To understand the reasons for the higher efficiency of substrate D’ methylation as compared to substrate D in the Pf sR26 RNP we set out to determine the structure of the corresponding half-loaded sRNPs, bound to either substrate D or substrate D’. We used a stabilized version of the Pf sR26 guide RNA, where the apical K-loop has been substituted by a K-turn sequence (stabilized sR26, st-sR26, Figure 1b). This modification was necessary to ensure that the complex remains stably assembled over several days at 55°C, as required by the NMR experiments, and does not affect the oligomerization state of the complex (Figure 1 and Figure 1—figure supplement 3).

First, we determined the oligomerization state of the RNP complexes assembled with st-sR26 from their radius-of-gyration (R_g), measured by SAXS or SANS. To estimate the compatibility of experimentally determined R_g values with the mono- or di-RNP assembly states, we evaluated the theoretical R_g distributions of 5000 di-RNP models with randomized positions of the fibrillarin copies not bound to the RNA in both apo and holo (fully-loaded) conformations from Lapinaite et al. (2013); Figure 1—figure supplement 4). We obtained a mean R_g value of 55.9 Å with a standard deviation (SD) of 2.0 Å for the apo di-RNP and a mean R_g of 58.1 ± 3.6 Å for the holo di-RNP. The SAXS curves of the apo sRNP assembled with st-sR26 (Figure 1c) correspond to a radius-of-gyration (R_g) of 54.3 Å, which is consistent with a di-RNP architecture (Figure 1—figure supplement 4). Addition of 1.25 molar equivalents of either substrate D or D’ reduces the R_g from 54.3 Å to 50.0 or 47.3 Å, respectively, with a further reduction to 45.0 Å, upon addition of both substrates (holo state) (Graziadei et al., 2016). These radii are no longer compatible with a di-RNP, demonstrating that both the half-loaded and holo st-sR26 complexes are mono-RNPs (Figure 1—figure supplement 4). The same transition from a di-RNP to a mono-RNP occurred for the Box C/D RNP assembled with sR26 upon substrate RNA binding (Figure 1—figure supplement 3). This is different from the holo complex assembled previously in our laboratory with the ssR26 RNA (symmetric and stabilized sR26), which contains two substrate D’ RNA binding sites of the same sequence (Figure 1b and Figure 1—figure supplement 2b). The RNP assembled with ssR26 remained a di-RNP after saturation of the substrate RNA binding sites (Lapinaite et al., 2013).

Before embarking upon the structural study of the sRNPs containing st-sR26, we wanted to understand which elements are responsible for the different oligomerization states of the holo ssR26- and holo st-sR26-RNPs. The ssR26 and the st-sR26 RNAs differ only in the sequence of the guide RNA at the box D position, which in the case of ssR26 is identical to that of guide D’. Thus, we generated two additional guide RNAs with distinct D and D’ sequences, st-sR26-1 and st-sR26-2: in st-sR26-1 (st-sR26-2), guide sequence D is a chimeric sequence, formed by the 5’ half of st-sR26 guide D (st-sR26 guide D’) and the 3’ half of st-sR26 guide D’ (st-sR26 guide D) (Figure 1—figure supplement 5a). Interestingly, the Box C/D enzyme containing st-sR26-1 maintained the di-RNP architecture upon binding of either substrate RNAs, while the sRNP containing st-sR26-2 transitioned to the mono-RNP state (Figure 1—figure supplement 5b). Mutation of the last nucleotide of st-sR26-1 guide D to either C or U (A61C and A61U with complementary substrate D) did not perturb the di-RNP architecture (Figure 1—figure supplement 5c). We conclude that the guide sequence strongly influences the oligomerization state of the holo complex.

Further evidence of the monomeric state of half-loaded and holo st-sR26 complexes emerges from the P(r) distribution calculated from the SANS curve of the complexes assembled with ²H-fibrillarin in 42%:58% D₂O:H₂O solvent: the number and relative intensities of the maxima are compatible with the presence of two fibrillarin copies but incompatible with the presence of four (Figure 1—figure supplement 6). As monomeric complexes, the substrate-loaded st-sR26 RNPs can serve as proxies for the eukaryotic snoRNP. As we showed previously (Graziadei et al., 2016), the sRNP assembled with this RNA catalyses the methylation of the substrate D’ more efficiently than substrate D, in a similar manner to the native Pf sR26 RNP.

To investigate whether the difference in methylation efficiency of substrate D and D’ correlates with structural differences, we assembled the Box C/D RNP with the st-sR26 guide RNA and saturated either its D or D’ guide site (Figure 1b) to obtain two half-loaded mono-RNPs. We then determined their structures in solution, where the conformational dynamics of the complexes are preserved. The mono-RNPs are ~190 kDa in size and thus not amenable to standard structure determination by NMR. In this molecular-weight range, solution NMR focuses on methyl-group resonances, which have favourable relaxation properties and show strong signal intensity (Sprangers and Kay, 2007; Tugarinov et al., 2003) Thus, to solve the structure of the two half-loaded sRNPs, we used a combination of methyl-group NMR spectroscopy and small-angle scattering (see Methods and Carlomagno, 2014).

