Despite tremendous efforts, the exact structure of SARS-CoV-2 and related betacoronaviruses remains elusive. SARS-CoV-2 envelope is a key structural component of the virion that encapsulates viral RNA. It is composed of three structural proteins, spike, membrane (M), and envelope, which interact with each other and with the lipids acquired from the host membranes. Here, we developed and applied an integrative multi-scale computational approach to model the envelope structure of SARS-CoV-2 with near atomistic detail, focusing on studying the dynamic nature and molecular interactions of its most abundant, but largely understudied, M protein. The molecular dynamics simulations allowed us to test the envelope stability under different configurations and revealed that the M dimers agglomerated into large, filament-like, macromolecular assemblies with distinct molecular patterns. These results are in good agreement with current experimental data, demonstrating a generic and versatile approach to model the structure of a virus de novo.
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With the alarming rise of antimicrobial resistance, studies on bacteria-surface interactions are both relevant and timely. Scanning electron microscopy and colony forming unit counting are commonly used techniques but require sophisticated sample preparation and long incubation time. Here, we present a direct method based on molecular dynamics simulation of nanostructured surfaces providing in silico predictions, complemented with time-lapse fluorescence imaging to study live interactions of bacteria at the membrane-substrate level. We evaluate its effectiveness in predicting and statistically analyzing the temporal evolution and spatial distribution of prototypical bacteria with costained nucleoids and membranes (E. coli) on surfaces with nanopillars. We observed cell reorientation, clustering, membrane damage, growth inhibition, and in the extreme case of hydrocarbon-coated nanopillars, this was followed by cell disappearance, validating the obtained simulation results. Contrary to commonly used experimental methods, microscopy data are fast processed, in less than 1 h. In particular, the bactericidal effects can be straightforwardly detected and correlated with surface morphology and/or wettability.
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BackgroundThe ROS1 G2032R mutation is the most common on-target resistance mutation in crizotinib treated ROS1-positive lung cancer patients. The aim of our study was to investigate resistance mechanisms in SCL34A2-ROS1G2032R positive Ba/F3 cells against second line treatment with lorlatinib.MethodsBa/F3 SLC34A2-ROS1G2032R cells were subjected to N-ethyl-N-nitrosourea (ENU) mutagenesis and clones were selected upon treatment with 1000 nM lorlatinib for 4 weeks. Resistant clones were analyzed for presence of on-target resistant mutations using Sanger sequencing. In addition, we generated subclones expressing SLC34A2-ROS1L2026M+G2032R and SLC34A2-ROS1L2026M in Ba/F3 cells. Sensitivity to ROS1 TKIs was determined by measuring cell viability and ROS1 phosphorylation. Molecular Dynamic simulations of the ATP binding pocket were performed for all ROS1 variants.ResultsThe ENU-screen of 41 lorlatinib resistant clones revealed one with a mutation in the kinase domain: L2026M. Cell viability assays of the ENU-induced resistant cell line and the Ba/F3 cells transfected with the mutant SCL34A2-ROS1 fusion gene constructs revealed a decreased sensitivity of SLC34A2-ROS1L2026M+G2032R cells for lorlatinib, crizotinib, entrectinib and repotrectinib compared to the single mutants. Consistent with these findings, we observed phosphorylation of ROS1 fusion protein in the double mutant cells which was not inhibited upon treatment with ROS1 TKIs. The single mutant cells showed as expected a clear reduction in phosphorylated ROS1 fusion protein . Molecular modeling to unravel the effect of the mutations demonstrated that the volume of the ATP-binding pocket was reduced in single and double mutants compared to wild type. The double L2026M+G2032R mutant displayed the smallest pocket.ConclusionsWe identified a novel on-target mutation after inducing lorlatinib resistance in SLC34A2-ROS1G2032R Ba/F3 cells. This SLC34A2-ROS1L2026M+G2032R cell line was also resistant to crizotinib, entrectinib and repotrectinib. The resistance can be explained by a smaller ATP binding pocket in the mutated ROS1 fusion protein preventing effective binding of the investigated TKIs.
