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

Dari Kimanius, Gustav Zickert, Takanori Nakane, Jonas Adler, Sebastian Lunz, Carola-Bibiane Schonlieb, Ozan Oktem, Sjors Scheres
2020 bioRxiv   pre-print
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 regularisation 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 it exploits compares
more » ... unfavourably to the knowledge about biological structures that has been accumulated over decades of research in Structural Biology. Here, we present a regularisation framework for cryo-EM structure determination that exploits prior knowledge about biological structures through a convolutional neural network that is trained on known macromolecular structures. We insert this neural network into the iterative cryo-EM structure determination process through an approach that is inspired by Regularisation by Denoising. We show that the new regularisation approach yields better reconstructions than the current state-of-the-art for simulated data and discuss options to extend this work for application to experimental cryo-EM data.
doi:10.1101/2020.03.25.007914 fatcat:5pglr3zwpbchbbeiazhu4veqcu