Toward transferable empirical valence bonds: Making classical force fields reactive.

The empirical valence bond technique allows classical force fields to model reactive processes. However, parametrization from experimental data or quantum mechanical calculations is required for each reaction present in the simulation. We show that the parameters present in the empirical valence bond method can be predicted using a neural network model and the SMILES strings describing a reaction. This removes the need for quantum calculations in the parametrization of the empirical valence bond technique. In doing so, we have taken the first steps toward defining a new procedure for enabling reactive atomistic simulations. This procedure would allow researchers to use existing classical force fields for reactive simulations, without performing additional quantum mechanical calculations.

SARS-CoV-2 Spike Protein Stimulates Macropinocytosis in Murine and Human Macrophages via PKC-NADPH Oxidase Signaling.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While recent studies have demonstrated that SARS-CoV-2 may enter kidney and colon epithelial cells by inducing receptor-independent macropinocytosis, it remains unknown whether this process also occurs in cell types directly relevant to SARS-CoV-2-associated lung pneumonia, such as alveolar epithelial cells and macrophages. The goal of our study was to investigate the ability of SARS-CoV-2 spike protein subunits to stimulate macropinocytosis in human alveolar epithelial cells and primary human and murine macrophages. Flow cytometry analysis of fluid-phase marker internalization demonstrated that SARS-CoV-2 spike protein subunits S1, the receptor-binding domain (RBD) of S1, and S2 stimulate macropinocytosis in both human and murine macrophages in an angiotensin-converting enzyme 2 (ACE2)-independent manner. Pharmacological and genetic inhibition of macropinocytosis substantially decreased spike-protein-induced fluid-phase marker internalization in macrophages both in vitro and in vivo. High-resolution scanning electron microscopy (SEM) imaging confirmed that spike protein subunits promote the formation of membrane ruffles on the dorsal surface of macrophages. Mechanistic studies demonstrated that SARS-CoV-2 spike protein stimulated macropinocytosis via NADPH oxidase 2 (Nox2)-derived reactive oxygen species (ROS) generation. In addition, inhibition of protein kinase C (PKC) and phosphoinositide 3-kinase (PI3K) in macrophages blocked SARS-CoV-2 spike-protein-induced macropinocytosis. To our knowledge, these results demonstrate for the first time that SARS-CoV-2 spike protein subunits stimulate macropinocytosis in macrophages. These results may contribute to a better understanding of SARS-CoV-2 infection and COVID-19 pathogenesis.

Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains.

Metal halide perovskites are multifunctional semiconductors with tunable structures and properties. They are highly dynamic crystals with complex octahedral tilting patterns and strongly anharmonic atomic behavior. In the higher temperature, higher symmetry phases of these materials, several complex structural features are observed. The local structure can differ greatly from the average structure and there is evidence that dynamic 2D structures of correlated octahedral motion form. An understanding of the underlying complex atomistic dynamics is, however, still lacking. In this work, the local structure of the inorganic perovskite CsPbI3 is investigated using a new machine learning force field based on the atomic cluster expansion framework. Through analysis of the temporal and spatial correlation observed during large-scale simulations, it is revealed that the low frequency motion of octahedral tilts implies a double-well effective potential landscape, even well into the cubic phase. Moreover, dynamic local regions of lower symmetry are present within both higher symmetry phases. These regions are planar and the length and timescales of the motion are reported. Finally, the spatial arrangement of these features and their interactions are investigated and visualized, providing a comprehensive picture of local structure in the higher symmetry phases.

First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects.

