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Molecular dynamics simulations data of α1β2γ2 GABAAR with positive and negative neurosteriods.&nbsp

This paper presents a planning and real-time pricing approach for EV charging stations (CSs). The approach takes the form of a bi-level model to fully consider the interest of both the government and EV charging station operators in the planning process. From the perspective of maximizing social welfare, the government acts as the decision-maker of the upper level that optimizes the charging price matrix, and uses it as a transfer variable to indirectly influence the decisions of the lower level operators. Then each operator at the lower level determines their scale according to the goal of maximizing their own revenue, and feeds the scale matrix back to the upper level. A Logit model is applied to predict the drivers' preference when selecting a CS. Furthermore, an improved particle swarm optimization (PSO) with the utilization of a penalty function is introduced to solve the nonlinear nonconvex bi-level model. The paper applies the proposed Bi-level planning model to a singlecenter small/medium-sized city with three scenarios to evaluate its performance, including the equipment utilization rate, payback period, traffic attraction ability, etc. The result verifies that the model performs very well in typical CS distribution scenarios with a reasonable station payback period (average 6.5 years), and relatively high equipment utilization rate, 44.32%.

Molecular dynamics simulations of the alpha-7 nicotinic receptor, both atomistic (ion permeation free energy calculation) and coarse-grained (lipid and drug binding)., Two amino residues were incorrectly assigned in the original PDB file. N35 should be S35; P111 should be S111. However, given they are neither inside the TMD region---affecting the lipid binding---nor facing toward to pore---affecting the ion permeation, the results in this manuscript remain intact. Control experiments have been performed.

Most general anaesthetics and classical benzodiazepine drugs act through positive modulation of γ-aminobutyric acid type A (GABAA) receptors to dampen neuronal activity in the brain1-5. However, direct structural information on the mechanisms of general anaesthetics at their physiological receptor sites is lacking. Here we present cryo-electron microscopy structures of GABAA receptors bound to intravenous anaesthetics, benzodiazepines and inhibitory modulators. These structures were solved in a lipidic environment and are complemented by electrophysiology and molecular dynamics simulations. Structures of GABAA receptors in complex with the anaesthetics phenobarbital, etomidate and propofol reveal both distinct and common transmembrane binding sites, which are shared in part by the benzodiazepine drug diazepam. Structures in which GABAA receptors are bound by benzodiazepine-site ligands identify an additional membrane binding site for diazepam and suggest an allosteric mechanism for anaesthetic reversal by flumazenil. This study provides a foundation for understanding how pharmacologically diverse and clinically essential drugs act through overlapping and distinct mechanisms to potentiate inhibitory signalling in the brain.

Cryo-EM structures and molecular simulations of a pentameric ligand-gated ion channel, GLIC, in resting and activating conditions implicate protonation sites and altered dynamics in sequential gating., Ligand-gated ion channels are critical mediators of electrochemical signal transduction across evolution. Biophysical and pharmacological characterization of these receptor proteins relies on high-quality structures in multiple, subtly distinct functional states. However, structural data in this family remain limited, particularly for resting and intermediate states on the activation pathway. Here, we report cryo-electron microscopy (cryo-EM) structures of the proton-activated Gloeobacter violaceus ligand-gated ion channel (GLIC) under three pH conditions. Decreased pH was associated with improved resolution and side chain rearrangements at the subunit/domain interface, particularly involving functionally important residues in the β1–β2 and M2–M3 loops. Molecular dynamics simulations substantiated flexibility in the closed-channel extracellular domains relative to the transmembrane ones and supported electrostatic remodeling around E35 and E243 in proton-induced gating. Exploration of secondary cryo-EM classes further indicated a low-pH population with an expanded pore. These results allow us to define distinct protonation and activation steps in pH-stimulated conformational cycling in GLIC, including interfacial rearrangements largely conserved in the pentameric channel family.

