Motif Search in Electron Tomography

Abstract

Cryoelectron tomography aims to act as an interface between two levels of 3D imaging: in vivo cell imaging and techniques achieving atomic resolution (e.g., X-ray crystallography). This most likely will happen through a computational motif search by mapping structures with atomic resolution into lower-resolution tomograms of cells and organelles. There exist a large variety of pattern recognition techniques in engineering, which can perform different types of motif search. This chapter will focus on cross-correlation techniques, which aim to identify a motif within a noisy 3D image (the tomogram or the 3D reconstruction). Generally, the success of the crosscorrelation approach depends on the resolution of the tomograms, the degree of corruption of the motif by noise as well as the fidelity with which the template matches the motif. For maximal detection signal, the template should have the same impulse response as the motif, which in this case is the macromolecule sought. Since the noise in the tomogram cannot be significantly decreased after data recording, the task of designing an accurate template reduces to the determination of the precise parameters of the image recording conditions, so that the searched motifs may be modeled as accurately as possible.

Keywords

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.