Electronic Polarizability Tunes the Function of the Human Bestrophin 1 Cl Channel.

Mechanisms of anion permeation within ion channels and nanopores remain poorly understood. Recent cryo-electron microscopy structures of the human bestrophin 1 Cl channel (hBest1) provide an opportunity to evaluate ion interactions predicted by molecular dynamics (MD) simulations against experimental observations. Here, we implement the fully polarizable force field AMOEBA in MD simulations on different conformations of hBest1.

Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents.

A significant challenge in computational chemistry is developing approximations that accelerate ab initio methods while preserving accuracy. Machine learning interatomic potentials (MLIPs) have emerged as a promising solution for constructing atomistic potentials that can be transferred across different molecular and crystalline systems. Most MLIPs are trained only on energies and forces in vacuum, while an improved description of the potential energy surface could be achieved by including the curvature of the potential energy surface.

Deposition simulations of realistic dosages in patient-specific airways with two- and four-way coupling.

Inhalers spray over 100 million drug particles into the mouth, where a significant portion of the drug may deposit. Understanding how the complex interplay between particle and solid phases influence deposition is crucial for optimising treatments. Existing modelling studies neglect any effect of particle momentum on the fluid (one-way coupling), which may cause poor prediction of forces acting on particles.

AutoXAI4Omics: an automated explainable AI tool for omics and tabular data.

Machine learning (ML) methods offer opportunities for gaining insights into the intricate workings of complex biological systems, and their applications are increasingly prominent in the analysis of omics data to facilitate tasks, such as the identification of novel biomarkers and predictive modeling of phenotypes. For scientists and domain experts, leveraging user-friendly ML pipelines can be incredibly valuable, enabling them to run sophisticated, robust, and interpretable models without requiring in-depth expertise in coding or algorithmic optimization. By streamlining the process of model development and training, researchers can devote their time and energies to the critical tasks of biological interpretation and validation, thereby maximizing the scientific impact of ML-driven insights.

Coarse-grained polarizable soft solvent models, with applications in dissipative particle dynamics.

We critically examine a broad class of explicitly polarizable soft solvent models aimed at applications in dissipative particle dynamics. We obtain the dielectric permittivity using the fluctuating box dipole method in linear response theory and verify the models in relation to several test cases, including demonstrating ion desorption from an oil-water interface due to image charge effects. We additionally compute the Kirkwood factor and find that it uniformly lies in the range gK≃0.

DGCNN approach links metagenome-derived taxon and functional information providing insight into global soil organic carbon.

Metagenomics can provide insight into the microbial taxa present in a sample and, through gene identification, the functional potential of the community. However, taxonomic and functional information are typically considered separately in downstream analyses. We develop interpretable machine learning (ML) approaches for modelling metagenomic data, combining the biological representation of species with their associated genetically encoded functions within models.

Electrothermal filamentation of igniting plasmas.

Dense, hot plasmas are susceptible to the electrothermal instability: a collisional process which permits temperature perturbations in electron currents to grow. It is shown here that linearizing a system comprised of two opposing currents and a mobile ion background as three distinct fluids yields unstable modes with rapid growth rates (∼10^{13}s^{-1}) for wavenumbers below a threshold k_{th}. An analytical threshold condition is derived, this being surpassed for typical hot-spot and shell parameters.

A Nonlinear Peptide Topology for Synthetic Virions.

a nonlinear peptide topology for the assembly of synthetic virions is reported. The topology is a backbone cyclized amino-acid sequence in which polar l- and hydrophobic d-amino acid residues of the same-type alternate. This arrangement introduces pseudo symmetries of side chains within the same cyclopeptide ring, allowing for the lateral propagation of cyclopeptides into networks with a [3/6, 4]-fold rotational symmetry closing into virus-like shells.

Relationship between Capillary Wettability, Mass, and Momentum Transfer in Nanoconfined Water: The Case of Water in Nanoslits of Graphite and Hexagonal Boron Nitride.

The flow of water confined in nanosize capillaries is subject of intense research due to its relevance in the fabrication of nanofluidic devices and in the development of theories for fluid transport in porous media. Here, using molecular dynamics simulations carried out on 2D capillaries made up of graphite, hexagonal boron nitride (hBN) and a mix of the two, and of sizes from subnanometer to few nanometers, we investigate the relationship between the wettability of the wall capillary, the water diffusion, and its flow rate. We find that the water diffusion is decoupled from its flow properties as the former is not affected either by the height or chemistry of the capillary (except for the subnanometer slits), while the latter is dependent on both.

How can quantum computing be applied in clinical trial design and optimization?

Clinical trials are necessary for assessing the safety and efficacy of treatments. However, trial timelines are severely delayed with minimal success due to a multitude of factors, including imperfect trial site selection, cohort recruitment challenges, lack of efficacy, absence of reliable biomarkers, etc. Each of these factors possesses a unique computational challenge, such as data management, trial simulations, statistical analyses, and trial optimization.

Are we fitting data or noise? Analysing the predictive power of commonly used datasets in drug-, materials-, and molecular-discovery.

Data-driven techniques for establishing quantitative structure property relations are a pillar of modern materials and molecular discovery. Fuelled by the recent progress in deep learning methodology and the abundance of new algorithms, it is tempting to chase benchmarks and incrementally build ever more capable machine learning (ML) models. While model evaluation has made significant progress, the intrinsic limitations arising from the underlying experimental data are often overlooked.

