Getting the signal across:
A crucial part of cellular physiology is the ability to transmit a variety of stimuli from outside the cell into the cell, triggering the right cellular response to the right stimuli. G-protein-coupled receptors (GPCRs) are a superfamily of proteins evolved precisely for this. Embedded on the cellular membrane, they sense the outside world and couple to G proteins on the inside of the cell. Combining molecular simulation with state-of-the-art biophysical and biochemical experiments we can know, with atomic precision, how this signal gets passed along, and the “routes” that it goes through, opening the possibility for better and newer drug development.

In this study flows over and through modelled aquatic plant canopies are investigated to better understand the interaction between the outer flow and the interior of the canopy. This is relevant for the resistance exerted by the canopy and the exchange of oxygen, pollutants, etc. between flow and canopy. Here, very detailed numerical simulations are conducted to resolve the canopy with all individual blades with an unprecedented detail. The configurations studied are densely arranged, highly flexible ribbons, which overall represent a situation very close to real seagrass meadows, much closer than in other studies. Unexpected, for example, is the observation that the blades move quite far up-wards and even further in horizontal direction.

Catalysis at liquid interfaces (CLINT) provides a fascinating new research area with great potential to develop more efficient and sustainable catalytic processes. Since such kind of catalysis, especially those with supported catalytically active liquid metal solutions (SCALMS) and surface catalysis with ionic liquid layers (SCILL), is still quite new, much more understanding needs to be gained on the underlying microscopic steps, leading to the know-how required for a knowledge-based development of highly active catalysts for specific reactions. Periodic density-functional theory (DFT) simulations can shed light on the processes taking place at the catalyst at an atomistic level. Recently, a new approach to generate machine-learning force

Lipid nanoparticles (LNPs) are crucial for RNA delivery in gene therapies and vaccines. Our research investigates how LNPs respond to pH changes, with a focus on their structural transitions. By combining molecular dynamics simulations and X-ray scattering experiments, we gain detailed insights into the structural phase transitions between inverse lipid mesophases and explore how structural adaptability influences fusion and release efficiency. This integrated approach advances our molecular-level understanding of LNP dynamics, paving the way for designing more effective gene delivery systems.