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Structure and Dynamics of Molecular Machines

Prof. Karl Duderstadt

Research Field

A description of the fascinating research topics follows soon. You may have a look at the group’s homepage (see links on the right).

Address/Contact

Am Klopferspitz 18
82152 Planegg
duderstadt@biochem.mpg.de
+49 89 8578-3033

Members of the Research Group

Professor

Scientists

Teaching

Course with Participations of Group Members

Offers for Theses in the Group

Development of fluorescent DNA topology sensors for single-molecule imaging
While the double-helical structure of duplex DNA is advantageous for the storage and maintenance of genetic information, it poses major challenges during essential cellular processes, such as transcription and replication, when the information-rich DNA bases must be accessed. Separation of the two single strands leads to the build-up of superhelical tension, which, if left unresolved, exerts extreme forces in the form of torque that disrupts critical enzymatic events on chromosomes. In this project, recently discovered topology sensing proteins will be fluorescently labeled and used in single-molecule imaging experiments to directly visualize DNA topology dynamics. These tools will then be used to characterize how topological energy migrates around the replication fork and where overwinding accumulates leading to replication fork collapse. In this project protein biochemistry techniques will be used to characterise the DNA binding properties of the labeled topology sensors. DNA substrates will be generated with defined topologies suitable for imaging. Single immobilized DNA molecules together with labeled fluorescent sensors will be imaged using multiwavelength TIRF microscopy. Dynamics will be quantified using custom written Fiji plugins and python notebooks. During each stage of the project supervision and needed expertise will be provided.
suitable as
  • Bachelor’s Thesis Physics
Supervisor: Karl Duderstadt

Current and Finished Theses in the Group

Entwurf und Implementierung eines Algorithmus basierend auf maschinellem Lernen, um den Peptidinferenzprozess in der datenunabhängigen Shotgun-Proteomik in der MaxQuant Software zu verbessern.
Abschlussarbeit im Masterstudiengang Physics (Applied and Engineering Physics)
Themensteller(in): Karl Duderstadt
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