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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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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