Large-scale deep learning identifies the antiviral potential of PKI-179 and MTI-31 against coronaviruses.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to the global pandemic of Coronavirus Disease (2019) (COVID-19), underscoring the urgency for effective antiviral drugs. Despite the development of different vaccination strategies, the search for specific antiviral compounds remains crucial. Here, we combine machine learning (ML) techniques with in vitro validation to efficiently identify potential antiviral compounds. We overcome the limited amount of SARS-CoV-2 data available for ML using various techniques, supplemented with data from diverse biomedical assays, which enables end-to-end training of a deep neural network architecture. We use its predictions to identify and prioritize compounds for in vitro testing. Two top-hit compounds, PKI-179 and MTI-31, originally identified as Pi3K-mTORC1/2 pathway inhibitors, exhibit significant antiviral activity against SARS-CoV-2 at low micromolar doses. Notably, both compounds outperform the well-known mTOR inhibitor rapamycin. Furthermore, PKI-179 and MTI-31 demonstrate broad-spectrum antiviral activity against SARS-CoV-2 variants of concern and other coronaviruses. In a physiologically relevant model, both compounds show antiviral effects in primary human airway epithelial (HAE) cultures derived from healthy donors cultured in an air-liquid interface (ALI). This study highlights the potential of ML combined with in vitro testing to expedite drug discovery, emphasizing the adaptability of AI-driven approaches across different viruses, thereby contributing to pandemic preparedness.

Electrolytes in conducting nanopores: Revisiting constant charge and constant potential simulations.

Simulating electrolyte-electrode systems poses challenges due to the need to account for the electrode's response to ion movements in order to maintain a constant electrode potential, which slows down the simulations. To circumvent this, computationally more efficient constant charge (CC) simulations are sometimes employed. However, the accuracy of CC simulations in capturing the behavior of electrolyte-electrode systems remains unclear, especially for microporous electrodes. Herein, we consider electrolyte-filled slit nanopores and systematically analyze the in-pore ion structure and diffusivity using CC and constant potential simulations. Our results indicate that CC simulations provide comparable pore occupancies at high bulk ion densities and for highly charged pores, but they fail to accurately describe the ion structure and dynamics, particularly in quasi-2D (single-layer) pores and at low ion densities. We attribute these results to the superionic state emerging in conducting nanoconfinement and its interplay with excluded volume interactions.

Unexpected Two-Stage Swelling of Weak Polyelectrolyte Brushes with Divalent Counterions.

We use particle-based, coarse-grained simulations to study the influence of divalent counterions on a weak polyelectrolyte brush. Our simulations show a profound influence of even small concentrations of divalent salt on the titration behavior of the brush, which is shown to be a combined effect of electrostatic interactions and the Donnan effect. Furthermore, we examine the partitioning of mono- and divalent counterions into the brush. We demonstrate the preferred uptake of divalent ions by the brush, which is further enhanced by electrostatic correlation effects. Finally, our simulations reveal a hitherto unobserved two-stage swelling of the brush as a function of the pH in the presence of divalent salt. This phenomenon arises as a consequence of charge regulation and ion partitioning.

Intermittent low-dose far-UVC irradiation inhibits growth of common mold below threshold limit value.

Mold infestations in buildings pose significant challenges to human health, affecting both private residences and hospitals. While molds commonly trigger asthma and allergies in the immunocompetent, they can cause life-threatening diseases in the immunocompromised. Currently, there is an unmet need for new strategies to reduce or prevent mold infestations. Far-UVC technology can inactivate microorganisms while remaining safe for humans. This study investigates the inhibitory efficacy of far-UVC light at 222 nm on the growth of common mold-producing fungi, specifically Penicillium candidum, when delivered in low-dose on-off duty cycles, a configuration consistent with its use in real-world settings. The inhibitory effect of the low-dose duty cycles was assessed on growth induced by i) an adjacent spore-producing P. candidum donor and ii) P. candidum spores seeded directly onto agar plates. In both setups, the far-UVC light significantly inhibited both vertical and horizontal growth of P. candidum, even when the UV doses were below the Threshold Value Limit of 23 mJ/cm2. These results suggest that far-UVC light holds the potential to improve indoor air quality by reducing or preventing mold growth, also when people are present.

Machine learning-driven investigation of the structure and dynamics of the BMIM-BF4 room temperature ionic liquid.

