Development of Tools for the Interpretation of Cryo-EM Data

Abstract

In this dissertation, I describe my efforts to build tools to address two gaps in the field of cryo-electron microscopy: deriving structural details about the conformational landscape from cryo-EM data and model validation for moderate resolution cryo-EM maps. Currently, there are few model validation metrics that can precisely evaluate the local quality of atomic models built into maps solved to the resolutions common for cryo-EM. I developed MEDIC (Model Error Detection in Cryo-EM), a robust statistical model to identify local errors in protein structures built into cryo-EM maps. In the second half of this dissertation, I describe my efforts to use atomic models to guide single particle analysis of cryo-EM datasets to obtain a mechanistic understanding of the protein conformational space. Revealing the protein conformational landscape contained in a cryo-EM dataset is notoriously difficult as individual 2D images have a very low signal-to-noise ratio. State of the art methods are only capable of resolving a few very distinct states or describing the motion at low resolutions.