Law and mechanism analysis of biodegradability of polychlorinated naphthalenes based on principal component analysis, QSAR models, molecular docking and molecular dynamics simulation

Exploring the progressive change in transformation and toxicity of polycyclic dyes during aerobic biodegradation

Structure-activity models can rapidly assess the biodegradability and toxicity of dye. However, these properties dynamically change during biodegradation due to byproduct formation. In this study, the aerobic biodegradation of common polycyclic dyes (PDs) and their precursors, including anthraquinone dyes, triarylmethane dyes, azo dyes, substituted naphthalene, and tricyclic aromatic hydrocarbons was studied. We used combined in vivo and silico approaches to analyze their biodegradation kinetics and toxicity evolution. Most compounds were rapidly degraded within 6-8 h, with substituted naphthalene exhibiting the highest median maximum degradation rate (kmax = 0.278 h-1). Our molecular dynamics simulations quantified the binding energies between compounds and oxidoreductases (-20.27 ± 2.61 to -53.24 ± 3.57 kcal/mol), revealing that stronger binding interactions correlated with lower kmax values. Furthermore, we developed a novel toxicity assessment method using the inhibition/TOC (I/TOC) ratio, revealing increased toxicity post-biodegradation for most compounds. Triarylmethane dyes exhibited significantly higher median I/TOC values (p < 0.05). HPLC-TOF-MS analysis identified 18 major transformation products. Toxicity estimation software tool (T.E.S.T) predictions confirmed that the transformation products exhibited higher toxicity than parent compounds. Our integrated analytical approach, combining experimental biodegradation kinetics, molecular simulation, and toxicity evolution, provides crucial insights for evaluating and managing environmental risks of emerging pollutants during wastewater treatment.

High-throughput prediction of oral acute toxicity in Rat and Mouse of over 100,000 polychlorinated persistent organic pollutants (PC-POPs) by interpretable data fusion-driven machine learning global models

This study utilized available oral acute toxicity data in Rat and Mouse for polychlorinated persistent organic pollutants (PC-POPs) to construct data fusion-driven machine learning (ML) global models. Based on atom-centered fragments (ACFs), the collected high-throughput data overcame the applicability limitations, enabling accurate toxicity prediction for a wide range of PC-POPs series compounds using only single models. The data variances in the Rat training and test sets were 1.52 and 1.34, respectively, while for the Mouse, the values were 1.48 and 1.36, respectively. Genetic algorithm (GA) was used to build multiple linear regression (MLR) models and pre-screen descriptors, addressing the "black-box" problem prevalent in ML and enhancing model interpretability. The best ML models for Rat and Mouse achieved approximately 90 % prediction reliability for over 100,000 true untested compounds. Ultimately, a warning list of highly toxic compounds for eight categories of polychlorinated atom-centered fragments (PCACFs) was generated based on the prediction results. The analysis of descriptors revealed that dioxin analogs generally exhibited higher toxicity, because the heteroatoms and ring systems increased structural complexity and formed larger conjugated systems, contributing to greater oral acute toxicity. The present study provides valuable insights for guiding the subsequent in vivo tests, environmental risk assessment and the improvement of global governance system of pollutants.

Investigating PCB degradation by indigenous fungal strains isolated from the transformer oil-contaminated site: degradation kinetics, Bayesian network, artificial neural networks, QSAR with DFT, molecular docking, and molecular dynamics simulation