Correlating Ultrastability with Fragility and Surface Mobility in Vapor Deposited Tetrahedral Glasses.

Several experiments on molecular and metallic glasses have shown that the ability of vapor deposition to produce ultrastable glasses is correlated to their structural and thermodynamic properties. Here we investigate the vapor deposition of a class of tetrahedral materials (including silicon and water) via molecular dynamics simulations of the generalized Stillinger-Weber potential. By changing a single parameter that controls the local tetrahedrality, we show that the emergence of ultrastable behavior is correlated with an increase in the fragility of the model.

High-dimensional order parameters and neural network classifiers applied to amorphous ices.

Amorphous ice phases are key constituents of water's complex structural landscape. This study investigates the polyamorphic nature of water, focusing on the complexities within low-density amorphous ice (LDA), high-density amorphous ice, and the recently discovered medium-density amorphous ice (MDA). We use rotationally invariant, high-dimensional order parameters to capture a wide spectrum of local symmetries for the characterization of local oxygen environments.

Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks.

For the one billion sufferers of respiratory disease, managing their disease with inhalers crucially influences their quality of life. Generic treatment plans could be improved with the aid of computational models that account for patient-specific features such as breathing pattern, lung pathology and morphology. Therefore, we aim to develop and validate an automated computational framework for patient-specific deposition modelling.

Developing semi-empirical water model for efficiently simulating temperature-dependent chemisorption of CO in amine solvents.

Classical molecular dynamics (MD) simulations without bond forming/breaking cannot be used to model chemical reactions (CRs) among small molecules. Although the first-principle MD simulation can adequately describe CRs with explicit water molecules, such simulation is normally too costly for most researchers to afford. Generally, water molecules in a solvent can exert hydrophobic forces on reacting molecules, which yields a so-called caging effect that cannot be ignored when constructing a free energy landscape for reacting molecules.

Lipid shape and packing are key for optimal design of pH-sensitive mRNA lipid nanoparticles.

The ionizable-lipid component of RNA-containing nanoparticles controls the pH-dependent behavior necessary for an efficient delivery of the cargo-the so-called endosomal escape. However, it is still an empirical exercise to identify optimally performing lipids. Here, we study two well-known ionizable lipids, DLin-MC3-DMA and DLin-DMA using a combination of experiments, multiscale computer simulations, and electrostatic theory.

Toward Accurate Post-Born-Oppenheimer Molecular Simulations on Quantum Computers: An Adaptive Variational Eigensolver with Nuclear-Electronic Frozen Natural Orbitals.

Nuclear quantum effects such as zero-point energy and hydrogen tunneling play a central role in many biological and chemical processes. The nuclear-electronic orbital (NEO) approach captures these effects by treating selected nuclei quantum mechanically on the same footing as electrons. On classical computers, the resources required for an exact solution of NEO-based models grow exponentially with system size.

Diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets.

Variability in case severity and in the range of symptoms experienced has been apparent from the earliest months of the COVID-19 pandemic. From a clinical perspective, symptom variability might indicate various routes/mechanisms by which infection leads to disease, with different routes requiring potentially different treatment approaches. For public health and control of transmission, symptoms in community cases were the prompt upon which action such as PCR testing and isolation was taken.

Electronic Polarizability Tunes the Function of the Human Bestrophin 1 Cl Channel.

Mechanisms of anion permeation within ion channels and nanopores remain poorly understood. Recent cryo-electron microscopy structures of the human bestrophin 1 Cl- channel (hBest1) provide an opportunity to evaluate ion interactions predicted by molecular dynamics (MD) simulations against experimental observations. Here, we implement the fully polarizable forcefield AMOEBA in MD simulations on different conformations of hBest1.

Nonadiabatic Nuclear-Electron Dynamics: A Quantum Computing Approach.

Coupled quantum electron-nuclear dynamics is often associated with the Born-Huang expansion of the molecular wave function and the appearance of nonadiabatic effects as a perturbation. On the other hand, native multicomponent representations of electrons and nuclei also exist, which do not rely on any a priori approximation. However, their implementation is hampered by prohibitive scaling.

Electrolyte Permeability in Plastic Ice VII.

Deep brines in water-rich planets form when electrolytes diffuse from the rocky interior through layers of thick dense ice such as ice VII and the hypothesized plastic ice VII. We perform classical molecular dynamics simulations of Li, Na, and K alkali ions and F and Cl halide ions in plastic ice VII at conditions similar to water-rich super-Earths, icy moons, and ocean worlds. We find that plastic ice VII is permeable to electrolytes on geological timescales.

Application of machine-learning algorithms to predict the transport properties of Mie fluids.

The ability to predict transport properties of fluids, such as the self-diffusion coefficient and viscosity, has been an ongoing effort in the field of molecular modeling. While there are theoretical approaches to predict the transport properties of simple systems, they are typically applied in the dilute gas regime and are not directly applicable to more complex systems. Other attempts to predict transport properties are performed by fitting available experimental or molecular simulation data to empirical or semi-empirical correlations.

Automated identification and tracking of cells in Cytometry of Reaction Rate Constant (CRRC).

Cytometry of Reaction Rate Constant (CRRC) is a method for studying cell-population heterogeneity using time-lapse fluorescence microscopy, which allows one to follow reaction kinetics in individual cells. The current and only CRRC workflow utilizes a single fluorescence image to manually identify cell contours which are then used to determine fluorescence intensity of individual cells in the entire time-stack of images. This workflow is only reliable if cells maintain their positions during the time-lapse measurements.