A Multi-view Molecular Pre-training with Generative Contrastive Learning.

Molecular representation learning can preserve meaningful molecular structures as embedding vectors, which is a necessary prerequisite for molecular property prediction. Yet, learning how to accurately represent molecules remains challenging. Previous approaches to learning molecular representations in an end-to-end manner potentially suffered information loss while neglecting the utilization of molecular generative representations. To obtain rich molecular feature information, the pre-training molecular representation model utilized different molecular representations to reduce information loss caused by a single molecular representation. Therefore, we provide the MVGC, a unique multi-view generative contrastive learning pre-training model. Our pre-training framework specifically acquires knowledge of three fundamental feature representations of molecules and effectively integrates them to predict molecular properties on benchmark datasets. Comprehensive experiments on seven classification tasks and three regression tasks demonstrate that our proposed MVGC model surpasses the majority of state-of-the-art approaches. Moreover, we explore the potential of the MVGC model to learn the representation of molecules with chemical significance.

Kinetic Ensemble of Tau Protein through the Markov State Model and Deep Learning Analysis.

The ordered assembly of Tau protein into filaments characterizes Alzheimer's and other neurodegenerative diseases, and thus, stabilization of Tau protein is a promising avenue for tauopathies therapy. To dissect the underlying aggregation mechanisms on Tau, we employ a set of molecular simulations and the Markov state model to determine the kinetics of ensemble of K18. K18 is the microtubule-binding domain of Tau protein and plays a vital role in the microtubule assembly, recycling processes, and amyloid fibril formation. Here, we efficiently explore the conformation of K18 with about 150 µs lifetimes in silico. Our results observe that all four repeat regions (R1-R4) are very dynamic, featuring frequent conformational conversion and lacking stable conformations, and the R2 region is more flexible than the R1, R3, and R4 regions. Additionally, it is worth noting that residues 300-310 in R2-R3 and residues 319-336 in R3 tend to form sheet structures, indicating that K18 has a broader functional role than individual repeat monomers. Finally, the simulations combined with Markov state models and deep learning reveal 5 key conformational states along the transition pathway and provide the information on the microsecond time scale interstate transition rates. Overall, this study offers significant insights into the molecular mechanism of Tau pathological aggregation and develops novel strategies for both securing tauopathies and advancing drug discovery.

Investigations of the reaction mechanism of sodium with hydrogen fluoride to form sodium fluoride and the adsorption of hydrogen fluoride on sodium fluoride monomer and tetramer.

CONTEXT: The reaction between Na and HF is a typical harpooning reaction which is of great interest due to its significance in understanding the elementary chemical reaction kinetics. This work aims to investigate the detailed reaction mechanisms of sodium with hydrogen fluoride and the adsorption of HF on the resultant NaF as well as the (NaF)4 tetramer. The results suggest that the reaction between Na and HF leads to the formation of sodium fluoride salt NaF and hydrogen gas. Na interacts with HF to form a complex HF···Na, and then the approaching of F atom of HF to Na results in a transition state H···F···Na. Accompanied by the broken of H-F bond, the bond forms between F and Na atoms as NaF, then the product NaF is yielded due to the removal of H atom. The resultant NaF can further form (NaF)4 tetramer. The interaction of NaF with HF leads to the complex NaF···HF; the form I as well as II of (NaF)4 can interact with HF to produce two complexes (i.e., (NaF)4(I-1)···HF, (NaF)4(I-2)···HF, (NaF)4(II-1)···HF and (NaF)4(II-2)···HF), but the form III of (NaF)4 can interact with HF to produce only one complex (NaF)4(III)···HF. These complexes were explored in terms of noncovalent interaction (NCI) and quantum theory of atoms in molecules (QTAIM) analyses. NCI analyses confirm the existences of attractive interactions in the complexes HF···Na, NaF···HF, (NaF)4(I-1)···HF, (NaF)4(I-2)···HF, (NaF)4(II-1)···HF and (NaF)4(II-2)···HF, and (NaF)4(III)···HF. QTAIM analyses suggest that the F···Na interaction forms in the HF···Na complex while the F···H hydrogen bonds form in NaF···HF, (NaF)4(I-1)···HF, (NaF)4(I-2)···HF, (NaF)4(II-1)···HF and (NaF)4(II-2)···HF, and (NaF)4(III)···HF complexes. Natural bond orbital (NBO) analyses were also applied to analyze the intermolecular donor-acceptor orbital interactions in these complexes. These results would provide valuable insight into the chemical reaction of Na and HF and the adsorption interaction between sodium fluoride salt and HF. METHODS: The calculations were carried out at the M06-L/6-311++G(2d,2p) level of theory which were performed using the Gaussian16 program. Intrinsic reaction coordinate (IRC) calculations were carried out at the same level of theory to confirm that the obtained transition state was true. The molecular surface electrostatic potential (MSEP) was employed to understand how the complex forms. Quantum theory of atoms in molecules (QTAIM) and noncovalent interaction (NCI) analysis was used to know the topology parameters at bond critical points (BCPs) and intermolecular interactions in the complex and intermediate. The topology parameters and the BCP plots were obtained by the Multiwfn software.

