The classification was performed after 8 cycles of interative alignment in emClarity, at a pixel size of 4.1 Å (3× binned). Multi-scale PCA and k-means classification implemented in emClarity were used to identify common organization features in NC-RNA layer in the subtomograms. A 49 × 49 × 47 Å region that comprised the CA-CTD, the six-helix bundle and the NC-RNA layer was used for focused alignment and classification at the SP1-NC junction (Supplementary Fig. 7a). No symmetry was imposed. The multi-scale PCA was done using 3 resolution bands: 15, 20 and 40 Å. 25 eigenvolumes were generated for each resolution band. Of these, we selected the volumes which showed the most variance at the NC-RNA layer. Then we performed k-means clustering based on these eigenvolumes into 32 3D classes. Among those, 3 major classes showing similar NC-RNA organization were combined, comprising 6747 subvolumes (Supplementary Fig. 7b). The final resolution was calculated at 15.1 Å at 0.143 FSC.

A composite model was built in Coot57 using the refined GagΔMAT8I model and the published NC-ΨRNASL2 model (PDB 1F6U)39. UCSF Chimera55 was used for rigid body fitting of the NC and RNA region of the composite model into the NC-RNA density map. This was repeated for each CA monomer. Bond lengths were regularized in Coot by using the Regularize Zone from residue M14 in SP1 to residue Q9 in NC.

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