Engineering von Proteinen an Oberflächen: Von komplementärer Charakterisierung zu Materialoberflächen mit maßgeschneiderten Funktionen

Engineering Proteins at Interfaces: From Complementary Characterization to Material Surfaces with Designed Functions

Abstract Once materials come into contact with a biological fluid containing proteins, proteins are generally—whether desired or not—attracted by the material's surface and adsorb onto it. The aim of this Review is to give an overview of the most commonly used characterization methods employed to gain a better understanding of the adsorption processes on either planar or curved surfaces. We continue to illustrate the benefit of combining different methods to different surface geometries of the material. The thus obtained insight ideally paves the way for engineering functional materials that interact with proteins in a predetermined manner.

Nitrated Fatty Acids Modulate the Physical Properties of Model Membranes and the Structure of Transmembrane Proteins

Nitrated fatty acids (NO2 -FAs) act as anti-inflammatory signal mediators, albeit the molecular mechanisms behind NO2 -FAs' influence on diverse metabolic and signaling pathways in inflamed tissues are essentially elusive. Here, we combine fluorescence measurements with surface-specific sum frequency generation vibrational spectroscopy and coarse-grained computer simulations to demonstrate that NO2 -FAs alter lipid organization by accumulation at the membrane-water interface. As the function of membrane proteins strongly depends on both, protein structure as well as membrane properties, we consecutively follow the structural dynamics of an integral membrane protein in presence of NO2 -FAs. …

Adversarial reverse mapping of condensed-phase molecular structures: Chemical transferability

Switching between different levels of resolution is essential for multiscale modeling, but restoring details at higher resolution remains challenging. In our previous study we have introduced deepBackmap: a deep neural-network-based approach to reverse-map equilibrated molecular structures for condensed-phase systems. Our method combines data-driven and physics-based aspects, leading to high-quality reconstructed structures. In this work, we expand the scope of our model and examine its chemical transferability. To this end, we train deepBackmap solely on homogeneous molecular liquids of small molecules, and apply it to a more challenging polymer melt. We augment the generator's objective w…

Adversarial reverse mapping of equilibrated condensed-phase molecular structures

A tight and consistent link between resolutions is crucial to further expand the impact of multiscale modeling for complex materials. We herein tackle the generation of condensed molecular structures as a refinement -- backmapping -- of a coarse-grained structure. Traditional schemes start from a rough coarse-to-fine mapping and perform further energy minimization and molecular dynamics simulations to equilibrate the system. In this study we introduce DeepBackmap: A deep neural network based approach to directly predict equilibrated molecular structures for condensed-phase systems. We use generative adversarial networks to learn the Boltzmann distribution from training data and realize reve…