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. Based on the quantitative RIN, four machine learning algorithms were applied to establish the classification and recognition model for 1100 compounds with transported by ABCG2 potential. The random forest (RF) models constructed with RIN presented the best and satisfied predictive ability with an accuracy of training set of 0.97 and the test set of 0.96 respectively. In conclusion, the construction of residue interaction network provided a new perspective for the quantitative characterization of protein conformation and the establishment of prediction models for transporter molecular recognition. The ABCG2 transport molecular recognition model based on residue interaction network provides a possible way for screening environmental chemistry transported through placenta.

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.

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 results also indicated that the binding to GPER was the MIE that led to the adverse outcome (AO) of PFOA. In addition, the downstream signal transduction pathways of GPER, as regulated by PFOA, were further investigated through genomics and proteomics to identify the KEs leading to thr endocrine disrupting effects. Two pathways (Endocrine resistance, ERP and Estrogen signaling pathway, ESP) containing GPER were regulated by different concentration of PFOA and identified as the KEs. The knowledge of MIE, KEs, and AO of PFOA is necessary to understand the links between PFOA and the possible pathways that lead to its negative effects.

Effects of polyethylene microplastics on cell membranes: A combined study of experiments and molecular dynamics simulations.

Microplastics (MPs), widely distributed within the environment, can be ingested by humans easily and cause various biological reactions such as oxidative stress, immune response and membrane damage, ultimately representing a threat to health. Cell membranes work as first barrier for MPs entering the cell and playing biological effects. For now, the researches on interactions of MPs on cell membranes lack an in-depth and effective theoretical model to understand molecular details and physicochemical behaviors. In present study, observations of calcein leakage established polyethylene plastic nanoparticles (PE PNPs), especially of high concentrations, harming cell membrane integrity. SYTOX green and lactate dehydrogenase (LDH) assays supported the evidence that the exposure of cells to PE PNPs caused significant cell membrane damage in dose-response. Molecular dynamics (MD) simulations were further applied to determine the effects of PE on the properties of dipalmitoyl phosphatidylcholine (DPPC) bilayer. PE permeated into lipid membranes easily resulting in significant variations in DPPC bilayer with lower density, fluidity changes and membrane thickening. Besides, PE aggregates bound were more likely to cause pore formation and serious damage to the DPPC bilayer. The interaction mechanisms between MPS and cell membrane were explored which provided valuable insights into membrane effect of MPs.