Speaker
Description
In single particle cryo-EM, the central problem is to reconstruct the three-dimensional structure of a protein from $10^4-10^7$ noisy and randomly oriented two-dimensional projections. However, the imaged protein molecules may exhibit structural variability, which complicates reconstruction and is typically addressed using discrete clustering approaches that fail to capture the full range of protein dynamics. Here, we present a novel framework using deep neural networks for cryo-EM reconstruction that extends naturally to modeling continuous generative factors of protein structural heterogeneity. We demonstrate that our framework, termed CryoNN, can perform ab initio reconstruction of 3D protein structures from simulated and real cryo-EM image data. To our knowledge, CryoNN is the first neural network-based approach for cryo-EM reconstruction and the first end-to-end method for directly reconstructing continuous ensembles of protein structures from cryo-EM images.
