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.
Original language | English (US) |
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Article number | 024003 |
Journal | Inverse Problems |
Volume | 36 |
Issue number | 2 |
DOIs | |
State | Published - 2020 |
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Signal Processing
- Applied Mathematics
- Computer Science Applications
- Mathematical Physics
Keywords
- Diffusion maps
- Heterogeneity
- Laplacian eigenmaps
- Manifold learning
- Molecular conformation space
- Single particle electron cryomicroscopy
- Tomographic reconstruction