Machine Learning for Reconstructing Structural Landscapes from CryoEM

Explore machine learning applications in cryo-electron microscopy through this seminar featuring two presentations from Princeton University researchers. Begin with a primer on heterogeneous reconstruction in cryo-EM by Rishwanth Raghu from the Department of Computer Science, followed by Ellen Zhong's comprehensive discussion on machine learning for visualizing structural landscapes inside cells. Learn about the algorithmic challenges at the frontier of structure determination via cryo-EM and discover cryoDRGN, a machine learning system for heterogeneous cryo-EM and cryo-ET reconstruction. Understand how deep learning breakthroughs have transformed structural biology and created new opportunities to study biomolecular complex dynamics and interactions. Gain insights into recent progress in reconstructing complex mixtures, developing benchmarks for structural heterogeneity, and visualizing dynamic biomolecular complexes in situ. Discover how advances in cryo-electron microscopy and tomography are revolutionizing our understanding of protein structure prediction and cellular structural landscapes through cutting-edge AI and computer vision methodologies.