First-principles study on the heterogeneous formation of environmentally persistent free radicals (EPFRs) over α-Fe2O3(0001) surface: Effect of oxygen vacancy.

Metal oxides with oxygen vacancies have a significant impact on catalytic activity for the transformation of organic pollutants in waste-to-energy (WtE) incineration processes. This study aims to investigate the influence of hematite surface oxygen point defects on the formation of environmentally persistent free radicals (EPFRs) from phenolic compounds based on the first-principles calculations. Two oxygen-deficient conditions were considered: oxygen vacancies at the top surface and on the subsurface. Our simulations indicate that the adsorption strength of phenol on the α-Fe2O3(0001) surface is enhanced by the presence of oxygen vacancies. However, the presence of oxygen vacancies has a negative impact on the dissociation of the phenol molecule, particularly for the surface with a defective point at the top layer. Thermo-kinetic parameters were established over a temperature range of 300-1000 K, and lower reaction rate constants were observed for the scission of phenolic O-H bonds over the oxygen-deficient surfaces compared to the pristine surface. The negative effects caused by the oxygen-deficient conditions could be attributed to the local reduction of FeIII to FeII, which lower the oxidizing ability of surface reaction sites. The findings of this study provide us a promising approach to regulate the formation of EPFRs.

Unravelling bioaccumulation, depletion and metabolism of organophosphate triesters in laying hens: Insight of in vivo biotransformation assisted by diester metabolites.

Organophosphate triesters (tri-OPEs) threaten human health through dietary exposure, but little is known about their feed-to-food transfer and in vivo behavior in farm animals. Herein 135 laying hens were fed with contaminated feed (control group, low-level group and high-level group) to elucidate the bioaccumulation, distribution, and metabolism of the six most commonly reported tri-OPEs. The storage (breast muscle), metabolism and mobilization (liver and blood) and non-invasive (feather) tissues were collected. The exposure-increase (D1∼14) and depuration-decrease (D15∼42) trends indicated that feed exposure caused tri-OPE accumulation in animal tissues. Tissue-specific and moiety-specific behavior was observed for tri-OPEs. The highest transfer factor (TF) and transfer rate (TR) were observed in liver (TF: 14.8%∼82.3%; TR: 4.40%∼24.5%), followed by feather, breast muscle, and blood. Tris(2-chloroisopropyl) phosphate (TCIPP) had the longest half-life in feather (72.2 days), while triphenyl phosphate (TPhP) showed the shortest half-life in liver (0.41 days). Tri-OPEs' major metabolites (organophosphate diesters, di-OPEs) were simultaneously studied, which exhibited dose-dependent and time-dependent variations following administration. In breast muscle, the inclusion of di-OPEs resulted in TF increases of 735%, 1108%, 798%, and 286% than considering TCIPP, tributyl phosphate, tris(2-butoxyethyl) phosphate and tris(2-ethylhexyl) phosphate alone. Feather was more of a proxy of birds' long-term exposure to tri-OPEs, while short-term exposure was better reflected by di-OPEs. Both experimental and in silico modeling methods validated aryl-functional group facilitated the initial accumulation and metabolism of TPhP in the avian liver compared to other moiety-substituted tri-OPEs.

Deciphering exogenous chemical carcinogenicity through interpretable deep learning: A novel approach for evaluating atmospheric pollutant hazards.

Cancer remains a significant global health concern, with millions of deaths attributed to it annually. Environmental pollutants play a pivotal role in cancer etiology and contribute to the growing prevalence of this disease. The carcinogenic assessment of these pollutants is crucial for chemical health evaluation and environmental risk assessments. Traditional experimental methods are expensive and time-consuming, prompting the development of alternative approaches such as in silico methods. In this regard, deep learning (DL) has shown potential but lacks optimal performance and interpretability. This study introduces an interpretable DL model called CarcGC for chemical carcinogenicity prediction, utilizing a graph convolutional neural network (GCN) that employs molecular structural graphs as inputs. Compared to existing models, CarcGC demonstrated enhanced performance, with the area under the receiver operating characteristic curve (AUCROC) reaching 0.808 on the test set. Due to air pollution is closely related to the incidence of lung cancers, we applied the CarcGC to predict the potential carcinogenicity of chemicals listed in the United States Environmental Protection Agency's Hazardous Air Pollutants (HAPs) inventory, offering a foundation for environmental carcinogenicity screening. This study highlights the potential of artificially intelligent methods in carcinogenicity prediction and underscores the value of CarcGC interpretability in revealing the structural basis and molecular mechanisms underlying chemical carcinogenicity.

