Speaker: Karissa Sanbonmatsu, Los Alamos National Laboratory, USA
While many methods exist to produce structural models for lower resolution cryo-EM reconstructions, high-resolution reconstructions are often modeled using crystallographic techniques and extensive manual intervention. Here, we present an automated fitting technique for high-resolution cryo-EM data sets that produces all-atom models consistent with the EM density. Using a molecular dynamics approach, atomic positions are optimized with a potential that includes the cross-correlation coefficient between the structural model and the cryo-EM electron density, as well as a biasing potential preserving the stereochemistry and secondary structure of the biomolecule. Specifically, we use a hybrid structure-based/ab initio molecular dynamics potential to extend molecular dynamics fitting. We obtain atomistic models of the human ribosome consistent with high-resolution cryo-EM reconstructions of the human ribosome. Automated methods such as these have the potential to produce atomistic models for a large number of ribosome complexes simultaneously that can be subsequently refined manually.
Dr. Sanbonmatsu has been a principal investigator at Los Alamos National Laboratory since 2001. She received her BA in Physics from Columbia University in 1992 and PhD in Astrophysical, Planetary and Atmospheric Sciences from University of Colorado at Boulder in 1997. She is a fellow of the American Physical Society. Dr. Sanbonmatsu’s research is focused on the mechanism of non-coding RNAs, including the ribosome, riboswitches and long non-coding RNAs. She has pioneered large-scale biomolecular simulations of nano-scale molecular machines such as the ribosome. She uses a variety of computational and experimental techniques, ranging from large-scale explicit solvent molecular dynamics simulation to biochemical structural probing methods to cryo-EM.
Date: Sept 20
Venue: Air&Fire auditorium, SciLifeLab Solna
Host: Alexey Amunts
This seminar is part of a seminar series hosted by SciLifeLab Fellows
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