Introduction

Welcome to the documentation for cryoDRGN-AI!

cryoDRGN-AI is an open-source software package for ab initio heterogeneous reconstruction in cryo-EM. This user guide is a work-in-progress. Please check back regularly for updates!

Inputs/Outputs

cryoDRGN-AI takes a collection of picked particles with estimated CTF parameters as input and outputs an approximation of the conformational space of the biomolecular complex. The output takes the form of a set of low-dimensional latents (one for each picked particle) and a neural network that associates latents to 3D density maps.

Method Overview

cryoDRGN-AI needs to jointly infer the pose of each particle in the dataset while reconstructing the structural variability of the molecule. To do so, it fuses the flexibility of implicit neural representations with a fast and scalable pose estimation strategy. Poses are optimized using a hierarchical search strategy and are later refined by stochastic gradient descent. Conformations are independently optimized in an autodecoding fashion. The whole model (neural field + conformations + poses) is optimized by gradient descent in order to minimize the difference between reconstructed images and real observations.

Tutorial Outline

The user guide is split into 3 parts:

1. Installation: details on how to install cryoDRGN-AI.

2. Running a job: tutorial explaining how to run cryoDRGN-AI, monitor your job and analyze the outputs.

3. Full API documentation: details on all the cryoDRGN-AI parameters.

Citing this Work

@article{cryodrgnai,
  title = "CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets",
  author = "Levy, Axel and Raghu, Rishwanth and Feathers, Ryan and Grzadkowski, Michal and Poitevin, Frederic and
            Johnston, Jake D, Vallese, Francesca and Clarke, Oliver B and Wetzstein, Gordon and Zhong, Ellen D",
  journal = "Nature Methods",
  doi = 10.1038/s41592-025-02720-4,
  url = nature.com/articles/s41592-025-02720-4,
  month = jun,
  year = 2025,
}