CatNet: Sequence-based deep learning with cross-attention mechanism for identifying endocrine-disrupting chemicals.

Endocrine-disrupting chemicals (EDCs) pose significant environmental and health risks due to their potential to interfere with nuclear receptors (NRs), key regulators of physiological processes. Despite the evident risks, the majority of existing research narrows its focus on the interaction between compounds and the individual NR target, neglecting a comprehensive assessment across the entire NR family. In response, this study assembled a comprehensive human NR dataset, capturing 49,244 interactions between 35,467 unique compounds and 42 NRs. We introduced a cross-attention network framework, "CatNet", innovatively integrating compound and protein representations through cross-attention mechanisms. The results showed that CatNet model achieved excellent performance with an area under the receiver operating characteristic curve (AUCROC) = 0.916 on the test set, and exhibited reliable generalization on unseen compound-NR pairs. A distinguishing feature of our research is its capacity to expand to novel targets. Beyond its predictive accuracy, CatNet offers a valuable mechanistic perspective on compound-NR interactions through feature visualization. Augmenting the utility of our research, we have also developed a graphical user interface, empowering researchers to predict chemical binding to diverse NRs. Our model enables the prediction of human NR-related EDCs and shows the potential to identify EDCs related to other targets.

Multiscale computational simulation of pollutant behavior at water interfaces.

The investigation of pollutant behavior at water interfaces is critical to understand pollution in aquatic systems. Computational methods allow us to overcome the limitations of experimental analysis, delivering valuable insights into the chemical mechanisms and structural characteristics of pollutant behavior at interfaces across a range of scales, from microscopic to mesoscopic. Quantum mechanics, all-atom molecular dynamics simulations, coarse-grained molecular dynamics simulations, and dissipative particle dynamics simulations represent diverse molecular interaction calculation methods that can effectively model pollutant behavior at environmental interfaces from atomic to mesoscopic scales. These methods provide a rich variety of information on pollutant interactions with water surfaces. This review synthesizes the advancements in applying typical computational methods to the formation, adsorption, binding, and catalytic conversion of pollutants at water interfaces. By drawing on recent advancements, we critically examine the current challenges and offer our perspective on future directions. This review seeks to advance our understanding of computational techniques for elucidating pollutant behavior at water interfaces, a critical aspect of water research.

Dynamic Behavior and Interaction Mechanism of Soil Organic Matter in Water Systems: A Coarse-Grained Molecular Dynamics Study.

Investigating soil organic matter's (SOM) microscale assembly and functionality is challenging due to its complexity. This study constructs comparatively realistic SOM models, including diverse components such as Leonardite humic acid (LHA), lipids, peptides, carbohydrates, and lignin, to unveil their spontaneous self-assembly behavior at the mesoscopic scale through microsecond coarse-grained molecular dynamics simulations. We discovered an ordered SOM aggregation creating a layered phase from its hydrophobic core to the aqueous phase, resulting in an increasing O/C ratio and declining structural amphiphilicity. Notably, the amphiphilic lipids formed a bilayer membrane, partnering with lignin to constitute SOM's hydrophobic core. LHA, despite forming a layer, was embedded within this structure. The formation of such complex architectures was driven by nonbonded interactions between components. Our analysis revealed component-dependent diffusion effects within the SOM system. Lipids, peptides, and lignin showed inhibitory effects on self-diffusion, while carbohydrates facilitated diffusion. This study offers novel insights into the dynamic behavior and assembly of SOM components, introducing an effective approach for studying dynamic SOM mechanisms in aquatic environments.

Machine learning strategy on activation energy of environmental heterogeneous reactions and its application to atmospheric formation of typical montmorillonite-bound phenoxy radicals.