As in our earlier work on the fully-loaded di-RNP complex (Lapinaite et al., 2013), we started from the assumption that the interaction interface of the Nop5-CTD with the L7Ae–K-turn-RNA complex and that of the Nop5-NTD with fibrillarin do not change with respect to those observed in the respective crystal structures (Liu et al., 2007; Xue et al., 2010; Aittaleb et al., 2003). To validate this assumption we acquired two-dimensional ¹H-¹³C correlation spectra of fibrillarin and L7Ae labelled specifically at the methyl groups of Ile, Val and Leu residues (Tugarinov and Kay, 2003). The chemical shift perturbations measured for L7Ae in the Box C/D mono-RNP with respect to L7Ae in the L7Ae–K-turn-sRNA complex map to the previously described interface between L7Ae and the Nop5-CTD (Xue et al., 2010; Figure 2—figure supplement 1). Similarly, the chemical-shift perturbations measured for fibrillarin in the Nop5-NTD–fibrillarin complex with respect to free fibrillarin map to the interaction interface observed in previous crystal structures (Aittaleb et al., 2003). These CSPs are conserved in the Nop5–fibrillarin complex and in the apo Box C/D mono-RNP (Figure 2—figure supplement 2), demonstrating that fibrillarin interacts exclusively with the Nop5-NTD in all complexes.

We then used the signals from the L7Ae and fibrillarin methyl groups to measure paramagnetic relaxation enhancements (PREs). In this technique, a paramagnetic tag (spin-label) carrying an unpaired electron is coupled to a unique cysteine engineered on one protein subunit within the complex. The PREs elicited on the methyl groups of a second protein subunit by the unpaired electron are translated into distance restraints (Battiste and Wagner, 2000), which define the position and relative orientation of the two subunits in the complex. For the D-loaded (D’-loaded) mono-RNP, we collected a total of 407 (442) PREs using spin-labels on L7Ae-Q45C, L7Ae-E58C/C68S, L7Ae-C68, Nop5-E196C, Nop5-D247C and Nop5-S343C while observing the methyl resonances of fibrillarin and on Nop5-E65C while observing the methyl resonances of L7Ae (Figure 2—figure supplement 3a). The PRE data were validated by means of intra-molecular PREs within the rigid fibrillarin module (Figure 2—figure supplement 4). The excellent fit between the experimental PRE intensity ratios and those predicted from the known distances confirms the reliability of the PRE-derived inter-molecular distances.

A second class of structural restraints was derived from SANS curves acquired with contrast-matching. In these experiments one or more proteins in the complex are ²H-labelled and contribute to the observed scattering signal, while the scattered intensity of the unlabelled proteins is masked by the solvent, which is prepared as a 42%:58% D₂O:H₂O mixture. A combination of such datasets provides sufficient information to restrain the relative position of several molecules within a multi-subunit complex. In our case we acquired SANS curves for ²H-L7Ae, ²H-Nop5, ²H-Fib, ²H-RNA, ²H-Fib/²H-RNA and ²H(70%)-Nop5/²H-RNA in 42%:58% D₂O:H₂O (Figure 2—figure supplement 3b). In addition, we also collected SAXS curves, which report on the shape of the entire complexes.

These data were then incorporated into a structure-calculation protocol adapted from that developed in our previous study (Lapinaite et al., 2013) (for a description of the adapted protocol, refer to Methods and Figure 3—figure supplement 1). We used the conformations of the modules L7Ae–K-turn-sRNA–Nop5-CTD and Nop5-NTD–fibrillarin observed in previous crystal structures, and restricted our conformational search to the relative orientations of the three domains of Nop5, the conformation of the sRNA in parts other than the K-turn motifs and A-form helices and the relative positions of the two copies of each protein in the mono-RNP.

The methyl-group NMR spectrum of fibrillarin in the apo RNP assembled with st-sR26 is identical to the spectrum of the RNP assembled with ssR26 (Figure 2a, left panel). This was expected, as in both di-RNPs all four fibrillarin copies are far from the RNA and thus their chemical shifts are independent of the RNA sequence used to assemble the complex.

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Methyl groups are rather sparse in the protein surfaces involved in recognition of the RNA backbone; in the RNA-bound form of fibrillarin, only the methyl groups of V35, I82, V110, L114, I117, V151 and V185 are expected to be within 8 Å of the RNA, while only V110 should be closer than 5 Å. Therefore, the chemical shift perturbations (CSPs) for fibrillarin upon RNA binding should be few and relatively small in magnitude. As expected, the methyl-group NMR spectrum of the substrate-bound RNPs showed only moderate CSPs; nonetheless, these were mainly localized in the spectral region containing V110, V151 and V185, thus confirming that fibrillarin recognizes the substrate D’–guide duplex (Figure 2a, right panel).

