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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This report presents the highlights of the 7th European Meeting on Molecular Diagnostics held in Scheveningen, The Hague, The Netherlands, 12-14 October 2011. The areas covered included molecular diagnostics applications in medical microbiology, virology, pathology, hemato-oncology,clinical genetics and forensics. Novel real-time amplification approaches, novel diagnostic applications and new technologies, such as next-generation sequencing, PCR lectrospray-ionization TOF mass spectrometry and techniques based on the detection of proteins or other molecules, were discussed. Furthermore, diagnostic companies presented their future visions for molecular diagnostics in human healthcare.
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Summary: Xpaths is a collection of algorithms that allow for the prediction of compound-induced molecular mechanisms of action by integrating phenotypic endpoints of different species; and proposes follow-up tests for model organisms to validate these pathway predictions. The Xpaths algorithms are applied to predict developmental and reproductive toxicity (DART) and implemented into an in silico platform, called DARTpaths.
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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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Development of novel testing strategies to detect adverse human health effects is of interest to replace in vivo-based drug and chemical safety testing. The aim of the present study was to investigate whether physiologically based kinetic (PBK) modeling-facilitated conversion of in vitro toxicity data is an adequate approach to predict in vivo cardiotoxicity in humans. To enable evaluation of predictions made, methadone was selected as the model compound, being a compound for which data on both kinetics and cardiotoxicity in humans are available. A PBK model for methadone in humans was developed and evaluated against available kinetic data presenting an adequate match. Use of the developed PBK model to convert concentration–response curves for the effect of methadone on human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) in the so-called multi electrode array (MEA) assay resulted in predictions for in vivo dose–response curves for methadone-induced cardiotoxicity that matched the available in vivo data. The results also revealed differences in protein plasma binding of methadone to be a potential factor underlying variation between individuals with respect to sensitivity towards the cardiotoxic effects of methadone. The present study provides a proof-of-principle of using PBK modeling-based reverse dosimetry of in vitro data for the prediction of cardiotoxicity in humans, providing a novel testing strategy in cardiac safety studies.
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Protein kinases function as pivotal regulators in biological events, governing essential cellular processes through the transfer of phosphate groups from ATP molecules to substrates. Dysregulation of kinase activity is frequently associated with cancer, ocasionally arising from chromosomal translocation events that relocate genes encoding kinases. Fusion proteins resulting from such events, particularly those involving the proto-oncogene tyrosine-protein kinase ROS (ROS1), manifest as constitutively active kinases, emphasizing their role in oncogenesis. Notably, the chromosomal reallocation of the ros1 gene leads to fusion of proteins with the ROS1 kinase domain, implicated in various cancer types. Despite their prevalence, targeted inhibition of these fusion proteins relies on repurposed kinase inhibitors. This review comprehensively surveys experimentally determined ROS1 structures, emphasizing the pivotal role of X-ray crystallography in providing high-quality insights. We delve into the intricate interactions between ROS1 and kinase inhibitors, shedding light on the structural basis for inhibition. Additionally, we explore point mutations identified in patients, employing molecular modeling to elucidate their structural impact on the ROS1 kinase domain. By integrating structural insights with in vitro and in silico data, this review advances our understanding of ROS1 kinase in cancer, offering potential avenues for targeted therapeutic strategies.
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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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An important step towards improving performance while reducing weight and maintenance needs is the integration of composite materials into mechanical and aerospace engineering. This subject explores the many aspects of composite application, from basic material characterization to state-of-the-art advances in manufacturing and design processes. The major goal is to present the most recent developments in composite science and technology while highlighting their critical significance in the industrial sector—most notably in the wind energy, automotive, aerospace, and marine domains. The foundation of this investigation is material characterization, which offers insights into the mechanical, chemical, and physical characteristics that determine composite performance. The papers in this collection discuss the difficulties of gaining an in-depth understanding of composites, which is necessary to maximize their overall performance and design. The collection of articles within this topic addresses the challenges of achieving a profound understanding of composites, which is essential for optimizing design and overall functionality. This includes the application of complicated material modeling together with cutting-edge simulation tools that integrate multiscale methods and multiphysics, the creation of novel characterization techniques, and the integration of nanotechnology and additive manufacturing. This topic offers a detailed overview of the current state and future directions of composite research, covering experimental studies, theoretical evaluations, and numerical simulations. This subject provides a platform for interdisciplinary cooperation and creativity in everything from the processing and testing of innovative composite structures to the inspection and repair procedures. In order to support the development of more effective, durable, and sustainable materials for the mechanical and aerospace engineering industries, we seek to promote a greater understanding of composites.
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Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.
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