Establishing Experimental Model Systems for Visual Proteomics
This team aims to harness the combined power of cryo-electron tomography (cryo-ET), quantitative mass spectrometry (MS), and molecular dynamics simulations for visual proteomics. Researchers will establish three different experimental model systems for visual proteomics, from fungi to human cells, by quantifying their molecular content with MS and by optimizing the acquisition parameters for cryo-ET. They will then develop a Bayesian statistical framework to holistically assign the molecular identity to features observed by cryo-ET.
The ultimate goal is to assign a probability to each voxel, or three-dimensional data point, in cryo-electron tomograms for being part of a certain macromolecular structure or subcellular feature. Molecular dynamics simulations will be used to account for structural heterogeneity, and synthetic data will be used to estimate statistical confidence, specificity, and sensitivity. The combination of these powerful experimental and computational methods into a single workflow will significantly improve the accuracy of the visual proteomics workflow. Disseminating high-quality proteomics and image data together with open-source software will have an enabling character for the wider scientific community.