Heterogeneous transformation of organic pollutants into more toxic chemicals poses substantial health risks to humans. Activation energy is an important indicator that help us to understand transformation efficacy of environmental interfacial reactions. However, the determination of activation energies for large numbers of pollutants using either the experimental or high-accuracy theoretical methods is expensive and time-consuming. Alternatively, the machine learning (ML) method shows the strength in predictive performance. In this study, using the formation of a typical montmorillonite-bound phenoxy radical as an example, a generalized ML framework RAPID was proposed for activation energy prediction of environmental interfacial reactions. Accordingly, an explainable ML model was developed to predict the activation energy via easily accessible properties of the cations and organics. The model developed by decision tree (DT) performed best with the lowest root-mean-squared error (RMSE = 0.22) and the highest coefficient of determination values (R2 score = 0.93), the underlying logic of which was well understood by combining model visualization and SHapley Additive exPlanations (SHAP) analysis. The performance and interpretability of the established model suggest that activation energies can be predicted by the well-designed ML strategy, and this would allow us to predict more heterogeneous transformation reactions in the environmental field.

Exogenous Chemicals Impact Virus Receptor Gene Transcription: Insights from Deep Learning.

Despite the fact that coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been disrupting human life and health worldwide since the outbreak in late 2019, the impact of exogenous substance exposure on the viral infection remains unclear. It is well-known that, during viral infection, organism receptors play a significant role in mediating the entry of viruses to enter host cells. A major receptor of SARS-CoV-2 is the angiotensin-converting enzyme 2 (ACE2). This study proposes a deep learning model based on the graph convolutional network (GCN) that enables, for the first time, the prediction of exogenous substances that affect the transcriptional expression of the ACE2 gene. It outperforms other machine learning models, achieving an area under receiver operating characteristic curve (AUROC) of 0.712 and 0.703 on the validation and internal test set, respectively. In addition, quantitative polymerase chain reaction (qPCR) experiments provided additional supporting evidence for indoor air pollutants identified by the GCN model. More broadly, the proposed methodology can be applied to predict the effect of environmental chemicals on the gene transcription of other virus receptors as well. In contrast to typical deep learning models that are of black box nature, we further highlight the interpretability of the proposed GCN model and how it facilitates deeper understanding of gene change at the structural level.

Generation Mechanism of Perfluorohexanesulfonic Acid from Polyfluoroalkyl Sulfonamide Derivatives During Chloramination in Drinking Water.

Per- and polyfluoroalkyl substances (PFASs), including perfluorohexanesulfonic acid (PFHxS), as emerging persistent organic pollutants widely detected in drinking water, have drawn increasing concern. The PFHxS contamination of drinking water always results from direct and indirect sources, especially the secondary generations through environmental transformations of precursors. However, the mechanism of the transformation of precursors to PFHXS during the drinking water treatment processes remains unclear. Herein, the potential precursors and formation mechanisms of PFHxS were explored during drinking water disinfection. Simultaneously, the factors affecting PFHxS generation were also examined. This study found PFHxS could be generated from polyfluoroalkyl sulfonamide derivatives during chlorination and chloramination. The fate and yield of PFHxS varied from different precursors and disinfection processes. In particular, monochloramine more favorably formed PFHxS. Several perfluoroalkyl oxidation products and decarboxylation intermediates were detected and identified in the chloraminated samples using Fourier-transform ion cyclotron resonance mass spectrometry. Combined with density functional theory calculations, the results indicated that the indirect oxidation via the attack of the nitrogen atom in sulfonamide groups might be the dominant pathway for generating PFHxS during chloramination, and the process could be highly affected by the monochloramine dose, pH, and temperature. This study provides important evidence of the secondary formation of PFHxS during drinking water disinfection and scientific support for chemical management of PFHxS and PFHxS-related compounds.

Insight into the formation and biological effects of natural organic matter corona on silver nanoparticles in water environment using biased cyclical electrical field-flow fractionation.

