کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
2828771 | 1162758 | 2011 | 9 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Maximum likelihood based classification of electron tomographic data Maximum likelihood based classification of electron tomographic data](/preview/png/2828771.png)
Classification and averaging of sub-tomograms can improve the fidelity and resolution of structures obtained by electron tomography. Here we present a three-dimensional (3D) maximum likelihood algorithm – MLTOMO – which is characterized by integrating 3D alignment and classification into a single, unified processing step. The novelty of our approach lies in the way we calculate the probability of observing an individual sub-tomogram for a given reference structure. We assume that the reference structure is affected by a ‘compound wedge’, resulting from the summation of many individual missing wedges in distinct orientations. The distance metric underlying our probability calculations effectively down-weights Fourier components that are observed less frequently. Simulations demonstrate that MLTOMO clearly outperforms the ‘constrained correlation’ approach and has advantages over existing approaches in cases where the sub-tomograms adopt preferred orientations. Application of our approach to cryo-electron tomographic data of ice-embedded thermosomes revealed distinct conformations that are in good agreement with results obtained by previous single particle studies.
Journal: Journal of Structural Biology - Volume 173, Issue 1, January 2011, Pages 77–85