Neutralizing antibodies that bind to viral fusion proteins represent a promising strategy for protection from viral infections. Such antibodies can also serve as templates for the generation of peptides, which retain the ability to bind to viral proteins. In the present project, the known complexes between antibodies and the SARS CoV-2 spike are analyzed to design antibody-derived peptides that bind to the spike protein thereby blocking viral infection. For that purpose, a computational workflow is developed that uses molecular dynamics (MD) simulations to identify the most promising peptides for further experimental testing.

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.

Computational tools such as Molecular Dynamics (MD) have revolutionized the way we study biomolecules; however, they are severely limited by the computational cost of running simulations on biological time- and length-scales. Various coarse-grained (CG) models have been developed which rely on simpler representations of molecular systems than atomistic MD. While these models are difficult to configure using physical intuition, we have shown that by using state-of-the-art machine learning methods, it is possible to design accurate and efficient CG models which can correctly reproduce protein dynamics. By enhancing both our training dataset and network architecture, we hope to produce a “universal” CG model to study biological systems.

Proteins are the workhorses of cells, driving all processes essential for survival. Each protein folds into a specific shape, uniquely tailored to its sequence and function. Predicting protein structures from sequences has long been one of biology’s greatest challenges. Recent breakthroughs in computational methods, like AlphaFold, which earned the 2024 Nobel Prize in Chemistry, have revolutionized this field.

The liver produces bile, which the intestine uses for digestion. For the transport of bile, the liver relies on a network of microscopic tubings, known as bile canaliculi, formed by liver cells called hepatocytes. When the outflow of bile to the intestine is blocked, it collects in the liver and can lead to serious liver disease. Researchers at the Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG) in Dresden together with experts from the Carl Gustav Carus University Hospital (UKD) and the Department for Information Services and High Performance Computing (ZIH) at TU Dresden as well as further clinics in Germany and Oslo University Hospital in Norway found that high pressure in the bile canaliculi alters the structure of

The design of new functional proteins can tackle many of the problems humankind is facing today but so far has proven very challenging1. Analogies between protein sequences and human languages have been long noted and a summary of their most prominent similarities is described. Given the tremendous success of Natural Language Processing (NLP) methods in recent years, its application to protein research opens a fresh perspective, shifting from the current energy-function centered paradigm to an unsupervised learning approach based entirely on sequences. To explore this opportunity further we have pre-trained a generative language model on the entire protein sequence space. We find that our language model, ProtGPT2, effectively speaks the

Increasing fungal resistance to fungicides prompt the urge for developing new antifungal peptides (AFP). This computational study aims the elucidation of mode of action of two parental AFPs as well as a large set of novel chimera peptides. Specifically, we will identify interaction hot spots with the fungal membrane by performing classical all-atom molecular dynamics simulations using enhanced sampling algorithms of large fungal membranes – AFP complexes.

Strong painkillers (such as opioids) are essential to medicine. However, they are mostly addictive and have potentially deadly side effects. Simulation and machine learning techniques help in the search for tailor-made active substances that do not have these side effects. The interaction of the various algorithms raises questions about which computer architecture can support the calculations most effectively.

Lipid nanoparticles (LNPs) are very successfully employed as novel transport vehicles for mRNA vaccines. A major gap in our understanding and thus obstacle for future developments of nanoparticle-mRNA drugs, however, is the lack of a molecular picture and molecular insight into LNPs. In this project we aim to provide unique insight at the atomistic scale into the structure and mechanisms of these carriers.