of the dataset. The complementation of the results obtained from the LOO procedure was performed to ensure the reproducibility of the cross-validated value (q2). Further, the stereoscopic and electrostatic fields were calculated using the CoMFA method. When compared with the CoMFA method, the CoMSIA method increased the number of hydrophobic fields as well as the number of hydrogen-bond donor and acceptor fields.

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Both these models were applied to validate and supplement each other because this was helpful to ultimately obtain a reliable and accurate prediction model. Using CN-70 as the target molecule, the substituted sites and groups that influenced the environmental

comprehensive effect values were determined through systematic analysis of the 3D contour maps in the multi-activity 3D-QSAR models.

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2.3 Modification of the PCN derivatives with reduced CEI values

The contour maps for CoMFA (two descriptor fields: steric and electrostatic fields)

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and CoMSIA (five descriptor fields: steric, electrostatic, hydrophobic, hydrogen bond, and donor-acceptor fields) were used to determine the substitution sites and groups that

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affected the CEI values of PCNs. Based on this analysis, the PCN derivatives were designed by considering CN-70 as an example. 2.4 Evaluation of the CEI values, POP characteristics, and functional

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characteristics of the PCN derivatives

The environmental comprehensive effects of the PCN derivatives were evaluated using the multi-activity 3D-QSAR model. The toxicity [4], the ability to undertake

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long-distance migration [14], and bioconcentration [13] of the new PCNs were predicted and evaluated according to the previously established 3D-QSAR models.

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Molecular docking was performed between the degrading enzyme and the CN-70 derivatives, and scoring functions were obtained. The CN-70 derivatives that were more easily degraded than CN-70 were selected using the scoring functions [15]. The functional characteristics of CN-70 and its derivatives, including stability, insulating capability, flame retardancy, and positive frequency, were obtained through the DFT method using the Gaussian 09 software (at the B3LYP/6-31G* basis group level) [23, 24]. 6

2.5 Environmental behavior simulation of the new PCN derivatives The environmental pollution control of PCNs mostly adopts the mean of the microbial degradation parameter; the biodegradation rate of aromatics is affected by the external environmental conditions. Arylcyclohydrolase, which is one of the key enzymes in the degradation of aromatics, is used to determine the rate of the whole process. In this study, naphthalene 1, 2-dioxidase (1O7G) was used as an example for studying the biodegradability of the new PCN derivatives. These derivatives were subsequently screened to determine the derivatives that would easily degrade in a realworld setting and establish the best external stimulation conditions to promote the said

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biodegradation. Research has shown that the pH (A), temperature (B), surfactant concentration (C), nitrogen-phosphorus ratio (D), oxygen promoter concentration (E), and voltage gradient (F) significantly affected the biodegradation of aromatic

hydrocarbons. As a special orthogonal experimental method, the Taguchi experimental design could analyze a large number of variables using a small number of experiments

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[25]. Further, the external stimulation conditions that promoted PCN biodegradation

were selected. The variables required to generate the orthogonal experimental table and

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the appropriate conditions for each variable were selected as the “experimental level” in the Taguchi orthogonal experimental design containing six factors and two levels. In

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addition, the molecular dynamics experimental simulation of the molecular biodegradation of the derivatives was conducted according to the generated orthogonal experimental table.

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The molecular dynamics simulations for the complexes formed between 1O7G and CN-70 as well as the complexes formed between 1O7G and the various CN-70 derivatives were conducted using the Gromacs software package found on a Dell

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PowerEdge R7425 server. The complexes’ systems of enzymes and molecules were placed in a cube box with a side length of 15 nm; the force field properties of

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GROMOS96 43a1 were subsequently used to suppress the molecules, and Na+ biodegradation process between the 1O7G enzyme and the 3-CH3-7-CH3-CN-70 derivative in accordance with the Taguchi experimental design. The signal-to-noise ratio (SNR) obtained from the Taguchi experiment and the binding energy obtained via the Molecular Mechanics/Poisson-Boltzmann Surface Area (MMPBSA) method were used to determine the optimal combination of the external environmental conditions needed to degrade 3-CH3-7-CH3-CN-70 derivatives through naphthalene dioxygenase 7

(NDO; PDB ID: 1O7G). MMPBSA is a terminal method for calculating the difference between the free energies of the two states. In addition, during the biodegradation processes between the 1O7G enzyme and CN-70, the remaining CN-70 derivatives were simulated under the optimal combination of external stimulations to evaluate the biodegradability of the CN-70 derivatives in a natural environment (as shown in Fig. S2). Subsequent screening was conducted on the new CN-70 derivatives that were easily degradable and exhibited reduced comprehensive effect indexes. Here, the guidelines related to the biodegradability of new derivatives exhibiting a low comprehensive effect index were established.

