Cancer mutanome vaccines targeting neoepitopes derived from somatic mutations have ideal properties to become an essential part of modern multimodal cancer therapy. Our goal is to fully realize this personalized cancer immunotherapy concept by addressing the key genomic and immunological challenges for successful application of this approach in patients with any type of cancer.

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

We work on imaging for particle therapy. Particle beams (e.g. protons) can precisely destroy tumors while sparing healthy tissue. To verify the irradiation, Compton cameras (CC) can be used to detect gamma rays emerging from the patient. CC require complex algorithms to reconstruct images. We employ Monte Carlo simulations to recreate the irradiation and test reconstruction algorithms. The simulations and development of reconstruction approaches require much computing power. Thanks to HLRN, we simulate realistic therapeutic beams, the emerging rays and its detection with CC, and can test our reconstruction. Our approaches notably improve image quality. Further improvements are planed using a-prori information and refined data selection.

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.