Studying the Conformational Changes of Protein Kinases Using In Silico Structural Approaches

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URI: http://hdl.handle.net/10900/136672
http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1366722
http://dx.doi.org/10.15496/publikation-78023
Dokumentart: PhDThesis
Date: 2023-02-20
Language: English
Faculty: 7 Mathematisch-Naturwissenschaftliche Fakultät
Department: Chemie
Advisor: Laufer, Stefan (Prof. Dr.)
Day of Oral Examination: 2023-02-06
DDC Classifikation: 500 - Natural sciences and mathematics
Keywords: Proteinkinasen , Molekulardynamik
License: http://tobias-lib.uni-tuebingen.de/doku/lic_mit_pod.php?la=de http://tobias-lib.uni-tuebingen.de/doku/lic_mit_pod.php?la=en
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Abstract:

Protein kinases were brought to the scientific community's attention with the remarkable approval of imatinib more than 20 years ago. Since then, enormous efforts have been made to identify, characterise, and investigate the dynamic and function of this broad protein family. Nowadays, protein kinases are associated with numerous human diseases, including the origin of cancer and beyond.Extensive research of protein kinases led not only to the steady development and approval of new inhibitors and therapeutics but also to the awareness that there are still plenty of questions to be answered about these dynamic proteins. Hence, the In Silico methods come in hand to study their highly dynamic nature. Offering a wide range of novel computational techniques, they provide a possibility to lift the veil of secrecy and glance into the minor details of kinase behaviour. The presentation will cover the introduction to the main research focus – protein kinases, and the central methodology – computer-aided drug design. Firstly, the classification, function, drug discovery trends and a detailed review of the kinase domain’s structural features will be presented. Next, the In Silico drug discovery methods, encompassing the sample workflows and analyses with the main emphasis on the theory and application of molecular dynamics will be discussed. The application of the computational chemistry approaches in kinase drug research will be illustrated in two scientific publications. The first is dedicated to the application of long-scale molecular dynamics in understanding the impact of phosphorylation and mutation on the autoinhibition and dimerization of mitogen- activated protein kinase kinase 4 (MKK4). The second publication focuses on the investigation of the statistical trends and patterns related to protein kinase regulatory hydrophobic spine (R-spine), emphasizing the alfa- helical hydrophobic spine residue three (RS3). The aforementioned publications demonstrate the successful application of In Silico approaches, particularly molecular dynamics in the field of drug discovery, and furthermore offer a framework modification for the investigation of geometrical motions within the protein structures during molecular dynamics simulation.

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