Maximum likelihood based classification of electron tomographic data

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• 作者：Michael Stö ; lken ; Florian Beck ; Thomas Haller ; Reiner Hegerl ; Irina Gutsche ; Jose-Maria Carazo ; Wolfgang Baumeister ; Sjors H.W. Scheres ; Stephan Nickell
• 关键词：Cryo-electron tomography ; Sub-tomogram averaging ; Expectation maximization
• 刊名：Journal of Structural Biology
• 出版年：2011
• 出版时间：January 2011
• 年：2011
• 卷：173
• 期：1
• 页码：77-85
• 全文大小：551 K

文摘

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.