Ensemble of local and global information for Protein-Ligand Binding Affinity Prediction.

Accurately predicting protein-ligand binding affinities is crucial for determining molecular properties and understanding their physical effects. Neural networks and transformers are the predominant methods for sequence modeling, and both have been successfully applied independently for protein-ligand binding affinity prediction. As local and global information of molecules are vital for protein-ligand binding affinity prediction, we aim to combine bi-directional gated recurrent unit (BiGRU) and convolutional neural network (CNN) to effectively capture both local and global molecular information. Additionally, attention mechanisms can be incorporated to automatically learn and adjust the level of attention given to local and global information, thereby enhancing the performance of the model. To achieve this, we propose the PLAsformer approach, which encodes local and global information of molecules using 3DCNN and BiGRU with attention mechanism, respectively. This approach enhances the model's ability to encode comprehensive local and global molecular information. PLAsformer achieved a Pearson's correlation coefficient of 0.812 and a Root Mean Square Error (RMSE) of 1.284 when comparing experimental and predicted affinity on the PDBBind-2016 dataset. These results surpass the current state-of-the-art methods for binding affinity prediction. The high accuracy of PLAsformer's predictive performance, along with its excellent generalization ability, is clearly demonstrated by these findings.

Heavy metal pollution alters reproductive performance and mate choice in an anuran, Strauchbufo raddei.

Understanding the effects of pollution on reproductive performance and sexual selection is crucial for the conservation of biodiversity in an increasingly polluted world. The present study focused on the effect of environmental heavy metal pollution on sexually selected traits, including morphological characteristics and acoustic parameters, as well as mate choice in Strauchbufo raddei, an anuran species widely distributed in Northern China. The results showed that male courtship signals, including forelimb length, forelimb force, and advertisement calls, have evolved under the pressure of heavy metal pollution in young S. raddei. In addition, the breeding age was lower in the polluted areas, and younger individuals had more mating opportunities. However, males with heightened reproductive performance did not show the expected higher individual quality. The current study suggests that exposure to heavy metal pollution can induce stress in males, altering reproductive performance and further disrupting mate choice.

EDC-DTI: An end-to-end deep collaborative learning model based on multiple information for drug-target interactions prediction.

Innovations in drug-target interactions (DTIs) prediction accelerate the progression of drug development. The introduction of deep learning models has a dramatic impact on DTIs prediction, with a distinct influence on saving time and money in drug discovery. This study develops an end-to-end deep collaborative learning model for DTIs prediction, called EDC-DTI, to identify new targets for existing drugs based on multiple drug-target-related information including homogeneous information and heterogeneous information by the way of deep learning. Our end-to-end model is composed of a feature builder and a classifier. Feature builder consists of two collaborative feature construction algorithms that extract the molecular properties and the topology property of networks, and the classifier consists of a feature encoder and a feature decoder which are designed for feature integration and DTIs prediction, respectively. The feature encoder, mainly based on the improved graph attention network, incorporates heterogeneous information into drug features and target features separately. The feature decoder is composed of multiple neural networks for predictions. Compared with six popular baseline models, EDC-DTI achieves highest predictive performance in the case of low computational costs. Robustness tests demonstrate that EDC-DTI is able to maintain strong predictive performance on sparse datasets. As well, we use the model to predict the most likely targets to interact with Simvastatin (DB00641), Nifedipine (DB01115) and Afatinib (DB08916) as examples. Results show that most of the predictions can be confirmed by literature with clear evidence.

