Toward Multiscale Modeling of Proteins and Bioagglomerates: An Orientation-Sensitive Diffusion Model for the Integration of Molecular Dynamics and the Discrete Element Method

Inhibition of the Janus kinase protein (JAK1) by the A. Pyrethrum Root Extract for the treatment of Vitiligo pathology. Design, Molecular Docking, ADME-Tox, MD Simulation, and in-silico investigation

This study delves into the therapeutic efficacy of A. pyrethrum in addressing vitiligo, a chronic inflammatory disorder known for inducing psychological distress and elevating susceptibility to autoimmune diseases. Notably, JAK inhibitors have emerged as promising candidates for treating immune dermatoses, including vitiligo. Our investigation primarily focuses on the anti-vitiligo potential of A. pyrethrum root extract, specifically targeting N-alkyl-amides, utilizing computational methodologies. Density Functional Theory (DFT) is deployed to meticulously scrutinize molecular properties, while comprehensive evaluations of ADME-Tox properties for each molecule contribute to a nuanced understanding of their therapeutic viability, showcasing remarkable drug-like characteristics. Molecular docking analysis probes ligand interactions with pivotal site JAK1, with all compounds demonstrating significant interactions; notably, molecule 6 exhibits the most interactions with crucial inhibition residues. Molecular dynamics simulations over 500ns further validate the importance and sustainability of these interactions observed in molecular docking, favoring energetically both molecules 6 and 1; however, in terms of stability, the complex with molecule 6 outperforms others. DFT analyses elucidate the distribution of electron-rich oxygen atoms and electron-poor regions within heteroatoms-linked hydrogens. Remarkably, N-alkyl-amides extracted from A. pyrethrum roots exhibit similar compositions, yielding comparable DFT and Electrostatic Potential (ESP) results with subtle distinctions. These findings underscore the considerable potential of A. pyrethrum root extracts as a natural remedy for vitiligo.

Hierarchical Coarse-Grained Strategy for Macromolecular Self-Assembly: Application to Hepatitis B Virus-Like Particles

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.

Use of Multiscale Data-Driven Surrogate Models for Flowsheet Simulation of an Industrial Zeolite Production Process

The production of catalysts such as zeolites is a complex multiscale and multi-step process. Various material properties, such as particle size or moisture content, as well as operating parameters—e.g., temperature or amount and composition of input material flows—significantly affect the outcome of each process step, and hence determine the properties of the final product. Therefore, the design and optimization of such processes is a complex task, which can be greatly facilitated with the help of numerical simulations. This contribution presents a modeling framework for the dynamic flowsheet simulation of a zeolite production sequence consisting of four stages: precipitation in a batch reactor; concentration and washing in a block of centrifuges; formation of droplets and drying in a spray dryer; and burning organic residues in a chain of rotary kilns. Various techniques and methods were used to develop the applied models. For the synthesis in the reactor, a multistage strategy was used, comprising discrete element method simulations, data-driven surrogate modeling, and population balance modeling. The concentration and washing stage consisted of several multicompartment decanter centrifuges alternating with water mixers. The drying is described by a co–current spray dryer model developed by applying a two-dimensional population balance approach. For the rotary kilns, a multi-compartment model was used, which describes the gas–solid reaction in the counter–current solids and gas flows.

DEM-Based Approach for the Modeling of Gelation and Its Application to Alginate

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.