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The binding energy of the enzyme and molecules represented the strength of the interaction between the enzyme and the naphthalene molecule. A high binding energy absolute value was associated with strong interactions between the 1O7G enzyme and

CN-70 as well as its CN-70 derivatives, indicating easy degradation of the molecule in

the natural environment. When the MMPBSA method was used to calculate the binding

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energy, the trimming trajectory of the enzyme-molecule complex was sampled, and the

binding energy of the complex, the enzyme, and the molecule were calculated using the

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following equation:

Gbind = Gcomplex - Gfree-protein - Gfree-ligand

(2)

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where the binding energy of the molecule in a solution can be given as follows: G = Egas - TSgas + Gsolvation

(3)

Further, the calculated formula of the solvent binding energy containing polar and non-

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polar parts can be given as follows: Gsolvation = Gpolar + Gnonpolar

(4)

2.6 Mechanism analysis method of the CEI values of the PCNs and characteristics

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of POPs

In this study, 2D-QSAR models were developed for the environmental

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comprehensive effect values, bioconcentration, and long-distance migration of PCNs and its derivatives based on a regression equation [27]. Here, the CEI, logKow [13] and logPL [14] values of CN-70 and its derivatives were used for the dependent variables, and the parameters of quantification and the substituents were considered to be independent variables [28-30]. Further, 2D-QSAR models were established with respect to the biotoxicity of the PCN derivatives [4, 15].

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The independent variables for QSAR modeling were the quantum chemical descriptors that were calculated using the Gaussian 09 software package [31]. The geometries of PCNs and their derivatives were initially optimized using the DFT method at the level of B3LYP/6-311G (d, p) [31-33]. The descriptors were then calculated using the following parameters: total energy (TE, eV), the energy of the highest occupied molecular orbital (EHOMO, eV), the energy of the lowest unoccupied molecular orbital (ELUMO, eV), the difference in energy ELUMO - EHOMO (ΔE, eV), the difference in energy EHOMO - ELUMO (energy gap, eV), the dipole moment (μ, Debye), the most negative atomic partial Mulliken charge (q-, e), and the most positive atomic partial Mulliken charge (q+, e).

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3 Results and Discussion

3.1 Calculation of the environmental comprehensive effect index for the PCN molecules

The environmental CEI values for seventy-five PCNs are presented in Table S1.

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The logEC50, logPL, logKow, and total-score values are presented in Tables S2-S5. 3.2 Evaluation and verification of the multi-activity 3D-QSAR model

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The results of the statistical parameters for the CoMFA and CoMSIA models in Table S6 denote that the two models were reliable and that they exhibited good

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predictive power and stability, respectively [4, 34]. The ratios of the non-crossvalidation correlation coefficients r2 and cross-validated value q2 were 9.52 and 7.01% (<25%), respectively, indicating that the models did not “overfit” the data [35]. Table

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S7 showed that the contribution rates of the steric and electrostatic fields were 38.80% and 61.20% in the CoMFA model, respectively, indicating that the electrostatic field significantly influenced the CEI values when compared with the steric field. In the

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CoMSIA model, the steric, electrostatic, hydrophobic, hydrogen-bond acceptor, and donor fields were 1.40%, 76.40%, 22.20%, 0.00%, and 0.00%, respectively. This also

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indicated that compared to other fields, the electrostatic field significantly influenced the CEI values of the PCN derivatives. Therefore, the results of the two models showed that electrostatic interaction was a major factor that affected the obtained CEI values. Further, external validation was conducted to assess the reliability and the predictive ability of the proposed models. The predictive ability of the models was expressed using the correlation coefficient of test set predictions r2 pred (r2pred > 0.6) and the standard errors of prediction SEP [34]. PLS of the test set, which included seventeen 9

compounds, was performed using a cross-validation method to obtain the predicted r2pred and SEP values (Table S6). SEP values of 0.356 and 0.282 and r2pred values of 0.910 (>0.6) and 0.922 (>0.6) were obtained, which proved the predictive ability of the two models [36]. The predicted and calculated CEI values were analyzed using best linear fit to evaluate the predictive capabilities of the multi-activity 3D-QSAR model (Fig. 1). The results revealed the linear dependence (R2 of 0.9763 and 0.9709, respectively) among the calculated and predicted values and that the slopes of the linear equations for the calculated and predicted values were 0.9631 and 0.9613 (Fig. 1), respectively; this could be observed from the scatter plot of the calculated versus

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predicted values for the CEI. 3.3 Analysis of the substitution characteristics based on the contour maps obtained for the multi-effect 3D-QSAR models

By considering CN-70 as an example, the block diagrams with different colors around CN-70 showed the effects of the steric, electrostatic, and hydrophobic properties

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on the CEI values (Fig. 2).