NoiseMol: A noise-robusted data augmentation via perturbing noise for molecular property prediction.

Simplified Molecular-Input Line-Entry System (SMILES) is one of a widely used molecular representation methods for molecular property prediction. We conjecture that all the characters in the SMILES string of a molecule are essential for making up the molecules, but most of them make little contribution to determining a particular property of the molecule. Therefore, we verified the conjecture in the pre-experiment. Motivated by the result, we propose to inject proper noisy information into the SMILES to augment the training data by increasing the diversity of the labeled molecules. To this end, we explore injecting perturbing noise into the original labeled SMILES strings to construct augmented data for alleviating the limitation of the labeled compound data and enhancing the model to extract more useful molecular representation for molecular property prediction. Specifically, we directly adopt mask, swap, deletion, and fusion operations on SMILES strings to randomly mask, swap, and delete atoms in SMILES strings. Then, the augmented data is used by two strategies: each epoch alternately feeds the original and perturbing noisy molecules, or each batch alternately feeds the original and perturbing noisy molecules. We conduct experiments on both Transformer and BiGRU models to validate the effectiveness by adopting widely used datasets from MoleculeNet and ZINC. Experimental results demonstrate that the proposed method outperforms strong baselines on all the datasets. NoiseMol obtains the best performance on BBBP and FDA when compared with state-of-the-art methods. Besides, NoiseMol achieves the best accuracy on LogP. Therefore, injecting perturbing noise into the labeled SMILES strings is an effective and efficient method, which improves the prediction performance, generalization, and robustness of the deep learning models.

TranGRU: focusing on both the local and global information of molecules for molecular property prediction.

Molecular property prediction is an essential but challenging task in drug discovery. The recurrent neural network (RNN) and Transformer are the mainstream methods for sequence modeling, and both have been successfully applied independently for molecular property prediction. As the local information and global information of molecules are very important for molecular properties, we aim to integrate the bi-directional gated recurrent unit (BiGRU) into the original Transformer encoder, together with self-attention to better capture local and global molecular information simultaneously. To this end, we propose the TranGRU approach, which encodes the local and global information of molecules by using the BiGRU and self-attention, respectively. Then, we use a gate mechanism to reasonably fuse the two molecular representations. In this way, we enhance the ability of the proposed model to encode both local and global molecular information. Compared to the baselines and state-of-the-art methods when treating each task as a single-task classification on Tox21, the proposed approach outperforms the baselines on 9 out of 12 tasks and state-of-the-art methods on 5 out of 12 tasks. TranGRU also obtains the best ROC-AUC scores on BBBP, FDA, LogP, and Tox21 (multitask classification) and has a comparable performance on ToxCast, BACE, and ecoli. On the whole, TranGRU achieves better performance for molecular property prediction. The source code is available in GitHub: https://github.com/Jiangjing0122/TranGRU.

Quantum Chemical Calculations with Machine Learning for Multipolar Electrostatics Prediction in RNA: An Application to Pentose.