Natural organic matter (NOM) readily interacts with nanoparticles, leading to the formation of NOM corona structures on their surface. NOM corona formation is closely related to the surface coatings and bioavailability of nanoparticles. However, the mechanism underlying NOM corona formation on silver nanoparticles (AgNPs) remains largely unknown due to the lack of effective analytical methods for identifying the changes in the AgNP surface. Herein, the separation ability of biased cyclical electrical field-flow fractionation (BCyElFFF) for same-sized polyvinyl pyrrolidone-coated and poly(ethylene glycol)-coated silver nanoparticles (AgNPs) with different electrophoretic mobilities was evaluated under various electrical conditions. Then, the mechanism behind the NOM corona formation on these AgNP surfaces was elucidated based on the changes in the elution time and off-line characterization of the collected fractions during their elution time in a BCyElFFF run. Finally, the survival rates of E. coli exposed to polyvinyl pyrrolidone-coated and poly(ethylene glycol)-coated AgNPs with or without NOM collected during repeated BCyElFFF runs were observed to increase with increasing NOM concentration, clearly demonstrating the negative effect of NOM corona structures on the bioavailability of AgNPs. These findings highlight the powerful separation and isolation ability of BCyElFFF in studying the transformation and fate of nanoparticles in aqueous environments.

Evaluation of the binding performance of flavonoids to estrogen receptor alpha by Autodock, Autodock Vina and Surflex-Dock.

Molecular docking is a widely used method to predict the binding modes of small-molecule ligands to the target binding site. However, it remains a challenge to identify the correct binding conformation and the corresponding binding affinity for a series of structurally similar ligands, especially those with weak binding. An understanding of the various relative attributes of popular docking programs is required to ensure a successful docking outcome. In this study, we systematically compared the performance of three popular docking programs, Autodock, Autodock Vina, and Surflex-Dock for a series of structurally similar weekly binding flavonoids (22) binding to the estrogen receptor alpha (ERα). For these flavonoids-ERα interactions, Surflex-Dock showed higher accuracy than Autodock and Autodock Vina. The hydrogen bond overweighting by Autodock and Autodock Vina led to incorrect binding results, while Surflex-Dock effectively balanced both hydrogen bond and hydrophobic interactions. Moreover, the selection of initial receptor structure is critical as it influences the docking conformations of flavonoids-ERα complexes. The flexible docking method failed to further improve the docking accuracy of the semi-flexible docking method for such chemicals. In addition, binding interaction analysis revealed that 8 residues, including Ala350, Glu353, Leu387, Arg394, Phe404, Gly521, His524, and Leu525, are the key residues in ERα-flavonoids complexes. This work provides reference for assessing molecular interactions between ERα and flavonoid-like chemicals and provides instructive information for other environmental chemicals.

Computational Insights into the Allosteric Effect and Dynamic Structural Features of the SARS-COV-2 Spike Protein.

Invited for the cover of this issue are Aiqian Zhang, Jianjie Fu, Guibin Jiang, and co-workers at the Chinese Academy of Sciences. The image depicts the molecular recognition of human angiotensin-converting enzyme 2 by the SARS-COV-2 spike protein. Read the full text of the article at 10.1002/chem.202104215.

Computational Insights into the Allosteric Effect and Dynamic Structural Features of the SARS-COV-2 Spike Protein.

COVID-19 caused by SARS-COV-2 is continuing to surge globally. The spike (S) protein is the key protein of SARS-COV-2 that recognizes and binds to the host target ACE2. In this study, molecular dynamics simulation was used to elucidate the allosteric effect of the S protein. Binding of ACE2 caused a centripetal movement of the receptor-binding domain of the S protein. The dihedral changes in Phe329 and Phe515 played a key role in this process. Two potential cleavage sites S1/S2 and S2' were exposed on the surface after the binding of ACE2. The binding affinity of SARS-COV-2 S protein and ACE2 was higher than that of SARS-COV. This was mainly due to the mutation of Asp480 in SARS-COV to Ser494 in SARS-COV-2, which greatly weakened the electrostatic repulsion. The result provides a theoretical basis for the SARS-COV-2 infection and aids the development of biosensors and detection reagents.

Social motility of biofilm-like microcolonies in a gliding bacterium.