In the CoMFA model, the contour map of the electrostatic field (a) showed that the

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red region was mainly distributed at positions 2, 3, 7, and 8, indicating that the introduction of positive substituents at these positions was conducive to reduce the

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environmental CEI values for PCNs, i.e., the new CN-70 molecules will show a reduction in their comprehensive pollution impact on the environment. Therefore, the CEI values for CN-67 and CN-68 were lower than those of CN-70, indicating that the

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introduction of -H at positions 7 and 8 on the CN-70 molecule reduced their comprehensive pollution impact on the environment. In the CoMFA steric contour map (b), the yellow area was near the 4- and 8-positions, whereas the green area was near 5-

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position. This indicated that the introduction of small groups at the 4- and 8-positions will reduce the comprehensive environmental pollution impact.

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In the CoMSIA model, the contour map of the electrostatic field (c) showed that

the red region was mainly distributed near the 3-, 5-, and 7-positions, whereas the blue region was mainly distributed near the 3-, 4- and 5-positions. This indicated that the introduction of a positive group at the 3-, 5-, and 7-positions was conducive to reduce the comprehensive environmental effect index. Indeed, the CEI value of CN-28 was smaller than that of CN-52 because the introduction of an H atom at 7-position of the CN-52 molecule reduced its CEI value. It can be seen from the contour map of the steric 10

field (d) that the yellow region was mainly distributed at 5-position, whereas the green region was mainly distributed near the 3-, 5-, and 8-positions. This indicated that the introduction of small groups at the 3-, 5-, and 8-positions will be conducive to reduce the comprehensive environmental pollution index. Another example is the CN-22 derivative. The CEI value of the CN-22 derivative was less than that of CN-43 because the introduction of the H atom, whose volume is less than that of -Cl, at the 5-position of CN-43 reduced its environmental comprehensive effect index. Furthermore, the hydrophobic field (e) showed that the yellow region was distributed near the 3-, 5-, 7-, and 8-positions, whereas the white region was distributed near the 5-position, indicating

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that the introduction of hydrophilic groups at the 3-, 5-, 7-, and 8-positions reduced the CEI values of the PCN derivatives. Thus, the introduction of positive and hydrophilic

groups at the 3- and 7-positions reduced the environmental comprehensive effect index of the PCN derivatives.

3.4 Evaluation results of the CEI values, POP characteristics, and practicability of

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the new PCN derivatives

3.4.1 Evaluation results of the CEI values and POP characteristics of the CN-70

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derivatives

Even though the modified information obtained via the two multi-activity 3D-

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QSAR models was consistent, it was difficult to accurately find the area of influence in the CoMFA model because the distribution of the molecular fields was extensive. Therefore, the contour maps of the CoMSIA model were selected to complete the

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modification of CN-70. The CEI value of the electropositive or hydrophilic groups introduced at the Cl3 and Cl7 positions should be initially reduced before modifying CN-70. Sixty-four CN-70 derivatives were designed using this technique to calculate

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their CEI values, biological toxicity (predicted by logEC50), bioconcentration (predicted by logKow), long-distance migration (predicted by logPL), and biodegradability

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(predicted by total-score). Based on these results, only nine CN-70 derivatives had CEI values lower than those of the target molecule. Further, their biological toxicity, bioconcentration, and long-distance migration values decreased, whereas their biodegradability increased (Table 1). It can be seen from Table 2 that when the positions of Cl3 and Cl7 were replaced by bromine (-Br), ethyne (-C ≡ CH), aldehyde (-CHO), methyl group (-CH3) whose electronegativity were less than that of -Cl, the CEI values’ reduced amplitude of new 11