Vibrational spectroscopy is a powerful approach to visualising interfacial phenomena. However, extracting structural and dynamical information from vibrational spectra is a challenge that requires first-principles simulations, including non-Condon and quantum nuclear effects. We address this challenge by developing a machine-learning enhanced first-principles framework to speed up predictive modelling of infrared, Raman, and sum-frequency generation spectra. Our approach uses machine learning potentials that encode quantum nuclear effects to generate quantum trajectories using simple molecular dynamics efficiently. In addition, we reformulate bulk and interfacial selection rules to express them unambiguously in terms of the derivatives of polarisation and polarisabilities of the whole system and predict these derivatives efficiently using fully-differentiable machine learning models of dielectric response tensors. We demonstrate our framework's performance by predicting the IR, Raman, and sum-frequency generation spectra of liquid water, ice and the water-air interface by achieving near quantitative agreement with experiments at nearly the same computational efficiency as pure classical methods. Finally, to aid the experimental discovery of new phases of nanoconfined water, we predict the temperature-dependent vibrational spectra of monolayer water across the solid-hexatic-liquid phases transition.

Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules.

We present a transferable MACE interatomic potential that is applicable to open- and closed-shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an accurate description of radical species extends the scope of possible applications to bond dissociation energy (BDE) prediction, for example, in the context of cytochrome P450 (CYP) metabolism. The transferability of the MACE potential was validated on the COMP6 data set, containing only closed-shell molecules, where it reaches better accuracy than the readily available general ANI-2x potential. MACE achieves similar accuracy on two CYP metabolism-specific data sets, which include open- and closed-shell structures. This model enables us to calculate the aliphatic C-H BDE, which allows us to compare reaction energies of hydrogen abstraction, which is the rate-limiting step of the aliphatic hydroxylation reaction catalyzed by CYPs. On the "CYP 3A4" data set, MACE achieves a BDE RMSE of 1.37 kcal/mol and better prediction of BDE ranks than alternatives: the semiempirical AM1 and GFN2-xTB methods and the ALFABET model that directly predicts bond dissociation enthalpies. Finally, we highlight the smoothness of the MACE potential over paths of sp3C-H bond elongation and show that a minimal extension is enough for the MACE model to start finding reasonable minimum energy paths of methoxy radical-mediated hydrogen abstraction. Altogether, this work lays the ground for further extensions of scope in terms of chemical elements, (CYP-mediated) reaction classes and modeling the full reaction paths, not only BDEs.

wfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows.

Predictive atomistic simulations are increasingly employed for data intensive high throughput studies that take advantage of constantly growing computational resources. To handle the sheer number of individual calculations that are needed in such studies, workflow management packages for atomistic simulations have been developed for a rapidly growing user base. These packages are predominantly designed to handle computationally heavy ab initio calculations, usually with a focus on data provenance and reproducibility. However, in related simulation communities, e.g., the developers of machine learning interatomic potentials (MLIPs), the computational requirements are somewhat different: the types, sizes, and numbers of computational tasks are more diverse and, therefore, require additional ways of parallelization and local or remote execution for optimal efficiency. In this work, we present the atomistic simulation and MLIP fitting workflow management package wfl and Python remote execution package ExPyRe to meet these requirements. With wfl and ExPyRe, versatile atomic simulation environment based workflows that perform diverse procedures can be written. This capability is based on a low-level developer-oriented framework, which can be utilized to construct high level functionality for user-friendly programs. Such high level capabilities to automate machine learning interatomic potential fitting procedures are already incorporated in wfl, which we use to showcase its capabilities in this work. We believe that wfl fills an important niche in several growing simulation communities and will aid the development of efficient custom computational tasks.

Gaussian approximation potentials: Theory, software implementation and application examples.

Gaussian Approximation Potentials (GAPs) are a class of Machine Learned Interatomic Potentials routinely used to model materials and molecular systems on the atomic scale. The software implementation provides the means for both fitting models using ab initio data and using the resulting potentials in atomic simulations. Details of the GAP theory, algorithms and software are presented, together with detailed usage examples to help new and existing users. We review some recent developments to the GAP framework, including Message Passing Interface parallelisation of the fitting code enabling its use on thousands of central processing unit cores and compression of descriptors to eliminate the poor scaling with the number of different chemical elements.

Structural Dynamics Descriptors for Metal Halide Perovskites.

