Data-driven regularization lowers the size barrier of cryo-EM structure determination

Abstract Macromolecular structure determination by electron cryo-microscopy (cryo-EM) is limited by the alignment of noisy images of individual particles. Because smaller particles have weaker signals, alignment errors impose size limitations on its applicability. Here, we explore how image alignment is improved by the application of deep learning to exploit prior knowledge about biological macromolecular structures that would otherwise be difficult to express mathematically. We train a denoising convolutional neural network on pairs of half-set reconstructions from the electron microscopy data bank (EMDB) and use this denoiser as an alternative to a commonly used smoothness prior. We demonstrate that this…

• TE
  Tertiary Education Commission
• UO
  University of Cambridge
• EC
  European Commission

  Award: 895412
• MR
  Medical Research Council

  Awards: MC_UP_A025_1013, MC UP A025 1013