| Literature DB >> 32394996 |
Amit Moscovich1, Amit Halevi1, Joakim Andén2, Amit Singer1,3.
Abstract
Single-particle electron cryomicroscopy is an essential tool for high-resolution 3D reconstruction of proteins and other biological macromolecules. An important challenge in cryo-EM is the reconstruction of non-rigid molecules with parts that move and deform. Traditional reconstruction methods fail in these cases, resulting in smeared reconstructions of the moving parts. This poses a major obstacle for structural biologists, who need high-resolution reconstructions of entire macromolecules, moving parts included. To address this challenge, we present a new method for the reconstruction of macromolecules exhibiting continuous heterogeneity. The proposed method uses projection images from multiple viewing directions to construct a graph Laplacian through which the manifold of three-dimensional conformations is analyzed. The 3D molecular structures are then expanded in a basis of Laplacian eigenvectors, using a novel generalized tomographic reconstruction algorithm to compute the expansion coefficients. These coefficients, which we name spectral volumes, provide a high-resolution visualization of the molecular dynamics. We provide a theoretical analysis and evaluate the method empirically on several simulated data sets.Entities:
Keywords: Laplacian eigenmaps; diffusion maps; heterogeneity; manifold learning; molecular conformation space; single particle electron cryomicroscopy; tomographic reconstruction
Year: 2020 PMID: 32394996 PMCID: PMC7213598 DOI: 10.1088/1361-6420/ab4f55
Source DB: PubMed Journal: Inverse Probl ISSN: 0266-5611 Impact factor: 2.407