Aug 8 – 10, 2019
Flatiron Institute
America/New_York timezone

Reconstructing continuous distributions of 3D protein structure from cryo-EM images

Aug 8, 2019, 5:15 PM
2h
Ingrid Daubechies Auditorium (Flatiron Institute)

Ingrid Daubechies Auditorium

Flatiron Institute

162 Fifth Ave, 2nd fl. New York, NY 10010

Speaker

Ellen Zhong (Massachusetts Institute of Technology)

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.

Primary author

Ellen Zhong (Massachusetts Institute of Technology)

Co-authors

Bonnie Berger (Massachusetts Institute of Technology) Prof. Joseph Davis (Massachusetts Institute of Technology) Tristan Bepler

Presentation materials

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