Toxicity assessment and i-QSTTR analysis of ionic liquids on D. magna, D. rerio, and R. subcapitata.

The study aimed to assess the impacts of ionic liquids (ILs) as innovative alternatives to traditional organic solvents on aquatic environments and human health. Five machine learning methods, including multiple linear regression (MLR), partial least squares regression (PLS), random forest regression (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost), were used to construct the prediction models of the toxicity of ILs to D. magna, D.

A Data-Driven Computational Framework for Assessing the Risk of Placental Exposure to Environmental Chemicals.

A computational framework based on placental gene networks was proposed in this work to improve the accuracy of the placental exposure risk assessment of environmental compounds. The framework quantitatively characterizes the ability of compounds to cross the placental barrier by systematically considering the interaction and pathway-level information on multiple placental transporters. As a result, probability scores were generated for 307 compounds crossing the placental barrier based on this framework.

Machine learning models based on residue interaction network for ABCG2 transportable compounds recognition.

As the one of the most important protein of placental transport of environmental substances, the identification of ABCG2 transport molecules is the key step for assessing the risk of placental exposure to environmental chemicals. Here, residue interaction network (RIN) was used to explore the difference of ABCG2 binding conformations between transportable and non-transportable compounds. The RIN were treated as a kind of special quantitative data of protein conformation, which not only reflected the changes of single amino acid conformation in protein, but also indicated the changes of distance and action type between amino acids.

Identification of molecular initiating events and key events leading to endocrine disrupting effects of PFOA: Integrated molecular dynamic, transcriptomic, and proteomic analyses.

Perfluorooctanoic acid (PFOA) can rapidly activate signaling pathways independent of nuclear hormone receptors through membrane receptor regulation, which leads to endocrine disrupting effects. In the present work, the molecular initiating event (MIE) and the key events (KEs) which cause the endocrine disrupting effects of PFOA have been explored and determined based on molecular dynamics simulation (MD), fluorescence analysis, transcriptomics, and proteomics. MD modeling and fluorescence analysis proved that, on binding to the G protein-coupled estrogen receptor-1 (GPER), PFOA could induce a conformational change in the receptor, turning it into an active state.

The adverse outcome pathway (AOP) of estrogen interference effect induced by triphenyl phosphate (TPP): Integrated multi-omics and molecular dynamics approaches.

Triphenyl phosphate (TPP) has been detected with increasing frequency in various biota and environmental media, and it has been confirmed that G protein-coupled estrogen receptor (GPER) was involved in the estrogenic activity of TPP. Therefore, it is necessary to link the estrogen-interfering effects and possible mechanisms of action of TPP with the molecular initiation event (MIE) to improve its adverse outcome pathway framework. In this study, transcriptomic and proteomic methods were used to analyze the estrogen interference effect of TPP mediated by GPER, and the causal relationship was supplemented by molecular dynamics simulation and fluorescence analysis.

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.