Fig. 2: The workflow of cryoPROS.

The cryoPROS protocol includes two components: generative module and co-refinement module. Using imaging parameters \(\Theta\), the generative module trains three networks: the encoding network \(q({z|}\Theta,)\), the prior network \(p({z|}\Theta )\), and the decoding network \(p({x|z},\Theta )\). After training, this module can generate auxiliary particles through \(p({z|}\Theta )\) and \(p({x|z},\Theta )\), indicated by a blue arrow. Additionally, the optional ReconDisMic algorithm can be employed to generate micelles in studies involving membrane proteins. The co-refinement module employs cryo-EM single-particle analysis software for global co-refinement with both raw and auxiliary particles, estimating the pose parameters for the raw particles. Optionally, these pose parameters can be further refined through a local refinement step. Co-classification can also be adopted with existing single-particle analysis software. The refined pose parameters are then utilized to reconstruct a volume for subsequent iterations.