SARS-CoV-2 contains four structural proteins in its genome. These proteins aid in the assembly and budding of new virions at the ER-Golgi intermediate compartment (ERGIC). Current fundamental research efforts largely focus on one of these proteins – the spike (S) protein. Since successful antiviral therapies are likely to target multiple viral components, there is considerable interest in understanding the biophysical role of its other structural proteins, in particular structural membrane proteins. Here, we have focused our efforts on the characterization of the full-length envelope (E) protein from SARS-CoV-2, combining experimental and computational approaches. Recombinant expression of the full-length E protein from SARS-CoV-2 reveals that this membrane protein is capable of independent multimerization, possibly as a tetrameric or smaller species. Fluorescence microscopy shows that the protein localizes intracellularly, and coarse-grained MD simulations indicate it causes bending of the surrounding lipid bilayer, corroborating a potential role for the E protein in viral budding. Although we did not find robust electrophysiological evidence of ion-channel activity, cells transfected with the E protein exhibited reduced intracellular Ca2+, which may further promote viral replication. However, our atomistic MD simulations revealed that previous NMR structures are relatively unstable, and result in models incapable of ion conduction. Our study highlights the importance of using high-resolution structural data obtained from a full-length protein to gain detailed molecular insights, and eventually permitting virtual drug screening.

Ligand-gated ion channels are critical mediators of electrochemical signal transduction across evolution. Biophysical and pharmacological development in this family relies on high-quality structural data in multiple, subtly distinct functional states. However, structural data remain limited, particularly for the unliganded or resting state. Here we report cryo-electron microscopy structures of the Gloeobacter violaceus ligand-gated ion channel (GLIC) under resting and activating conditions (neutral and low pH). Parallel models were built either manually or using recently developed density-guided molecular simulations. The moderate resolution of resting-state reconstructions, particularly in the extracellular domain, was improved under activating conditions, enabling the visualization of residues at key subunit interfaces including loops B, C, F, and M2–M3. Combined with molecular dynamics simulations, the cryo-electron microscopy structures at different pH describe a heterogeneous population of closed channels, with activating conditions condensing the closed-channel energy landscape on a pathway towards gating.

Density guided simulations files .mrc files - CryoEM density half-maps for pH 7, pH 5, pH 3 .mdp - Option files for GROMACS simulations. One density guided mdp file and one annealing mdp file per pH data set .gro - Equilibrated starting model of manually built pH 7,the same was used for all three pH data sets .itp - topology files .top - topology master file Example command for density guided simulations gmx grompp --f mdrun_ip_ph3_tc40_sp02_tau0_3.mdp --p topology.top --o mdrun_ip_ph3_tc40_sp02_tau0_3.tpr --c minimized.gro gmx mdrun --s mdrun_ip_ph3_tc40_sp02_tau0_3_test.tpr --v --x mdrun.xtc --g md.log --e ener.edr --c final_ip_ph3_tc40_sp02_tau0_3_test.gro Example command for custom GROMACS FSC tool, available at (https://github.com/blauc/gromacs/tree/fscavg) gromacs-fsc/bin/gmx fsc -f whole.xtc -s mdrun_ip_ph3_tc40_sp02_tau0_3_test.tpr -refmap pH3_half1_class001_unfil.mrc-dt 100 -xvg none -shells 186 -o fsc_ip_ph3_tc40_sp02_tau0_3.xvg -similarity similarity_ip_ph3_tc40_sp02_tau0_3.xvg -avg fscavg_ip_ph3_tc40_sp02_tau0_3.xvg -fscmove fscmove_ip_ph3_tc40_sp02_tau0_3.xvg -ordinate-axis fscaxis_ip_ph3_tc40_sp02_tau0_3.xvg -fscmoveavg fscmoveavg_ip_ph3_tc40_sp02_tau0_3.xvg Molecular dynamics simulations .prep_file -topology files and forcefield files for preparing the simulations. .run_tpr -tpr files for production runs .prodution_traj -concatenated trajectories and starting snapshots (after equilibration). analysis_results - csv: analaysis data - ipynb: jupyter notebook for generating the figures in the paper.