The widespread prevalence of polychlorinated biphenyls (PCBs) in the environment has raised major concerns due to the associated risks to human health, wildlife, and ecological systems. Here, we investigated the degradation kinetics, Bayesian network (BN), quantitative structure-activity relationship-density functional theory (QSAR-DFT), artificial neural network (ANN), molecular docking (MD), and molecular dynamics stimulation (MS) of PCB biodegradation, i.e., PCB-10, PCB-28, PCB-52, PCB-138, PCB-153, and PCB-180 in the soil system using fungi isolated from the transformer oil-contaminated sites. Results revealed that the efficacy of PCB biodegradation best fits the first-order kinetics (R2 ≥ 0.93). The consortium treatment (29.44-74.49%) exhibited more efficient degradation of PCBs than those of Aspergillus tamarii sp. MN69 (27.09-71.25%), Corynespora cassiicola sp. MN69 (23.76-57.37%), and Corynespora cassiicola sp. MN70 (23.09-54.98%). 3'-Methoxy-2, 4, 4'-trichloro-biphenyl as an intermediate derivative was detected in the fungal consortium treatment. The BN analysis predicted that the biodegradation efficiency of PCBs ranged from 11.6 to 72.9%. The ANN approach showed the importance of chemical descriptors in decreasing order, i.e., LUMO > MW > IP > polarity no. > no. of chlorine > Wiener index > Zagreb index > HOMU > Pogliani index > APE in PCB removal. Furthermore, the QSAR-DFT model between the chemical descriptors and rate constant (log K) exhibited a high fit and good robustness of R2 = 99.12% in predicting ability. The MD and MS analyses showed the lowest binding energy through normal mode analysis (NMA), implying stability in the interactions of the docked complexes. These findings provide crucial insights for devising strategies focused on natural attenuation, holding substantial potential for mitigating PCB contamination within the environment.

An in silico study on human carcinogenicity mechanism of polybrominated biphenyls exposure

Polybrominated biphenyls (PBBs) are associated with an increased risk of thyroid cancer; however, relevant mechanistic studies are lacking. In this study, we investigated the mechanisms underlying PBB-induced human thyroid cancer. Molecular docking and molecular dynamics methods were employed to investigate the metabolism of PBBs by the cytochrome P450 enzyme under aryl hydrocarbon receptor mediation into mono- and di-hydroxylated metabolites. This was taken as the molecular initiation event. Subsequently, considering the interactions of PBBs and their metabolites with the thyroxine-binding globulin protein as key events, an adverse outcome pathway for thyroid cancer caused by PBBs exposure was constructed. Based on 2D quantitative structure activity relationship (2D-QSAR) models, the contribution of amino acid residues and binding energy were analyzed to understand the mechanism underlying human carcinogenicity (adverse effect) of PBBs. Hydrogen bond and van der Waals interactions were identified as key factors influencing the carcinogenic adverse outcome pathway of PBBs. Analysis of non-bonding forces revealed that PBBs and their hydroxylation products were predominantly bound to the thyroxine-binding globulin protein through hydrophobic and hydrogen bond interactions. The key amino acids involved in hydrophobic interactions were alanine 330, arginine 381 and lysine 270, and the key amino acids involved in hydrogen bond interactions were arginine 381 and lysine 270. This study provides valuable insights into the mechanisms underlying human health risk associated with PBBs exposure.

Evaluation of surfactant-aided polycyclic aromatic hydrocarbon biodegradation by molecular docking and molecular dynamic simulation in the marine environment

Marine oil spills directly cause polycyclic aromatic hydrocarbons (PAHs) pollution and affect marine organisms due to their toxic property. Chemical and bio-based dispersants composed of surfactants and solvents are considered effective oil spill-treating agents. Dispersants enhance oil biodegradation in the marine environment by rapidly increasing their solubility in the water column. However, the effect of dispersants, especially surfactants, on PAHs degradation by enzymes produced by microorganisms has not been studied at the molecular level. The role of the cytochrome P450 (CYP) enzyme in converting contaminants into reactive metabolites during the biodegradation process has been evidenced, but the activity in the presence of surfactants is still ambiguous. Thus, this study focused on the evaluation of the impact of chemical and bio-surfactants (i.e., Tween 80 (TWE) and Surfactin (SUC)) on the biodegradation of naphthalene (NAP), chrysene (CHR), and pyrene (PYR), the representative components of PAHs, with CYP enzyme from microalgae Parachlorella kessleri using molecular docking and molecular dynamics (MD) simulation. The molecular docking analysis revealed that PAHs bound to residues at the CYP active site through hydrophobic interactions for biodegradation. The MD simulation showed that the surfactant addition changed the enzyme conformation in the CYP-PAH complexes to provide more interactions between the enzyme and PAHs. This led to an increase in the enzyme's capability to degrade PAHs. Binding free energy (ΔG‌‌Bind) calculations confirmed that surfactant treatment could enhance PAHs degradation by the enzyme. The SUC gave a better result on NAP and PYR biodegradation based on ΔG‌‌Bind, while TWE facilitated the biodegradation of CHR. The research outputs could greatly facilitate evaluating the behaviors of oil spill-treating agents and oil spill response operations in the marine environment.

Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches

Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical methods to reveal quantitative correlations between the pharmacokinetics of compounds and their molecular structures, as well as their physical and chemical properties. QSPR models have been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties still needs improvement. Therefore, this paper comprehensively reviews the tools employed in various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches to building QSPR models, systematically analyzing the advantages and limitations of each modeling method to ensure their judicious application. We provide an overview of recent advancements in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review explores the inherent challenges in QSPR modeling while also proposing a range of considerations aimed at enhancing model prediction accuracy. The objective is to enhance the predictive capabilities of QSPR models in the field of drug development and provide valuable reference and guidance for researchers in this domain.

Exploring the role of microbial proteins in controlling environmental pollutants based on molecular simulation

Molecular simulation has been widely used to study microbial proteins' structural composition and dynamic properties, such as volatility, flexibility, and stability at the microscopic scale. Herein, this review describes the key elements of molecular docking and molecular dynamics (MD) simulations in molecular simulation; reviews the techniques combined with molecular simulation, such as crystallography, spectroscopy, molecular biology, and machine learning, to validate simulation results and bridge information gaps in the structure, microenvironmental changes, expression mechanisms, and intensity quantification; illustrates the application of molecular simulation, in characterizing the molecular mechanisms of interaction of microbial proteins with four different types of contaminants, namely heavy metals (HMs), pesticides, dyes and emerging contaminants (ECs). Finally, the review outlines the important role of molecular simulations in the study of microbial proteins for controlling environmental contamination and provides ideas for the application of molecular simulation in screening microbial proteins and incorporating targeted mutagenesis to obtain more effective contaminant control proteins.

Bioremediation potential of laccase for catalysis of glyphosate, isoproturon, lignin, and parathion: Molecular docking, dynamics, and simulation

The present study aimed to investigate the catalytic degradation produced by laccase in the detoxification of glyphosate, isoproturon, lignin polymer, and parathion. We explored laccase-glyphosate, laccase-lignin polymer, laccase-isoproturon, and laccase-parathion using molecular docking (MD) and molecular dynamics simulation (MDS) approaches. The results suggest that laccase interacts well with glyphosate, lignin polymer, isoproturon, and parathion during biodegradation. We calculated the root mean square deviations (RMSD) of laccase-glyphosate, laccase-lignin polymer, laccase-isoproturon, and laccase-parathion as 0.24 ± 0.02, 0.59 ± 0.32, 0.43 ± 0.07, and 0.43 ± 0.06 nm, respectively. In an aqueous solution, the stability of laccase with glyphosate, lignin polymer, isoproturon, and parathion is mediated through the formation of hydrophobic interactions, hydrogen bonds, and van der Waals interactions. The presence of xenobiotic toxic compounds in the active site changed the conformation of laccase. MDS of the laccase-substrate complexes confirmed their stability during catalytic degradation. Laccase assay results confirmed that the degradation of syringol, dihydroconiferyl alcohol, guaiacol, parathion, isoproturon, and glyphosate were 100%, 99.31%, 95.69%, 60.96%, 54.51%, and 48.34% within 2 h, respectively. Taken together, we describe a novel method to understand the molecular-level biodegradation of xenobiotic compounds through laccase and its potential application in contaminant removal.

