Towards tailoring hydrophobic interaction with uranyl(VI) oxygen for C-H activation.

Bovine serum albumin (BSA) has a uranyl(VI) binding hotspot where uranium is tightly bound by three carboxylates. Uranyl oxygen is "soaked" into the hydrophobic core of BSA. Isopropyl hydrogen of Val is trapped near UO22+ and upon photoexcitation, C-H bond cleavage is initiated. A unique hydrophobic contact with "yl"-oxygen, as observed here, can be used to induce C-H activation.

Enhancement of energy decomposition analysis in fragment molecular orbital calculations.

Energy decomposition analysis is one of the most attractive features of fragment molecular orbital (FMO) calculations from the point of view of practical applications. Here we report some enhancements for PIEDA in the ABINIT-MP program. One is a separation of the dispersion-type stabilization from the electron correlation energy, traditionally referred to as the "dispersion interaction" (DI). Another is an alternative evaluation of the electrostatic (ES) interaction using the restrained electrostatic potential (RESP) charges. The GA:CT stacked base pair and the Trp-Cage miniprotein were used as illustrative examples.

Regression analysis for predicting the elasticity of liquid crystal elastomers.

It is highly desirable but difficult to understand how microscopic molecular details influence the macroscopic material properties, especially for soft materials with complex molecular architectures. In this study we focus on liquid crystal elastomers (LCEs) and aim at identifying the design variables of their molecular architectures that govern their macroscopic deformations. We apply the regression analysis using machine learning (ML) to a database containing the results of coarse grained molecular dynamics simulations of LCEs with various molecular architectures. The predictive performance of a surrogate model generated by the regression analysis is also tested. The database contains design variables for LCE molecular architectures, system and simulation conditions, and stress-strain curves for each LCE molecular system. Regression analysis is applied using the stress-strain curves as objective variables and the other factors as explanatory variables. The results reveal several descriptors governing the stress-strain curves. To test the predictive performance of the surrogate model, stress-strain curves are predicted for LCE molecular architectures that were not used in the ML scheme. The predicted curves capture the characteristics of the results obtained from molecular dynamics simulations. Therefore, the ML scheme has great potential to accelerate LCE material exploration by detecting the key design variables in the molecular architecture and predicting the LCE deformations.

Screening toward the Development of Fingerprints of Atomic Environments Using Bond-Orientational Order Parameters.

A combination of atomic numbers and bond-orientational order parameters is considered a candidate for a simple representation that involves information on both the atomic species and their positional relation. The 504 candidates are applied as the fingerprint of the molecules stored in QM9, a data set of computed geometric, energetic, electronic, and thermodynamic properties for 133 885 stable small organic molecules made up of carbon, hydrogen, oxygen, nitrogen, and fluorine atoms. To screen the fingerprints, a regression analysis of the atomic charges given by Open Babel was performed by supervised machine learning. The regression results indicate that the 60 fingerprints successfully estimate Open Babel charges. The results of the dipole moments, an example of a property expressed by charge and position, also had a high accuracy in comparison with the values computed from Open Babel charges. Therefore, the screened 60 fingerprints have the potential to precisely describe the chemical and structural information on the atomic environment of molecules.

Redox Sensor Array with 23.5-µm Resolution for Real-Time Imaging of Hydrogen Peroxide and Glutamate Based on Charge-Transfer-Type Potentiometric Sensor.

Towards clarifying the spatio-temporal neurotransmitter distribution, potentiometric redox sensor arrays with 23.5-µm resolution were fabricated. The sensor array based on a charge-transfer-type potentiometric sensor comprises 128×128 pixels with gold electrodes deposited on the surface of pixels. The sensor output corresponding to the interfacial potential of the electrode changed logarithmically with the mixture ratio of K3Fe(CN)6 and K4Fe(CN)6, where the redox sensitivity reached 49.9 mV/dec. By employing hydrogen peroxidase as an enzyme and ferrocene as an electron mediator, the sensing characteristics for hydrogen peroxide (H2O2) were investigated. The analyses of the sensing characteristics revealed that the sensitivity was about 44.7 mV/dec., comparable to the redox sensitivity, while the limit of detection (LOD) was achieved to be 1 µM. Furthermore, the oxidation state of the electron mediator can be the key to further lowering the LOD. Then, by immobilizing oxidizing enzyme for H2O2 and glutamate oxidase, glutamate (Glu) measurements were conducted. As a result, similar sensitivity and LOD to those of H2O2 were obtained. Finally, the real-time distribution of 1 µM Glu was visualized, demonstrating the feasibility of our device as a high-resolution bioimaging technique.