CN-70 derivatives was 0.96% - 51.73% compared to the CN-70 molecule. Therefore, the environmental comprehensive pollution was reduced (the CEI value of the new 3CH3-7-CH3-CN-70 derivative was 51.73% lower than that of CN-70). 3D-QSAR models for the toxicity, bioconcentration, and long-distance migration characteristics of PCNs, which used the logEC50, logKow, and logPL values of the derivatives as the dependent variables [4, 13, 14] and the molecular structure as the independent variable, were applied for the prediction of the logPL, logKow, and logEC50 values for CN-70 derivatives. Furthermore, the molecular docking method was used to calculate the totalscore values for the interaction between nine of the CN-70 derivatives and 1O7G to

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determine the degradability of these compounds. When compared with the target molecule, the toxicity, bioconcentration, and long-distance migration values associated

with the new CN-70 derivatives were observed to decrease by 0.34%-34.06%, 1.93%28.77%, and 0.89%-14.18%, respectively. However, the biodegradability increased by 1.89%-22.49%, indicating that the CEI values of the CN-70 derivatives generally

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decreased with declining characteristics of the POPs when compared with those of the parent molecule. Further, the changing degree of the CEI values was greater than that

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of the corresponding POPs’ characteristics, and the biological toxicity and bioconcentration of the CN-70 derivatives were higher than their long-distance mobility

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and biodegradability traits. Therefore, a new 3Q-QSPR model was established by assigning different weights to various environmental characteristics (POPs’ characteristics) of the PCN derivatives in the vector analysis method to coordinate the

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degree of influence of various environmental characteristics. In addition, when compared with the parent molecule, the CEI values and POPs’ characteristics of 3methyl-7-methyl-CN-70, 3-aldehyde-7-aldehyde-CN-70, and 3-aldehyde-7-hydroxyl-

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CN-70 decreased significantly. Furthermore, 3-methyl-7-methyl-CN-70 showed the most significant decline, and the logKow value of 3-methyl-7-methyl-CN-70 was 4.937

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(<5).

3.4.2 Evaluation results of stability, insulation, and flame retardancy of the CN-70 derivatives

The values of total energy (representing stability), energy gap (insulation), C-Cl dissociation enthalpy (representing flame retardancy), and positive frequency of CN70 and its derivatives are presented in Table 2. Generally, the stability increases with increasing total energy. A large energy gap translates to reduced conductivity, whereas 12

improved flame retardancy can be observed when the variation in enthalpy is small. As shown in Table 2, the stability, insulation, and flame retardancy traits of the three most environment-friendly CN-70 derivatives (i.e., molecules with a significantly reduced CEI value, biological toxicity, bioconcentration, long-range transport potential, and persistence) remained unchanged, with the change rates for each parameter varying from 5.073% to 9.781%, from -1.935% to 2.581%, and from -3.461% to 8.110% respectively; the positive frequencies of these CN-70 derivatives were greater than 0, indicating that these derivatives were generally stable [35]. Therefore, these three CN70 derivatives were selected for further verification studies because they exhibited

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significantly reduced CEI values, toxicity, bioconcentration, long-range migration, and persistence, whereas their practical properties remained unchanged.

3.5 Verification and analysis of the environmental behavior of the CN-70 derivatives

By considering the biodegradation characteristics as an example, we used the

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Taguchi experimental design-assisted molecular dynamics simulation method to verify the reliability of the predictions for the environmental characteristics of the selected

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CN-70 derivatives. The Taguchi experimental design-assisted molecular dynamics method was used to simulate the biodegradation process of CN-70 and its derivatives

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in real-world settings, verifying the degradation properties of the CN-70 derivatives in comparison with those of CN-70 and determining the optimal combination of external stimulations required to promote degradation. This served to improve the theoretical

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guidelines for the degradation of these derivatives. The results showed that a strong binding affinity between the oxidase and the target aromatics favored the hydroxylation degradation reactions between the oxidase

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and the target aromatics [36-38]. The amount of binding energy between the oxidase and naphthalene generally represented the strength of the interaction between the

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enzyme and the molecule [39]. Here, the binding energies between 1O7G and CN-70 as well as between the oxidase and the three selected CN-70 derivatives were calculated based on the molecular dynamics simulation with different types of external stimulations that were generated from the Taguchi orthogonal experiment (Table 5). An SNR with few calculation errors for the test data was selected as the evaluation standard for combining energy (Table 3). The most significant external stimulation conditions that influenced the biodegradation of the CN-70 derivatives were explored based on the 13

combination containing the 1O7G enzyme and the 3-methyl-7-methyl-CN-70 molecule. The binding energy of the interactions between 1O7G and CN-70, 3-aldehyde-7aldehyde-CN-70, and 3-aldehyde-7-hydroxyl-CN-70 was calculated based on the most significant external stimulation conditions using molecular dynamics simulations to judge the degradability of these three CN-70 derivatives (in comparison with CN-70) in a natural setting. This provided theoretical guidelines for designing molecules with a low environmental comprehensive index and helped to establish the external stimulation conditions that were most suitable for the degradation of aromatic hydrocarbons.

