Determining structures in a native environment using single-particle cryoelectron microscopy images

Cryo-electron tomography is a powerful tool for structure determination in the native environment. However, this method requires the acquisition of tilt series, which is time-consuming and severely slows structure determination. By treating the densities of non-target protein as non-Gaussian noise, we developed a new target function that greatly improves the efficiency of recognizing the target protein in a single cryoelectron microscopy image. Moreover, we developed a sorting function that effectively eliminates the model dependence and improved the resolution during the subsequent structure refinement procedure. By eliminating model bias, our method allows using homolog proteins as models to recognize the target proteins in a complex context. Together, we developed an in situ single-particle analysis method. Our method was successfully applied to solve structures of glycoproteins on the surface of a non-icosahedral virus and Rubisco inside the carboxysome. Both data were collected within 24 h, thus allowing fast and simple structural determination.