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 gather.town 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 https://kiharalab.org/emsuites/.

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 gather.town 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.



Other “One Worlds” (not affiliated): https://www.owprobability.org/other-worlds