Further evidence of substrate–guide recognition by fibrillarin was provided by the PRE data. As shown in Figure 2b for substrate D’, upon fibrillarin binding to the substrate–guide duplex (on-state, upper left), the Nop5-E65C spin-label (red) comes close to one L7Ae copy and would lead to PRE intensity-ratios of less than 0.8 for the L7Ae-ILV residues shown as yellow spheres. In contrast, when fibrillarin is not bound to the substrate–guide duplex (off-state, upper right), the Nop5-E65C spin-label is far from L7Ae and cannot cause any PRE attenuation of L7Ae peaks. Thus, the low PRE intensity-ratios observed experimentally for the methyl groups of the residues marked in yellow (Figure 2b, bottom) indicates the presence of conformers in which fibrillarin is bound to the substrate–guide duplex.

In an half-loaded mono-RNP, one fibrillarin copy is necessarily in the off-state, due to the lack of the corresponding substrate; the second fibrillarin copy could be either stably bound to the substrate–guide duplex (yielding a complex in the [on,off]-conformation) or exchanging between the on- and off-states (corresponding to the RNP exchanging between the RNP [on,off]- and [off,off]-conformations, Figure 2c). The NMR data are qualitatively compatible with both scenarios, as the broad line-widths and the overlap of the fibrillarin NMR peaks that show the largest CSPs upon RNA binding preclude a quantitative analysis of the magnitude of the CSPs in terms of relative proportions of the two conformations. Thus, we decided to consider both scenarios in the interpretation of the structural data.

To determine the [on,off]- and [off,off]-conformations of both the substrate D- and D’-loaded sRNPs, we adapted our previously developed structure-calculation protocol (Lapinaite et al., 2013). We initially performed two structure calculations per complex: in the first calculation, we imposed the restraint that one fibrillarin copy is in contact with the corresponding substrate–guide duplex, while the other copy is not ([on,off]-state); in the second calculation, we left both fibrillarin copies free to adopt any position compatible with the PRE data ([off,off]-state). We then recursively binned the PRE-derived distance-restraints into two sets, according to their compatibility with the the [on,off]- or [off,off]-conformations (Figure 3—figure supplement 1). The majority of restraints were found to be consistent with both states and therefore appeared in both sets. One notable exception is the set of PRE restraints derived from the methyl-groups of L7Ae in the presence of spin-labelled Nop5-E65C, which are compatible only with fibrillarin being in contact with the substrate–guide duplex (Figure 2b). In total, we performed four structure-calculation runs, two for each of the half-loaded complexes. The [on,off]-conformations were compatible with nearly all PRE-derived restraints (400 out of 407 for the substrate D-loaded and 436 out of 442 for the substrate D’-loaded RNP, respectively), while the [off,off]-conformations were compatible with 364 and 414 restraints for the substrate D- and substrate D’-loaded RNP, respectively.

Each individual structure calculation proceeded through a global and a local search stage. At each stage, the total and distance-restraint energies as well as the back-calculated fits to the SANS curves were used for structure selection. The two final structure ensembles corresponding to the [on,off]-states (Figure 3) are defined to a precision of better than 2.5 Å (root-mean-square-deviation, RMSD, of the protein Cα and RNA P atoms, excluding flexible regions). When compared to the existing structure of the holo mono-RNP from Sulfolobus solfataricus (PDB entry 3pla, Lin et al., 2011), the substrate D- and substrate D’-loaded complexes show a reasonable similarity (Figure 3—figure supplement 2). All major features of the substrate-bound site are conserved: the RNA-guide sequences lie on the coiled-coil Nop5 domain at an angle of about 70° and the C-terminal tip of L7Ae is in proximity to the short Nop5 β-sheet 77–79 and α-helix 64–73. However, the solution structures differ from the crystallographic structure in many details, demonstrating that the sRNP architecture is flexible enough to adapt to different guide- and substrate-RNAs. As expected, a significant divergence from the structure of PDB entry 3pla is observed in the substrate-unbound half of the complexes.

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Importantly, neither the [on,off]- nor the [on,on]-ensemble are able to reproduce the combination of PRE and SAS data satisfactorily for each of the substrate D- or the substrate D’-loaded RNPs. The PRE intensity-ratios measured for the Nop5-NTD-E65C mutant on the methyl-groups of L7Ae indicate the presence of conformers in the [on,off]-state. In agreement with this, the [on,off]-structures of Figure 3 reproduce the PRE data reasonably well both for the substrate D- and substrate D’-loaded complexes (Figure 3—figure supplements 3 and 4). However, these structures are unable to fit the ²H-Fib SANS, ²H-Fib/²H-RNA SANS and SAXS curves in a satisfactory manner (Figure 3—figure supplement 5). Thus, the combination of PRE and SAS data is incompatible with a single state for each of the substrate D- or substrate D’-loaded RNPs, but rather reveals the presence of conformational ensembles.

