Maximum likelihood based classification of electron tomographic data.
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.
Stölken
Michael
M
Max Planck Institute of Biochemistry, D-82152 Martinsried, Germany.
Beck
Florian
F
Haller
Thomas
T
Hegerl
Reiner
R
Gutsche
Irina
I
Carazo
Jose-Maria
JM
Baumeister
Wolfgang
W
Scheres
Sjors H W
SH
Nickell
Stephan
S
D000465:Algorithms; D003627:Data Interpretation, Statistical; D055032:Electron Microscope Tomography / Q000145:classification / Q000379:methods* / Q000706:statistics & numerical data*; D016013:Likelihood Functions; D008958:Models, Molecular; D056405:Thermosomes / Q000737:chemistry*
D056405:Thermosomes