Redox-active chemical chaperones exhibiting promiscuous binding promote oxidative protein folding under condensed sub-millimolar conditions.

Proteins form native structures through folding processes, many of which proceed through intramolecular hydrophobic effect, hydrogen bond and disulfide-bond formation. In vivo, protein aggregation is prevented even in the highly condensed milieu of a cell through folding mediated by molecular chaperones and oxidative enzymes. Chemical approaches to date have not replicated such exquisite mediation. Oxidoreductases efficiently promote folding by the cooperative effects of oxidative reactivity for disulfide-bond formation in the client unfolded protein and chaperone activity to mitigate aggregation. Conventional synthetic folding promotors mimic the redox-reactivity of thiol/disulfide units but do not address client-recognition units for inhibiting aggregation. Herein, we report thiol/disulfide compounds containing client-recognition units, which act as synthetic oxidoreductase-mimics. For example, compound ßCDWSH/SS bears a thiol/disulfide unit at the wide rim of ß-cyclodextrin as a client recognition unit. ßCDWSH/SS shows promiscuous binding to client proteins, mitigates protein aggregation, and accelerates disulfide-bond formation. In contrast, positioning a thiol/disulfide unit at the narrow rim of ß-cyclodextrin promotes folding less effectively through preferential interactions at specific residues, resulting in aggregation. The combination of promiscuous client-binding and redox reactivity is effective for the design of synthetic folding promoters. ßCDWSH/SS accelerates oxidative protein folding at highly condensed sub-millimolar protein concentrations.

Photothermally Heated Asymmetric Thin Nanopores Suggest the Influence of Temperature on the Intermediate Conformational State of Cytochrome c in an Electric Field.

Nanopore sensing is a label-free single-molecule technique that enables the study of the dynamical structural properties of proteins. Here, we detect the translocation of cytochrome c (Cyt c) through an asymmetric thin nanopore with photothermal heating to evaluate the influence of temperature on Cyt c conformation during its translocation in an electric field. Before Cyt c translocates through an asymmetric thin SiNx nanopore, ∼1 ms trapping events occur due to electric field-induced denaturation. These trapping events were corroborated by a control analysis with a transmission electron microscopy-drilled pore and denaturant buffer. Cyt c translocation events exhibited markedly greater broad current blockade when the pores were photothermally heated. Collectively, our molecular dynamics simulation predicted that an increased temperature facilitates denaturation of the α-helical structure of Cyt c, resulting in greater blockade current during Cyt c trapping. Our photothermal heating method can be used to study the influence of temperature on protein conformation at the single-molecule level in a label-free manner.

Synthesis and Characterization of Ionic Li+ @C70 Endohedral Fullerene.

Ion-endohedral-fullerene has attracted growing interest due to the unique electronic and structural characteristics arising from its distinctive ionic nature. Although there has been only one reported ion-encapsulated fullerene, Li+ @C60 , a significant number of fundamental and applied studies have been conducted, making a substantial impact not only in chemistry and physics but also across various interdisciplinary research fields. Nevertheless, studies on ion-endohedral fullerenes are still in their infancy due to the limitations in variety, and hence, it remains an open question how the size and symmetry of fullerene, as well as the motion and position of the encapsulated ion, affect their physical/chemical properties. Herein, we report the synthesis of lithium-ion-endohedral [70]fullerene (Li+ @C70 X- , X=PF6 - and TFSI- ), a novel ionic endohedral fullerene. X-ray crystallography confirmed the encapsulation of Li+ by C70 cage as well as its ion-pair structure stabilized by external TFSI- counter anion. The encapsulated Li+ drastically lowered the orbital energy of the C70 cage by Coulomb interactions but did not affect the orbital energy gap and degeneracy. DFT studies were also performed, which supported the experimentally observed electronic effects caused by the encapsulated Li+ .

Prediction of water transport properties on an anisotropic wetting surface via deep learning.

Understanding the water flow behavior on an anisotropic wetting surface is of practical significance in nanofluidic devices for their performance improvement. However, current methods of experiments and simulations face challenges in measuring water transportation in real time and visually displaying it. Here, molecular dynamics simulation was integrated with our developed multi-attribute point cloud dataset and a customized network of deep learning to achieve mapping from an anisotropic wetting surface to the static and dynamic behaviors of water molecules and realize the high-performance prediction of water transport behavior. More importantly, for the chaotic phenomenon of water molecule flow caused by thermal fluctuation and limited sampling, we proposed a nanoparticle tracking optimization strategy to improve the prediction performance of the velocity field. The prediction results proved that the deep learning framework proposed in this work had superior performance in terms of accuracy, computational cost and visualization, and had the potential for generality to model the transport behavior of different molecules. Our framework can be expected to motivate the development of real-time water flow prediction at an interface and contribute to the optimization and design of surface structures in nanofluidic devices.

