Statistical characterization of ensembles of symmetric virus particles: 3-D stochastic signal reconstruction from...

Stochastic models of nano-biomachines have been studied by 3-D reconstruction from cryo electron microscopy images in recent years. The image data is the projection of many heterogeneous instances of the object under study (e.g., a virus). Initial reconstruction algorithms require different instances of the object, while still heterogeneous, to have the same symmetry. This paper presents a maximum likelihood reconstruction approach which allows each object to lack symmetry while constraining the statistics of the ensemble of objects to have symmetry. This algorithm is demonstrated on bacteriophage HK97 and is contrasted with the former algorithm. Reconstruction results show that the proposed algorithm provides estimates that make more biological sense.