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Using the rank exclusion results calculated via the SNR (Table 4), the degradation efficiency of 1O7G promoted by the prevailing external stimulation conditions is listed from large to small; these include the nitrogen and phosphorus dosage ratios,

temperature (K), pH, concentration of sodium dodecyl sulfate (mg/L) present in the

system, voltage gradient (V/cm), and concentration of H2O2 (mg/L) in the system. The

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nutrient elements considerably affect the biodegradation rate of pollutants; the proportion of nitrogen and phosphorus that provided the most significant external

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stimulus for biodegradation was in accordance with the research results [27]. From Figure 3, the optimal external stimulation conditions are as follows: pH (5), temperature

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(288.15 K), the concentration of sodium dodecyl sulfate (450 mg/L), the ratio of nitrogen and phosphorus (1:1), the concentration of H2O2 (350 mg/L), and the operating voltage (0.5 V/cm).

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The root mean square deviation (RMSD) values for the complexes formed between the 1O7G enzyme and CN-70, 3-methyl-7-methyl-CN-70, 3-aldehyde-7aldehyde-CN-70, and 3-aldehyde-7-hydroxyl-CN-70 were calculated to establish the

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structural stability of the complexes (Fig. 4), whereas the binding energy was calculated based on the average structural pattern observed after dynamic stability was achieved.

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As shown in Figure 6, the complexes maintained equilibrium for the final 2 ns, and the average RMSD stability value of these compounds was approximately 0.3 A. Therefore, the 8-10 ns trajectory for dynamic simulation was selected for calculating the binding energy. The binding energies of the interactions between the 1O7G enzyme and CN-70, 3-methyl-7-methyl-CN-70,

3-aldehyde-7-aldehyde-CN-70,

and

3-aldehyde-7-

hydroxy-CN-70 were -127.739, -142.516, -112.118, and -145.072 kJ/mol, respectively, in the same external environment conditions. When compared with CN-70, the changes 14

in the binding energies of 1O7G that complexed with 3-methyl-7-methyl-CN-70, 3aldehyde-7-aldehyde-CN-70, and 3-aldehyde-7-hydroxyl-CN-70 were 11.57%, 12.23%, and 13.57%, respectively. The binding energies of 3-methyl-7-methyl-CN-70 and 3-aldehyde-7-hydroxyl-CN-70 were generally higher than that of the CN-70 parent molecule, i.e., these derivatives degraded easily in natural settings. The validation results reported for the biodegradation of the CN-70 derivatives showed that the prediction and evaluation results obtained via the molecular docking simulations (amplitude increase: 14.94%-22.49%) were consistent with the simulation results obtained from molecular dynamics (amplitude increase: 11.57%-13.57%). Thus, the

ro of

prediction and evaluation of the environmental characteristics of the CN-70 derivatives were observed to be reliable in this study.

3.6 Reduction mechanism analysis of the CEI values and POP characteristics of PCNs and their derivatives

The 2D-QSAR models, in which the CEI, logKow [13], and logPL [14] values of

-p

PCNs were used as the dependent variables and the quantification parameters for PCN

were used as the independent variables, were developed to explore the reduction

re

mechanisms by which the comprehensive effect and the POP characteristics of PCN and its new derivatives exerted their influence. The equations of CEI, logKow, and logPL presented in Table 5.

lP

for PCNs (Equation 5, 7, and 9) and the CN-70 derivatives (Equation 6, 8, and 10) are The R values of PCN and the CN-70 derivatives for the 2D-QSAR models (CEI,

na

logKow and logPL) were 0.991, 0.978, and 0.998 (where n = 58/16/13, p = 0.01, rmin > r: 0.3541/0.6226/0.6835), respectively, as well as 0.803, 0.796, and 0.858 (where n = 9/9/10, p = 0.01, rmin > r: 0.7977/0.7977/0.7646), respectively. The sig. values were

ur

0.000, 0.000, 0.000 and 0.049, 0.012, 0.026 (< 0.05), respectively. All the models passed the significance test. The coefficients of the total energy, the dipole moment (μ,

