Speaker
Description
A single biological molecule imaged in cryo-EM typically exhibits multiple structural configurations, each with a different function. These configurations may exist along a continuum of states, or conformations, giving rise to a manifold of continuous variability known as the conformational manifold. We propose to estimate this manifold by combining low-resolution reconstruction methods and graph Laplacian techniques. A covariance-based method is used to first obtain low-resolution estimates, which are used to construct a graph over the projection images. Computing the graph Laplacian and extracting its eigenvectors then allows us to characterize the underlying conformational manifold. Among other things, these Laplacian eigenvectors allow us to visualize the topology of the manifold, but they also provide a means for constructing higher-resolution molecular reconstructions through the method of “spectral volumes.” Both applications are evaluated on synthetic and experimental datasets.
