A Bayesian View on Cryo-EM Structure Determination

By Sjors H. W. Scheres. IEEE International Symposium on Biomedical Imaging, 2012

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Three-dimensional (3D) structure determination by single-particle analysis of cryo-electron microscopy (cryo-EM) images requires many parameters to be determined from extremely noisy data. This makes the method prone to overfitting, that is, when structures describe noise rather than signal, in particular near their resolution limit where noise levels are highest. Cryo-EM structures are typically filtered using ad hoc procedures to prevent overfitting, but the tuning of arbitrary parameters may lead to subjectivity in the results. I describe a Bayesian interpretation of cryo-EM structure determination, where smoothness in the reconstructed density is imposed through a Gaussian prior in the Fourier domain. The statistical framework dictates how data and prior knowledge should be combined, so that the optimal 3D linear filter is obtained without the need for arbitrariness and objective resolution estimates may be obtained. Application to experimental data indicates that the statistical approach yields more reliable structures than existing methods and is capable of detecting smaller classes in data sets that contain multiple different structures.

Subfields: Structural Biology, Surfaces, Coatings and Films

View Paper: https://www.semanticscholar.org/paper/0174adba872fba26b57c4285c592cc412a8681ce

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In Surfaces, Coatings and Films

Citations in the corpus

Reconstructing continuous distributions of 3D protein structure from cryo-EM images (2019, methodology): “Despite these challenges, continuing advances in hardware and software have enabled structure determination at near-atomic resolution for rigid proteins (Kühlbrandt (2014); Scheres (2012b); Renaud et al. (2018); Li et al. (2013)).”
Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning (2016, positive): “Thus, we employed the adaptive MAP method (Scheres, 2012; Tagare, et al., 2010) to align single-particle images prior to SML-based clustering (Fig.”
New tools for automated high-resolution cryo-EM structure determination in RELION-3 (2018, methodology): “The availability of the first direct-electron detectors coincided with the introduction of the RELION software ( Scheres, 2012b ).”
High-resolution noise substitution to measure overfitting and validate resolution in 3D structure determination by single particle electron cryomicroscopy (2013, methodology): “Scheres introduced a Bayesian interpretation of the cryoEM method that led to an optimal 3D linear filter [41], which required none of the arbitrary parameter”
BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images (2016, methodology): “BioEM requires relatively few images to discrimi- nate the correct model within a pool of plausible structures (e. g., 1500 particles for GroEL [35]), whereas, for full 3D reconstructions, Relion typically requires tens to hundreds of thousands of particles and costly computational resources to imp”
RELION: Implementation of a Bayesian approach to cryo-EM structure determination (2012, methodology): “Recently, I described a Bayesian approach to cryo-EM structure determination, in which the reconstruction problem is expressed as the optimization of a single target function (Scheres, 2012).”
Molecular architecture of a eukaryotic translational initiation complex (2013, background): “Cryo–electron microscopy (cryo-EM) makes it possible to determine the structure of a minor population in a heterogeneous sample, from several orders of magnitude less material (12).”