Because the SAS data that are in disagreement with the [on,off]-conformations of Figure 3 all report on the position of the fibrillarin copies in the complexes, we deduced that the conformational equilibria present in solution must be related to the position of fibrillarin. Different types of conformational equilibria are conceivable. In the simplest scenario, only the fibrillarin in the off-state samples multiple conformations, with the second fibrillarin remaining stably in the on-state; in a more complex scenario, the second fibrillarin copy may sample both the on- and off-states (in addition to the conformational flexibility of the fibrillarin copy in the off-state).

To represent both scenarios and obtain structural ensembles compatible with both PRE and SAS experimental data, we developed an ensemble scoring protocol (Figure 3—figure supplement 1b, Methods). For both the substrate D- and substrate D’-loaded RNPs, we used representative structures of the [on,off]- and [off,off]-state ensembles (Figure 3) — defined as the structure closest to the mean structure — as starting points to generate four sets of ~4000 conformations, in which the positions of the Nop5-NTD–fibrillarin units not bound to the substrate–guide duplex were randomized, in order to account for their flexibility. We then used a pseudo-genetic algorithm to select ensembles of either exclusively [on,off]-conformers or of both [on,off]- and [off,off]-conformers that best fit the PRE data, as well as the ²H-Fib and ²H-Nop5 SANS, ²H-Fib/²H-RNA SANS, ²H(70%)-Nop5/²H-RNA SANS and SAXS curves (Figure 3—figure supplement 1).

Despite the reasonable fit of the PRE intensity ratios of the substrate D’-loaded sRNP with the representative structure of the [on,off]-conformers of (Figure 3; Figure 3—figure supplement 3), the larger R_g of the experimental ²H-Fib SANS curve with respect to the theoretical one indicated the presence of conformers where the two copies of fibrillarin are more distant from each other than in this set of [on,off]-conformers (Figure 3—figure supplement 5).

We thus set out to improve the fit to the experimental data by deriving mixed ensembles containing both [on,off]- and [off,off]-conformers using the ensemble scoring protocol described above. The resulting best-fit ensembles contained 66 ± 8% [on,off]-conformers and showed a much improved fit to both the SAXS and ²H-Fib SANS curves (Figure 4). The agreement between experimental and predicted PREs also improved (Figure 5).

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To verify that an acceptable fit to the experimental data requires the combination of both [on,off]- and [off,off]-conformers in the structural ensemble, we repeated the ensemble scoring protocol selecting from only [on,off]- or [off,off]-conformers. The fit to the SAS curves remained unsatisfactory for both these ensembles (Figure 4—figure supplement 1), with the [on,off]-ensemble yielding a poor fit to the ²H-Fib SANS curve and the [off,off]-ensemble being unable to reproduce the SAXS curve. In addition, the fit of the [on,off]-ensemble to the PRE data (Figure 5—figure supplement 1a) remained inferior to that of the ensemble containing both [on,off]- and [off,off]-structures.

The higher values of the PRE intensity-ratios measured for the L7Ae methyl-groups in the presence of the spin-labelled Nop5-NTD-E65C mutant in the substrate D-loaded mono-RNP as compared to the substrate D’-loaded mono-RNP indicated that the proportion of fibrillarin bound to the substrate–guide duplex is lower for the mono-RNP loaded with substrate D than for that loaded with substrate D’. Accordingly, the combination of PRE and SAS data could not be fit with an ensemble consisting of [on,off]-conformers only, as the χ² value of the SAXS curve remained as poor as that obtained with a single [on,off]-conformer (>250) (Figure 4—figure supplement 1). Conversely, ensembles containing only [off,off]-conformers failed to reproduce the PRE dataset of the complex containing spin-labelled Nop5-E65C (Figure 5—figure supplement 1b).

When we fitted the PRE and SAS data with ensembles consisting of both [on,off]- and [off,off]-conformers, we could reproduce all experimental data satisfactorily with a population of [on,off]-conformers of 34 ± 10% (Figures 4 and 5).

The combination of the NMR and SAS data demonstrated the existence of a conformational equilibrium between [on,off]- and [off,off]-conformers for both substrate D- and substrate D’-loaded RNPs. The ensemble of conformations representing the substrate D’-loaded sRNP (Figure 4) contained a reproducibly higher proportion of conformers with fibrillarin in the [on,off]-state (66 ± 8%) than did the ensemble representing the substrate D-loaded sRNP (34 ± 10%), as was expected from the stronger PRE effects induced on L7Ae by the Nop5-E65C paramagnetic tag for the substrate D’-loaded sRNP (Figure 2b). Thus, despite the lack of sequence-specific interactions with the RNA, fibrillarin binds more strongly to the substrate D’–guide duplex than to the substrate D–guide duplex in the context of the Box C/D RNP.

