Professorship of Bioinformatics
Protein-protein interactions play a crucial role for the transduction of information in biological systems. The identification of the underlying principles of molecular recognition is important for the understanding of regulatory mechanisms as well as for the prediction of novel, physiologically relevant protein interactions. The bioinformatics group is primarily interested in investigating molecular interactions by a variety of computational tools (e.g. sequence data analysis, molecular modeling, and molecular dynamics).
Molecular dynamics simulations are used to study the dynamics of viral proteins, the conformational transitions of human proteins (e.g. Alzheimer Aβ-Amyloid), or the effect of covalent modifications on molecular recognition processes. Molecular modeling is used to generate the structure of isolated proteins or biomolecular complexes and provides the basis for a molecular understanding of mutational effects on protein stability and binding properties. In addition, sequence based methods are developed that allow an improved detection of functional linear interaction motifs. Such motifs play an important role for the interactions of numerous pathogens with the target molecules of their host.
Specific interactions with host proteins are pivotal for a successful infection by a pathogen. This project focuses on the prediction and structural characterization of host-pathogen protein interactions using computational tools. The recognition processes either occur between short sequence motifs that bind complementary adapter modules or between pairs of globular protein domains. These types of interactions do not only differ from a structural point of view, but also with respect to the computational tools required for their prediction and analysis.
One particular challenge for the prediction of functional interaction motifs is the short length of the respective sequence patterns resulting in a large number of false-positive hits which prove to be non-functional in subsequent experiments. Therefore, we aim at improving the specificity of the predictions by assessing the importance of motif-specific flanking sequence regions. In order to further increase the reliability of the predictions, modeling of sequence motifs in complex with the respective adapter domains is performed, thus allowing to judge the likelihood of an interaction based on a three-dimensional structure.
For the analysis of host-pathogen interactions formed between globular protein domains, a combination of molecular modeling, docking, and molecular dynamics simulations is used. The latter technique provides information about the conformational stability and energetics of an interaction that can hardly be deduced from static structures alone. These methods are for example applied to study the structure of herpesviral glycoproteins that are pivotal for binding to the host cell and following fusion with the cell membrane. Furthermore, we investigate the molecular dynamics of viral regulator proteins and their interaction with cellular targets.
Protein conformational diseases are unique since they result from a drastic change in protein three-dimensional structure. Most often, the change in conformation involves a structural conversion from primarily α-helical conformation with good solubility to an insoluble β-sheet conformation. Cells have evolved mechanisms to clear these insoluble deposits; however, once clearance pathways are overloaded, these proteins are deposited in the form of insoluble intracellular inclusions or extracellular plaques. Protein deposits or aggregates are also hallmark of many neurodegenerative diseases.
The most prevalent neurodegenerative disease is Alzheimer’s disease which is characterized by extracellular protein deposition of the peptide fragment Aβ from the amyloid precursor protein, and intracellular tau-containing filaments, called neurofibrillary tangles. The 3D structure of the Aβ deposits revealed the overall topology of the fibrils, but gives only limited information about the role of individual residues for fibril formation. The latter type of information, however, is important for the development of novel drugs that are capable of preventing aggregation or of solubilizing aggregates by targeting those residues that represent the hot spots of binding affinity in the fibrillar structure. We address this point by molecular dynamics simulations of Aβ oligomers and thermodynamic analyses of the aggregation interfaces. In addition, we investigate the effect of different solvent environments on the conformational stability of such Aβ oligomers.
Molecular docking represents a versatile computational method for determining the structure of protein-protein complexes. Despite considerable efforts to enhance the accuracy of docking predictions during the past years, a general solution to this problem is not yet within reach. One major challenge is the definition of suitable criteria for a scoring function that allows the identification of a good docking solution among many false arrangements.
In our group, we have adapted the concepts from information theory to treat the biological problem of protein-protein docking. We have developed a formalism based on the concept of mutual information (MI) to investigate different features with respect to their information content in protein docking. We have also shown that the MI-values of these features can successfully be converted into a scoring function. Current work includes the analysis of larger datasets and more sophisticated structural features to obtain a robust and widely applicable approach.
Changes in pH regulate many biological processes in bacteria, viruses, vertebrates, and plants. For example, some bacteria are able to survive the acidic conditions in the stomach of their host by using acid-activated chaperones which protect substrate proteins from aggregation. In viruses, some of the fusion proteins that mediate cell entry were described to act pH-dependently. Other proteins in vertebrates undergo pH changes on their way through the endoplasmic reticulum and the Golgi apparatus. In order to mimic pH-titration experiments, we investigate pH-dependent proteins by conducting molecular dynamics (MD) simulations, in which pH is changed gradually changed. This method allows the calculation of titration curves and pKa values of ionizable groups. By using this strategy, we investigate on an atomic level the effects of pH changes which affect protein local conformations, macromolecular assemblies as well as structural stability.
Signal and substrate processing in living cells has been optimized by nature during millions of years. A key aspect to high efficiency is the spatial proximity of the proteins involved in biological systems. Synthetic biology aims at replication and optimization of biological processes for tailor-made applications. Artificial bioreactors implement the concept of proximity mostly via modular scaffolds: individual building blocks are combined into a larger structure of defined stoichiometry and spatial organization facilitating substrate channeling like in natural systems.
A promising method of protein-based scaffolding uses small protein adaptor domains and specific peptide ligands to connect the enzymes. Our first approach relies on scaffolding via noncovalent protein-ligand interactions formed by SH3, SH2, and PDZ domains. In the second approach, we design adaptor systems that form covalent interactions upon association resulting in a permanent linkage. For that purpose, we investigate several CnaB-fold domains containing an intramolecular isopeptide bond as candidates for the generation of orthogonal split-protein systems.