GA-Based Inference of Euler Angles for Single Particle Analysis

Shusuke Saeki¹, Kiyoshi Asai², Katsutoshi Takahashi², Yutaka Ueno², Katsunori Isono³, and Hitoshi Iba¹

¹Graduate School of Frontier Science
The University of Tokyo
Hongo 7-3-1 Bunkyo-ku
Tokyo, 113-8656, Japan
{saeki,iba}@miv.t.u-tokyo.ac.jp

²Computational Biology Research Center
National Institute of Advanced Industrial Science and Technology
Aomi 2-41-6, Koutou-ku
Tokyo 135-0064, Japan
{asai-cbrc,takahashi-k,yutaka.ueno}@aist.go.jp

³INTEC Web and Genome Informatics Corporation
1-3-3 Shinsuna, Koto-ku
Tokyo 136-0075, Japan
isono@isl.intec.co.jp

Abstract. Single particle analysis is one of the methods for structural studies of protein and macromolecules developed in image analysis on electron microscopy. Reconstructing 3D structure from microscope images is not an easy analysis because of the low resolution of images and lack of the directional information of images in 3D structure. To improve the resolution, different projections are aligned, classified and averaged. Inferring the orientations of these images is so difficult that the task of reconstructing 3D structures depends upon the experience of researchers. But recently, a method to reconstruct 3D structures is automatically devised [6]. In this paper, we propose a new method for determining Euler angles of projections by applying Genetic Algorithms (i.e., GAs). We empirically show that the proposed approach has improved the previous one in terms of computational time and acquired precision.

LNCS 2724, p. 2288 ff.

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