Metal halide perovskites have shown extraordinary performance in solar energy conversion technologies. They have been classified as "soft semiconductors" due to their flexible corner-sharing octahedral networks and polymorphous nature. Understanding the local and average structures continues to be challenging for both modeling and experiments. Here, we report the quantitative analysis of structural dynamics in time and space from molecular dynamics simulations of perovskite crystals. The compact descriptors provided cover a wide variety of structural properties, including octahedral tilting and distortion, local lattice parameters, molecular orientations, as well as their spatial correlation. To validate our methods, we have trained a machine learning force field (MLFF) for methylammonium lead bromide (CH3NH3PbBr3) using an on-the-fly training approach with Gaussian process regression. The known stable phases are reproduced, and we find an additional symmetry-breaking effect in the cubic and tetragonal phases close to the phase-transition temperature. To test the implementation for large trajectories, we also apply it to 69,120 atom simulations for CsPbI3 based on an MLFF developed using the atomic cluster expansion formalism. The structural dynamics descriptors and Python toolkit are general to perovskites and readily transferable to more complex compositions.

ACEpotentials.jl: A Julia implementation of the atomic cluster expansion.

We introduce ACEpotentials.jl, a Julia-language software package that constructs interatomic potentials from quantum mechanical reference data using the Atomic Cluster Expansion [R. Drautz, Phys. Rev. B 99, 014104 (2019)]. As the latter provides a complete description of atomic environments, including invariance to overall translation and rotation as well as permutation of like atoms, the resulting potentials are systematically improvable and data efficient. Furthermore, the descriptor's expressiveness enables use of a linear model, facilitating rapid evaluation and straightforward application of Bayesian techniques for active learning. We summarize the capabilities of ACEpotentials.jl and demonstrate its strengths (simplicity, interpretability, robustness, performance) on a selection of prototypical atomistic modelling workflows.

Global analysis of energy landscapes for materials modeling: A test case for C60.

In this contribution, we employ computational tools from the energy landscape approach to test Gaussian Approximation Potentials (GAPs) for C60. In particular, we apply basin-hopping global optimization and explore the landscape starting from the low-lying minima using discrete path sampling. We exploit existing databases of minima and transition states harvested from previous work using tight-binding potentials. We explore the energy landscape for the full range of structures and pathways spanning from the buckminsterfullerene global minimum up to buckybowls. In the initial GAP model, the fullerene part of the landscape is reproduced quite well. However, there are extensive families of C1@C59 and C2@C58 structures that lie lower in energy. We succeeded in refining the potential to remove these artifacts by simply including two minima from the C2@C58 families found by global landscape exploration. We suggest that the energy landscape approach could be used systematically to test and improve machine learning interatomic potentials.

A Reappraisal of the Utility of L-012 to Measure Superoxide from Biologically Relevant Sources.

The detection of superoxide anion (O2●-) in biological tissues remains challenging. Barriers to convenient and reproducible measurements include expensive equipment, custom probes, and the need for high sensitivity and specificity. The luminol derivative, L-012, has been used to measure O2●- since 1993 with mixed results and concerns over specificity. The goal of this study was to better define the conditions for use and their specificity. We found that L-012 coupled with depolymerized orthovanadate, a relatively impermeable tyrosine phosphatase inhibitor, yielded a highly sensitive approach to detect extracellular O2●-. In O2●- producing HEK-NOX5 cells, orthovanadate increased L-012 luminescence 100-fold. The combination of L-012 and orthovanadate was highly sensitive, stable, scalable, completely reversed by superoxide dismutase, and selective for O2●- generating NOXes versus NOX4, which produces H2O2. Moreover, there was no signal from cells transfected with NOS3 (NO●) and NOS2(ONOO-). To exclude the effects of altered tyrosine phosphorylation, O2●- was detected using non-enzymatic synthesis with phenazine methosulfate and via novel coupling of L-012 with niobium oxalate, which was less active in inducing tyrosine phosphorylation. Overall, our data shows that L-012 coupled with orthovanadate or other periodic group 5 salts yields a reliable, sensitive, and specific approach to measuring extracellular O2●- in biological systems.

Extravasation of Blood and Blood Toxicity Drives Tubular Injury from RBC Trapping in Ischemic AKI.

