Physics-based and data-driven multiscale modeling of the structural formation in macromolecular systems
Creators: name: Depta, Philipp Nicolas
Publisher: name: Cuvillier Verlag
Year: 2024
Subjects: subject: multiscale modeling
subject: molecular modeling
subject: Molecular Discrete Element Method
subject: MDEM
subject: Discrete Element Method
subject: DEM
subject: coarse-graining
subject: Molecular Dynamics
subject: MD
subject: Langevin dynamics
subject: machine learning
subject: ML
subject: supervised learning
subject: Kriging
subject: macromolecular self-assembly
subject: structural formation simulation
subject: anisotropic macromolecules
subject: assembly pathways
subject: assembly kinetics
subject: molecular collisions
subject: 6D intermolecular interaction potentials
subject: specialized force-fields
subject: molecular binding
subject: bonded interaction
subject: hepatitis B core antigen
subject: HBcAg
subject: capsid formation
subject: virus-like particles
subject: VLP
subject: pyruvate dehydrogenase complex
subject: PDC
subject: alginate
subject: alginic acid
subject: biopolymer
subject: gelation
subject: gel
subject: aerogel
subject: porous nanomaterial
subject: anisotropic diffusion
subject: ion binding model
subject: calcium
subject: proteins
subject: enzymes
subject: multi-enzymatic biocatalysis
subject: metabolic channeling
subject: high performance computing
subject: HLRS
subject: GPU implementation
subject: MUSEN
classificationCode: 5
subject: Natural Sciences and Mathematics::540: Chemistry
classificationCode: 5
subject: Natural Sciences and Mathematics::570: Life Sciences, Biology
classificationCode: 6
subject: Technology::620: Engineering::620.1: Engineering Mechanics and Materials Science

description: Macromolecular 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.