Olaf G. Othersen

Difluoro-dioxolo-benzoimidazol-benzamides As Potent Inhibitors of CK1δ and ε with Nanomolar Inhibitory Activity on Cancer Cell Proliferation

Deregulation of CK1 (casein kinase 1) activity can be involved in the development of several pathological disorders and diseases such as cancer. Therefore, research interest in identifying potent CK1-specific inhibitors is still increasing. A previously published potent and selective benzimidazole-derived CK1δ/ε-specific inhibitor compound with significant effects on several tumor cell lines was further modified to difluoro-dioxolo-benzoimidazole derivatives displaying remarkable inhibitory effects and increased intracellular availability. In the present study, we identified two heterocyclic molecules as new CK1-specific inhibitor compounds with favorable physicochemical properties and notable selectivity in a kinome-wide screen. Being compared to other CK1 isoforms, these compounds exhibited advanced isoform selectivity toward CK1δ. Moreover, newly designed compounds showed increased growth inhibitory activity in a panel of different tumor cell lines as determined by analyses of cell viability and cell cycle distribution. In summary, presented lead optimization resulted in new highly selective CK1δ-specific small molecule inhibitors with increased biological activity.

Application of information theory to feature selection in protein docking

In the era of structural genomics, the prediction of protein interactions using docking algorithms is an important goal. The success of this method critically relies on the identification of good docking solutions among a vast excess of false solutions. We have adapted the concept of mutual information (MI) from information theory to achieve a fast and quantitative screening of different structural features with respect to their ability to discriminate between physiological and nonphysiological protein interfaces. The strategy includes the discretization of each structural feature into distinct value ranges to optimize its mutual information. We have selected 11 structural features and two datasets to demonstrate that the MI is dimensionless and can be directly compared for diverse structural features and between datasets of different sizes. Conversion of the MI values into a simple scoring function revealed that those features with a higher MI are actually more powerful for the identification of good docking solutions. Thus, an MI-based approach allows the rapid screening of structural features with respect to their information content and should therefore be helpful for the design of improved scoring functions in future. In addition, the concept presented here may also be adapted to related areas that require feature selection for biomolecules or organic ligands.

Investigating protein-protein and protein-ligand interactions by molecular dynamics simulations

In recent years, the earlier view of proteins as relatively rigid structures has been replaced by a dynamic model in which the internal motions and resulting conformational changes play an essential role in their function. In this context, molecular dynamics (MD) simulations have become an important computational tool for understanding the physical basis of the structure and function of biological macromolecules. Also in the process of finding new drugs MD simulations play an important role. Our workgroup uses molecular dynamics simulations to study proteins of biological and medical relevance, e.g. signal transduction proteins or human integrin complexes. The general aim of these investigations is to find possible new lead structures or drugs and also to understand the basic and essential mechanisms behind the mode of action of our target systems. In MD simulation, the problem size is fixed and a large number of iterations must be executed, so the MD simulation suites have to scale to hundreds or thousands CPUs to get detailed view inside biomolecular systems. The used programs AMBER and GROMACS scale well up to 64 or 32 CPUs, respectively. A typical run for about 100 ns simulation time consumes 5500 up to 21000 CPU hours.