Prediction of adsorption capacity and biodegradability of polybrominated diphenyl ethers in soil

Polybrominated diphenyl ethers (PBDEs) are widely used brominated flame retardants with strong toxicity concerns. Understanding the behaviors of PBDEs in soil is essential to evaluate their environmental impact. However, the limited, incoherent, and inaccurate data has challenged predicting the adsorption capacity and biodegradability of all 209 PBDE congeners in the soil. Moreover, there are minimal studies regarding the interactions between adsorption and biodegradation behaviors of PBDEs in the soil. Herein, in this study, we adopted quantitative structure-property relationship (QSAR) modeling to predict the adsorption behavior of 209 PBDE congeners by estimating their organic carbon-water partition coefficient (K_OC) values. In addition, the biodegradability of commonly occurring PBDE congeners was evaluated by analyzing their affinity to extracellular enzymes responsible for biodegradation using molecular docking. The results highlight that the degree of bromination plays a significant role in both the absorption and biodegradation of PBDEs in the soil due to compound stability and molecular geometry. Our findings help to advance the knowledge on PBDE behaviors in the soil and facilitate PBDE remediation associated with a soil environment.

Coupling strategies for ecotoxicological assessment of neonicotinoid insecticides based on their selective lethal effects: Design, screening, and regulation

The recently recognized adverse environmental and toxic effects of neonicotinoid insecticides (NNIs) on non-target organisms are alarming. A comprehensive design, screening, and regulatory system was developed to generate NNI derivatives and mutant receptors with selective-ecotoxicological effects to overcome such adverse effects. For ligand design, taking ACE-09 derivative as an example, the toxicity on non-target animals (aboveground: bees; underground: earthworms), plant absorption, and soil absorption decreased by 4.80% and 13.7%, 10.0%, and 121%, while the toxicity on target animals (aboveground: aphids; underground: B. odoriphagas), plant metabolism, and soil degradation increased by 70.2% and 51.7%, 5.08%, and 8.28%. For receptor modification, the ability of mutants to absorb ACE-09 derivative decreased by 31.0%, while the ability of mutants to metabolize ACE-09 derivative increased by 28.0% in scenario 2 (mainly plant selectivity); the ability of mutants to degrade ACE-09 derivative increased by 11.6% in scenario 3 (mainly soil selectivity). The above results indicated that the selective-ecotoxicological effects of ligand design and receptor modification were both improved. Additionally, the combined effects of the ACE-09 derivative on plant absorption and metabolic mutants improved by 31.1% and 31.4% in scenario 2, respectively, while the effect on microbial degradation mutant improved by 14.9%, indicating that there was a synergistic effect between ligand design and receptor modification. Finally, based on the interaction between the ACE-09 derivative and mutants, the optimal environmental factors that improved the selectivity of their ecotoxicological effects were determined. For example, alternate application of nitrogen and phosphorus fertilizers effectively reduced the oxidative damage to plants caused by NNI residues. The novel ligand-receptor joint modification method, combined with the regulation of environmental factors under multiple scenarios, can biochemically address the ecotoxicological concern and highlight the harmful effects of pesticides on the environment and non-target organisms.

Validation and inhibition study for toxic expression of quinolone antibiotic resistance genes in agricultural soils of eastern China