To develop a realistic electrostatic model that allows for the anisotropy of the atomic electron density, high-rank atomic multipole moments computed by quantum chemical calculations have been studied extensively. However, it is hard to process huge RNA systems only relying on quantum chemical calculations due to its highly computational cost. In this study, we employ five machine learning methods of Gaussian process regression with automatic relevance determination (ARDGPR), Kriging, radial basis function neural networks, Bagging, and generalized regression neural network to predict atomic multipole moments. Atom-atom electrostatic interaction energies are subsequently computed using the predicted atomic multipole moments in the pilot system pentose of RNA. Here, the performance of the five methods is compared in terms of both the multipole moment prediction errors and the electrostatic energy prediction errors. For the predicted high-rank multipole moments of the four elements (O, C, N, and H) in capped pentose, ARDGPR and Kriging consistently outperform the other three methods. Therefore, the multipole moments predicted by the two best methods of ARDGPR and Kriging are then used to predict electrostatic interaction energy of each pentose. Finally, the absolute average energy errors of ARDGPR and Kriging are 1.83 and 4.33 kJ mol-1, respectively. Compared to Kriging, the ARDGPR method achieves a 58% decrease in the absolute average energy error. These satisfactory results demonstrated that the ARDGPR method with the strong feature extraction ability can predict the electrostatic interaction energy of pentose in RNA correctly and reliably.

MultiGran-SMILES: multi-granularity SMILES learning for molecular property prediction.

MOTIVATION: Extracting useful molecular features is essential for molecular property prediction. Atom-level representation is a common representation of molecules, ignoring the sub-structure or branch information of molecules to some extent; however, it is vice versa for the substring-level representation. Both atom-level and substring-level representations may lose the neighborhood or spatial information of molecules. While molecular graph representation aggregating the neighborhood information of a molecule has a weak ability in expressing the chiral molecules or symmetrical structure. In this article, we aim to make use of the advantages of representations in different granularities simultaneously for molecular property prediction. To this end, we propose a fusion model named MultiGran-SMILES, which integrates the molecular features of atoms, sub-structures and graphs from the input. Compared with the single granularity representation of molecules, our method leverages the advantages of various granularity representations simultaneously and adjusts the contribution of each type of representation adaptively for molecular property prediction. RESULTS: The experimental results show that our MultiGran-SMILES method achieves state-of-the-art performance on BBBP, LogP, HIV and ClinTox datasets. For the BACE, FDA and Tox21 datasets, the results are comparable with the state-of-the-art models. Moreover, the experimental results show that the gains of our proposed method are bigger for the molecules with obvious functional groups or branches. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this work are available on GitHub at https://github. com/Jiangjing0122/MultiGran. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Club Convergence of Economies' Per Capita Carbon Emissions: Evidence from Countries That Proposed Carbon Neutrality.

To achieve the long-term goals outlined in the Paris Agreement that address climate change, many countries have committed to carbon neutrality targets. The study of the characteristics and emissions trends of these economies is essential for the realistic formulation of accurate corresponding carbon neutral policies. In this study, we investigate the convergence characteristics of per capita carbon emissions (PCCEs) in 121 countries with carbon neutrality targets from 1990 to 2019 using a nonlinear time-varying factor model-based club convergence analysis, followed by an ordered logit model to explore the mechanism of convergence club formation. The results reveal three relevant findings. (1) Three convergence clubs for the PCCEs of countries with proposed carbon neutrality targets were evident, and the PCCEs of different convergence clubs converged in multiple steady-state levels along differing transition paths. (2) After the Kyoto Protocol came into effect, some developed countries were moved to the club with lower emissions levels, whereas some developing countries displayed elevated emissions, converging with the higher-level club. (3) It was shown that countries with higher initial emissions, energy intensity, industrial structure, and economic development levels are more likely to converge with higher-PCCEs clubs, whereas countries with higher urbanization levels are more likely to converge in clubs with lower PCCEs.

A general RNA force field: comprehensive analysis of energy minima of molecular fragments of RNA.

Force fields are actively used to study RNA. Development of accurate force fields relies on a knowledge of how the variation of properties of molecules depends on their structure. Detailed scrutiny of RNA's conformational preferences is needed to guide such development. Towards this end, minimum energy structures for each of a set of 16 small RNA-derived molecules were obtained by geometry optimization at the HF/6-31G(d,p), B3LYP/apc-1, and MP2/cc-pVDZ levels of theory. The number of minima computed for a given fragment was found to be related to both its size and flexibility. Atomic electrostatic multipole moments of atoms occurring in the [HO-P(O3)-CH2-] fragment of 30 sugar-phosphate-sugar geometries were calculated at the HF/6-31G(d,p) and B3LYP/apc-1 levels of theory, and the transferability of these properties between different conformations was investigated. The atomic multipole moments were found to be highly transferable between different conformations with small standard deviations. These results indicate necessary elements of the development of accurate RNA force fields.

