A Bayesian inference approach to determining structural ensembles using cryo-EM and molecular dynamics.

Abstract

Advances in cryo-EM have led to an enormous increase in the number of high-resolution structures solved and deposited within the Protein Data Bank. However, the way protein models are represented has not kept pace as static single structure models continue to be widely accepted as accurate depictions of proteins despite their underlying conformational heterogeneity and dynamics. While advances in image processing allows for the construction of multiple distinct conformational states directly from raw 2D images, functionally important highly dynamic regions of proteins, including highly-dynamic side chains, are often averaged out resulting in low resolution and an inability to accurately determine local structures. Here we present ‘EMMIVox’, a Bayesian inference approach to determining structural ensembles of biological entities by combining cryo-EM data with molecular dynamics (MD) simulations. EMMIVox, which is able to automatically detect and downweigh noisy experimental data, calculates accurate structural ensembles of proteins and protein complexes including any lipids, small-molecules and ordered water present in experimental maps. Using EMMIVox, we generated single structure models for a variety of high-resolution cryo-EM maps (1.9 Å - 3.5 Å), including membrane proteins and ligand-protein complexes. EMMIVox models outperformed deposited structures in terms of stereochemical descriptors (MolProbity and Clashcore) as well as metrics that quantify how well the model fits the density map (map-model cross correlation and EMRinger). Finally, we determined the structural ensembles of the type 1a tau filament (1.9 Å) and the SPP1 bacteriophage (4 Å) in detail, demonstrating the accuracy of our structural ensemble compared to standard MD simulations. Inference of structural ensembles and their representative populations will have wide ranging applications in structural biology and drug discovery. EMMIVox is available in the Integrative Structural and Dynamical Biology module of the open-source, freely-available PLUMED library (www.plumed.org).