With the extensive use of antibiotics, the problem of antibiotic resistance genes (ARGs) has gradually emerged. As agricultural soil is an important enrichment media of antibiotics and ARGs, it is particularly important to study the toxicity of ARGs, the effects of various nutrients and pollutants, and how to control them through source modification and process regulation. In this study, a combination of source modification and process regulation was used to weaken the toxic expression of Quinolones' (QNs') ARGs in soils from different agricultural areas. And the influence of soil nutrients and pollutants on this process will be explored. Protein-protein docking and molecular docking were used to construct a target protein complex for the toxic expression of QNs' ARGs and characterize the toxicity of QNs' ARGs. The two-dimensional and three-dimensional quantitative structure-activity relationships model construction and sensitivity analysis were used to molecular modification and related validation. Molecular dynamics simulations assisted by sampling survey method based on agricultural soils in Northeast China and the lower-middle reaches of the Yangtze River were carried out to generate four scenarios. The main results are: (a) A functionally improved and environmentally friendly quinolone derivative (ORB-19) was designed. It can effectively inhibit the expression of QNs' ARGs and weaken the antibiotic selection pressure risk. The application of ORB-19 in agricultural areas could significantly inhibit the toxic expression of QNs' ARGs (112.75%~169.59%); (b) QNs' ARGs have a stronger toxic expression in agricultural areas of Northeast China, which have higher nutrient elements; (c) The contribution of different types of agricultural pollution to suppressing the toxic expression of QNs' ARGs in agricultural soils varies; (d) The options of applied field measures given for the inhibition of QNs' ARG toxic expression varied between plots with different agricultural pollution types. This study provides theoretical support for inhibiting the toxic expression of QNs' ARGs in the soil environment, reducing the spread of ARGs in microbial populations, replacing green QNs derivatives, and sustainable development of agricultural soils.

Dermal exposure to synthetic musks: Human health risk assessment, mechanism, and control strategy

Synthetic musks (SMs) have been widely used as odor additives in personal care products (PCPs). Dermal exposure to SMs is the main pathway of the accumulation of these chemicals in human kerateins and poses potential health risks. In this study, in silico methods were established to reduce the human health risk of SMs from dermal exposure by investigating the risk mechanisms, designing lower bioaccumulation ability SMs and suggesting proper PCP ingredients using molecular docking, molecular dynamics simulation, and quantitative structure-activity relationship (QSAR) models. The binding energy, a parameter reflecting the binding ability of SMs and human keratin protein (4ZRY), was used as the indicator to assess the human health risk of SMs. According to the mechanism analysis, total energy was found as the most influential molecular structural feature influencing the bioaccumulation ability of a SM, and as one of the main factors influencing the function (i.e., odor sensitivity) of an SM. The 3D-QSAR models were constructed to control the human health risk of SMs by designing lower-risk SMs derivatives. The phantolide (PHAN)- 58 was determined to be the optimum SM derivative with lower bioaccumulation ability (reduced 17.25%) and improved odor sensitivity (increased 7.91%). A further reduction of bioaccumulation ability of PHAN-58 was found when adding proper body wash ingredients (i.e., alkyl ethoxylate sulfate (AES), dimethyloldimethyl (DMDM), EDTA-Na4, ethylene glycol distearate (EGDS), hydroxyethyl cellulose (HEC), lemon yellow and octyl glucose), leading to a significant reduction of the bioaccumulation ability (42.27%) compared with that of PHAN. Results demonstrated that the proposed theoretical mechanism and control strategies could effectively reduce the human health risk of SMs from dermal exposure.

An Evaluation of the Potential of Essential Oils against SARS-CoV-2 from In Silico Studies through the Systematic Review Using a Chemometric Approach

Essential oils (EOs) and their compounds have attracted particular attention for their reported beneficial properties, especially their antiviral potential. However, data regarding their anti-SARS-CoV-2 potential are scarce in the literature. Thus, this study aimed to identify the most promising EO compounds against SARS-CoV-2 based on their physicochemical, pharmacokinetic, and toxicity properties. A systematic literature search retrieved 1669 articles; 40 met the eligibility criteria, and 35 were eligible for analysis. These studies resulted in 465 EO compounds evaluated against 11 human and/or SARS-CoV-2 target proteins. Ninety-four EO compounds and seven reference drugs were clustered by the highest predicted binding affinity. Furthermore, 41 EO compounds showed suitable drug-likeness and bioactivity score indices (≥0.67). Among these EO compounds, 15 were considered the most promising against SARS-CoV-2 with the ADME/T index ranging from 0.86 to 0.81. Some plant species were identified as EO potential sources with anti-SARS-CoV-2 activity, such as Melissa officinalis Arcang, Zataria multiflora Boiss, Eugenia brasiliensis Cambess, Zingiber zerumbet Triboun & K.Larsen, Cedrus libani A.Rich, and Vetiveria zizanoides Nash. Our work can help fill the gap in the literature and guide further in vitro and in vivo studies, intending to optimize the finding of effective EOs against COVID-19.