Effect of short-chain chlorinated paraffins (SCCPs) on lipid membranes: Combination of molecular dynamics and membrane damage experiments.

In recent years, more attention has been paid to the biological effects of short-chain chlorinated paraffin (SCCP). Studies have shown that SCCPs exposure could cause metabolic damage and lipid metabolic damage. In the present work, based on E. coli membrane damage experiments and molecular dynamics (MD) simulation, the effects of SCCPs on the membrane structure and membrane properties were studied to explore the possible toxic damage effects of SCCPs on cell membrane. Experiments results showed that SCCPs had a significant inhibitory effect on E. coli. The E. coli cell membrane of the bacteria was broken and the macromolecules of the cell flowed out when exposed to SCCPs. SCCPs would lead to the decrease and depolarization of cell membrane potential, and then affect the integrity and permeability of cell membrane. The further molecular dynamic simulation revealed that SCCP molecules can easily enter the lipid DPPC membranes from the aqueous phase and tended to aggregate inside bilayer stably. The bound of SCCPs could lead to significant variations in DPPC bilayer with a less dense, more disorder and rougher layer, which thus made the damage of cell membrane. In a word, although the overall toxicity of SCCPs to cell was relatively weak, the damage to the cell membrane may be one of the mechanisms of its toxicity. MAIN FINDING OF THE WORK: The exposure of SCCPs could cause structural change of cell membrane in E. coli, which verified the damage to the cell membrane may be one of the mechanisms of its toxicity.

Multipolar electrostatics for hairpin and pseudoknots in RNA: Improving the accuracy of force field potential energy function.

Molecular dynamics (MD) simulations that rely on force field methods has been widely used to explore the structure and function of RNAs. However, the current commonly used force fields are limited by the electrostatic description offered by atomic charge, dipole and at most quadrupole moments, failing to capture the anisotropic picture of electronic features. Actually, the distribution of electrons around atomic nuclei is not spherically symmetric but is geometry dependent. A multipolar electrostatic model based on high rank multipole moments is described in this work, which allows us to combine polarizability and anisotropy of electron density. RNA secondary structure was taken as a research system, and its substructures including stem, loops (hairpin loop, bulge loop, internal loop, and multi-branch loop), and pseudoknots (H-type and K-type) were investigated, respectively, as well as the hairpin. First, the atom-atom electrostatic properties derived from one chain of a duplex RNA 2MVY in our previous work (Ref. 58) were measured by the pilot RNA systems of hairpin, hairpin loop, stem, and H-type pseudoknot, respectively. The prediction results were not satisfactory. Consequently, to obtain a general set of electrostatic parameters for RNA force fields, the convergence behavior of the atom-atom electrostatic interactions in the pilot RNA systems was explored using high rank atomic multipole moments. The pilot RNA systems were cut into four types of different-sized molecular fragments, and the single nucleotide fragment and nucleotide-paired fragment proved to be the most reasonable systems for base-unpairing regions and base-pairing regions to investigate the convergence behavior of all types of atom-atom electrostatic interactions, respectively. Transferability of the electrostatic properties drawn from the pilot RNA systems to the corresponding test systems was also investigated. Furthermore, the convergence behavior of atomic electrostatic interactions in other substructures including bulge loop, internal loop, multi-branch loop, and K-type pseudoknot was expected to be modeled via the hairpin.

Echinacoside Suppresses Amyloidogenesis and Modulates F-actin Remodeling by Targeting the ER Stress Sensor PERK in a Mouse Model of Alzheimer's Disease.

