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Item type:Publication, Access status: without files , Multiscale model-based investigation of functional macromolecular agglomerates for biotechnological applicationsThe formation of macromolecular agglomerates and ordered assemblies has diverse applications in both nature and technology. Especially biotechnology often relies heavily on (semi-)ordered macromolecular assemblies to enable specific functions. Examples are enzymatic biocatalysis enhanced through metabolic channeling as a result of structural organization of enzymes and virus-like particles (VLPs) as self-assembled transport containers for genetic materials or pharmaceuticals. In order to better understand such systems, we developed a novel multiscale method using data-driven parameterization approaches to simulate formation and properties of large macromolecular assemblies on scales of micro-meters and milli-seconds. Thus, enabling the investigation well beyond traditional methods such as molecular dynamics (MD). The method is termed the molecular discrete element method (MDEM). As model systems the calcium mediated gelation of the biopolymer alginate, self-assembly of the hepatitis B core antigen (HBcAg) into VLPs, and semistructured agglomerate formation of the pyruvate dehydrogenase complex (PDC) were investigated at various process conditions. Experimental validation was performed using both available literature data and experimental results of collaborators showing good agreement with model predictions.Publicationtype: Book Part5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Data-driven multiscale modeling of self-assembly and hierarchical structural formation in biological macro-molecular systems: pyruvate dehydrogenase complexMacro-molecular self-assembly and hierarchical structural formation are crucial for a variety of systems in nature and technology. Especially biological systems often rely on a specific structural organization to enable their function. Examples are multi-enzyme complexes enabling catalytic activity through structure-based phenomena such as metabolic channeling or the self-assembly of virus capsids necessary for transport of the genetic material and overall infection process. This project attempts to improve understanding and modeling capabilities of such systems by developing a multiscale modeling methodology for self-assembly on the scales of micro-meters and milli-second including a data-driven parameterization approach. As model systems the hepatitis B core antigen (HBcAg) and pyruvate dehydrogenase complex (PDC) are used, which feature a macro-molecular self-assembly crucial in enabling their function.Publicationtype: Book part14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Physics-based and data-driven multiscale modeling of the structural formation in macromolecular systemsMacromolecular 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.Publicationtype: Doctoral Thesis44 35 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Data-driven multiscale modeling of self-assembly and hierarchical structural formation in biological macro-molecular systemsPublicationtype: Conference Paper39 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Hierarchical coarse-grained strategy for macromolecular self-assembly : application to hepatitis B virus-like particles(Multidisciplinary Digital Publishing Institute (MDPI), 2022-11-24); ; ; ; Macromolecular self-assembly is at the basis of many phenomena in material and life sciences that find diverse applications in technology. One example is the formation of virus-like particles (VLPs) that act as stable empty capsids used for drug delivery or vaccine fabrication. Similarly to the capsid of a virus, VLPs are protein assemblies, but their structural formation, stability, and properties are not fully understood, especially as a function of the protein modifications. In this work, we present a data-driven modeling approach for capturing macromolecular self-assembly on scales beyond traditional molecular dynamics (MD), while preserving the chemical specificity. Each macromolecule is abstracted as an anisotropic object and high-dimensional models are formulated to describe interactions between molecules and with the solvent. For this, data-driven protein–protein interaction potentials are derived using a Kriging-based strategy, built on high-throughput MD simulations. Semi-automatic supervised learning is employed in a high performance computing environment and the resulting specialized force-fields enable a significant speed-up to the micrometer and millisecond scale, while maintaining high intermolecular detail. The reported generic framework is applied for the first time to capture the formation of hepatitis B VLPs from the smallest building unit, i.e., the dimer of the core protein HBcAg. Assembly pathways and kinetics are analyzed and compared to the available experimental observations. We demonstrate that VLP self-assembly phenomena and dependencies are now possible to be simulated. The method developed can be used for the parameterization of other macromolecules, enabling a molecular understanding of processes impossible to be attained with other theoretical models.Publicationtype: Journal ArticleScopus© Citations 2 70 172 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , DEM-based approach for the modeling of gelation and its application to alginate(American Chemical Society (ACS), 2022-01-10); ; ; ; The gelation of biopolymers is of great interest in the material science community and has gained increasing relevance in the past few decades, especially in the context of aerogels lightweight open nanoporous materials. Understanding the underlying gel structure and influence of process parameters is of great importance to predict material