Heinrich, StefanStefanHeinrich1328470940000-0002-7901-1698Depta, Philipp NicolasPhilipp NicolasDepta2024-09-022024-09-022024Cuvillier Verlag (ISSN 2943-8500, Bd. 25, 2024)978-3-7369-7972-7978-3-7369-6972-8https://hdl.handle.net/11420/48892Macromolecular structural formation and hierarchical self-assembly is crucial for a variety of systems in both nature and technology. Such systems may retain a remarkable structural organization from the atomistic up to the macroscopic scale enabling crucial features for their function. Many of these systems achieve this through self-assembly and consequently do not rely on external assembly mechanisms. In the field of material science one example is hydrogels, which achieve significant mechanical strength through polymer network formation on the molecular level. In the field of biology examples are abundant including most enzymes and viruses. One example is the hepatitis B virus, which contains a structural protein that assembles into regular spherical structures to transport the genetic material of the virus. Another example is the pyruvate dehydrogenase complex, which is crucial for cellular respiration and achieves its high biocatalytic activity through structural formation, thereby enabling features such as metabolic channeling. While there is an abundant amount of examples, investigation is challenging both experimentally and numerically. The phenomena involved in such structural assembly spread over vast scales in length and time and contain not only regular structures, but often also disordered elements. Consequently, capturing the mechanisms of formation, especially their kinetics, is inherently difficult. In order to improve understanding of these phenomena, this work proposes a physics-based and data-driven multiscale modeling framework capable of describing structural formation on the micro-meter and milli-second scale, while retaining large amounts of molecular detail. The framework achieves this by abstracting the elementary macromolecules of a system as anisotropic unit objects and describes the interaction between units as well as the environment through data-driven models, e.g. 6D interaction potential fields. The models are parameterized in a bottom-up fashion and validated top-down. The framework is applied to and validated on three model systems: the gelation of alginate in CaCl2 solution, the self-assembly of the hepatitis B core antigen into virus-like particles, and the assembly and agglomeration of the pyruvate dehydrogenase complex. Results are validated using literature data and experimental data provided by collaborators, which show good agreement with measurable characteristics. Consequently, the developed framework enables novel scales to be investigated using numerical simulations and proposes a streamlined bottom-up parameterization, thus paving the way towards physically-mechanistic modeling of such structural assembly processes.enhttps://creativecommons.org/licenses/by/4.0/multiscale modelingmolecular modelingMolecular Discrete Element MethodMDEMDiscrete Element MethodDEMcoarse-grainingMolecular DynamicsMDLangevin dynamicsmachine learningMLsupervised learningKrigingmacromolecular self-assemblystructural formation simulationanisotropic macromoleculesassembly pathwaysassembly kineticsmolecular collisions6D intermolecular interaction potentialsspecialized force-fieldsmolecular bindingbonded interactionhepatitis B core antigenHBcAgcapsid formationvirus-like particlesVLPpyruvate dehydrogenase complexPDCalginatealginic acidbiopolymergelationgelaerogelporous nanomaterialanisotropic diffusionion binding modelcalciumproteinsenzymesmulti-enzymatic biocatalysismetabolic channelinghigh performance computingHLRSGPU implementationMUSENNatural Sciences and Mathematics::540: ChemistryNatural Sciences and Mathematics::570: Life Sciences, BiologyTechnology::620: Engineering::620.1: Engineering Mechanics and Materials ScienceDoctoral Thesis10.15480/882.13251https://cuvillier.de/de/shop/publications/9000-physics-based-and-data-driven-mulitiscale-modeling-of-the-structural-formation-in-macromolecular-systems10.15480/882.13251Gurikov, PavelPavelGurikovSchilde, CarstenCarstenSchildeOther