Bacterial biofilms are aggregates of surface-associated cells embedded in an extracellular polysaccharide (EPS) matrix, and are typically stationary. Studies of bacterial collective movement have largely focused on swarming motility mediated by flagella or pili, in the absence of a biofilm. Here, we describe a unique mode of collective movement by a self-propelled, surface-associated biofilm-like multicellular structure. Flavobacterium johnsoniae cells, which move by gliding motility, self-assemble into spherical microcolonies with EPS cores when observed by an under-oil open microfluidic system. Small microcolonies merge, creating larger ones. Microscopic analysis and computer simulation indicate that microcolonies move by cells at the base of the structure, attached to the surface by one pole of the cell. Biochemical and mutant analyses show that an active process drives microcolony self-assembly and motility, which depend on the bacterial gliding apparatus. We hypothesize that this mode of collective bacterial movement on solid surfaces may play potential roles in biofilm dynamics, bacterial cargo transport, or microbial adaptation. However, whether this collective motility occurs on plant roots or soil particles, the native environment for F. johnsoniae, is unknown.

Long-Range Transport, Trophic Transfer, and Ecological Risks of Organophosphate Esters in Remote Areas.

Organophosphate esters (OPEs) have been a focus in the field of environmental science due to their large volume production, wide range of applications, ubiquitous occurrence, potential bioaccumulation, and worrisome ecological and health risks. Varied physicochemical properties among OPE analogues represent an outstanding scientific challenge in studying the environmental fate of OPEs in recent years. There is an increasing number of studies focusing on the long-range transport, trophic transfer, and ecological risks of OPEs. Therefore, it is necessary to conclude the OPE pollution status on a global scale, especially in the remote areas with vulnerable and fragile ecosystems. The present review links together the source, fate, and environmental behavior of OPEs in remote areas, integrates the occurrence and profile data, summarizes their bioaccumulation, trophic transfer, and ecological risks, and finally points out the predominant pollution burden of OPEs among organic pollutants in remote areas. Given the relatively high contamination level and bioaccumulation/biomagnification behavior of OPEs, in combination with the sensitivity of endemic species in remote areas, more attention should be paid to the potential ecological risks of OPEs.

Data-driven coarse-grained modeling of non-equilibrium systems.

Modeling a high-dimensional Hamiltonian system in reduced dimensions with respect to coarse-grained (CG) variables can greatly reduce computational cost and enable efficient bottom-up prediction of main features of the system for many applications. However, it usually experiences significantly altered dynamics due to loss of degrees of freedom upon coarse-graining. To establish CG models that can faithfully preserve dynamics, previous efforts mainly focused on equilibrium systems. In contrast, various soft matter systems are known to be out of equilibrium. Therefore, the present work concerns non-equilibrium systems and enables accurate and efficient CG modeling that preserves non-equilibrium dynamics and is generally applicable to any non-equilibrium process and any observable of interest. To this end, the dynamic equation of a CG variable is built in the form of the non-stationary generalized Langevin equation (nsGLE), where the two-time memory kernel is determined from the data of the auto-correlation function of the observable of interest. By embedding the nsGLE in an extended dynamics framework, the nsGLE can be solved efficiently to predict the non-equilibrium dynamics of the CG variable. To prove and exploit the equivalence of the nsGLE and extended dynamics, the memory kernel is parameterized in a two-time exponential expansion. A data-driven hybrid optimization process is proposed for the parameterization, which integrates the differential-evolution method with the Levenberg-Marquardt algorithm to efficiently tackle a non-convex and high-dimensional optimization problem.

Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics.

The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters. In particular, we focus on implicit-solvent CG modeling of polymer solutions. The CG model is based on the generalized Langevin equation, where the memory kernel plays the critical role in determining the dynamics in all time scales. Thus, we propose methods for transfer learning of memory kernels. The key ingredient of our methods is Gaussian process regression. By integration with the model order reduction via proper orthogonal decomposition and the active learning technique, the transfer learning can be practically efficient and requires minimum training data. Through two example polymer solution systems, we demonstrate the accuracy and efficiency of the proposed transfer learning methods in the construction of transferable memory kernels. The transferability allows for out-of-sample predictions, even in the extrapolated domain of parameters. Built on the transferable memory kernels, the CG models can reproduce the dynamic properties of polymers in all time scales at different thermodynamic conditions (such as temperature and solvent viscosity) and for different systems with varying concentrations and lengths of polymers.

