Instructor, Baylor College of Medicine
Date and time: December 1st, 2021
Wednesday, at 8am PT / 11am ET / 4pm GMT / 5pm CET / 12am China (Dec 2nd)
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Deep learning based Gaussian mixture model for characterizing variability in CryoEM
Structural flexibility and/or dynamic interactions with other molecules is a critical aspect of protein function. CryoEM provides direct visualization of individual macromolecules sampling different conformational and compositional states, but characterization of continuous conformational changes or large numbers of discrete states without human supervision remains challenging. Here, we present a machine learning algorithm to determine a conformational landscape for proteins or complexes using a 3-D Gaussian mixture model mapped onto 2-D particle images in known orientations. Using a deep neural network architecture, the method can automatically resolve the structural heterogeneity within the protein complex and map particles onto a small latent space describing conformational and compositional changes. The algorithm is applied to multiple biological systems, to explore compositional and conformational changes at a range of scales.