CryoGAN: A New Reconstruction Paradigm for Single-Particle Cryo-EM via Deep Adversarial Learning

Abstract

We present CryoGAN, a new paradigm for single-particle cryo-EM reconstruction based on unsupervised deep adversarial learning. The major challenge in single-particle cryo-EM is that the imaged particles have unknown poses. Current reconstruction techniques are based on a marginalized maximum-likelihood formulation that requires calculations over the set of all possible poses for each projection image, a computationally demanding procedure. CryoGAN sidesteps this problem by using a generative adversarial network (GAN) to learn the 3D structure that has simulated projections that most closely match the real data in a distributional sense. The architecture of CryoGAN resembles that of standard GAN, with the twist that the generator network is replaced by a model of the cryo-EM image acquisition process. CryoGAN is an unsupervised algorithm that only demands projection images and an estimate of the contrast transfer function parameters. No initial volume estimate or prior training is needed. Moreover, CryoGAN requires minimal user interaction and can provide reconstructions in a matter of hours on a high-end GPU. In addition, we provide sound mathematical guarantees on the recovery of the correct structure. CryoGAN currently achieves a 8.6 Å resolution on a realistic synthetic dataset. Preliminary results on real β-galactosidase data demonstrate CryoGAN’s ability to exploit data statistics under standard experimental imaging conditions. We believe that this paradigm opens the door to a family of novel likelihood-free algorithms for cryo-EM reconstruction.