Strategies to Control Human Health Risks Arising from Antibiotics in the Environment: Molecular Modification of QNs for Enhanced Plant-Microbial Synergistic Degradation

In the present work, a comprehensive screening and evaluation system was established to improve the plant-microbial synergistic degradation effects of QNs. The study included the construction of a 3D-QSAR model, the molecular modification, environmental friendliness and functional evaluation of drugs, degradation pathway simulation, and human health risk assessment. Molecular dynamics was applied to quantify the binding capacity of QNs toward the plant degradation enzyme (peroxidase) and microbial degradation enzymes (manganese peroxidase, lignin peroxidase, and laccase). The fuzzy comprehensive evaluation method was used in combination with the weighted average method for normalization and assigning equal weights to the plant and microbial degradation effect values of the QNs. Considering the synergistic degradation effect value as the dependent variable and the molecular information of the QNs as the independent variable, a 3D-QSAR model was constructed for the plant-microbial synergistic degradation effect of QNs. The constructed model was then employed to conduct the molecular modification, environmental friendliness and functional evaluation, degradation pathway simulation, and human health risk assessment of transformation products using pharmacokinetics and toxicokinetics. The results revealed that the synergistic degradation effect 3D-QSAR (CoMSIA) model exhibited good internal and external prediction ability, fitting ability, stability, and no overfitting phenomenon. Norfloxacin (NOR) was used as the target molecule in the molecular modification. A total of 35 NOR derivatives with enhanced plant-microbial synergistic degradation effect (1.32-21.51%) were designed by introducing small-volume, strongly electronegative, and hydrophobic hydrogen bond receptor groups into the active group of the norfloxacin structure. The environment-friendliness and the functionality of NOR were evaluated prior to and after the modification, which revealed seven environment-friendly FQs derivatives exhibiting moderate improvement in stability and bactericidal efficacy. The simulation of the NOR plant and microbial degradation pathways prior to and after the modification and the calculation of the reaction energy barrier revealed Pathway A (D-17 to D-17-2) and Pathway B (D-17 to D-17-4) as the most prone degradation pathways in plants and Pathway A (D-17 to D-17-1) and Pathway B (D-17 to D-17-4) as the most prone degradation pathways in microorganisms. This demonstrated that the degradation of the modified NOR derivatives was significantly enhanced, with the hydroxylation and piperazine ring substitution reaction playing an important role in the degradation process. Finally, the parameters, including hepatotoxicity, mutagenicity, and rodent carcinogenicity, among others, predicted using the pharmacokinetics and toxicokinetics analyses revealed a significant reduction in the human health risk associated with the modified NOR, along with a considerable reduction in the toxicity of its transformation products, implying that the human health risk associated with the transformation products was reduced remarkably. The present study provides a theoretical basis for novel ideas and evaluation programs for improving the plant-microbial synergistic degradation of the QNs antibiotics for source control and drug design, thereby reducing the residues of these antibiotics and the associated hazard in the complex plant-soil environment, ultimately decreasing the potential risks to human health.

Intracellular GSH/GST antioxidants system change as an earlier biomarker for toxicity evaluation of iron oxide nanoparticles