Red blood cell (RBC) trapping is common in ischemic acute kidney injury (AKI) and presents as densely packed RBCs that accumulate within and engorge the kidney medullary circulation. In this study, we tested the hypothesis that "RBC trapping directly promotes tubular injury independent of extending ischemia time." Studies were performed on rats. Red blood cell congestion and tubular injury were compared between renal arterial clamping, venous clamping, and venous clamping of blood-free kidneys. Vessels were occluded for either 15 or 45 min with and without reperfusion. We found that RBC trapping in the medullary capillaries occurred rapidly following reperfusion from renal arterial clamping and that this was associated with extravasation of blood from congested vessels, uptake of blood proteins by the tubules, and marked tubular injury. To determine if this injury was due to blood toxicity or an extension of ischemia time, we compared renal venous and arterial clamping without reperfusion. Venous clamping resulted in RBC trapping and marked tubular injury within 45 min of ischemia. Conversely, despite the same ischemia time, RBC trapping and tubular injury were minimal following arterial clamping without reperfusion. Confirming the role of blood toward tubular injury, injury was markedly reduced in blood-free kidneys with venous clamping. Our data demonstrate that RBC trapping results in the rapid extravasation and uptake of blood components by tubular cells, causing toxic tubular injury. Tubular toxicity from extravasation of blood following RBC trapping appears to be a major component of tubular injury in ischemic AKI, which has not previously been recognized.

Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration.

Predicting the rate constants of elementary reaction steps is key for the computational modeling of catalytic processes. Within transition state theory (TST), this requires an accurate estimation of the corresponding free energy barriers. While sophisticated methods for estimating free energy differences exist, these typically require extensive (biased) molecular dynamics simulations that are computationally prohibitive with the first-principles electronic structure methods that are typically used in catalysis research. In this contribution, we show that machine-learning (ML) interatomic potentials can be trained in an automated iterative workflow to perform such free energy calculations at a much reduced computational cost as compared to a direct density functional theory (DFT) based evaluation. For the decomposition of CHO on Rh(111), we find that thermal effects are substantial and lead to a decrease in the free energy barrier, which can be vanishingly small, depending on the DFT functional used. This is in stark contrast to previously reported estimates based on a harmonic TST approximation, which predicted an increase in the barrier at elevated temperatures. Since CHO is the reactant of the putative rate limiting reaction step in syngas conversion on Rh(111) and essential for the selectivity toward oxygenates containing multiple carbon atoms (C2+ oxygenates), our results call into question the reported mechanism established by microkinetic models.

Direct endothelial ENaC activation mitigates vasculopathy induced by SARS-CoV2 spike protein.

Introduction: Although both COVID-19 and non-COVID-19 ARDS can be accompanied by significantly increased levels of circulating cytokines, the former significantly differs from the latter by its higher vasculopathy, characterized by increased oxidative stress and coagulopathy in lung capillaries. This points towards the existence of SARS-CoV2-specific factors and mechanisms that can sensitize the endothelium towards becoming dysfunctional. Although the virus is rarely detected within endothelial cells or in the circulation, the S1 subunit of its spike protein, which contains the receptor binding domain (RBD) for human ACE2 (hACE2), can be detected in plasma from COVID-19 patients and its levels correlate with disease severity. It remains obscure how the SARS-CoV2 RBD exerts its deleterious actions in lung endothelium and whether there are mechanisms to mitigate this. Methods: In this study, we use a combination of in vitro studies in RBD-treated human lung microvascular endothelial cells (HL-MVEC), including electrophysiology, barrier function, oxidative stress and human ACE2 (hACE2) surface protein expression measurements with in vivo studies in transgenic mice globally expressing human ACE2 and injected with RBD. Results: We show that SARS-CoV2 RBD impairs endothelial ENaC activity, reduces surface hACE2 expression and increases reactive oxygen species (ROS) and tissue factor (TF) generation in monolayers of HL-MVEC, as such promoting barrier dysfunction and coagulopathy. The TNF-derived TIP peptide (a.k.a. solnatide, AP301) -which directly activates ENaC upon binding to its a subunit- can override RBD-induced impairment of ENaC function and hACE2 expression, mitigates ROS and TF generation and restores barrier function in HL-MVEC monolayers. In correlation with the increased mortality observed in COVID-19 patients co-infected with S. pneumoniae, compared to subjects solely infected with SARS-CoV2, we observe that prior intraperitoneal RBD treatment in transgenic mice globally expressing hACE2 significantly increases fibrin deposition and capillary leak upon intratracheal instillation of S. pneumoniae and that this is mitigated by TIP peptide treatment.