This observation prompted us to analyse in more detail the structural differences between the [on,off]-states of the substrate D- and D’-loaded RNPs, as well as their stability in a 150-ns molecular-dynamics (MD) simulation. In the [off,off]-state, both half-loaded RNPs display a regular A-form helix of 11 base-pairs formed by the guide and substrate RNAs and positioned far from the Nop5 coiled-coil domains. The geometry of this helix was given as a restraint in the structure calculations, because of the perfect complementarity of the substrate–guide sequences over these 11 nucleotides. Binding of fibrillarin pushes the substrate–guide duplex towards the Nop5 coiled-coil domain, thereby perturbing the base-pairing at the substrate 3’ end (Figure 6). This observation is in agreement with a recent study, reporting that a substrate–guide duplex of only 10 base-pairs results in the highest level of in vitro methylation for a S. solfataricus enzyme (Yang et al., 2016). During the 150-ns MD trajectory of the D’-loaded complex, the two base-pairs at the 3’ end of substrate D’ are disrupted and the Nop5 α10 helix and its flanking loops form many electrostatic contacts with the RNA (Figure 7). In addition, W319 forms a face-to-face interaction with the no-longer base-paired G15 of the guide RNA. In contrast, in the substrate D-loaded complex, only one base-pair is melted at the 3’ end of the substrate (the second last), fewer new contacts are formed between the protein and the RNA and some other contacts are lost during the simulation (Figures 6 and 7). Furthermore, in the substrate D’-loaded complex the A–U base-pair at the 5’ end of substrate D’ iwas often disrupted during the simulation, allowing for the formation of electrostatic contacts between E289 and A25/C5 and K290 and U4 (Figure 7). Conversely, in the substrate D-loaded complex, the C–G base-pair at the 5’ end of the substrate remains stable throughout the simulation (Figure 6). The first-base paired nucleotide of substrate D is kept in place by hydrogen bonds between G22 of the unpaired guide and its sugar backbone. In agreement with our MD simulations, Yang et al. (2016) demonstrated that high levels of methylation occur for a substrate–guide duplex length of 8–10 base pairs.

Figure 6

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Figure 7

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We conclude that the stability of the fibrillarin-bound form depends on a delicate balance between the loss of entropy due to fibrillarin localization, and the positive and negative enthalpy changes associated with base-pair melting and formation of new protein–RNA contacts, respectively. Given that:

where $G_{on,off}^{D\text{'}}\left(G_{on,off}^D\right)$ and $G_{off,off}^{D\text{'}}\left(G_{off,off}^D\right)$ are the free energies of the substrate D’ (D)-loaded complex in the [on,off]- and [off,off]-states, respectively, and $\frac{p_{on,off}^{D\text{'}}}{p_{off,off}^{D\text{'}}}\left(\frac{p_{on,off}^D}{p_{off,off}^D}\right)$ is the ratio of the populations of the substrate D’ (D)-loaded complex in the [on,off]- and [off,off]-states, we can calculate that the difference between the ΔG values for the [off,off]→[on,off] transition of the substrate D’- and substrate D-loaded complexes in the st-sR26 RNP is only 0.86 ± 0.55 kcal/mol. This small value suggests that fine differences in the stability of the substrate–guide helices may regulate the affinity of fibrillarin for the methylation site and thus the fractional population of active enzyme.

L7Ae (UniProtKB accession code Q8U160), Nop5 (Q8U4M1) and archaeal fibrillarin (Q8U4M2) were expressed, purified and reconstituted with sRNAs as described previously (Graziadei et al., 2016). Nop5 was expressed with the L113K and V223E mutations in order to prevent the formation of aggregates. Deuterated proteins were expressed in 100% D₂O M9 minimal medium using ²H-glycerol as the sole carbon source. Deuterated proteins with ¹H,¹³C-labelled ILV methyl groups were produced following protocols developed in the Kay laboratory (Tugarinov and Kay, 2003). Stereospecific pro-S ¹H,¹³C-labelling of valine and leucine methyl groups was obtained by expression with the appropriate metabolic precursor according to the specifications of the manufacturer (TLAM-I^δ1LV^proS; NmrBio). Leucine-specific labelling was achieved using the protocol described by Lichtenecker et al. (2013). All NMR samples were assembled with ²H-Nop5, and, in the case of ¹H,¹³C -ILV methyl-labelled L7Ae, with both ²H-Nop5 and ²H-fibrillarin. The ²H(70%)-Nop5 sample for SANS experiments was obtained by expression in 100% D₂O M9 minimal medium with ¹H-glucose as the sole carbon source; deuteration levels for this sample were verified by MALDI mass spectrometry.

Guide-RNAs were produced by in vitro transcription from double-stranded plasmid DNA templates using T7 RNA polymerase produced in-house and rNTPs (Roth). RNAs were purified by denaturing 12–20% polyacrylamide gel electrophoresis, and extracted by electro-elution. For ²H-RNA samples, RNA synthesis was performed using ²H-labelled rNTPs (Silantes).

