Research On Key Techniques Of Cryo-Electron Microscopy Macromolecular Three-Dimensional Reconstruction

Mordern computer technology has made a significant contribution to the study of structural biology on expressing, storing, acquiring and analyzing structural information. Structural biology is one of the most important subjects of life sciences. Its key research content, the three-dimensional (3D) structure of biological macromolecules, is the premise and foundation of studying the relationship between macromolecule structure and function. Determining high resolution 3D structure of macromolecule is significant to understanding its corresponding function. Electron cryomicroscopy (Cryo-EM) is one of the main methods to obtain the macromolecules structure.There are three key issues which is crucial to Cryo-EM 3D reconstruction. The first problem is segmentation of Cryo-EM image, which is crucial to the high-level processing of electron micrograph, including feature extraction, particle recognition, image registration and so on. But Cryo-EM image has the characteristic of low contrast and low signal-to-noise ratio (SNR) and is difficult to acquire ideal segmentation effect. The second is macromolecule particle recognition from Cryo-EM image, which is a crucial step of single particle reconstruction and has directly effect on the efficiency of Cryo-EM 3D reconstruction and the resolution of 3D structure. Selecting hundreds of thousands of particles from low contrast and low SNR Cryo-EM image is one of the major bottle-necks in advancing toward achieving atomic resolution reconstruction of biological macromolecules. The third problem is alignment of transmission electron microscope tilting series. Mechanical inaccuracies of the specimen holder cause specimen movement during tilting, which requires appropriate compensating adjustment. Accurate image alignment is needed for computing three-dimensional reconstructions from transmission electron microscope (TEM) tilt series.This paper has made deeply research on how to use modern computer technology and image processing technology to enhance the efficiency of CryoEM three-dimensional reconstruction and the resolution of 3D structure. Several finding has been made, which is as follows.1. This paper provides a new Cryo-EM image segmentation method based on wavelet transform and difference of Gaussian (DoG), which performs multi-scale decomposition based on wavelet transform at first. And then this method gets the DoG image of low-resolution image and does threshing segmentation based on the combination of gray and gradient information. At last, the segmentation result in low-resolution image is transferred to the high-resolution image based on the gradient. This method can overcome the flaw of the low SNR, and experiments show that it can get better segmentation effect than the canny algorithm and OTSU method.2. It is difficult to get particle region in Cryo-EM images, which leads to the difficulty of shape fearture extraction. This paper proposed a shape feature extraction algorithm based on DoG mask. This algorithm only obtains the approximate region of objects, from which the shape features are extracted. Experiments show the shape feature extracted by this method has better ability of classification than the method based on variance image.3. As to the problem of particle recognition, a two step approach for the particle recognition from Cryo-EM images has been developed in this paper, which is based on the principle of partial least square (PLS). This approach involves two steps: the detection of candidate particles based on Gaussian function and the recognition from these candidates based on PLS. This method is characterized by small sample study and can obtain satisfied recognition result with a few samples needed. Experiments show it can get low error-select-rate, which is 14% while selecting rectangular particle and only 7% while selecting circle particle. Also, this method uses a wide range of feature set to recognize particles, which is suitable to different type of particles and different image qualities. The experiment results show it has advantage on the generability.4. As to the problem of tilting alignment, a TEM tilting alignment optimization model based on local cross-correlation is established in this paper. This model can do well in noisy TEM images. And it also has good extensibility, which means it can change the similarity evaluation function freely without the change of the model. Experiments show that this method has more accurate aligning result than the global cross-correlation method.5. To obtain the optimum, it is needed to search the whole transform coefficients space, which is time-consuming. This paper proposes an adaptive searching mechanism according to the relativity between two consecutive tilt images, which can greatly reduce searching range. Experiment shows that it can enhance efficiency by 70% at least.