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A projection matching algorithm is proposed to efficiently reveal all match projections according to the projective properties of spatial edges.
To improve resolution, the preliminary maps were refined using projection matching.
Orientations of class images were refined using the projection matching procedure in Spider [51].
Particles were selected interactively from the micrographs using Boxer program (EMAN) [20] and classified according to their oligomeric state and orientation by multi-reference projection matching based methods using SPIDER [21].
The best parameters are used to create a model for further iterations of projection matching.
Untitled and tilted particles from the first five classes were merged into a single dataset for further projection matching refinement.
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We performed projection-matching refinement, first against reference-free class averages and then against single particles.
Projection-Matching Refinement We initially refined the OTR models against class averages of cryo-NS data.
3D volumes of ORC were reconstructed by iterative projection-matching refinement using SPIDER (Frank et al., 1996).
Particles in each class were sorted into two independent stacks and subjected to further projection-matching refinement.
An initial set of angles was assigned to the Group I particle stack using Xmipp projection-matching refinement.
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