Mining of Effective Local Order Parameters to Classify Ice Polymorphs.

Order parameters make it possible to quantify the degree of structural ordering in a material and thus to apply as the reaction coordinates during the free-energy analysis of phase or structure transitions. Furthermore, order parameters are useful in determining the local structures of molecular groups during transition stages. However, identifying or developing local order parameters (LOPs) that are sensitive for specific materials and phases is a non-trivial task. In this study, the ability of LOPs to classify the solid and liquid structures of water at coexistence or triple points is investigated with the aid of supervised machine learning. The classification accuracy of a total of 179,738,433 combinations of 493 LOPs is automatically and systematically compared for water structures at the ice Ih-Ic-liquid coexistence point and the ice III-V-liquid and ice V-VI-liquid triple points. The optimal sets of two LOPs are found for each point, and sets of three LOPs are suggested for better accuracy.

Searching for local order parameters to classify water structures at triple points.

The diversity of ice polymorphs is of interest in condensed-matter physics, engineering, astronomy, and biosphere and climate studies. In particular, their triple points are critical to elucidate the formation of each phase and transitions among phases. However, an approach to distinguish their molecular structures is lacking. When precise molecular geometries are given, order parameters are often computed to quantify the degree of structural ordering and to classify the structures. Many order parameters have been developed for specific or multiple purposes, but their capabilities have not been exhaustively investigated for distinguishing ice polymorphs. Here, 493 order parameters and their combinations are considered for two triple points involving the ice polymorphs ice III-V-liquid and ice V-VI-liquid. Supervised machine learning helps automatic and systematic searching of the parameters. For each triple point, the best set of two order parameters was found that distinguishes three structures with high accuracy. A set of three order parameters is also suggested for better accuracy.

Searching local order parameters to classify water structures of ice Ih, Ic, and liquid.

Identifying molecular structures of water and ice helps reveal the chemical nature of liquid and solid water. Real-space geometrical information on molecular systems can be precisely obtained from molecular simulations, but classifying the resulting structure is a non-trivial task. Order parameters are ordinarily introduced to effectively distinguish different structures. Many order parameters have been developed for various kinds of structures, such as body-centered cubic, face-centered cubic, hexagonal close-packed, and liquid. Order parameters for water have also been suggested but need further study. There has been no thorough investigation of the classification capability of many existing order parameters. In this work, we investigate the capability of 493 order parameters to classify the three structures of ice: Ih, Ic, and liquid. A total of 159 767 496 combinations of the order parameters are also considered. The investigation is automatically and systematically performed by machine learning. We find the best set of two bond-orientational order parameters, Q4 and Q8, to distinguish the three structures with high accuracy and robustness. A set of three order parameters is also suggested for better accuracy.

CMOS-Based Redox-Type Label-Free ATP Image Sensor for In Vitro Sensitive Imaging of Extracellular ATP.

Adenosine 5'-triphosphate (ATP) plays a crucial role as an extracellular signaling molecule in the central nervous system and is closely related to various nerve diseases. Therefore, label-free imaging of extracellular ATP dynamics and spatiotemporal analysis is crucial for understanding brain function. To decrease the limit of detection (LOD) of imaging extracellular ATP, we fabricated a redox-type label-free ATP image sensor by immobilizing glycerol-kinase (GK), L-α-glycerophosphate oxidase (LGOx), and horseradish peroxidase (HRP) enzymes in a polymer film on a gold electrode-modified potentiometric sensor array with a 37.3 µm-pitch. Hydrogen peroxide (H2O2) is generated through the enzymatic reactions from GK to LGOx in the presence of ATP and glycerol, and ATP can be detected as changes in its concentration using an electron mediator. Using this approach, the LOD for ATP was 2.8 µM with a sensitivity of 77 ± 3.8 mV/dec., under 10 mM working buffers at physiological pH, such as in in vitro experiments, and the LOD was great superior 100 times than that of the hydrogen ion detection-based image sensor. This redox-type ATP image sensor may be successfully applied for in vitro sensitive imaging of extracellular ATP dynamics in brain nerve tissue or cells.

Mining of effective local order parameters for classifying crystal structures: A machine learning study.