Room-temperature ionic liquids are an exciting group of materials with the potential to revolutionize energy storage. Due to their chemical structure and means of interaction, they are challenging to study computationally. Classical descriptions of their inter- and intra-molecular interactions require time intensive parametrization of force-fields which is prone to assumptions. While ab initio molecular dynamics approaches can capture all necessary interactions, they are too slow to achieve the time and length scales required. In this work, we take a step towards addressing these challenges by applying state-of-the-art machine-learned potentials to the simulation of 1-butyl-3-methylimidazolium tetrafluoroborate. We demonstrate a learning-on-the-fly procedure to train machine-learned potentials from single-point density functional theory calculations before performing production molecular dynamics simulations. Obtained structural and dynamical properties are in good agreement with computational and experimental references. Furthermore, our results show that hybrid machine-learned potentials can contribute to an improved prediction accuracy by mitigating the inherent shortsightedness of the models. Given that room-temperature ionic liquids necessitate long simulations to address their slow dynamics, achieving an optimal balance between accuracy and computational cost becomes imperative. To facilitate further investigation of these materials, we have made our IPSuite-based training and simulation workflow publicly accessible, enabling easy replication or adaptation to similar systems.

Development of nitroalkene-based inhibitors to target STING-dependent inflammation.

Stimulator of Interferon Genes (STING) is essential for the inflammatory response to cytosolic DNA. Despite that aberrant activation of STING is linked to an increasing number of inflammatory diseases, the development of inhibitors has been challenging, with no compounds in the pipeline beyond the preclinical stage. We previously identified endogenous nitrated fatty acids as novel reversible STING inhibitors. With the aim of improving the specificity and efficacy of these compounds, we developed and tested a library of nitroalkene-based compounds for in vitro and in vivo STING inhibition. The structure-activity relationship study revealed a robustly improved electrophilicity and reduced degrees of freedom of nitroalkenes by conjugation with an aromatic moiety. The lead compounds CP-36 and CP-45, featuring a ß-nitrostyrene moiety, potently inhibited STING activity in vitro and relieved STING-dependent inflammation in vivo. This validates the potential for nitroalkene compounds as drug candidates for STING modulation to treat STING-driven inflammatory diseases, providing new robust leads for preclinical development.

Influence of bacterial swimming and hydrodynamics on attachment of phages.

Bacteriophages ("phages") are viruses that infect bacteria. Since they do not actively self-propel, phages rely on thermal diffusion to find target cells-but can also be advected by fluid flows, such as those generated by motile bacteria themselves in bulk fluids. How does the flow field generated by a swimming bacterium influence how it encounters phages? Here, we address this question using coupled molecular dynamics and lattice Boltzmann simulations of flagellated bacteria swimming through a bulk fluid containing uniformly-dispersed phages. We find that while swimming increases the rate at which phages attach to both the cell body and flagellar propeller, hydrodynamic interactions strongly suppress this increase at the cell body, but conversely enhance this increase at the flagellar bundle. Our results highlight the pivotal influence of hydrodynamics on the interactions between bacteria and phages, as well as other diffusible species, in microbial environments.

Modeling microgel swelling: Influence of chain finite extensibility.

Microgels exhibit the ability to undergo reversible swelling in response to shifts in environmental factors that include variations in temperature, concentration, and pH. While several models have been put forward to elucidate specific aspects of microgel swelling and its impact on bulk behavior, a consistent theoretical description that chains throughout the microscopic degrees of freedom with suspension properties and deepens into the full implications of swelling remains a challenge yet to be met. In this work, we extend the mean-field swelling model of microgels from Denton and Tang [J. Chem. Phys. 145, 164901 (2016)] to include the finite extensibility of the polymer chains. The elastic contribution to swelling in the original work is formulated for Gaussian chains. By using the Langevin chain model, we modify this elastic contribution in order to account for finite extensibility effects, which become prominent for microgels containing highly charged polyelectrolytes and short polymer chains. We assess the performance of both elastic models, namely for Gaussian and Langevin chains, comparing against coarse-grained bead-spring simulations of ionic microgels with explicit electrostatic interactions. We examine the applicability scope of the models under a variation of parameters, such as ionization degree, microgel concentration, and salt concentration. The models are also tested against experimental results. This work broadens the applicability of the microgel swelling model toward a more realistic description, which brings advantages when describing the suspensions of nanogels and weak-polyelectrolyte micro-/nanogels.

Generating Minimal Training Sets for Machine Learned Potentials.

