Assistant Professor at University of Washington Bothell
Artificial Intelligence Advances for De Novo Molecular Structure Modeling in Cryo-EM
Date and time: May 5, 2021
Wednesday, at 8am PT / 11am ET / 3pm GMT / 5pm CET / 11pm China
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Although cryo-EM has been drastically improved to generate high-resolution three-dimensional (3D) maps that contain detailed structural information about macromolecules, the computational methods for using the data to automatically build structure models are lagging far behind. The traditional cryo-EM model building approach is template-based homology modeling. Manual de novo modeling is very time-consuming when no template model is found in the database. In recent years, de novo cryo-EM modeling using machine learning (ML) and deep learning (DL) has ranked among the top-performing methods in macromolecular structure modeling. Deep-learning-based de novo cryo-EM modeling is an important application of artificial intelligence, with impressive results and great potential for the next generation of molecular biomedicine. Accordingly, I will talk about the representative ML/DL-based de novo cryo-EM modeling methods that we developed.