Structural Basis for RNA Processing by the Human. RISC-Loading Complex

Transcription

1 Supplementary Information Structural Basis for RNA Processing by the Human RISC-Loading Complex Hong-Wei Wang, Cameron Noland, Bunpote Siridechadilok, David W. Taylor, Enbo Ma, Karin Felderer, Jennifer A. Doudna, and Eva Nogales

2 Supplementary Figures 55 o a 1 FSC Å b c d Supplementary Figure 1. 3D reconstruction of human Dicer. (a) Representative tilted-pair electron micrographs of the negatively stained human Dicer particles at 0 and -55 degrees respectively. Some individual particles are marked in yellow circles. The scale bars correspond to 50 nm. (b) Reprojections of the Dicer 3D reconstruction (first and fourth rows) and corresponding reference-free class averages (second and fifth rows), as matched by cross-correlation. Representative single particle images of each class are shown in the third and sixth rows. The box dimension corresponds to 41.8 nm. (c) Angular distribution map for the human Dicer reconstruction. The size of the spot relates to the number of particles falling in that specific view. (d) FSC curve for the human Dicer reconstruction showing the resolution of the map to be 34 Å using the 0.5 criterion.

3 1 Human Dicer DExH/ D DUF PAZ RNase IIIa RNase IIIb Ago2 binding motif 191 dsrbd 2 Giardia Dicer PAZ RNase IIIa RNase IIIb a b Supplementary Figure 2. Domain comparison between human and Giardia Dicer proteins and sequence alignment between human DDX3X and the DExH/D domain of human dicer. (a) The sequences of human and Giardia Dicer are illustrated with their domains color-coded. The Ago2 binding motif within the human RNase IIIa domain discovered by biochemical data is shown in blue. The diagrams lengths relate linearly to sequence lengths. (b) The sequence of the full length human DDX3X (residues 1-662) and of the N-terminal fragment of human Dicer (residues 1-592) were aligned using ClustalW2. The aligned sequences are annotated with the secondary structure elements of human DDX3X obtained from its crystal structure (PDB 2i4i) using WEBespript2.0.

4 Supplementary Figure 3. Comparison of projections of the synthetic basebranching 3D model to their corresponding reference-free 2D class averages of the DExH/D Dicer mutant sample. The depiction is the same as in Figure 1C.

5 Supplementary Figure 4. Electron micrograph of negatively stained uncross-linked Dicer-Ago2-TRBP complex. Small globular particles with dimensions and shape corresponding to Ago2 are indicated by the red arrowheads.

6 a b

7 c Supplementary Figure 5. Glutaraldehyde cross-linked RLC and 2D EM analysis on the complex. (a) SDS-PAGE of the cross-linked RLC at different concentrations of glutaraldehyde. (b) An electron micrograph of the negatively stained RLC crosslinked using 0.02% glutaraldehyde. (c) Gallery comparing reference-free class averages of cross-linked RLC with their best matched projection views of the 3D reconstruction of Dicer. The depiction is the same as in Fig. 2.

8 hdicer and hago2 form complex DExH/D interacts with hago2, but not TRBP a b c Supplementary Figure 6. In vitro reconstitution of human Dicer and human Ago2 and of human Ago2, TRBP, and DExH/D human Dicer mutant by size-exclusion chromatography. (a) Human Dicer and human Ago2 form stable complexes. Fractions C2 and C3, containing both Dicer and Ago2, were subjected to EM examination and analysis with and without glutaraldehyde cross-linking. (b) DExH/D human Dicer mutant binds to human Ago2 but not to TRBP. (c) The chromatograph of human Ago2 and TRBP alone does not show the two proteins in the fast eluted fractions. From top to bottom, the chromatographs are Ago2, TRBP, Dicer, Dicer+TRBP, Dicer+TRBP+Ago2 respectively.

9 a b Supplementary Figure 7. Supervised heterogeneity analysis of the crosslinked RLC. (a) Histogram of cross-correlation values obtained for the cross-linked RLC particles which compares two initial models: apo-dicer (left, gray isosurface), and the all-in reconstruction from the cross-linked RLC sample (right, gold isosurface). The X-axis is the difference value between the cross-correlation coefficients of the raw particle with the right model (CC right ) and the left model (CC left ) respectively. The values were scaled to a range from -1 to 1. The histogram was split into three groups of particles (I, II, III) to be used for the three different reconstructions shown in (b).

10 GraFix Gradient 25 % M Dicer Ago TRBP 37 a b c

11 1 FSC Å d Supplementary Figure 8. GraFix prepared RLC for single particle reconstruction (a) SDS-PAGE of GraFix sedimentation fractions. The fraction shown in lane 6 was used for further single particle EM analysis. (b) A micrograph of negatively stained particles of the GraFix prepared RLC. (c) Comparison of reference-free 2D class averages (left column of each panel) matched with reprojections (middle column of the same panel) and the corresponding views (right column of the panel) of the final 3D reconstruction of GraFix prepared RLC. Ago2 densities in certain views are marked with yellow circle. (d) Angular distribution map and the FSC curve of the reconstruction.