Glutathione (GSH) and glutathione-S-transferases (GSTs) are two frontlines of cellular defense against both acute and chronic toxicity of xenobiotics-induced oxidative stress. The contribution of GSH and GST enzymes to signaling pathways and the regulation of GSH homeostasis play a central role in the detoxification of numerous environmental toxins and impurities. Iron oxide nanoparticles stemmed from traffic exhaust, steel manufacturing, or welding as a potential environmental pollution can lead to adverse respiratory outcomes and aggravate the risk of chronic health conditions via persistent oxidative stress. In this work, two kinds of acute exposure experiments of iron oxide (Fe₂O₃ and Fe₃O₄) nanoparticles in cells and in vivo were conducted to evaluate the GSH levels and GST activity. Our current research presented Fe₃O₄ nanoparticles at lower concentrations (≤100 μg/ml) seem to be more toxic to the human bronchial epithelial cells as their consumption of GSH and decrease of GST activity. The catalysis activity of Fe₃O₄ nanoparticles per se may contribute to the intracellular GSH consumption along with inhibition of glutathione-S-transferase class mu 1 and P (GSTM1 and GSTP1) active site and expression decrease of GSTM1 and GSTP1. Accordingly, the GSH consumption and decrease in GST activity directed to the further lipid peroxidation regarded as an earlier marker for toxicity evaluation of iron oxide nanoparticles, and relevant intervention may be effective for prevention of respiratory exposure induced damage from iron oxide nanoparticles.

Combined remediation of polychlorinated naphthalene-contaminated soil under multiple scenarios: An integrated method of genetic engineering and environmental remediation technology

This study explored the types of polychlorinated naphthalene (PCN)-contaminated soil and determined the practicable scheme of combined remediation using an integrated method of genetic engineering and environmental remediation technology. A multi-scenario comprehensive evaluation system of a plant-microbial combined bioremediation of PCN-contaminated soil was established using the intelligent integration of analytic hierarchy process and formula evaluation methods based on the current situation of PCN contamination in China, which showed the bioremediation of PCN-contaminated soil by the plant-microbial system could be divided into four scenarios. QSAR models were constructed to quantify the remediation mechanism that electronic parameter ∆E was the key factor changing the efficiency of combined bioremediation. Moreover, the macro-control scheme of PCN-contaminated soil was established, which indicated that four new multifunctional proteins promoted the absorption, degradation, and mineralization of PCNs in specific soil pollution types significantly, were obtained through cross gene recombination. The molecular dynamics (MD) simulation results showed the efficiency of the plant-microbial combined bioremediation were increased by 15.45% (Scenario 1, 2, 3) and 20.02% (Scenario 4) under the optimal regulation scheme. The findings will be helpful to realize the regional control of PCN-contaminated soil.

Environmentally Friendly Quinolones Design for a Two-Way Choice between Biotoxicity and Genotoxicity through Double-Activity 3D-QSAR Model Coupled with the Variation Weighting Method

Quinolone (QN) antibiotics are widely used, which lead to their accumulation in soil and toxic effects on ryegrass in pasture. In this study, we employed ryegrass as the research object and selected the total scores of 29 QN molecules docked with two resistant enzyme structures, superoxide dismutase (SOD, PDB ID: 1B06) and proline (Pro, PPEP-2, PDB ID: 6FPC), as dependent variables. The structural parameters of QNs were used as independent variables to construct a QN double-activity 3D-QSAR model for determining the biotoxicity on ryegrass by employing the variation weighting method. This model was constructed to determine modification sites and groups for designing QNs molecules. According to the 3D contour map of the model, by considering enrofloxacin (ENR) and sparfloxacin (SPA) as examples, 23 QN derivatives with low biotoxicity were designed, respectively. The functional properties and environmental friendliness of the QN derivatives were predicted through a two-way selection between biotoxicity and genotoxicity before and after modification; four environmentally friendly derivatives with low biotoxicity and high genotoxicity were screened out. Mixed toxicity index and molecular dynamics methods were used to verify the combined toxicity mechanism of QNs on ryegrass before and after modification. By simulating the combined pollution of ENR and its derivatives in different soils (farmland, garden, and woodland), the types of combined toxicity were determined as partial additive and synergistic. Binding energies were calculated using molecular dynamics. The designed QN derivatives with low biotoxicity, high genotoxicity, and environmental friendliness can highly reduce the combined toxicity on ryegrass and can be used as theoretic reserves to replace QN antibiotics.