This Letter presents a novel approach for identifying uncorrelated atomic configurations from extensive datasets with a nonstandard neural network workflow known as random network distillation (RND) for training machine-learned interatomic potentials (MLPs). This method is coupled with a DFT workflow wherein initial data are generated with cheaper classical methods before only the minimal subset is passed to a more computationally expensive ab initio calculation. This benefits training not only by reducing the number of expensive DFT calculations required but also by providing a pathway to the use of more accurate quantum mechanical calculations. The method's efficacy is demonstrated by constructing machine-learned interatomic potentials for the molten salts KCl and NaCl. Our RND method allows accurate models to be fit on minimal datasets, as small as 32 configurations, reducing the required structures by at least 1 order of magnitude compared to alternative methods. This reduction in dataset sizes not only substantially reduces computational overhead for training data generation but also provides a more comprehensive starting point for active-learning procedures.

Octyl itaconate enhances VSVΔ51 oncolytic virotherapy by multitarget inhibition of antiviral and inflammatory pathways.

The presence of heterogeneity in responses to oncolytic virotherapy poses a barrier to clinical effectiveness, as resistance to this treatment can occur through the inhibition of viral spread within the tumor, potentially leading to treatment failures. Here we show that 4-octyl itaconate (4-OI), a chemical derivative of the Krebs cycle-derived metabolite itaconate, enhances oncolytic virotherapy with VSVΔ51 in various models including human and murine resistant cancer cell lines, three-dimensional (3D) patient-derived colon tumoroids and organotypic brain tumor slices. Furthermore, 4-OI in combination with VSVΔ51 improves therapeutic outcomes in a resistant murine colon tumor model. Mechanistically, we find that 4-OI suppresses antiviral immunity in cancer cells through the modification of cysteine residues in MAVS and IKKß independently of the NRF2/KEAP1 axis. We propose that the combination of a metabolite-derived drug with an oncolytic virus agent can greatly improve anticancer therapeutic outcomes by direct interference with the type I IFN and NF-κB-mediated antiviral responses.

Collaboration on Machine-Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly.

The field of machine learning potentials has experienced a rapid surge in progress, thanks to advances in machine learning theory, algorithms, and hardware capabilities. While the underlying methods are continuously evolving, the infrastructure for their deployment has lagged. The community, due to these rapid developments, frequently finds itself split into groups built around different implementations of machine-learned potentials. In this work, we introduce IPSuite, a Python-driven software package designed to connect different methods and algorithms from the comprehensive field of machine-learned potentials into a single platform while also providing a collaborative infrastructure, helping ensure reproducibility. Furthermore, the data management infrastructure of the IPSuite code enables simple model sharing and deployment in simulations. Currently, IPSuite supports six state-of-the-art machine learning approaches for the fitting of interatomic potentials as well as a variety of methods for the selection of training data, running of ab initio calculations, learning-on-the-fly strategies, model evaluation, and simulation deployment.

Amyloid-ß aggregates activate peripheral monocytes in mild cognitive impairment.

The peripheral immune system is important in neurodegenerative diseases, both in protecting and inflaming the brain, but the underlying mechanisms remain elusive. Alzheimer's Disease is commonly preceded by a prodromal period. Here, we report the presence of large Aß aggregates in plasma from patients with mild cognitive impairment (n = 38). The aggregates are associated with low level Alzheimer's Disease-like brain pathology as observed by 11C-PiB PET and 18F-FTP PET and lowered CD18-rich monocytes. We characterize complement receptor 4 as a strong binder of amyloids and show Aß aggregates are preferentially phagocytosed and stimulate lysosomal activity through this receptor in stem cell-derived microglia. KIM127 integrin activation in monocytes promotes size selective phagocytosis of Aß. Hydrodynamic calculations suggest Aß aggregates associate with vessel walls of the cortical capillaries. In turn, we hypothesize aggregates may provide an adhesion substrate for recruiting CD18-rich monocytes into the cortex. Our results support a role for complement receptor 4 in regulating amyloid homeostasis.

Disentanglement of Surface and Confinement Effects for Diene Metathesis in Mesoporous Confinement.

We study the effects of a planar interface and confinement on a generic catalytically activated ring-closing polymerization reaction near an unstructured catalyst. For this, we employ a coarse-grained polymer model using grand-canonical molecular dynamics simulations with a Monte Carlo reaction scheme. Inspired by recent experiments in the group of M. Buchmeiser that demonstrated an increase in ring-closing selectivity under confinement, we show that both the interface effects, i.e., placing the catalyst near a planar wall, and the confinement effects, i.e., locating the catalyst within a pore, lead to an increase of selectivity. We furthermore demonstrate that curvature effects for cylindrical mesopores (2 nm < d < 12.3 nm) influence the distribution of the chain ends, leading to a further increase in selectivity. This leads us to speculate that specially corrugated surfaces might also help to enhance catalytically activated polymerization processes.