DNA Nanopore-Tethered Gold Needle Electrodes for Channel Current Recording.

Synthetic DNA nanopores are attracting attention as alternatives to conventional biological nanopores in nanopore sensors because of the high designability of their pore structures and functionability. However, the efficient insertion of DNA nanopores into a planar bilayer lipid membrane (pBLM) remains challenging. Although hydrophobic modifications such as the use of cholesterol are required to insert DNA nanopores into pBLMs, these modifications also induce negative effects, including the undesired aggregation of DNA structures. Herein, we describe an efficient method to insert DNA nanopores into pBLMs and measure the channel currents of DNA nanopores using a DNA nanopore-tethered gold electrode. When the pBLM is formed at the electrode tip by immersing the electrode into a layered bath solution comprising an oil/lipid mixture and an aqueous electrolyte, the electrode-tethered DNA nanopores are physically inserted into the pBLM. In this study, we designed a DNA nanopore structure that can be immobilized on the gold electrode based on a reported six-helix bundle DNA nanopore structure and prepared DNA nanopore-tethered gold electrodes. Thereafter, we demonstrated the channel current measurements of the electrode-tethered DNA nanopores, and a high insertion probability of the DNA nanopores was achieved. We believe that this efficient DNA nanopore insertion method can accelerate the application of DNA nanopores in stochastic nanopore sensors.

Deep Learning to Reveal the Distribution and Diffusion of Water Molecules in Fuel Cell Catalyst Layers.

Water management in the catalyst layers (CLs) of proton-exchange membrane fuel cells is crucial for its commercialization and popularization. However, the high experimental or computational cost in obtaining water distribution and diffusion remains a bottleneck in the existing experimental methods and simulation algorithms, and further mechanistic exploration at the nanoscale is necessary. Herein, we integrate, for the first time, molecular dynamics simulation with our customized analysis framework based on a multiattribute point cloud dataset and an advanced deep learning network. This was achieved through our workflow that generates simulated transport data of water molecules in the CLs as the training and test dataset. Deep learning framework models the multibody solid-liquid system of CLs on a molecular scale and completes the mapping from the Pt/C substrate structure and Nafion aggregates to the density distribution and diffusion coefficient of water molecules. The prediction results are comprehensively analyzed and error evaluated, which reveals the highly anisotropic interaction landscape between 50,000 pairs of interacting nanoparticles and explains the structure and water transport property relationship in the hydrated Nafion film on the molecular scale. Compared to the conventional methods, the proposed deep learning framework shows computational cost efficiency, accuracy, and good visual display. Further, it has a generality potential to model macro- and microscopic mass transport in different components of fuel cells. Our framework is expected to make real-time predictions of the distribution and diffusion of water molecules in CLs as well as establish statistical significance in the structural optimization and design of CLs and other components of fuel cells.

Molecular Dynamics Study on the Effect of Cyclic Conducting Moieties on Poly(2,6-dimethyl-1,4-phenylene oxide) Anion Exchange Membranes.

We investigate PPO quaternized with different azoles (five-membered heterocyclic compounds) with a different odd number of Nitrogen atoms (1N-pyrrole, 3N-1,2,3-triazole, and 5N-pentazole) to form pyrrolium-PPO(py-PPO), 1,2,3,-triazolium-PPO(tri-PPO) and pentazolium-PPO(pen-PPO) AEMs, using molecular dynamics (MD) simulations to compare and evaluate their OH- transport via the vehicular mechanism. OH- diffusivity at the hydration level λ = 12 is 3.10 × 10-10 m2/s, 1.92 × 10-10 m2/s m2/s, and 1.91 × 10-10 m2/s for py-PPO, tri-PPO, and pen-PPO, respectively. This trend is due to the shorter distance between adjacent groups of py-PPO (7.5 Å) leading to an efficient hydroxide transport than tri-PPO (7.8 Å) and pen-PPO (8.1 Å) at λ = 12. Also, this trend is justified by the smaller average number of clusters for py-PPO (1.2), smaller than tri-PPO(2.0), and pen-PPO (1.5) at λ = 12, which suggests better connectivity and hence better conductivity.

Heat-Induced Conformational Transition Mechanism of Heat Shock Factor 1 Investigated by Tryptophan Probe.

