Wavelets filtering for classification of very noisy electron microscopic single particles images- application on structure determination of VP5-VP19C recombinant

摘要

Background: Images of frozen hydrated [vitrified] virus particles were taken close-to-focus in anelectron microscope containing structural signals at high spatial frequencies. These images had verylow contrast due to the high levels of noise present in the image. The low contrast made particleselection, classification and orientation determination very difficult. The final purpose of theclassification is to improve the signal-to-noise ratio of the particle representing the class, which isusually the average. In this paper, the proposed method is based on wavelet filtering and multiresolutionprocessing for the classification and reconstruction of this very noisy data. A multivariatestatistical analysis (MSA) is used for this classification.Results: The MSA classification method is noise dependant. A set of 2600 projections from a 3Dmap of a herpes simplex virus -to which noise was added- was classified by MSA. The classificationshows the power of wavelet filtering in enhancing the quality of class averages (used in 3Dreconstruction) compared to Fourier band pass filtering. A 3D reconstruction of a recombinantvirus (VP5-VP19C) is presented as an application of multi-resolution processing for classificationand reconstruction.Conclusion: The wavelet filtering and multi-resolution processing method proposed in this paper offers a new way for processing very noisy images obtained from electron cryo-microscopes. The multi-resolution and filtering improves the speed and accuracy of classification, which is vital for the 3D reconstruction of biological objects. The VP5-VP19C recombinant virus reconstruction presented here is an example, which demonstrates the power of this method. Without this processing, it is not possible to get the correct 3D map of this virus.