In silico health effect prioritization of environmental chemicals through transcriptomics data exploration from a chemo-centric view.

With the explosive growth of synthetic compounds, the health effects caused by exogenous chemical exposure have attracted more and more public attention. The prediction of health effect is a never-ending story. Collective resource of transcriptomics data offers an opportunity to understand and identify the multiple health effects of small molecule. Inspired by the fact that environmental chemicals of high health risk frequently share both similar gene expression profile and common structural feature of certain drugs, we here propose a novel computational effect prioritization method for environmental chemicals through transcriptomics data exploration from a chemo-centric view. Specifically, non-negative matrix factorization (NMF) method has been adopted to get the association network linking structural features with transcriptomics characteristics of drugs with specific effects. The model yields 13 pivotal types of effects, so-called components, that represent drug categories with common chemo- and geno- type features. Moreover, the established model effectively prioritizes potential toxic effects for the external chemicals from the endocrine disruptor screening program (EDSP) for their potential estrogenicity and other verified risks. Even if only the highest priority is set for the estrogenic effect, the precision and recall can reach 0.76 and 0.77 respectively for these chemicals. Our effort provides a successful endeavor as to profile potential toxic effects simultaneously for environmental chemicals using both chemical and omics data.

Data-driven coarse-grained modeling of polymers in solution with structural and dynamic properties conserved.

We present data-driven coarse-grained (CG) modeling for polymers in solution, which conserves the dynamic as well as structural properties of the underlying atomistic system. The CG modeling is built upon the framework of the generalized Langevin equation (GLE). The key is to determine each term in the GLE by directly linking it to atomistic data. In particular, we propose a two-stage Gaussian process-based Bayesian optimization method to infer the non-Markovian memory kernel from the data of the velocity autocorrelation function (VACF). Considering that the long-time behaviors of the VACF and memory kernel for polymer solutions can exhibit hydrodynamic scaling (algebraic decay with time), we further develop an active learning method to determine the emergence of hydrodynamic scaling, which can accelerate the inference process of the memory kernel. The proposed methods do not rely on how the mean force or CG potential in the GLE is constructed. Thus, we also compare two methods for constructing the CG potential: a deep learning method and the iterative Boltzmann inversion method. With the memory kernel and CG potential determined, the GLE is mapped onto an extended Markovian process to circumvent the expensive cost of directly solving the GLE. The accuracy and computational efficiency of the proposed CG modeling are assessed in a model star-polymer solution system at three representative concentrations. By comparing with the reference atomistic simulation results, we demonstrate that the proposed CG modeling can robustly and accurately reproduce the dynamic and structural properties of polymers in solution.

The formation of PBDFs from the ortho-disubstituted phenol precursors: A comprehensive theoretical study on the PBDD/Fs formation from 2,4,6-tribromophenol.

Bromophenols are known as direct precursors of the notorious polybrominated dibenzo-p-dioxin/dibenzofurans (PBDD/Fs). There is a long-held viewpoint that only the more toxic dioxin-type products could be formed from the ortho-disubstituted phenols, totally contrary to the experimental observations that both PBDDs and PBDFs are generated. To tackle the issue, the gaseous formation mechanism of PBDD/Fs from 2,4,6-tribromophenol (TBP), a typical ortho-disubstituted phenol, was investigated in this study. Firstly, the reactions between TBP and the active H radical produce three key radical species including the bromophenoxyl radical, the substituted phenyl radical and phenoxyl diradical. The self- and cross-combinations of these radical species and TBP yield not only the dioxin-type products 1,3,6,8-TeBDD and 1,3,7,9-TeBDD, but also the brominated dibenzofurans 1,3,6,8-TeBDF and 2,4,6,8-TeBDF. Notably, the reactions involving the phenyl C sites in the substituted phenyl and phenoxyl diradicals are demonstrated to be both thermodynamically and kinetically more favorable than those involving the bromophenoxyl radical and the TBP molecule. Most importantly, the findings of the present work are of great importance as it provides feasible pathways to form less toxic dibenzofuran-type products from the ortho-disubstituted phenols. These results will improve the understanding of the PBDD/Fs formation mechanism from phenol precursors.