By analyzing the spatial distribution of genetic variants in three-dimensional structures of proteins harboring them and their homologs, we can produce hypotheses about the functional consequences of these variants. For example, if a mutation caused by a single-nucleotide polymorphism lie on an interaction interface with another protein or in a ligand-binding pocket, it may affect the corresponding binding affinity, and mutations lying in the protein core can be detrimental for its stability. We develop methods that can annotate very large datasets in this way, providing insight into the relation between mutations’ annotated pathogenic or functional effect and their location in the three-dimensional structures of proteins and their complexes.
We build machine-learning methods for predicting the impact of mutations using a variety of features related to protein three-dimensional structures, interactions, and evolution. The methods can be trained to predict the impact on protein function, as well as their pathogenicity, which correlates with protein function. Additionally, we explore the possibility of training such methods to predict the impact on more specific phenotypes, such as resistance towards antibacterial compounds.
We employ a data mining technique called frequent subgraph mining to detect recurring structural patterns in three-dimensional structures of a set of distantly related proteins. These structural patterns that are significantly conserved over very long evolutionary distances represent known and novel functionally and structurally important motifs in the corresponding proteins.
We focus on residues that form pockets and cavities in protein structures and apply frequent subgraph mining to residue interaction network in proteins, chemical structures of potential binders, as well as their interactions to detect specific patterns of recognition for particular chemical moieties.
We apply classical methods of molecular dynamics simulations to investigate the impact of mutations that confer resistance in pathogens, as well as during cancer treatment, on the dynamics of the corresponding drug targets. In this way we can explain the mechanisms of resistance development even in cases when the immediate drug binding site is not visibly affected.
In this BMBF-funded project in cooperation with the Technical University Munich and University Hospital Greifswald, we apply our expertise in structural modelling and annotation to investigate novel mechanisms of pathogenesis in cardiac and renal diseases, focussing on the alterations of protein sequences caused by disease-specific alternative splicing events.
We use a combination of phylogenetic reconstruction and structural modelling in order to explore the mechanisms of resistance towards novel antibacterial compounds. We can trace the evolutionary spread of potential resistance factors and thus predict yet unobserved resistances in bacterial populations.
A current overview of the team and further information about the research group can be found on the HIPS page.