12 a b Supplementary Figure 9. Docking of Giardia Dicer atomic model in the 3-D reconstruction of human Dicer. Two solutions were obtained by the Fit-Model-in-Map program in Chimera for the docking of Giardia Dicer atomic model in the 3D reconstruction of human Dicer, both with the RNase III domains positioned within the top part of the platform. The two solutions differ with each other by a 180 degree rotation along their elongated axis. Both of them have very similar cross-correlation coefficient (0.55). (a) places the RNA-binding platform of Dicer (thick dashed line) facing the shaft of the RISC-loading complex, and thus positions the RNA where it can be engaged by TRBP and/or Ago2, while the only other alternative (b) places the RNA binding site facing far away from the shaft and the rest of the RLC components. This alternative would require total release of the diced RNA into the solution before being able to reengage the rest of the complex components and would make a RISC-loading complex functionally meaningless. We thus chose the docking (a) as the most biological relevant solution to present in Figure 1b and Figure 4.

13 Supplementary Methods Image processing For human Dicer, tilted pairs of particles were picked manually using WEB 1. Particle images from the micrographs were high pass and low pass filtered, normalized, boxed and decimated into boxes of square pixels, with final pixel size of 5.18 Å using SPIDER 1. For the 3D reconstruction of human Dicer, the untilted images were imported into IMAGIC for reference-free 2D alignment and classification 2 through several iterations of multi-variant statistical analysis and multi-reference alignment. The tilted partners in each class were then used to generate 3D reconstructions by back projection, using the random conical tilt (RCT) reconstruction method in SPIDER 3. The best six RCT class volumes were aligned, merged, and low-pass filtered to 50 Å resolution, then utilized as an initial model for projection matching refinement 4 using the untilted particles in an iterative process with decreasing angular steps. A final volume of human Dicer at a resolution of 34 Å was calculated from 3433 untilted particles. The GraFix prepared RLC was also reconstructed using the same strategy, i.e. RCT method followed by projection matching refinement. For this sample, total of 2600 tilted pairs were collected and used for RCT to generate an initial model. About 9000 untilted single particle images of the GraFix prepared RLC underwent heterogeneity analysis against the initial model by a maximumlikelihood algorithm using Xmipp 5 and 2000 particles were used for the final reconstruction (see below). For the other specimens, the particle images underwent reference-free 2D alignment and classification in IMAGIC to generate a final 100 to 200 class averages with 20~40 particles in each class. The class averages were compared to the 2D reprojections of either ab initio reconstructed or synthetic 3D models by multi-reference alignment in SPIDER. The best matched pairs were aligned and tiled together for clear illustration of their similarity and difference. The difference maps were calculated between class averages of the analyzed sample and the WT Dicer protein both matching the same projection view. Supervised heterogeneity analysis of the RLC dataset was done following the same strategy as in Gao et al. 6 using two initial models. One initial model was the 3D reconstruction of human Dicer low-pass filtered to 50 Å resolution. The other was the 50 Å low-pass filtered model of the back-projected 3D reconstruction from all the cross-linked RLC class averages using their corresponding matched partners Euler angles. Each particle in the RLC dataset was assigned two cross correlation coefficients to the top matched reprojections of each of the two models. The difference between the two coefficients was used as a measure of the similarity of each particle to a certain model. The crosscorrelation histogram of the entire dataset against the two models was then used to sort the particles into three groups, and to generate independent reconstructions from each group. The uncross-linked Dicer-Ago2-TRBP, Dicer-Ago2 complexes, and GraFix prepared RLC were subjected to heterogeneity 3D analysis using the maximumlikelihood reconstruction method 5. Briefly, each dataset was first aligned to the

14 Dicer 3D model (for GraFix prepared RLC, its own, ab initio RCT initial model was used), and an arbitrary number (4 for the Dicer-Ago2-TRBP, 2 for the Dicer- Ago2, and 4 for the GraFix prepared RLC dataset, respectively) of initial models from randomly defined subgroups were generated for further analysis. For each dataset, the initial models only have statistical differences. We then used the Xmipp_ml_refine3d command in the Xmipp software package 5, to reconstruct the four 3D models for the Dicer-Ago2-TRBP complex, the two models for the Dicer- Ago2 sample, and the four models for the GraFix prepared RLC using an iterative maximum-likelihood reconstruction algorithm. The most abundant subpopulation of the GraFix prepared RLC among the four had about 3000 particles and was used for projection matching refinement to gain higher resolution than the maximum-likelihood model. The final reconstruction utilized ~2000 particles after projection matching refinement. With overall similar shape to the above map, reconstructions from the other populations of the GraFix prepared RLC particles have relatively low resolution and are noisy, probably due to the flexibility of the assemblies. References 1. Frank, J. et al. SPIDER and WEB: processing and visualization of images in 3D electron microscopy and related fields. J Struct Biol 116, (1996). 2. van Heel, M., Harauz, G., Orlova, E.V., Schmidt, R. & Schatz, M. A new generation of the IMAGIC image processing system. J Struct Biol 116, (1996). 3. Radermacher, M., Wagenknecht, T., Verschoor, A. & Frank, J. Threedimensional reconstruction from a single-exposure, random conical tilt series applied to the 50S ribosomal subunit of Escherichia coli. J Microsc 146, (1987). 4. Penczek, P.A., Grassucci, R.A. & Frank, J. The ribosome at improved resolution: new techniques for merging and orientation refinement in 3D cryo-electron microscopy of biological particles. Ultramicroscopy 53, (1994). 5. Scheres, S.H. et al. Disentangling conformational states of macromolecules in 3D-EM through likelihood optimization. Nat Methods 4, 27-9 (2007). 6. Gao, H., Valle, M., Ehrenberg, M. & Frank, J. Dynamics of EF-G interaction with the ribosome explored by classification of a heterogeneous cryo-em dataset. J Struct Biol 147, (2004).