Abstract
Single particle analysis, which can be regarded as an average of signals from thousands or even millions of particle projections, is an efficient method to study the three-dimensional structures of biological macromolecules. An intrinsic assumption in single particle analysis is that all the analyzed particles must have identical composition and conformation. Thus specimen heterogeneity in either composition or conformation has raised great challenges for high-resolution analysis. For particles with multiple conformations, inaccurate alignments and orientation parameters will yield an averaged map with diminished resolution and smeared density. Besides extensive classification approaches, here based on the assumption that the macromolecular complex is made up of multiple rigid modules whose relative orientations and positions are in slight fluctuation around equilibriums, we propose a new method called as local optimization refinement to address this conformational heterogeneity for an improved resolution. The key idea is to optimize the orientation and shift parameters of each rigid module and then reconstruct their three-dimensional structures individually. Using simulated data of 80S/70S ribosomes with relative fluctuations between the large (60S/50S) and the small (40S/30S) subunits, we tested this algorithm and found that the resolutions of both subunits are significantly improved. Our method provides a proof-of-principle solution for high-resolution single particle analysis of macromolecular complexes with dynamic conformations.
Highlights
Single-particle analysis (SPA) of electron cryo-microscopy has become an efficient method to reveal structural information of macromolecular complexes
A couple of classification methods have been developed, such as the normal mode analysis (NMA) method that uses simulated models as references for multireference supervised classification (Brink et al, 2004; Jin et al, 2014), 3D multivariate statistical analysis (MSA) that projects a 3D mask of the area with the most variance to a series of 2D images in the same orientation and performs classification focusing on the masked highly varied regions (Penczek et al, 2006a; Penczek et al, 2006b; Zhang et al, 2008), and a Bayesian based 3D classification method (Scheres, 2012b)
Starting from the preliminary alignment parameters, we focus on a single rigid module, optimize its orientation and position and thereafter compute a new reconstruction with the refined parameters
Summary
Single-particle analysis (SPA) of electron cryo-microscopy (cryo-EM) has become an efficient method to reveal structural information of macromolecular complexes. A couple of classification methods have been developed, such as the normal mode analysis (NMA) method that uses simulated models as references for multireference supervised classification (Brink et al, 2004; Jin et al, 2014), 3D multivariate statistical analysis (MSA) that projects a 3D mask of the area with the most variance to a series of 2D images in the same orientation and performs classification focusing on the masked highly varied regions (Penczek et al, 2006a; Penczek et al, 2006b; Zhang et al, 2008), and a Bayesian based 3D classification method (Scheres, 2012b) These classification methods can work well upon the assumption that the heterogeneous sample only contains a finite number of compositions/conformations
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