Jo

Debye), and the most negative Millikan (q-, e) in the model (5) (PCNs) were negative. The coefficients of the lowest occupied orbital energy (ELUMO, eV), energy gap, the most positive Millikan charge number (q+, e), the number of substituents in the ortho position (No), the number of substituents in the meta position (Nm), and the number of paralogous substituents (NP) were positive. This indicated that these parameters actively influenced the environmental comprehensive effect index of the PCN derivatives under investigation. The parameters related to the molecular CEI in the 15

model (6) were changed to μ, and ELUMO - EHOMO (∆E, eV) was obtained when compared with the model itself (5). The coefficient of μ and ∆E became positive, which positively affected the CEI values. Therefore, the decrease in the comprehensive effect for the CN-70 derivatives when compared with the values for CN-70 was mainly caused by the changes in μ and ∆E. Similarly, the decrease in the bioconcentration of the PCN derivatives was mainly caused by the change in ELUMO and μ, whereas the decrease in the long-distance migration values for the derivatives was mainly caused by the change in ∆E and μ; this was established by comparing the models (7), (8) and models (9), (10). Previous studies [4, 15] have shown that the decrease in the toxicity of the PCN derivatives can be attributed to the change in μ; the change in their biodegradability was

ro of

related to EHOMO, which was reflected in the observed energy gap, q+, the quadrupole moment (Qxy and Qyz) substituents, and the total number of substituents (NT) as well as the related infrared and Raman spectra. The reduction mechanism of the environmental

comprehensive effect for the PCN derivatives was approximately similar to the

-p

mechanism that influenced the toxicity and bioconcentration values, which can be

mainly attributed to the change in μ. μ and ∆E were the main parameters that decreased

re

the CEI and long-distance migration values for the PCN derivatives. ∆E positively affected the environmental comprehensive effect but negatively impacted their long-

lP

distance migration properties. In addition, the mechanisms by which the environmental comprehensive effect and biodegradation of the PCN molecules exerted their influence were considerably different. Furthermore, the changes observed in the biological

na

toxicity and bioconcentration of the PCN derivatives were more noticeable than their long-distance migration capabilities and biodegradability. 4 Conclusion

ur

In this study, a multi-activity 3D-QSAR model was established based on the vector analysis method to measure the toxicity, bioconcentration, long-distance migration, and

Jo

biodegradability of the PCNs. This model was successfully applied to modify the environment-friendly PCN derivatives. Therefore, three PCN derivatives with reduced CEI, biological toxicity, bioconcentration, long-distance migration, and high biodegradability were designed to maintain their insulation and flame retardancy properties when compared with the parent CN-70 molecule. By considering the biodegradability of the CN-70 derivatives as an example, the binding energies of the interactions between the 1O7G enzyme and CN-70 as well as the interactions between 16

the enzyme and the three CN-70 derivatives were calculated via molecular dynamics simulation based on the different types of external stimulation conditions that were generated from the Taguchi experiment design; this was conducted to verify the degradation capacity of the CN-70 derivatives in a natural setting. The results obtained in the real-world settings were consistent with the results obtained for the biodegradability of the CN-70 derivatives, which was calculated using the molecular docking prediction methods. Thus, two CN-70 derivatives were more easily degraded than the CN-70 molecule under real-world conditions. Moreover, the optimal external stimulation conditions to promote the degradation of PCN and its derivatives were pH

ro of

= 5, temperature = 288.15 K, concentration of sodium dodecyl sulfate = 450 mg/L, ratio of nitrogen and phosphorus = 1:1, concentration of H2O2 = 350 mg/L, and voltage = 0.5 V/cm.

This study will extend the application scope of the traditional single-effect 3DQSAR models and provide the theoretical support needed for the design and

-p

degradation of future POP substitutes. Furthermore, it will help to clearly distinguish the differences in the reduction mechanism between the environmental comprehensive

re

effect and the POP characteristics of the PCNs. Thus, the biological toxicity and bioaccumulation of the CN-70 derivatives will be designed for achieving more notable

lP

variation when compared with respect to their long-distance migration capabilities and biodegradability. A novel 3D-QSPR model could be constructed by assigning different weights to different environmental effect parameters during vector analysis to

na

coordinate the impact of different environmental effects and provide a theoretical guideline for designing environment-friendly substitutes.

ur

Author Contributions Section:

Wenwen Gu is the main contributor of this paper, and she is mainly responsible for the

Jo

ideas’ design, data calculation and writing of this paper. Qing Li is mainly responsible for sorting out data and assisting in drawing. Yu Li is mainly responsible for guiding this paper. We confirm that this manuscript has not been published elsewhere and is not under consideration by another journal. All authors have approved the manuscript and agree with submission to the Journal Hazardous Materials.

