Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination

Abstract

Three-dimensional reconstruction of the electron-scattering potential of biological macromolecules from electron cryo-microscopy (cryo-EM) projection images is an ill-posed problem. The most popular cryo-EM software solutions to date rely on a regularization approach that is based on the prior assumption that the scattering potential varies smoothly over three-dimensional space. Although this approach has been hugely successful in recent years, the amount of prior knowledge that it exploits compares unfavorably with the knowledge about biological structures that has been accumulated over decades of research in structural biology. Here, a regularization framework for cryo-EM structure determination is presented that exploits prior knowledge about biological structures through a convolutional neural network that is trained on known macromolecular structures. This neural network is inserted into the iterative cryo-EM structure-determination process through an approach that is inspired by regularization by denoising. It is shown that the new regularization approach yields better reconstructions than the current state of the art for simulated data, and options to extend this work for application to experimental cryo-EM data are discussed.

New tools for automated cryo-EM single-particle analysis in RELION-4.0

“…Although not illustrated here, the VDAM algorithm can also be used for 3D classification and 3D auto-refinement. The latter applications may be particularly interesting in the context of injecting more prior knowledge about protein structures into the 3D reconstruction process [ 51 ], which will be a direction of future research.…”

New tools for automated cryo-EM single-particle analysis in RELION-4.0

“…Although not illustrated here, the VDAM algorithm can also be used for 3D classification and 3D auto-refinement. The latter applications may be particularly interesting in the context of injecting more prior knowledge about protein structures into the 3D reconstruction process [54], which will be a direction of future research.…”

Bayesian Inference: The Comprehensive Approach to Analyzing Single-Molecule Experiments

“…Gaussian distributions centered at zero for these Fourier components, the use of priors in RELION enables high-resolution mapping of the electrostatic potential of a molecule while simultaneously avoiding spurious noise from the high spatial frequencies where there is little structural information present in the raw data. Work to incorporate more information into these priors is underway, for instance by including information about the inherent spatial frequencies found in all biomolecular structures (17).…”