Hepatocyte-specific disruption of soluble epoxide hydrolase attenuates abdominal aortic aneurysm formation: novel role of the liver in aneurysm pathogenesis.

Introduction: Inflammation is a key pathogenic feature of abdominal aortic aneurysm (AAA). Soluble epoxide hydrolase (sEH) is a pro-inflammatory enzyme that converts cytochrome P450-derived epoxides of fatty acids to the corresponding diols, and pharmacological inhibition of sEH prevented AAA formation. Both cytochrome P450 enzymes and sEH are highly expressed in the liver. Here, we investigated the role of hepatic sEH in AAA using a selective pharmacological inhibitor of sEH and hepatocyte-specific Ephx2 (which encodes sEH gene) knockout (KO) mice in two models of AAA [angiotensin II (AngII) infusion and calcium chloride (CaCl 2 ) application]. Methods and results: sEH expression and activity were strikingly higher in mouse liver compared with aorta and further increased the context of AAA, in conjunction with elevated expression of the transcription factor Sp1 and the epigenetic regulator Jarid1b, which have been reported to positively regulate sEH expression. Pharmacological sEH inhibition, or liver-specific sEH disruption, achieved by crossing sEH floxed mice with albumin-cre mice, prevented AAA formation in both models, concomitant with reduced expression of hepatic sEH as well as complement factor 3 (C3) and serum amyloid A (SAA), liver-derived factors linked to AAA formation. Moreover, sEH antagonism markedly reduced C3 and SAA protein accumulation in the aortic wall. Co-incubation of liver ex vivo with aneurysm-prone aorta resulted in induction of sEH in the liver, concomitant with upregulation of Sp1, Jarid1b, C3 and SAA gene expression, suggesting that the aneurysm-prone aorta secretes factors that activate sEH and downstream inflammatory signaling in the liver. Using an unbiased proteomic approach, we identified a number of dysregulated proteins [ e.g., plastin-2, galectin-3 (gal-3), cathepsin S] released by aneurysm-prone aorta as potential candidate mediators of hepatic sEH induction. Conclusion: We provide the first direct evidence of the liver's role in orchestrating AAA via the enzyme sEH. These findings not only provide novel insight into AAA pathogenesis, but they have potentially important implications with regard to developing effective medical therapies for AAA.

Machine-learned acceleration for molecular dynamics in CASTEP.

Machine learning (ML) methods are of rapidly growing interest for materials modeling, and yet, the use of ML interatomic potentials for new systems is often more demanding than that of established density-functional theory (DFT) packages. Here, we describe computational methodology to combine the CASTEP first-principles simulation software with the on-the-fly fitting and evaluation of ML interatomic potential models. Our approach is based on regular checking against DFT reference data, which provides a direct measure of the accuracy of the evolving ML model. We discuss the general framework and the specific solutions implemented, and we present an example application to high-temperature molecular-dynamics simulations of carbon nanostructures. The code is freely available for academic research.

Evaluation of the MACE force field architecture: From medicinal chemistry to materials science.

The MACE architecture represents the state of the art in the field of machine learning force fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we further evaluate MACE by fitting models for published benchmark datasets. We show that MACE generally outperforms alternatives for a wide range of systems, from amorphous carbon, universal materials modeling, and general small molecule organic chemistry to large molecules and liquid water. We demonstrate the capabilities of the model on tasks ranging from constrained geometry optimization to molecular dynamics simulations and find excellent performance across all tested domains. We show that MACE is very data efficient and can reproduce experimental molecular vibrational spectra when trained on as few as 50 randomly selected reference configurations. We further demonstrate that the strictly local atom-centered model is sufficient for such tasks even in the case of large molecules and weakly interacting molecular assemblies.