st-sR26: 5’-GCGAGCAAUGAUGAGUGAUGGGCGAACUGAGCUCGAAAGAGCAAUGAUGACGGAGGUGAUCACUGAGCUCGC-3’ st-sR26-1: 5’-CGAGCAAUGAUGAGUGAUGGGCGAACUGAGCUCGAAAGAGCAAUGAUGACGGAGGGGCGAACUGAGCUGCG-3’

st-sR26-2: 5’-CGAGCAAUGAUGAGUGAUGGGCGAACUGAGCUCGAAAGAGCAAUGAUGAGUGAUGUGAUCACUGAGCUGCG-3’ sR26: 5'-GCGAGCAAUGAUGAGUGAUGGGCGAACUGAAAUAGUGAUGACGGAGGUGA UCUCUGAGCUCGC-3’

Substrate RNAs for st-sR26 were produced in-house using synthetic DNA oligonucleotides:

Substrate D′: 5′-GCUUCGCCCAUCAC-3’

Substrate D: 5′-GUAGAUCACCUCCG-3’

st-sR26-1 substrate D: 5’-GUAUCGCCCCUCCG-3’

st-sR26-2 substrate D: 5’-GUAGAUCACAUCAC-3’

In the free state, fibrillarin methyl resonances were stereospecifically assigned by means of 3D NOESY–¹³C-HMQC spectra, acquired on ILV and ILV^proS-labelled samples, in combination with 3D TOCSY–¹³C-HMQC spectra and by comparison to the NOEs expected from the fibrillarin structure. The assignment was transferred stepwise from the free fibrillarin to the Nop5-NTD–fibrillarin complex, the Nop5–fibrillarin complex and finally to the full Box C/D complex. For the ILV-labelled Nop5-NTD–fibrillarin complex, we also acquired a 3D NOESY–¹³C-HMQC spectrum; for all complexes we acquired ¹³C-HMQC spectra on ILV-labelled, ILV^proS-labelled and L-labelled samples. For the ILV-labelled Nop5–fibrillarin complex, pairings of HMQC peaks from the diastereotopic methyl-groups of leucine and valine residues were verified with the assistance of a 3D experiment in which the ¹H and ¹³C resonances of the methyl groups were correlated with the ¹³C resonances of the directly bonded methine carbon (Cγ and Cβ for leucine and valine residues, respectively), thereby allowing methyl-pairs to be identified from their common methine resonance. The pulse-sequence for this experiment comprises an out-and-back magnetization-transfer-pathway starting and ending on the methyl protons, using COSY-type transfers between the methyl and methine carbons and constant-time chemical-shift evolution periods for both indirect ¹³C dimensions.

Mutants were generated following the QUIKCHANGE-XL protocol (Agilent Technologies) and purified in the presence of 5 mM β-mercaptoethanol in order to prevent disulfide bond formation. For L7Ae, the native C68 was mutated to serine prior to the introduction of cysteine residues at other sites. The purified protein was then buffer exchanged into 50 mM NaPi, 500 mM NaCl, pH 6.6 using a HiPrep 26/10 desalting column (GE Healthcare) and eluted directly into tubes containing a 10-fold molar excess of the 3-(2-iodoacetoamido)-PROXYL radical (Sigma-Aldrich) in the dark. The spin-labelling reaction was allowed to proceed overnight at room temperature. Spin-labelled proteins were used for complex reconstitution; the free spin-label was removed during the gel-filtration step. The final reconstitution step was carried out in 100% D₂O buffer (50 mM NaPi, 500 mM NaCl, pH 6.6), prior to concentration with a 10 kDa-cutoff Amicon centrifugal concentrator (Merck Millipore).

All substrate-loaded sRNPs were obtained by addition of 1.25 molar equivalents of substrate RNA. This ratio yields full saturation of the substrate RNA-binding sites of the guide RNA. We verified this by monitoring the appearance of peaks indicative of free RNA (sharp peaks) in one-dimensional ¹H spectra of the sRNP upon addition of increasing concentrations of substrate RNA. Sharp peaks began to appear after a 1:1 molar ratio of substrate:guide RNA was reached.

¹³C-HMQC spectra were acquired on Bruker Avance 800 and 850 MHz spectrometers, equipped with TCI cryoprobes, at 55°C with sample concentrations between 10 and 40 μM (2–8 mg/ml). Diamagnetic spectra were recorded after reduction of the spin-label by addition of ascorbic acid to a final concentration of 5 mM.

All spectra were processed using apodization with an exponential function in order to preserve Lorentzian line-shapes. Peaks were fitted with the program FUDA (http://www.ucl.ac.uk/hansen-lab/fuda/) assuming Lorentzian line-shapes. When necessary, overlapped peaks were fitted as groups. The fitted volumes and line-widths were then converted into peak-heights. The heights in the paramagnetic and diamagnetic states were used to calculate the distance between the nitroxide group of the paramagnetic tag and the respective methyl-group (see below).

The diamagnetic R₂ rates corresponding to the transverse relaxation rates of ¹H single-quantum coherence (R₂^diaH) and ¹H-¹³C multiple-quantum coherence (R₂^diaHC) of each individual peak were quantified using the pulse-schemes from the Kay laboratory (Tugarinov and Kay, 2006; Tugarinov and Kay, 2013), modified to remove the fast-relaxing-component purging-element. Relaxation delays were 0, 2, 3, 4, 6, 7, 10 and 16 ms for fibrillarin, and 0, 2, 3, 4, 6, 7 and 10 ms for L7Ae. The peak-heights were fitted to a mono-exponential decay function to extract R₂^diaH and R₂^diaHC.

