Global ETD Search

1	In Silico Identification of Novel Cancer Drugs with 3D Interaction Profiling Salentin, Sebastian 01 August 2018 (has links) (PDF) Cancer is a leading cause of death worldwide. Development of new cancer drugs is increasingly costly and time-consuming. By exploiting massive amounts of biological data, computational repositioning proposes new uses for old drugs to reduce these development hurdles. A promising approach is the systematic analysis of structural data for identification of shared binding pockets and modes of action. In this thesis, I developed the Protein-Ligand Interaction Profiler (PLIP), which characterizes and indexes protein-ligand interactions to enable comparative analyses and searching in all available structures. Following, I applied PLIP to identify new treatment options in cancer: the heat shock protein Hsp27 confers resistance to drugs in cancer cells and is therefore an attractive target with a postulated drug binding site. Starting from Hsp27, I used PLIP to define an interaction profile to screen all structures from the Protein Data Bank (PDB). The top prediction was experimentally validated in vitro. It inhibits Hsp27 and significantly reduces resistance of multiple myeloma cells against the chemotherapeutic agent bortezomib. Besides computational repositioning, PLIP is used in docking, binding mode analysis, quantification of interactions and many other applications as evidenced by over 12,000 users so far. PLIP is provided to the community online and as open source. Protein-Ligand Interaktionen Interaktionsmuster Drug Repositioning Krebs Virtuelles Screening Fingerprinting PDB protein-ligand interactions interaction patterns drug repositioning BVDU cancer virtual screening fingerprinting PDB ddc:540 rvk:VS 9107
2	Computer-Aided Drug Design for Membrane Channel Proteins / Computergestützte Medikamentenentwicklung für Membrankanalproteine Wacker, Sören 07 August 2012 (has links) No description available. 500 Naturwissenschaften WCC 000 Physics Leitstruktur Docking Virtuelles Screening Kaliumkanal Kv1.2 Aquaporin 9 AQP9 Molecular Dynamik Simulationen GlnPQ lead structure Docking Virtual Screening Potassium Channel Kv1.2 Aquaporin 9 AQP9 Molecular Dynamics Simulations GlnPQ 30.00
3	In Silico Identification of Novel Cancer Drugs with 3D Interaction Profiling Salentin, Sebastian 06 February 2017 (has links) Cancer is a leading cause of death worldwide. Development of new cancer drugs is increasingly costly and time-consuming. By exploiting massive amounts of biological data, computational repositioning proposes new uses for old drugs to reduce these development hurdles. A promising approach is the systematic analysis of structural data for identification of shared binding pockets and modes of action. In this thesis, I developed the Protein-Ligand Interaction Profiler (PLIP), which characterizes and indexes protein-ligand interactions to enable comparative analyses and searching in all available structures. Following, I applied PLIP to identify new treatment options in cancer: the heat shock protein Hsp27 confers resistance to drugs in cancer cells and is therefore an attractive target with a postulated drug binding site. Starting from Hsp27, I used PLIP to define an interaction profile to screen all structures from the Protein Data Bank (PDB). The top prediction was experimentally validated in vitro. It inhibits Hsp27 and significantly reduces resistance of multiple myeloma cells against the chemotherapeutic agent bortezomib. Besides computational repositioning, PLIP is used in docking, binding mode analysis, quantification of interactions and many other applications as evidenced by over 12,000 users so far. PLIP is provided to the community online and as open source. info:eu-repo/classification/ddc/540 ddc:540

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