Determining local structures of molecular systems helps the scientific and technological understanding of the function of materials. Molecular simulations provide microscopic information on molecular systems, but analyzing the resulting local structures is a non-trivial task. Many kinds of order parameters have been developed for detecting such local structures. Bond-orientational order parameters are promising for classifying local structures and have been used to analyze systems with such structures as body-centered cubic, face-centered cubic, hexagonal close-packed, and liquid. A specific set of order parameters derived from Lechner's definitional equation are widely used to classify complex local structures. However, there has been no thorough investigation of the classification capability of other Lechner parameters, despite their potential to precisely distinguish local structures. In this work, we evaluate the classification capability of 112 species of bond-orientational order parameters including Lechner's definitions. A total of 234 248 combinations of these parameters are also evaluated. The evaluation is systematically and automatically performed using machine learning techniques. To distinguish the four types of local structures, we determine the better set of two order parameters by comparing with a conventional set. A set of three order parameters is also suggested for better accuracy. Therefore, the machine learning scheme in the present study enables the systematic, accurate, and automatic mining of effective order parameters for classifying crystal structures.

Stabilization Mechanism for a Nonfibrillar Amyloid ß Oligomer Based on Formation of a Hydrophobic Core Determined by Dissipative Particle Dynamics.

Neurotoxicity caused by nonfibrillar amyloid ß (Aß) oligomers in the brain is suggested to be associated with the onset of Alzheimer's disease (AD). Elucidating the structural features of Aß oligomers is critical for promoting drug discovery research for AD. One of the Aß oligomers, known as Aß*56, is a dodecamer that impairs memory when injected into healthy rats, suggesting that Aß*56 may contribute to cognitive deficits in AD patients. Another dodecamer structure, formed by 20-residue peptide segments derived from the Aß peptide (Aß17-36), has been revealed by X-ray crystallography. The structure of the Aß17-36 dodecamer is composed of trimer units and shows the oligomer antibody A11 reactivity, which are characteristic of Aß*56, indicating that Aß*56 and the Aß17-36 dodecamer share a similar structure. However, the structure of the C-terminal regions (Aß37-42) remains unclear. The C-terminal region, which is abundant in hydrophobic residues, is thought to play a key role in stabilizing the oligomer structure by forming a hydrophobic core. In this study, we employed dissipative particle dynamics, a coarse-grained simulation method with soft core potentials, utilizing the crystal structure information to unravel Aß dodecamer structures with C-terminal regions. The simulation results were validated by the reported experimental data. Hence, an analysis of the simulation results can provide structural insights into Aß oligomers. Our simulations revealed the stabilization mechanism of the dodecamer structure at the molecular level. We showed that C-terminal regions spontaneously form a hydrophobic core in the central cavity, contributing to stabilizing the dodecamer structure. Furthermore, four consecutive hydrophobic residues in the C-terminal region (i.e., Val39-Ala42) are important for core formation.

Machine learning-aided analysis for complex local structure of liquid crystal polymers.

Elucidation of mesoscopic structures of molecular systems is of considerable scientific and technological interest for the development and optimization of advanced materials. Molecular dynamics simulations are a promising means of revealing macroscopic physical properties of materials from a microscopic viewpoint, but analysis of the resulting complex mesoscopic structures from microscopic information is a non-trivial and challenging task. In this study, a Machine Learning-aided Local Structure Analyzer (ML-LSA) is developed to classify the complex local mesoscopic structures of molecules that have not only simple atomistic group units but also rigid anisotropic functional groups such as mesogens. The proposed ML-LSA is applied to classifying the local structures of liquid crystal polymer (LCP) systems, which are of considerable scientific and technological interest because of their potential for sensors and soft actuators. A machine learning (ML) model is constructed from small, and thus computationally less costly, monodomain LCP trajectories. The ML model can distinguish nematic- and smectic-like monodomain structures with high accuracy. The ML-LSA is applied to large, complex quenched LCP structures, and the complex local structures are successfully classified as either nematic- or smectic-like. Furthermore, the results of the ML-LSA suggest the best order parameter for distinguishing the two mesogenic structures. Our ML model enables automatic and systematic analysis of the mesogenic structures without prior knowledge, and thus can overcome the difficulty of manually determining the specific order parameter required for the classification of complex structures.

Fragment Molecular Orbital Based Parametrization Procedure for Mesoscopic Structure Prediction of Polymeric Materials.

