CS & BioE Seminar Speaker: Ellen Zhong, Monday, Feb 14 at 12:30
CS & BioE Seminar Speaker: Ellen Zhong, Massachusetts Institute of Technology Date: Monday, Feb 14, 2022 Time: 12:30pm EST Location: CS 105 Host: Clifford Brangwynne, Chemical and Biological Engineering Event page: [ https://www.cs.princeton.edu/events/26161 | https://www.cs.princeton.edu/events/26161 ] *Attendees can view this talk in person or remotely over Zoom here: [ https://princeton.zoom.us/j/98415856885 | https://princeton.zoom.us/j/98415856885 ] **Zoom link is available to Princeton authenticated users only. Title: Machine learning for reconstructing dynamic protein structures from cryo-EM images Abstract: Over the last decade, cryo-electron microscopy (cryo-EM) has emerged as a powerful imaging technology for visualizing the 3D structure of proteins and other biomolecules at near-atomic resolution. Unlike other methods in structural biology, cryo-EM is uniquely poised to image large and dynamic protein complexes. However, this promise is limited by the computational task of 3D reconstruction, where a dataset of noisy and unlabeled 2D projection images are combined to infer the 3D structure(s) of the molecule of interest. In this seminar, I will introduce cryoDRGN, a machine learning system for heterogeneous cryo-EM reconstruction. Underpinning the cryoDRGN method is a deep generative model parameterized by a new neural representation of 3D volumes and a learning algorithm combining exact and variational inference to optimize this model from unlabeled 2D cryo-EM images. Extended to real datasets and released as an open-source tool, cryoDRGN has been used to discover new protein structures and visualize continuous trajectories of their motion. I will discuss various extensions of the method to broaden the scope of cryo-EM to new classes of dynamic protein complexes. Finally, I will discuss how recent advances in machine learning for protein structure prediction (e.g. AlphaFold and Rosettafold) can complement methods for cryo-EM structure determination and what key algorithmic challenges remain to realize the next era of 3D visual biology.
CS & BioE Seminar Speaker: Ellen Zhong, Massachusetts Institute of Technology Date: Monday, Feb 14, 2022 Time: 12:30pm EST Location: CS 105 Host: Clifford Brangwynne, Chemical and Biological Engineering Event page: [ https://www.cs.princeton.edu/events/26161 | https://www.cs.princeton.edu/events/26161 ] *Attendees can view this talk in person or remotely over Zoom here: [ https://princeton.zoom.us/j/98415856885 | https://princeton.zoom.us/j/98415856885 ] **Zoom link is available to Princeton authenticated users only. Title: Machine learning for reconstructing dynamic protein structures from cryo-EM images Abstract: Over the last decade, cryo-electron microscopy (cryo-EM) has emerged as a powerful imaging technology for visualizing the 3D structure of proteins and other biomolecules at near-atomic resolution. Unlike other methods in structural biology, cryo-EM is uniquely poised to image large and dynamic protein complexes. However, this promise is limited by the computational task of 3D reconstruction, where a dataset of noisy and unlabeled 2D projection images are combined to infer the 3D structure(s) of the molecule of interest. In this seminar, I will introduce cryoDRGN, a machine learning system for heterogeneous cryo-EM reconstruction. Underpinning the cryoDRGN method is a deep generative model parameterized by a new neural representation of 3D volumes and a learning algorithm combining exact and variational inference to optimize this model from unlabeled 2D cryo-EM images. Extended to real datasets and released as an open-source tool, cryoDRGN has been used to discover new protein structures and visualize continuous trajectories of their motion. I will discuss various extensions of the method to broaden the scope of cryo-EM to new classes of dynamic protein complexes. Finally, I will discuss how recent advances in machine learning for protein structure prediction (e.g. AlphaFold and Rosettafold) can complement methods for cryo-EM structure determination and what key algorithmic challenges remain to realize the next era of 3D visual biology.
CS & BioE Seminar Speaker: Ellen Zhong, Massachusetts Institute of Technology Date: Monday, Feb 14, 2022 Time: 12:30pm EST Location: CS 105 Host: Clifford Brangwynne, Chemical and Biological Engineering Event page: [ https://www.cs.princeton.edu/events/26161 | https://www.cs.princeton.edu/events/26161 ] *Attendees can view this talk in person or remotely over Zoom here: [ https://princeton.zoom.us/j/98415856885 | https://princeton.zoom.us/j/98415856885 ] **Zoom link is available to Princeton authenticated users only. Title: Machine learning for reconstructing dynamic protein structures from cryo-EM images Abstract: Over the last decade, cryo-electron microscopy (cryo-EM) has emerged as a powerful imaging technology for visualizing the 3D structure of proteins and other biomolecules at near-atomic resolution. Unlike other methods in structural biology, cryo-EM is uniquely poised to image large and dynamic protein complexes. However, this promise is limited by the computational task of 3D reconstruction, where a dataset of noisy and unlabeled 2D projection images are combined to infer the 3D structure(s) of the molecule of interest. In this seminar, I will introduce cryoDRGN, a machine learning system for heterogeneous cryo-EM reconstruction. Underpinning the cryoDRGN method is a deep generative model parameterized by a new neural representation of 3D volumes and a learning algorithm combining exact and variational inference to optimize this model from unlabeled 2D cryo-EM images. Extended to real datasets and released as an open-source tool, cryoDRGN has been used to discover new protein structures and visualize continuous trajectories of their motion. I will discuss various extensions of the method to broaden the scope of cryo-EM to new classes of dynamic protein complexes. Finally, I will discuss how recent advances in machine learning for protein structure prediction (e.g. AlphaFold and Rosettafold) can complement methods for cryo-EM structure determination and what key algorithmic challenges remain to realize the next era of 3D visual biology.
participants (1)
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Emily C. Lawrence