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Understanding taste is key for optimizing the palatability of seaweeds and other non-animal-based foods rich in protein. The lingual papillae in the mouth hold taste buds with taste receptors for the five gustatory taste qualities. Each taste bud contains three distinct cell types, of which Type II cells carry various G protein-coupled receptors that can detect sweet, bitter, or umami tastants, while type III cells detect sour, and likely salty stimuli. Upon ligand binding, receptor-linked intracellular heterotrimeric G proteins initiate a cascade of downstream events which activate the afferent nerve fibers for taste perception in the brain. The taste of amino acids depends on the hydrophobicity, size, charge, isoelectric point, chirality of the alpha carbon, and the functional groups on their side chains. The principal umami ingredient monosodium l-glutamate, broadly known as MSG, loses umami taste upon acetylation, esterification, or methylation, but is able to form flat configurations that bind well to the umami taste receptor. Ribonucleotides such as guanosine monophosphate and inosine monophosphate strongly enhance umami taste when l-glutamate is present. Ribonucleotides bind to the outer section of the venus flytrap domain of the receptor dimer and stabilize the closed conformation. Concentrations of glutamate, aspartate, arginate, and other compounds in food products may enhance saltiness and overall flavor. Umami ingredients may help to reduce the consumption of salts and fats in the general population and increase food consumption in the elderly.
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Many origin of life theories argue that molecular self-organization explains the spontaneous emergence of structural and dynamical constraints. However, the preservation of these constraints over time is not well-explained because ofthe self-undermining and self-limiting nature of these same processes. A process called autogenesis has been proposed in which a synergetic coupling between self-organized processes preserves the constraints thereby accumulated. Thispaper presents a computer simulation of this process (the AutogenicAutomaton) and compares its behavior to the same self-organizing processes when uncoupled. We demonstrate that this coupling produces a second-order constraint that can both resist dissipation and become replicated in new substrates over time.
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Fouling plays a major role in the Dairy industry. Five criteria: defined flow, no circulation, real factory product, defined product temperature and defined wall temperature, are used to review articles on this topic published between 2003 and 2020. To show the effect of those criteria in experiments, a simulation model is developed. For a good experimental design to measure fouling, the use of a dairy product in a tubular heater with a known developed flow is advised. The temperature-time history of the product and the wall temperature of the heater should be recorded. Circulation of a product will increase the fouling and decrease the flow. Although none of the reviewed articles complied to all criteria, 71% of the reviewed articles met at least two criteria. If not all criteria are met, the results are of less use for the application for process lines on industrial scale. A simulated computer model can be helpful.
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Genomics has multiplied the number of targets for new therapeutic interventions, but this has not yet lead to a marked increase of pharma pipeline outputs. The complexity of protein function in higher order biological systems is often underestimated. Translation from in vitro and in vivo results to the human setting frequently fails due to unforeseen toxicity and efficacy issues. Biosimulation addresses these issues by capturing the complex dynamics of interacting molecules and cells in mechanistic, predictive models. A central concept is that of the virtual patient, an encapsulation of a specific pathophysiological behaviour in a biosimulation model. The authors describe how virtual patients are being used in target identification, target validation and clinical development, and discuss challenges for the acceptance of biosimulation methods.
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The evolution of emerging technologies that use Radio Frequency Electromagnetic Field (RF-EMF) has increased the interest of the scientific community and society regarding the possible adverse effects on human health and the environment. This article provides NextGEM’s vision to assure safety for EU citizens when employing existing and future EMF-based telecommunication technologies. This is accomplished by generating relevant knowledge that ascertains appropriate prevention and control/actuation actions regarding RF-EMF exposure in residential, public, and occupational settings. Fulfilling this vision, NextGEM commits to the need for a healthy living and working environment under safe RF-EMF exposure conditions that can be trusted by people and be in line with the regulations and laws developed by public authorities. NextGEM provides a framework for generating health-relevant scientific knowledge and data on new scenarios of exposure to RF-EMF in multiple frequency bands and developing and validating tools for evidence-based risk assessment. Finally, NextGEM’s Innovation and Knowledge Hub (NIKH) will offer a standardized way for European regulatory authorities and the scientific community to store and assess project outcomes and provide access to findable, accessible, interoperable, and reusable (FAIR) data.
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