Locating and planning charging stations for Low-Emission Vehicles (LEVs) such as Battery Electric Vehicle (BEV), Hydrogen Fuel-Cell Vehicle (HFCV), and Natural Gas Vehicle (NGV) are becoming increasingly important for LEV users, government, and the automobile industry. Conventional planning approach of charging station usually plans single functional charging station that can only serve one kind of LEVs, and other factors such as fuel type, driving range, initial fuel tank level, and refueling time of the LEV are less considered in the planning stage. In this article, we propose a bi-level planning model to locate and size Multi-Functional Charging Station (MFCS) which can recharge BEV, HFCV, and NGV at the same time in a medium-sized city with different functional areas (e.g., residential area, industrial area, CBD area). We also established a method for generating a daily route considering vehicle attributes and user habits, and we loaded these traveling data into the upper model to select a set of optimal combinations of refueling station locations with a relatively high success ratio. In the lower model, we introduced the mathematical relationship between number of chargers and average user waiting time, and set the total social cost factor, including investment cost and waiting time cost, to evaluate each optimal combination, and then identified the optimum locational result and defined the size of each station. In the case study, we verify the proposed model in several scenarios and conclude that multifunctional refueling station performs better in terms of investment cost and users’ satisfaction level.

Nicotinic acetylcholine receptors are pentameric ion channels that mediate fast chemical neurotransmission. The α3β4 nicotinic receptor subtype forms the principal relay between the central and peripheral nervous systems in the autonomic ganglia. This receptor is also expressed focally in brain areas that affect reward circuits and addiction. Here, we present structures of the α3β4 nicotinic receptor in lipidic and detergent environments, using functional reconstitution to define lipids appropriate for structural analysis. The structures of the receptor in complex with nicotine, as well as the α3β4-selective ligand AT-1001, complemented by molecular dynamics, suggest principles of agonist selectivity. The structures further reveal much of the architecture of the intracellular domain, where mutagenesis experiments and simulations define residues governing ion conductance.

Allosteric Gating Determinants in the Transmembrane Domain of Pentameric Ligand-Gated Ion Channels

In traditional supervised methods, model training requires a large number of manual labeled samples, which are time-consuming and labor-intensive. A new unsupervised method for rolling bearing fault diagnosis based on clustering and stacked auto-encoder is proposed in this paper. The new method provides a high-efficiency model, which consists of three parts. Raw vibration signals are extracted into multidimensional feature matrix, of which the dimension is further reduced by laplacian eigenmaps (LE) algorithm. The reduced feature matrix are then clustered using a K-means algorithm to form the training set, which are the inputs into a stacked auto-encoder (SAE). By fine tuning the parameters of the deep network, the trained SAE is employed to classify fault type and fault size. The experiment uses the proposed method to perform fault diagnosis from the data of rolling bearing system, and the results show that the method is able to distinguish different fault categories and sizes accurately and effectively. More importantly, it indicates that the proposed method removes the initial steps of artificial calibration samples and thus improves the intelligent level.

Allosteric Modulation via Transmembrane Interfaces in a Pentameric Ligand-Gated Ion Channel

Air quality reflects the extent of air pollution, and it is evaluated based on the concentration of air pollutants. Air quality level is traditionally assessed by mathematical formula, which cannot precisely represent the level of air pollution in some circumstances. This paper introduces a novel Air Quality Evaluation system based on Stacked Auto-Encoder (named AQES-SAE) to tackle these problems. The data of air pollutant concentrations and the air quality index are collected and clustered with improved K-means algorithm. The labeled clusters as training samples are then transferred to a deep neural networks and trained on the SAE model, which can be used as the classifier in the air quality evaluation. The proposed method is compared with traditional mathematical formula, and it is verified that the AQES-SAE system is prior to the traditional method because it can identify air quality level more accurately and more reasonably.

Traditional methods for bearing fault diagnosis mostly utilized a shallow model like support vector machine (SVM) that required professional machinery skills and much of knowledge. Deep models like deep belief network (DBN) had shown its advantage in fault feature extraction without prior knowledge. In this paper, an end-to-end approach based on deep convolution neural network (DCNN) is presented. The approach embodying the idea of end to end diagnosis has only one simple and elegant convolution neural network and don’t need any exquisite hierarchical structure that was used in the traditional methods. The samples of time-domain signals are inputted into the proposed model without any frequency transformation, and the approach can diagnosis bearing fault types and fault sizes simultaneously as output. Experimental researches had shown that the approach has the advantages such as a simple structure, less iteration and real-time, while its accuracy on the diagnosis of fault types and fault sizes can still be guaranteed.