properties such as mechanical strength. In order to improve understanding of the gelation mechanism in aqueous solution, this work presents a novel approach based on the discrete element method for the mesoscale for modeling gelation of hydrogels, similarly to an extremely coarse-grained molecular dynamics (MD) approach. For this, polymer chains are abstracted as dimer units connected by flexible bonds and interactions between units and with the environment, that is, diffusion in implicit water, are described. The model is based on Langevin dynamics and includes an implicit probabilistic ion model to capture the effects of ion availability during ion-mediated gelation. The model components are fully derived and parameterized using literature data and theoretical considerations based on a simplified representation of atomistic processes. The presented model enables investigations of the higher-scale network formation during gelation on the micrometer and millisecond scale, which are beyond classical modeling approaches such as MD. As a model system, calcium-mediated alginate gelation is investigated including the influence of ion concentration, polymer composition, polymer concentration, and molecular weight. The model is verified against numerous literature data as well as own experimental results for the corresponding Ca-alginate hydrogels using nitrogen porosimetry, NMR cryoporometry, and small-angle neutron scattering. The model reproduces both bundle size and pore size distribution in a reasonable agreement with the experiments. Overall, the modeling approach paves the way to physically motivated design of alginate gels.Publicationtype: Journal ArticleScopus© Citations 9 195 1442 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Data‐driven multiscale modeling of self‐assembly and hierarchical structural formation in biological macromolecular systemsMacromolecular systems are present inmany applications of biotechnology andprocess engineering and the physical phenomena involved therein often spreadover vast scales of size and time. To gaininsight, generally applicable models are developed to transfer the essential dynamics (including directional dependency)[1] and complex interaction of biologicalmacromolecules from MD [2] to DEM ina modeling methodology termed by usthe ‘‘molecular discrete element method’’(MDEM). The models are parameterized bottom-up and validated top-down bycomparison with experimental data, whichis obtained from BLI and DLS. As a modelsystem the multi-enzyme pyruvate dehy-drogenase complex (PDC) is used, whichfeatures organized self-structuring pro-cesses and a highly regulated multi-enzymatic machinery dependent upon thestructure.Obtained results for the PDC compo-nent E2 show that the continuous formation and breakup of enzymatic agglomerates can be predicted using the developedMDEM methodology. This approach requires no experimental data fitting andproduces accurate scale-bridging kineticsas well as agglomerate sizes matching corresponding dynamic light scattering data.Publicationtype: Conference Poster not in Proceedings102 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Toward Multiscale Modeling of Proteins and Bioagglomerates: An Orientation-Sensitive Diffusion Model for the Integration of Molecular Dynamics and the Discrete Element Method(2019-01-28); ; ; ; Most processes involved in biological and biotechnological systems spread over many scales in space and time. For example, the interaction of multiple enzymes in heterogeneous enzymatic agglomerates or clusters, necessary for efficient enzymatic conversion, is of high interest for research and enzyme engineering. In order to understand and predict their overall behavior and performance, it is important to describe these scales as completely as possible, known as multiscale modeling. While many different approaches have been presented in recent years, knowledge about protein formation and bioagglomeration at the micro scale is still very limited. In an attempt to address such systems, we propose a bottom- up multiscale modeling methodology, bridging the gaps between molecular dynamics (MD) with an explicit solvent and the larger scale discrete element method (DEM) using an implicit solvent and abstracting macromolecules (e.g., proteins) as objects with anisotropic properties. We term this approach the molecular discrete element method (MDEM). For this, we present an orientation-sensitive diffusion model for DEM, which describes the dynamics of anisotropic translational and rotational diffusion, while implicitly considering solvent molecules and enforcing a canonical ensemble. A general-purpose model and parametrization approach is presented, which can be used to simulate any process involving diffusion of discrete particles. Effects of temperature and viscosity changes can be considered, and guidance is provided concerning time step selection. This model is generally applicable and serves as a precondition to enforce the proper dynamics (i.e., diffusion characteristics and canonical ensemble, similar to a thermostat in MD) for the proposed multiscale modeling methodology with anisotropic properties. Thereby, it presents a first step toward modeling at the micro scale and is integral to enforcing dynamics of such systems and therefore extensively validated. As a next step, interaction models are to be defined and added to the presented model. In comparison to atomistic and coarse-grained (CG) MD, a speedup of 5-7 orders of magnitude can be achieved. The approach is demonstrated on multiple components of the pyruvate dehydrogenase enzyme complex, a multienzymatic machinery that involves very different types of enzymes and is of high value to further elucidate the mechanisms of bioagglomeration and metabolic channeling.Publicationtype: Journal Article124