17

Funding acquisition: This research was supported by China Scholarship Council and supported by the Fundamental Research Funds for the Central Universities 2019QN084.

Declarations of interest: None

Acknowledgments This research was supported by China Scholarship Council and supported by the

ro of

Fundamental Research Funds for the Central Universities 2019QN084.

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Tables and Figures Captions Table 1. CN-70 derivatives’ predicted values of the CEI、logKow、logEC50、logPL and total-score by CoMSIA models and molecular docking Table 2. Evaluation parameters (total energy, energy gap and C-Cl bond dissociation

ro of

enthalpy) of CN-70 derivatives’ practicability

Table 3. Effects of external stimulation conditions on the complex system of 1O7G and 3-methyl-7-methyl-CN-70 derivative

Table 4. Signal-to-noise ratio and rank ranking results of external stimulation conditions

-p

for promoting the biodegradation between 1O7G and CN-70 derivatives

Table 5. The equations of CEI, logKow and logPL for the PCNs (Equation 5, 7, 9) and

re

the CN-70 derivatives (Equation 6, 8, 10)

Fig. 1. The plot of calculated vs predicted CEI values using the multi-activity 3D-QSAR models

lP

Fig. 2. Contour maps of the CoMFA and CoMSIA models, electrostatic (a) and steric fields (b) of the CoMFA model; electrostatic (c), steric fields (d) and hydrophobic fields

na

(e) of the CoMSIA model

Fig. 3. Effects of external stimulation conditions on the complex system of 1O7G and 3-methyl-7-methyl-CN-70 derivative

ur

Fig. 4. Function relationship between RMSD and simulation time of 1O7G, CN-70 and

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its derivatives complex under optimal external stimulation conditions

21

f

oo

Table 1. CN-70 derivatives’ predicted values of the CEI、logKow、logEC50、logPL and total-score by CoMSIA models and molecular docking

values of

rate of CEI (%)

values of

CEI

logEC50

CoMSIA

CoMSIA

of logEC50 (%)

e-

Compounds

Change rate Predicted

Change rate

Predicted

pr

Change Predicted

values of

Change rate

of logKow

Predicted

(%)

values of

Change

of logPL

Predicted

rate of

(%)

values of

total-score （%）

logKow

logPL

total-score

CoMSIA

CoMSIA

CoMSIA

4.682

-

-2.907

-

6.931

-

-2.912

-

64.633

-

3-bromine-7-bromine-CN-70

4.468

-4.57

-2.988

-2.79

6.797

-1.93

-2.959

-1.61

70.998

9.85

3-ethyne-7-ethyne-CN-70

4.539

-3.05

-3.021

-3.92

6.680

-3.62

-2.960

-1.65

74.288

14.94

3-methyl-7-methyl-CN-70

2.260

-51.73

-3.897

-34.06

4.937

-28.77

-3.325

-14.18

72.067

11.50

3-aldehyde-7-aldehyde-CN-70

3.972

-15.16

-3.343

-15.00

6.174

-10.92

-3.272

-12.36

76.351

18.13

3-aldehyde-7-hydroxyl-CN-70 3-ether-7-aldehyde-CN-70

4.623

-1.26

-2.948

-1.41

6.777

-10.65

-2.979

-2.30

65.852

1.89

2. 609

-44.28

-3.260

-12.14

6.193

-10.64

-3.199

-9.86

79.172

22.49

4.637

-0.96

-2.917

-0.34

6.690

-3.48

-2.938

-0.89

74.800

15.73

4.271

-8.91

-2.998

-3.13

6.468

-2.22

-2.983

-2.44

75.354

16.59

-7.92

-2.912

-0.50

6.444

-7.03

-3.135

-7.66

69.276

7.18

Jo ur

3- hydroxy-7-aldehyde-CN-70

na l

3-ethyne-CN-70

7-aldehyde-CN-70

Pr

CN-70

4.311

22

Table 2. Evaluation parameters (total energy, energy gap and C-Cl bond dissociation enthalpy) of CN-70 derivatives’ practicability C-Cl bond

Change rate Compounds

Energy gap

of energy gap

Change rate of

Total energy

Change rate

dissociation

C-Cl bond

(a.u.)