In order to derive the correlation-time for the electron-nucleus interaction vector, τ_C, we quantified paramagnetic (I_para: oxidized, paramagnetic state of the spin-label) and diamagnetic (I_dia: reduced, diamagnetic state of the spin-label) peak-heights corresponding to known distances within fibrillarin in complexes reconstituted with the Fib-R109C mutant. For L7Ae, we used known distances between the Nop5-CTD and L7Ae in complexes reconstituted with the Nop5-S343C mutant. The ratios of peak-heights were converted into PREs (Γ₂), using Equation 2 and the R₂^diaHC and R₂^diaH rates measured for the respective peaks.

where t_HMQC represents the magnetization transfer time in the HMQC sequence (7.6 ms). As this equation is non-invertible, Γ₂ was derived by plotting the simulated bleaching ratio, I_para/I_dia, as a function of Γ₂ for a given set of diamagnetic rates, with the experimental errors on I_para/I_dia, R₂^diaH and R₂^diaHC used to determine the upper and lower bounds of the derived PRE. These PREs were then used as restraints in the protocol developed in the Clore Lab (Iwahara et al., 2004), which optimizes an ensemble of multiple spin-label conformations in combination with τ_C. For L7Ae, we used isoleucine resonances only. The minimization was run using the recommended ‘obsig’ setting for the weighting of the different PREs. After minimization of 20 structures, τ_C was 51.8 ± 5.7 ns for fibrillarin and 50.4 ± 9.4 ns for L7Ae.

For a given value of τ_C, distances r between the unpaired electron and the methyl protons were extracted from the equation:

where K is a constant ($1.23\times {10}^{-23}$ cm⁶ s⁻²) and ω is the proton Larmor frequency in rad/s. The errors on the distances were again estimated by using the errors in τ_C, experimental I_para/I_dia ratios and R₂ rates to yield upper and lower bounds on a calibration curve. A lower-bound of 10% was used for the errors of I_para/I_dia, as recommended by Battiste & Wagner (Battiste & Wagner, 2000). A lower-bound of 2 Å was imposed for the errors on the distances in order to account for tag flexibility. Finally, a minimum error of −4 Å was used as lower bound for the distances extracted from the PRE ratios in the calculation of the [on,off]-structures, to account for the possibility that the methyl group of only one fibrillarin copy is close the paramagnetic tag: in this case, the effective distance of the methyl group of the one fibrillarin copy to the paramagnetic tag would be smaller than the distance calculated from the sum of the two overlapping fibrillarin peaks (one with PRE intensity-ratios < 0.8 and one with PRE intensity-ratios close to 1).

In the structure calculations (CNS), distances were imposed from the nitrogen atom of the nitroxide group of the paramagnetic tag to the carbon atoms of fibrillarin methyl groups. For L7Ae, where stereospecific assignment of LV methyl groups was not available, the distance restraint was imposed to both methyl group carbons with an ‘OR’ statement. For complexes with both fibrillarin copies positioned away from the RNA, the same set of distance restraints was imposed on each fibrillarin copy; for complexes with one fibrillarin copy close to the RNA, distance restraints were imposed with an ‘OR’ statement.

Box C/D sRNPs reconstituted in 50 mM NaPi pH 6.6, 500 mM NaCl were recorded at 40°C and concentrations varying from 0.4 to 5 mg/ml, unless otherwise specified. In most experiments a temperature of 40°C instead of 55°C was used for SAXS measurements due to the difficulty in collecting data with high salt concentrations at the higher temperature. For all measurements, 2 mM dithiothreitol (DTT) was added to mitigate radiation damage. Data collection was performed at the ESRF bioSAXS beamline BM29 with exposure of 10 frames each of 1 s duration. The curves were compared, merged, and the buffer contribution subtracted by the beamline software BsxCube (Pernot et al., 2013). Forward scattering intensity I(0) values were normalized relative to an ideal protein in an ideal solution, and were reported as 288, 194, 215 and 197 for the apo st-sR26 RNP, the substrate D’-bound st-sR26 RNP, the substrate D-bound st-sR26 RNP and the holo st-sR26 RNP, respectively, all at 5 mg/ml. The R_g and I(0) values were extracted according to the Guinier approximation using PRIMUS in ATSAS 2.7.5 (Konarev et al., 2003). All R_g values were computed using an s.R_g upper limit of 1.3 (where s is the modulus of the scattering vector), as recommended for globular particles.

