Project Group Leader, Structural Biochemistry
Computational Structural Biology
The Computational Structural Biology group explores new software-based methods to stretch the boundaries of what image processing and machine learning can currently do for structural biology.
Methodologically, the focus is on object localization and segmentation of cryo-electron microscopy data sets. In addition, the group is working on the automation of existing workflows and pipelines. The group supports other scientists in the department with projects involving image- and data-analysis.
These methods enable scientists to gain insights from experiments that cannot be obtained by manual analysis, or only with a great deal of time.
Schöenfeld F, Stabrin M, Shaikh TR, Wagner T, Raunser S (2022). Accelerated 2D Classification With ISAC Using GPUs. Front Mol Biosci.
Wang Z, Grange M, Pospich S, Wagner T, Kho A.L, Gautel M, Raunser S (2022). Structures from intact myofibrils reveal mechanism of thin filament regulation through nebulin. Science
Wagner T, Raunser S (2020). The evolution of SPHIRE-crYOLO particle picking and its application in automated cryo-EM processing workflows. Communications Biology
Stabrin M, Schoenfeld F, Wagner T, Pospich S, Gatsogiannis C, Raunser S (2020). TranSPHIRE: automated and feedback-optimized on-the-fly processing for cryo-EM Nat Commun
Wagner T, Lusnig L, Pospich S, Stabrin M, Schönfeld F, Raunser S (2020).Two particle-picking procedures for filamentous proteins: SPHIRE-crYOLO filament mode and SPHIRE-STRIPER. Acta Cryst Sect D Struct Biol.