Fig. 4. Using prior knowledge to extract a pre-selected conformational state.

a The processed data set contains the Tc holotoxin in both the pre-pore state (left) and the more rare pore state (right). In this experiment, we specifically target the pore state. b Progression of the number of picked particles (blue), those accounted during 2D classification (gray) and particles labeled “good” i.e. representing the pore state (green) when applying the intermediate picking models of the feedback loop to a fixed subset of 500 micrographs. Initial picking is dominated by pre-pore state particles. This overhead is reduced with each iteration, while the amount of picked pore state particle remains stable. c Representative 2D class averages depicting the decrease of unwanted classes (pore state or low quality; marked magenta) from an initial 68% in the first feedback round (left) to 26% after the last feedback round (right). d Representative 2D class averages depicting the pore state as selected by Cinderella in the final iteration of the feedback loop. e 3D reconstruction of the Tc holotoxin pore state computed from 500 micrographs using the final optimized picking model.