The One World Cryo-EM seminar series is a platform for discussion of algorithms, computational methods and mathematical problems in cryo-EM.

Online, Every Other Wednesday, at 8am PT / 11am ET/ 4pm BST / 5pm CET / 11pm China.

Registration has changed. For zoom links and announcements, please register to our mailing list. This is a read-only mailing list (users cannot post). Please use your institution/ company email – we approve registrations manually and this helps us know who you are . You may receive an email asking you to confirm your registration, it is important that you click the link, otherwise we will not see your request.

Organizers: Joakim Andén, Dorit Hanein, Roy R. Lederman and Steven J. Ludtke

All TalksPast talksUpcoming Talks

Next Talk

Daisuke Kihara

Professor of Biological Sciences, Computer Science, Purdue University

Date and time: October 5th, 2022
Wednesday, at 8am PT / 11am ET / 4pm BST / 5pm CEST / 11pm China

For the zoom and links, please join the One World Cryo-EM mailing list.

Validating and Building Protein Structure Models for cryo-EM Maps Using Deep Learning

Cryo-electron microscopy (cryo-EM) has become one of the main experimental methods for determining protein structures. Protein structure modeling from cryo-EM is in general more difficult than X-ray crystallography since the resolution of maps is often not high enough to specify atom positions. We have been developing a series of computational methods for modeling protein structures from cryo-EM maps. For maps at medium resolution, deep learning can provide useful structure information for structure validation and modeling. Particularly, we have recently developed a protein model quality assessment score, DAQ, which compares local density patterns captured by deep learning with amino acid positions in a model, and detect potential errors in the model. In a large-scale analysis of protein models from cryo-EM, we found that not a small number of models may have some errors. All the tools we developed are available at

Last Talk

Frank DiMaio

Associate Professor, Institute for Protein Design, University of Washington

Date and time: Wednesday September 7th, 2022 at 8am PT / 11am ET / 3pm GMT / 4pm CET / 11pm China

For the zoom and links, please join the One World Cryo-EM mailing list.

Machine learning structure prediction and cryoEM map interpretation

The machine-learning structure prediction tools AlphaFold and RoseTTAfold have dramatically simplified low-resolution cryoEM map interpretation.  Following this, we describe three tools our lab has developed in conjunction with these approaches.  First, we describe novel machine-learning methodology for detecting errors in models built against low-resolution cryoEM density.  Next, we describe tools for modelling ligands into low-resolution cryoEM density.  Finally, we describe our efforts at adding to RoseTTAFold the ability to predict the structure of protein/nucleic-acid complexes.

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