of energy (%)

enthalpy

dissociation

(kcal/mol)

enthalpy (%)

(%)

Frequency

CN-70

0.155

-

-3143.420

-

75.921

-

18.500

3-methyl-7-methyl-CN-70

0.159

2.581

-3302.883

5.073

75.751

-0.224

18.220

3-aldehyde-7-aldehyde-CN-70

0.152

-1.935

-3450.871

9.781

73.293

-3.461

26.980

3-aldehyde-7-hydroxyl-CN-70

0.153

-1.290

-3412.781

8.569

82.078

8.110

22.260

7-methyl-CN-70 derivative B

C

D

E

1

5

288.15

50

1:1

2

5

288.15

50

10:1

3

5

288.15

450

1:1

4

5

288.15

450

10:1

5

5

323.15

50

6

5

323.15

50

7

5

323.15

8

5

323.15

9

9

10

9

11

Bind Energy

SNR

50

0.5

-128.858

42.2022

50

1.5

-130.516

42.3133

350

0.5

-123.930

41.8635

350

1.5

-116.716

41.3426

1:1

350

1.5

-122.293

41.7480

10:1

350

0.5

-128.884

42.2040

450

1:1

50

1.5

-125.130

41.9472

450

10:1

50

0.5

-112.776

41.0443

re

lP

na 288.15

50

1:1

350

1.5

-124.182

41.8812

288.15

50

10:1

350

0.5

-116.102

41.2968

9

288.15

450

1:1

50

1.5

-129.151

42.2220

9

288.15

450

10:1

50

0.5

-128.398

42.1712

Jo

12

F

-p

A

ur

Experiment number

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Table 3. Effects of external stimulation conditions on the complex system of 1O7G and 3-methyl-

13

9

288.15

50

1:1

50

0.5

-120.616

41.6281

14

9

288.15

50

10:1

50

1.5

-83.192

38.4016

15

9

288.15

450

1:1

350

0.5

-125.004

41.9385

16

9

288.15

450

10:1

350

1.5

-126.646

42.0518

23

Table 4. Signal-to-noise ratio and rank ranking results of external stimulation conditions for promoting the biodegradation between 1O7G and CN-70 derivatives Level

A

B

C

D

E

F

1

41.83

41.91

41.46

41.93

41.49

41.79

2

41.45

41.37

41.82

41.35

41.79

41.49

Delta

0.38

0.54

0.36

0.58

0.30

0.31

Patch

3

2

4

1

6

5

Table 5. The equations of CEI, logKow and logPL for the PCNs (Equation 5, 7, 9) and the CN-70 derivatives (Equation 6, 8, 10)

ro of

CEI = 2.859-0.002TE+18.083ELUMO+0.527Energy gap-0.100μ-0.455q-+8.552q+ 0.0493NT+ 0.223No+ 0.147Nm (5) CEI = 7.853+0.160μ+30.336∆E

(6)

logKow = -7.663-0.002TE-20.750ELUMO+33.184Energy gap-0.020μ

(7)

logKow = 8.126-0.879ELUMO+0.475μ

(8)

(9)

-p

logPL = 5.869+0.001TE+34.307ELUMO+18.832Energy gap-0.072μ

na

lP

re

logPL = 4.599-32.138∆E +0.085μ

Jo

ur

Fig. 1. The plot of calculated vs predicted CEI values using the multi-activity 3D-QSAR models

Fig. 2. Contour maps of the CoMFA and CoMSIA models, electrostatic (a) and steric fields (b) of the CoMFA model; electrostatic (c), steric fields (d) and hydrophobic fields (e) of the CoMSIA model

24

(10)

ro of

Fig. 3. Main effect diagram of signal-to-noise ratio of external stimulation conditions for promoting biodegradation between 1O7G and CN-70 derivatives CN-70 3-methyl-7-methyl-CN-70

0.35

0.30

0.30

0.25

0.25

0.15

0.20 0.15 0.10

0.05

0.05

re

0.10

3-aldehyde-7-hydroxyl-CN-70

-p

0.35

0.20

3-aldehyde-7-aldehyde-CN-70

0.40

RMSD/Ang

RMSD/Ang

0.40

0.00

0.00

-0.05

-0.05 0

2000

4000

6000

8000

10000

0

lP

Time/ps

2000

4000

6000

8000

10000

Time/ps

Fig. 4. Function relationship between RMSD and simulation time of 1O7G, CN-70 and its

Jo

ur

na

derivatives complex under optimal external stimulation conditions

25