A transcriptional regulatory system called heat shock response (HSR) has been developed in eukaryotic cells to maintain proteome homeostasis under various stresses. Heat shock factor-1 (Hsf1) plays a central role in HSR, mainly by upregulating molecular chaperones as a transcription factor. Hsf1 forms a complex with chaperones and exists as a monomer in the resting state under normal conditions. However, upon heat shock, Hsf1 is activated by oligomerization. Thus, oligomerization of Hsf1 is considered an important step in HSR. However, the lack of information about Hsf1 monomer structure in the resting state, as well as the structural change via oligomerization at heat response, impeded the understanding of the thermosensing mechanism through oligomerization. In this study, we applied solution biophysical methods, including fluorescence spectroscopy, nuclear magnetic resonance, and circular dichroism spectroscopy, to investigate the heat-induced conformational transition mechanism of Hsf1 leading to oligomerization. Our study showed that Hsf1 forms an inactive closed conformation mediated by intramolecular contact between leucine zippers (LZs), in which the intermolecular contact between the LZs for oligomerization is prevented. As the temperature increases, Hsf1 changes to an open conformation, where the intramolecular LZ interaction is dissolved so that the LZs can form intermolecular contacts to form oligomers in the active form. Furthermore, since the interaction sites with molecular chaperones and nuclear transporters are also expected to be exposed in the open conformation, the conformational change to the open state can lead to understanding the regulation of Hsf1-mediated stress response through interaction with multiple cellular components.

Revealing the Anticorrelation Behavior Mechanism between the Grotthuss and Vehicular Diffusions for Proton Transport in Concentrated Acid Solutions.

In this study, we performed reactive molecular dynamics simulations to characterize proton solvation and transport in concentrated hydrochloric acid solutions. The correlation contribution to the total proton mean square displacement is found to be negative for all acid concentrations, indicating the anticorrelation between the Grotthuss and vehicular diffusions. For the vehicular diffusion, the hydronium ions tend to move freely toward the lone pair side independent of acid concentrations, whereas for the Grotthuss diffusion, the proton hopping direction is limited to one of the hydrogen-bonded water molecules on the opposite side of the lone pair region, which are specifically oriented with respect to the neighboring hydronium ion at higher acid concentrations. This result is justified by our findings of the higher fraction of proton rattling with the single hopping event and longer hydrogen bond lifetimes at higher acid concentrations. However, the angular distribution for both the vehicular and Grotthuss diffusions is found to be rather broad and comparable for all acid concentrations, and thus, the anticorrelation shows a minimal dependence on the acid concentration. Our results reveal that the anticorrelation behavior between the vehicle and Grotthuss diffusions is attributed to the amphiphilic nature of hydronium ions and thus is independent of the acid concentrations in solutions.

Prediction of nanoscale thermal transport and adsorption of liquid containing surfactant at solid-liquid interface via deep learning.

HYPOTHESIS: Recent advances in deep learning (DL) have enabled high level of real-time prediction of thermophysical properties of materials. On the other hand, molecular dynamics (MD) have been long used as a numerical microscope to observe detailed interfacial conditions but require separate simulations that are computationally costly. Hence, it should be possible to combine MD and DL to obtain high resolution interfacial details at a low computational cost. EXPERIMENT: We proposed a novel DL encoding-decoding convolutional neural network (CNN) coupled with MD to realize the mapping from micro solid-liquid interface geometry to molecular temperature and density distribution of liquid containing surfactant. A multi-nanoscale optimization scheme was further proposed to reduce the uncertainty of DL prediction at the expense of local details to obtain more resilient predictors. FINDINGS: The statistical results showed that the proposed CNN had high prediction accuracy and could reproduce the heat transfer and adsorption phenomena under the influence of various factors including liquid composition, wettability, and solid surface roughness, while the computational efficiency was greatly improved. Our DL method with the support of multi-nanoscale learning strategies can achieve the fast and accurate visualization and prediction of various interfacial properties of liquid and assist for interfacial material design.

Molecular Dynamics Study of Ion Transport in Polymer Electrolytes of All-Solid-State Li-Ion Batteries.