Endoplasmic reticulum stress (ERS) plays a vital and pathogenic role in the onset and progression of Alzheimer's disease (AD). Phosphorylation of PKR-like endoplasmic reticulum kinase (PERK) induced by ERS depresses the interaction between actin-binding protein filamin-A (FLNA) and PERK, which promotes F-actin accumulation and reduces ER-plasma membrane (PM) communication. Echinacoside (ECH), a pharmacologically active component purified from Cistanche tubulosa, exhibits multiple neuroprotective activities, but the effects of ECH on ERS and F-actin remodeling remain elusive. Here, we found ECH could inhibit the phosphorylation of PERK. Firstly ECH can promote PERK-FLNA combination and modulate F-actin remodeling. Secondly, ECH dramatically decreased cerebral Aß production and accumulation by inhibiting the translation of BACE1, and significantly ameliorated memory impairment in 2 × Tg-AD mice. Furthermore, ECH exhibited high affinity to either mouse PERK or human PERK. These findings provide novel insights into the neuroprotective actions of ECH against AD, indicating that ECH is a potential therapeutic agent for halting and preventing the progression of AD.

Halogen bonds and other noncovalent interactions in the crystal structures of trans-1,2-diiodo alkenes: an ab initio and QTAIM study.

A series of interatomic interactions interpretable as halogen bonds involving I…I, I…O, and I…C(π), as well as the noncovalent interactions I…H and O…O, were observed in the crystal structures of trans-1,2-diiodoolefins dimers according to ab initio calculations and the quantum theory of "atoms in molecules" (QTAIM) method. The interplay between each type of halogen bond and other noncovalent interactions was studied systematically in terms of bond length, electrostatic potential, and interaction energy, which are calculated via ab initio methods at the B3LYP-D3/6-311++G(d,p) and B3LYP-D3/def2-TZVP levels of theory. Characteristics and nature of the halogen bonds and other noncovalent interactions, including the topological properties of the electron density, the charge transfer, and their strengthening or weakening, were analyzed by means of both QTAIM and "natural bond order" (NBO). These computational methods provide additional insight into observed intermolecular interactions and are utilized to explain the differences seen in the crystal structures. Graphical abstract The contour map presents the regions of electronic concentration and depletion along each bond in one dimer. The blue points denote the BCPs. The blue lines denote positive Laplacian of electron density, which indicate the ionic interactions, van der Waals or intermolecular interactions, and the red lines denote negative Laplacian of electron density which indicate the covalent bonds.

Multipolar Description of Atom-Atom Electrostatic Interaction Energies in Single/Double-Stranded DNAs.

Molecular force field simulation is an effective method to explore the properties of DNA molecules in depth. Almost all current popular force fields calculate atom-atom electrostatic interaction energies for DNAs based on the atomic charge and dipole or quadrupole moments, without considering high-rank atomic multipole moments for more accurate electrostatics. Actually, the distribution of electrons around atomic nuclei is not spherically symmetric but is geometry dependent. In this work, a multipole expansion method that allows us to combine polarizability and anisotropy was applied. One single-stranded DNA and one double-stranded DNA were selected as pilot systems. Deoxynucleotides were cut out from pilot systems and capped by mimicking the original DNA environment. Atomic multipole moments were integrated instead of fixed-point charges to calculate atom-atom electrostatic energies to improve the accuracy of force fields for DNA simulations. Also, the applicability of modeling the behavior of both single-stranded and double-stranded DNAs was investigated. The calculation results indicated that the models can be transferred from pilot systems to test systems, which is of great significance for the development of future DNA force fields.

The Regulator PltZ Regulates a Putative ABC Transporter System PltIJKNOP of Pseudomonas aeruginosa ATCC 27853 in Response to the Antimicrobial 2,4-Diacetylphloroglucinol.

