Model-based Reconstruction for Single Particle Cryo-Electron Microscopy

TL;DR

This paper introduces a model-based reconstruction method for cryo-electron microscopy that improves 3D protein structure determination by accounting for microscope effects and particle orientations, especially useful with noisy or sparse data.

Contribution

The paper presents a novel regularized cost function approach that incorporates a detailed forward model, enhancing 3D reconstructions in cryo-EM with known particle orientations.

Findings

Significant improvement over standard methods in simulated data

Effective handling of low signal-to-noise ratio data

Robust reconstruction despite data sparsity and flexibility

Abstract

Single particle cryo-electron microscopy is a vital tool for 3D characterization of protein structures. A typical workflow involves acquiring projection images of a collection of randomly oriented particles, picking and classifying individual particle projections by orientation, and finally using the individual particle projections to reconstruct a 3D map of the electron density profile. The reconstruction is challenging because of the low signal-to-noise ratio of the data, the unknown orientation of the particles, and the sparsity of data especially when dealing with flexible proteins where there may not be sufficient data corresponding to each class to obtain an accurate reconstruction using standard algorithms. In this paper we present a model-based image reconstruction technique that uses a regularized cost function to reconstruct the 3D density map by assuming known orientations for the particles. Our method casts the reconstruction as minimizing a cost function involving a novel forward model term that accounts for the contrast transfer function of the microscope, the orientation of the particles and the center of rotation offsets. We combine the forward model term with a regularizer that enforces desirable properties in the volume to be reconstructed. Using simulated data, we demonstrate how our method can significantly improve upon the typically used approach.