Cryo-electron tomography enables 3D visualization of cells in a near native state at molecular resolution. The produced cellular tomograms contain detailed information about all macromolecular complexes, their structures, their abundances and their specific spatial locations in the cell. However, extracting this information is very challenging and current methods usually rely on templates of known structure. Here, we formulate a template-free visual proteomics analysis as a de novo pattern mining problem and propose a new framework called "Multi Pattern Pursuit" for supporting proteome-scale de novo discovery of macromolecular complexes in cellular tomograms without using templates of known structures. Our tests on simulated and experimental tomograms show that our method is a promising tool for template-free visual proteomics analysis.
Dr. Min Xu is a Senior Research Associate (with Dr. Frank Alber) lab within the Computational Biology and Bioinformatics Program at the University of Southern California (USC). He obtained bachelor’s and master’s degrees in Computer Science and Applied Mathematics respectively, then performed PhD and postdoctoral research in Computational Biology at USC. Dr. Xu has more than 15 years of research experience in various areas of Computational Biology. He is currently developing methods for the structural analysis of cell systems at molecular resolution and in close-to-native states. In particular, his research focuses on determining the structures and spatial organizations of macromolecular complexes in cellular cryo-electron tomograms.