Pseudomonas aeruginosa is an opportunistic pathogen commonly infecting immunocompromised patients with diseases like cystic fibrosis (CF) and cancers and has high rates of recurrence and mortality. The treatment efficacy can be significantly worsened by the multidrug resistance (MDR) of P. aeruginosa, and there is increasing evidence showing that it is easy for this pathogen to develop MDR. Here, we identified a gene cluster, pltZ-pltIJKNOP, which was originally assumed to be involved in the biosynthesis of an antimicrobial pyoluteorin, significantly contributing to the antibiotic resistance of P. aeruginosa ATCC 27853. Moreover, the TetR family regulator PltZ binds to a semi-palindromic sequence in the promoter region of the pltIJKNOP operon and recognizes the antimicrobial 2,4-diacetylphloroglucinol (2,4-DAPG), which in turn induces the expression of the pltIJKNOP operon. Using quantitative proteomics method, it was indicated that the regulator PltZ also plays an important role in maintaining metabolic hemostasis by regulating the transporting systems of amino acids, glucose, metal ions, and bacteriocins.

Molecular dynamics exploring of atmosphere components interacting with lung surfactant phospholipid bilayers.

Sulfur dioxide (SO2), nitrogen oxide (NO2) and ozone (O3) in the atmosphere are significantly correlated with various respiratory and cardiovascular diseases. High doses of each of these gases or a mixture can change the physical and chemical properties of the lung membrane, thus leading to an increased pulmonary vascular permeability and structural failure of the alveolar cell membrane. In the present study, detailed molecular dynamic (MD) modeling was applied to investigate the effects of SO2, NO2, O3 and mixtures of these gases on the dipalmitoyl phosphatidylcholine (DPPC) phospholipid bilayer. The results showed that several key physical properties, including the mass density, lipid ordering parameter, lipid diffusion, and electrostatic potential of the cell membrane, have been changed by the binding of different compounds. This resulted in significant variations and more disorder in the DPPC bilayer. The multiple analyses of membrane properties proved the toxicity of NO2, O3, and SO2 to the DPPC bilayer, providing a theoretical basis for the experimental phenomenon that SO2, NO2 and O3 can cause lung cell apoptosis. For the single systems, the damage to DPPC bilayer caused by O3 was more serious than NO2 and SO2. More importantly, the MD simulations using the mixtures of SO2, NO2, and O3 showed a much greater decline of membrane fluidity and the aggravation of membrane damage than the single systems, indicating a synergistic effect when NO2, SO2, and O3 coexisted in the atmosphere, which could lead to much more severe damage and greater toxicities to the lung.

Exploring toxicity of perfluorinated compounds through complex network and pathway modeling.

Perfluorinated compounds (PFCs) have serious impacts on human health, which could interfere with the body's signal pathways and affect the normal hormone balance of humans. PFCs were reported to bind to many proteins causing a series of biological effects. It was quite possible that the in vivo action of PFCs was not a single target or a single pathway, suggesting the toxic effect was due to the disturbance of protein or gene network, not limited to the modification of a single target protein or gene. Thus, a PFCs-targets interaction network was constructed and the significant differences in the characteristics of complex networks between the branched PFCs and linear PFCs were observed. A molecular dynamics simulation proved that binding ability of the branched PFCs to the target protein was much weaker than that of the linear PFCs, explaining why the branched PFCs presented significantly difference from the linear PFCs in terms of complex network characteristics. In addition, four target genes were identified as the central node genes of the network. The four target genes were proved to present certain influences on some diseases, which suggested a high correlation between PFCs to these diseases, including obesity, hepatocellular carcinoma and diabetes. The present work was helpful to develop new approaches to identify the key toxic targets of compounds and to explore the toxicity effects on pathways. AbbreviationsARandrogen receptorBPAbisphenol AESR1estrogen receptor 1ESR2estrogen receptor 2GLTPglycolipid transfer proteinHbFthe fetal hemoglobinHBG1hemoglobin subunit γ-1hERαhuman ERαHSD17B1hydroxysteroid 17-ß dehydrogenase 1KEGGKenya encyclopedia of genes and genomesMDmolecular dynamics simulationPFCsperfluorinated compoundsPFOAperfluorooctanoic acidPFOSperfluorooctane sulfonatePOPspersistent organic pollutantsRMSDroot-mean-square deviationSHBGsex hormone binding globulinSPC/Eextended simple point charge modelTRthyroid hormone receptorCommunicated by Ramaswamy H. Sarma.