Image Alignment of Heterogeneous Macromolecules from Electron Microscopy

Electron microscopy (EM) is one of the key experimental techniques in modern structural biology[4]. Three-dimensional structures of large macro-molecular complexes can be solved by EM which otherwise would be impossible using X-ray crystallography or NMR. A common reconstruction technique, from 2D EM images to 3D structure, assigns 2D images to 2D projections of some initial 3D template[5, 1]. Due to high levels of noise and unknown orientation of imaged particles, the quality of the reconstruction procedure relies on two factors: an accurate alignment between a raw image and a template, and a scoring function that should be able to assign a raw particle to a correct template projection. Most of the methods for particle alignment apply a very efficient Fast Fourier Transform to compute a superposition between two images. This approach implies that only a normalized cross-correlation (NCC) function, or its variants, can be used as a similarity measure between two images[5, 1]. One of the major problems in EM reconstruction is heterogeneous data. In such cases a single template model does not accurately account for the heterogeneous data. Consequently, an application of the cross-correlation function produces inaccurate alignments. Here we present an alignment method that is able to accommodate various similarity scoring functions while efficiently sampling the 2D transformational space. In our preliminary results we apply a scoring function based on Mutual Information of two images. For a heterogeneous sample containing incomplete molecules it allows accurate alignment using a model of the complete structure. In our preliminary results, we successfully tested our approach on a model data set containing a mixed population of 70S and 50S E. coli Ribosomes.