Fully Automated Particle Selection and Verification in Single-Particle Cryo-EM

Cryo-electron microscopy combined with single-particle reconstruction is a promising technique for solving the high-resolution structure of macromolecular complexes, even in the presence of conformational or compositional heterogeneity. However, the usual workflow leading to one or several structures is mired in subjective decisions that must be made by an expert. One problem, in particular, has been the difficulty finding algorithms capable of automatically selecting and verifying individual views of a macromolecular complex from the electron micrograph, due to the extremely low signal-to-noise ratio and the presence of contaminants. We present a novel machine-learning algorithm that overcomes these problems. The performance of the algorithm is demonstrated with electron micrographs of ribosomes.