In the analyses of miscibility behaviors of macromolecules and polymers, dissipative particle dynamics (DPD) simulations are generally performed. In these simulations, the so-called χ parameters describing the effective interactions among particles are crucial. It has been known that such parameters can be obtained within the classical or empirical force field frameworks. However, there is a potential problem that charge transfer and polarization occasionally occur. Additionally, satisfactory reference parameters are not available for some cases. Therefore, we developed a new procedure to evaluate the set of parameters by using the ab initio fragment molecular orbital (FMO) method which can provide the set of interaction energies among segments as polymer units. Moreover, we evaluated the anisotropy of molecules by using the FMO-based effective interaction parameters for three standard binary mixture systems (hexane-nitrobenzene, polyisobutylene-diisobutyl ketone, and polyisoprene-polystyrene). The calculated values showed good agreement with the experimental values with about 10% errors.

Theoretical analyses on water cluster structures in polymer electrolyte membrane by using dissipative particle dynamics simulations with fragment molecular orbital based effective parameters.

The mesoscopic structures of polymer electrolyte membrane (PEM) affect the performances of fuel cells. Nafion® with the Teflon® backbone has been the most widely used of all PEMs, but sulfonated poly-ether ether-ketone (SPEEK) having an aromatic backbone has drawn interest as an alternative to Nafion. In the present study, a series of dissipative particle dynamics (DPD) simulations were performed to compare Nafion and SPEEK. These PEM polymers were modeled by connected particles corresponding to the hydrophobic backbone and the hydrophilic moiety of sulfonic acid group. The water particle interacting with Nafion particles was prepared as well. The crucial interaction parameters among DPD particles were evaluated by a series of calculations based on the fragment molecular orbital (FMO) method in a non-empirical way (Okuwaki et al., J. Phys. Chem. B, 2018, 122, 338-347). Through the DPD simulations, the water and hydrophilic particles aggregated, forming cluster networks surrounded by the hydrophobic phase. The structural features of formed water clusters were investigated in detail. Furthermore, the differences in percolation behaviors between Nafion and SPEEK revealed much better connectivity among water clusters by Nafion. The present FMO-DPD simulation results were in good agreement with available experimental data.

Approaches for Controlling the Temperature and Pressure Range in Generalized NPT Ensembles.

Isobaric-multithermal and multibaric-isothermal methods are powerful methods for sampling in a wide energy and/or volume space. A finite temperature or pressure is required to study phase diagrams and many properties of many types of systems. However, it is difficult to control the temperature or pressure range because these systems move randomly in energy or volume space. Here, we develop a method to control the temperature range in the isobaric-multithermal ensemble and a method to control the pressure range in the multibaric-isothermal ensemble. These methods have the advantage of adequately determining the weight factor to create the multicanonical ensemble and can be applied to study thermodynamic properties.

Theoretically optimum position of the prosthesis in total hip arthroplasty to fulfill the severe range of motion criteria due to neck impingement.

BACKGROUND: The purpose of this investigation is to determine the optimum position of the prosthesis in total hip arthroplasty for reducing neck impingement using a mathematical formula. METHODS: We calculated the cup inclination, cup anteversion, and stem antetorsion in cases with various sizes of femoral head (28, 32, 36, and 44 mm in diameter) to fulfill severe range of motion criteria: (1) flexion more than 120°, (2) extension more than 30°, (3) internal rotation at 90° flexion more than 60°, and (4) external rotation at neutral more than 40°. RESULTS: When the areas to fulfill the severe range of motion criteria were compared by femoral head diameter, the area for 28 mm was extremely small relative to those of 32, 36, and 44 mm. Theoretically, the optimum position of the prosthesis in total hip arthroplasty without neck impingement should be oriented at a cup inclination of 45° combined with the cup anteversion and stem antetorsion so that the sum of the cup anteversion plus 0.7 times the stem antetorsion equals 42° with a head diameter more than 32 mm. This study also recommends the optimum position of the prosthesis as 45° cup inclination, 25° cup anteversion, and 25° stem antetorsion when the surgeon can choose a freely adjustable modular stem system. However, this theory assumes that the pelvic inclination has no changes caused by aging and can be validated in the lying, sitting, and standing positions. CONCLUSIONS: The prosthesis in total hip arthroplasty without neck impingement should be oriented at a cup inclination of 45° combined with cup anteversion and stem antetorsion determined by the formula: cup anteversion + 0.7 × stem antetorsion = 42°. A range of acceptable positions would be more helpful and realistic to a surgeon trying to ensure adequate prosthesis positions.