Bayesian cryo-EM Refinement

Abstract

In light of recent high-resolution cryo-electron-microscopy (cryo-EM) structures of large complexes, modeling atom coordinates into cryo-EM densities faces new challenges. Though high-resolution structures provide more data, finding the best-fitting structure with current refinement methods is more difficult with increased resolution, because the used refinement potentials become more rugged the higher the resolution. Currently used refinement potentials are defined by empirically chosen measures of similarity between a calculated cryo-EM density and the given experimental map, e.g. cross-correlation or absolute distance.Here, we present a new refinement potential that is based on a statistical physics model of the cryo-EM measuring and reconstruction process using Bayesian statistics. Our method contains previously previously developed algorithms as limiting cases.The minima of the refinement potential and its shape both influence the efficiency of the refinement algorithm; a smoother energy landscape allows a more efficient exploration of the minima, i.e. fitting structures, in this landscape. Compared to earlier methods, our refinement energy landscape is smoother, allowing more efficient sampling of the energy landscape. Further, our refinement protocol provides an appropriate refinement force constant and takes into account the thermal fluctuation of the atoms. Additionally, our algorithm allows us to generate molecular dynamics ensembles that represent the simultaneous input from multiple cryo-EM maps.