Tensor-Reduced Atomic Density Representations.

Density-based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modeling, and the visualization and analysis of material datasets. The standard mechanism used to incorporate chemical element information is to create separate densities for each element and form tensor products between them. This leads to a steep scaling in the size of the representation as the number of elements increases. Graph neural networks, which do not explicitly use density representations, escape this scaling by mapping the chemical element information into a fixed dimensional space in a learnable way. By exploiting symmetry, we recast this approach as tensor factorization of the standard neighbour-density-based descriptors and, using a new notation, identify connections to existing compression algorithms. In doing so, we form compact tensor-reduced representation of the local atomic environment whose size does not depend on the number of chemical elements, is systematically convergable, and therefore remains applicable to a wide range of data analysis and regression tasks.

NADPH oxidase 1 in chronic pancreatitis-activated pancreatic stellate cells facilitates the progression of pancreatic cancer.

Patients suffering from chronic pancreatitis (CP) have a higher risk of pancreatic ductal adenocarcinoma (PDAC) compared to the general population. For instance, the presence of an activated pancreatic stellate cell (PaSC)-rich stroma in CP has facilitated the progression of non-invasive pancreatic intraepithelial neoplasia (PanIN) lesions to invasive PDAC. We have previously found that in a mouse model of CP, NADPH oxidase 1 (Nox1) in activated PaSCs forms fibrotic tissue and up-regulates both matrix metalloproteinase (MMP) 9 and the transcription factor Twist1. Yet, the role and mechanism of Nox1 in activated PaSCs from mice with CP (CP-activated PaSCs) in the progression of PDAC is unknown. For that, we tested the ability of Nox1 in CP-activated PaSCs to facilitate the growth of pancreatic cancer cells, and the mechanisms involved in these effects by identifying proteins in the secretome of CP-activated PaSCs whose production were Nox1-dependent. We found that, in vitro, Nox1 evoked a pro-invasive and cancer-promoting phenotype in CP-activated PaSCs via Twist1/MMP-9 expression, causing changes in the extracellular matrix composition. In vivo, Nox1 in CP-activated PaSCs facilitated tumor growth and stromal expansion. Using mass spectrometry, we identified proteins protecting from endoplasmic reticulum, oxidative and metabolic stresses in the secretome of CP-activated PaSCs whose production was Nox1-dependent, including peroxiredoxins (Prdx1 and Prdx4), and thioredoxin reductase 1. In conclusion, inhibiting the Nox1 signaling in activated PaSCs from patients with CP at early stages can reduce the reorganization of extracellular matrix, and the protection of neoplastic cells from cellular stresses, ameliorating the progression of PDAC.

Unraveling Thermal Transport Correlated with Atomistic Structures in Amorphous Gallium Oxide via Machine Learning Combined with Experiments.

Thermal transport properties of amorphous materials are crucial for their emerging applications in energy and electronic devices. However, understanding and controlling thermal transport in disordered materials remains an outstanding challenge, owing to the intrinsic limitations of computational techniques and the lack of physically intuitive descriptors for complex atomistic structures. Here, it is shown how combining machine-learning-based models and experimental observations can help to accurately describe realistic structures, thermal transport properties, and structure-property maps for disordered materials, which is illustrated by a practical application on gallium oxide. First, the experimental evidence is reported to demonstrate that machine-learning interatomic potentials, generated in a self-guided fashion with minimum quantum-mechanical computations, enable the accurate modeling of amorphous gallium oxide and its thermal transport properties. The atomistic simulations then reveal the microscopic changes in the short-range and medium-range order with density and elucidate how these changes can reduce localization modes and enhance coherences' contribution to heat transport. Finally, a physics-inspired structural descriptor for disordered phases is proposed, with which the underlying relationship between structures and thermal conductivities is predicted in a linear form. This work may shed light on the future accelerated exploration of thermal transport properties and mechanisms in disordered functional materials.