Optimal motility strategies for self-propelled agents to explore porous media.

Microrobots for, e.g., biomedical applications, need to be equipped with motility strategies that enable them to navigate through complex environments. Inspired by biological microorganisms we re-create motility patterns such as run-and-reverse, run-and-tumble, or run-reverse-flick applied to active rodlike particles in silico. We investigate their capability to efficiently explore disordered porous environments with various porosities and mean pore sizes ranging down to the scale of the active particle. By calculating the effective diffusivity for the different patterns, we can predict the optimal one for each porous sample geometry. We find that providing the agent with very basic sensing and decision-making capabilities yields a motility pattern outperforming the biologically inspired patterns for all investigated porous samples.

Explaining Giant Apparent pK_{a} Shifts in Weak Polyelectrolyte Brushes.

Recent experiments on weak polyelectrolyte brushes found marked shifts in the effective pK_{a} that are linear in the logarithm of the salt concentration. Comparing explicit-particle simulations with mean-field calculations we show that for high grafting densities the salt concentration effect can be explained using the ideal Donnan theory, but for low grafting densities the full shift is due to a combination of the Donnan effect and the polyelectrolyte effect. The latter originates from electrostatic correlations that are neglected in the Donnan picture and that are only approximately included in the mean-field theory. Moreover, we demonstrate that the magnitude of the polyelectrolyte effect is almost invariant with respect to salt concentration but depends on the grafting density of the brush. This invariance is due to a complex cancellation of multiple effects. Based on our results, we show how the experimentally determined pK_{a} shifts may be used to infer the grafting density of brushes, a parameter that is difficult to measure directly.

A novel model for biofilm initiation in porous media flow.

Bacteria often form biofilms in porous environments where an external flow is present, such as soil or filtration systems. To understand the initial stages of biofilm formation, one needs to study the interactions between cells, the fluid and the confining geometries. Here, we present an agent based numerical model for bacteria that takes into account the planktonic stage of motile cells as well as surface attachment and biofilm growth in a lattice Boltzmann fluid. In the planktonic stage we show the importance of the interplay between complex flow and cell motility when determining positions of surface attachment. In the growth stage we show the applicability of our model by investigating how external flow and biofilm stiffness determine qualitative colony morphologies as well as quantitative measurements of, e.g., permeability.

Solvent Effects on Structure and Screening in Confined Electrolytes.

Using classical density functional theory, we investigate the influence of solvent on the structure and ionic screening of electrolytes under slit confinement and in contact with a reservoir. We consider a symmetric electrolyte with implicit and explicit solvent models and find that spatially resolving solvent molecules is essential for the ion structure at confining walls, excess ion adsorption, and the pressure exerted on the walls. Despite this, we observe only moderate differences in the period of oscillations of the pressure with the slit width and virtually coinciding decay lengths as functions of the scaling variable σ_{ion}/λ_{D}, where σ_{ion} is the ion diameter and λ_{D} the Debye length. Moreover, in the electrostatic-dominated regime, this scaling behavior is practically independent of the relative permittivity and its dependence on the ion concentration. In contrast, the crossover to the hard-core-dominated regime depends sensitively on all three factors.

Renormalized charge and dielectric effects in colloidal interactions: a numerical solution of the nonlinear Poisson-Boltzmann equation for unknown boundary conditions.

 The Derjaguin-Landau-Verwey-Overbeek (DLVO) theory, introduced more than 70 years ago, is a hallmark of colloidal particle modeling. For highly charged particles in the dilute regime, it is often supplemented by Alexander's prescription (Alexander et al. in J Chem Phys 80:5776, 1984) for using a renormalized charge. Here, we solve the problem of the interaction between two charged colloids at finite ionic strength, including dielectric mismatch effects, using an efficient numerical scheme to solve the nonlinear Poisson-Boltzmann (NPB) equation with unknown boundary conditions. Our results perfectly match the analytical predictions for the renormalized charge by Trizac and coworkers (Aubouy et al. in J Phys A 36:5835, 2003). Moreover, they allow us to reinterpret previous molecular dynamics (MD) simulation results by Kreer et al. (Phys Rev E 74:021401, 2006), rendering them now in agreement with the expected behavior. We furthermore find that the influence of polarization becomes important only when the Debye layers overlap significantly.