ABSTRACT
We examined the statistical performance of clustering single particle molecular images by bottom-up clustering, a hierarchical algorithm, using simulated protein images with a low signal-to-noise ratio. Using covariance for the measure of similarity together with the iterative alignment, our method was found to be fairly robust against noise. Clustering tests of four known protein structures were performed at three levels of noise and with three levels of smoothing. A significant effect of smoothing was confirmed in our results for images with noise suggesting an effective degree of smoothing depending on the noise and structural features of the target molecule. The consistency of clustering results was evaluated by the average solid angle of projection, and the precision of our clustering results was checked by the average image correlation between the obtained cluster image and the true projection. Once image features are extracted appropriately, the average solid angle also represents the degree of clustering precision.
Subject(s)
Algorithms , Computational Biology/methods , Computational Biology/statistics & numerical data , Computer Simulation , Microscopy, Electron/statistics & numerical data , Models, Molecular , Sequence Alignment/methods , Sequence Alignment/statistics & numerical dataABSTRACT
Single-particle analysis is one of the methods for structural studies of protein and macromolecules; it requires advanced image analysis of electron micrographics. Reconstructing three-dimensional (3D) structure from microscope images is not an easy analysis because of the low image 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 was automatically devised. In this paper, we propose a new method for determining Euler angles of projections by applying genetic algorithms. We empirically show that the proposed approach has improved the previous one in terms of computational time and acquired precision.
Subject(s)
Algorithms , Crystallography/methods , Image Interpretation, Computer-Assisted/methods , Imaging, Three-Dimensional/methods , Microscopy, Electron, Scanning/methods , Proteins/chemistry , Proteins/ultrastructure , Macromolecular Substances , Myosins/analysis , Myosins/chemistry , Myosins/ultrastructure , Particle Size , Protein Conformation , Proteins/analysis , Reproducibility of Results , Rotation , Sensitivity and SpecificityABSTRACT
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. 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.
