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Hans-Peter Lenhof

Understanding protein structures is a crucial step in creating molecular insight for researchers as well as students and pupils. The enormous scaling gap between an atomic point of view and objects in daily life hampers developing an intuitive relation between them. Especially for high school students, it can be dif?cult to understand the spatial relations of a protein structure. Due to lack of direct imaging techniques, molecules can only be explored by studying abstract molecular models. Here, the use of Augmented reality (AR) techniques has proven to strongly improve structural perception.
We present a Lamarckian genetic algorithm (LGA) variant for flexible ligand-receptor docking which allows to handle a large number of degrees of freedom. Our hybrid method combines a multi-deme LGA with a recently published gradient-based method for local optimization of molecular complexes. We compared the performance of our new hybrid method to two non gradient-based search heuristics on the Astex diverse set for flexible ligand-receptor docking. Our results show that the novel approach is clearly superior to other LGAs employing a stochastic optimization method.
Recently we reported differential miRNA signatures in blood cells of lungcancer patients and healthy controls. With the present study we wanted to investigate if miRNA blood signatures are also suited to differentiate lungcancer patients from COPD patients. We compared the expression of 863 human miRNAs in blood cells of lungcancer patients, COPD patients, and healthy controls. The miRNA pattern from patients with lungcancer and COPD were more similar to each other than to the healthy controls.

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