Atomistic analysis of the ion transport in polymer electrolytes for all-solid-state Li-ion batteries was performed using molecular dynamics simulations to investigate the relationship between Li-ion transport and polymer morphology. Polyethylene oxide (PEO) and poly(diethylene oxide-alt-oxymethylene), P(2EO-MO), were used as the electrolyte materials, and the effects of salt concentrations and polymer types on the ion transport properties were explored. The size and number of LiTFSI clusters were found to increase with increasing salt concentrations, leading to a decrease in ion diffusivity at high salt concentrations. The Li-ion transport mechanisms were further analyzed by calculating the inter/intra-hopping rate and distance at various ion concentrations in PEO and P(2EO-MO) polymers. While the balance between the rate and distance of inter-hopping was comparable for both PEO and P(2EO-MO), the intra-hopping rate and distance were found to be higher in PEO than in P(2EO-MO), leading to a higher diffusivity in PEO. The results of this study provide insights into the correlation between the nanoscopic structures of ion solvation and the dynamics of Li-ion transport in polymer electrolytes.

Relationship between Proton Transport and Morphology of Perfluorosulfonic Acid Membranes: A Reactive Molecular Dynamics Approach.

A reactive molecular dynamics simulation has been performed for the characterization of the relationship between proton transport and water clustering in polymer electrolyte membranes. We have demonstrated that the anharmonic two-state empirical valence bond model is capable of describing efficiently excess proton transport through the Grotthuss hopping mechanism within the simplicity of the theoretical framework. To explore the long-time diffusion behavior in perfluorosulfonic acid membranes with statistical certainty, simulations that are longer than 10 ns are needed. The contribution of the Grotthuss mechanism to the proton transport yields a larger fraction compared to the vehicular mechanism, when the estimated percolation threshold of λ = 5.6 is surpassed. The cluster analyses elicit a consistent outlook in regard to the relationship between the connectivity and the confinement of water clusters and proton transport. The cluster growth behavior findings reveal that, below the percolation threshold, the water domains grow along the channel length to form the connected, elongated clusters, thus contributing to an increase in connectivity and a decrease in confinement, whereas above the percolation threshold the channel widths of water domains increase, while the elongated structure of clusters is retained, thereby contributing to further confinement decreases.

Molecular Dynamics Simulation of Proton Transport in Polymer Electrolyte Membrane.

We have performed a detailed analysis of proton solvation and transport properties in hydrated Nafion using molecular dynamics simulation. The revised empirical valence bond (EVB) method was developed in order to treat the excess proton transport through the Grotthuss mechanism. The new EVB model predicts a significantly enhanced transport in comparison with previous hopping models as well as the classical hydronium diffusion, which largely improves the agreement with the available experimental data. Our results suggest that a proton hopping mechanism has a small effect on the proton dissociation from the first solvation shell of sulfonate groups, namely that protons are not enhanced to separate from the sulfonate groups by the hopping mechanisms. From diffusion comparison between the Grotthuss and vehicular mechanism, the Grotthuss mechanism dominates the proton diffusion at the studied hydration levels including a hydration level of 3. It was also found that the vehicular mechanism dominates the electroosmotic transport of water molecules at the studied hydration levels.

A modified two-state empirical valence bond model for proton transport in aqueous solutions.

A detailed analysis of the proton solvation structure and transport properties in aqueous solutions is performed using classical molecular dynamics simulations. A refined two-state empirical valence bond (aTS-EVB) method, which is based on the EVB model of Walbran and Kornyshev and the anharmonic water force field, is developed in order to describe efficiently excess proton transport via the Grotthuss mechanism. The new aTS-EVB model clearly satisfies the requirement for simpler and faster calculation, because of the simplicity of the two-state EVB algorithm, while providing a better description of diffusive dynamics of the excess proton and water in comparison with the previous two-state EVB models, which significantly improves agreement with the available experimental data. The results of activation energies for the excess proton and water calculated between 300 and 340 K (the temperature range used in this study) are also found to be in good agreement with the corresponding experimental data.

Effect of bound state of water on hydronium ion mobility in hydrated Nafion using molecular dynamics simulations.

We have performed a detailed analysis of the structural properties of the sulfonate groups in terms of isolated and overlapped solvation shells in the nanostructure of hydrated Nafion membrane using classical molecular dynamics simulations. Our simulations have demonstrated the correlation between the two different areas in bound water region, i.e., the first solvation shell, and the vehicular transport of hydronium ions at different water contents. We have employed a model of the Nafion membrane using the improved force field, which is newly modified and validated by comparing the density and water diffusivity with those obtained experimentally. The first solvation shells were classified into the two types, the isolated area and the overlapped area. The mean residence times of solvent molecules explicitly showed the different behaviors in each of those areas in terms of the vehicular transport of protons: the diffusivity of classical hydronium ions in the overlapped area dominates their total diffusion at lower water contents while that in the isolated area dominates for their diffusion at higher water contents. The results provided insights into the importance role of those areas in the solvation shells for the diffusivity of vehicular transport of hydronium ions in hydrated Nafion membrane.