To estimate the compatibility of the experimentally determined R_g values with the mono- or di-RNP assembly states, we evaluated the theoretical R_g distributions of 5000 di-RNP models in both apo and holo conformations from Lapinaite et al. (2013) and 500 half-loaded mono-RNP models generated in both [on,off] and [off,off]-states using the torsion-angle simulated-annealing protocol described below. The apo di-RNP showed a mean R_g value of 55.9 Å with a standard deviation (SD) of 2.0 Å; the holo di-RNP showed a mean R_g of 58.1 ± 3.6 Å; the [on,off]-state of the mono-RNP showed a mean R_g of 44.7 ± 1.4 Å; and the [off,off]-state of the mono-RNP showed a mean R_g of 48.5 ± 1.7 Å (Figure 1—figure supplement 3).

²H-L7Ae, ²H-Nop5, ²H-fibrillarin, ²H-RNA, ²H-fibrillarin/²H-RNA and ²H(70%)-Nop5/²H-RNA samples were measured in 50 mM NaPi pH 6.6, 500 mM NaCl, 42%:58% D₂O:H₂O solutions, in order to mask the contribution of the ¹H-proteins. The curves corresponding to ²H-L7Ae, ²H-Nop5, ²H-RNA and ²H(70%)-Nop5/²H-RNA were acquired at D22 at the Institute Laue Langevin (ILL, Grenoble, France), with a neutron wavelength of 6 Å. The ²H-fibrillarin and ²H-fibrillarin/²H-RNA curves were acquired at KWS-1 at JCNS (Munich, Germany) (Feoktystov et al., 2015) with a neutron wavelength of 5 Å. Both instruments were configured with sample-detector distances of 4 m and collimation lengths of 4 m. Data reduction and radial integration were done with standard procedures using beamline-specific software. Buffer subtraction was done in PRIMUS. Pair-wise distance-distribution functions P(r) were calculated from experimental data using GNOM in ATSAS 2.7.5 (Svergun, 1992). All SANS curves were acquired at 55°C.

Structures were calculated using an adapted version of the protocol described in Lapinaite et al. (2013); Nilges, 1995 according to the workflow described in Figure 3—figure supplement 1. The starting st-sR26 RNA structures, bound to either substrate D or substrate D’, were generated in separate calculation runs using restraints to impose an A-form helical geometry on the substrate–guide duplex, and to yield the appropriate K-turn structures. Starting protein conformations were generated from the PDB entry 3nmu and assembled into two L7Ae–Nop5–Fib protomers, in which the L7Ae–Nop5-CTD and Nop5-NTD–Fib interaction interfaces of 3nmu were preserved, but not the relative orientation of the Nop5-NTD and CTD, which were randomised. The two copies of the protomers within the sRNP were separated and randomly rotated with respect to each other. The building-blocks L7Ae–Nop5-CTD, Nop5-NTD–Fib and the Nop5 coiled-coil domain were kept rigid throughout the calculations. Structures were calculated for both the substrate D- and substrate D’-loaded sRNPs. For each sRNP the proteins and RNA were subjected to two sets of parallel torsion-angle simulated-annealing procedures; one included a set of restraints positioning one fibrillarin copy on the methylation site of the substrate–guide duplex ([on,off]-state); in another no restraints were imposed between fibrillarin and the RNA ([off,off]-state). The conformational sampling was driven by PRE-derived distance restraints, distance restraints positioning the two L7Ae–Nop5-CTD modules onto the RNA K-turns and a loose distance restraint between the centres of mass of the two L7Ae modules (90 ± 15 Å), which was derived from the P(r) curve of ²H-L7Ae in 42%:58% D₂O:H₂O. Restraints positioning the Nop5-α9’ helix between the two guide regions (from Nop5-K301 and K304 to the phosphate backbone of the nucleotide linking the K-turn and substrate–guide helix) were also used. With this set up, we started an iterative procedure, to generate two lists of PRE-derived distance-restraints compatible with either the [on,off]- or [off,off]-state. 500 structures were calculated per iteration. At the end of each iteration, restraint violations were evaluated: restraints violated by more than 10 Å in either set of calculations were classified, eliminated from that particular set, but kept in the other. After 5 iterations, this led to two restraint-lists per sRNP, corresponding to the [on,off]- and [off,off]-states of the sRNP.

With these four sets of restraints (two for the substrate D-loaded and two for the substrate D’-loaded sRNP), four separate runs of torsion-angle simulated-annealing calculations were performed; we generated 2500 structures per run, using the settings described in Lapinaite et al. (2013).

The fitness of the experimental SAS and PRE data with respect to the calculated structures was assessed by calculation of the χ² statistic (Equation 4) and by visual inspection of fits between back-calculated and experimental data:

where I_calc represents the back-calculated data-point (I_para/I_diaintesity-ratios or SAS intensities), I_exp is the corresponding experimental value, N is the number of experimental points, σ represents the experimental error and c is the scaling factor:

The structures ranking in the top 2% in both total energy and restraint energy were selected. To further narrow down the selection on the basis of the SAS data, we evaluated the χ² distribution of the ²H-Nop5, ²H-L7Ae and ²H-RNA SANS curves. The SAS curves including the contribution from fibrillarin were left out, because we expected the position of fibrillarin to be variable when it is not in contact with the RNA. SAS fitness was calculated with the programs CRYSOL and CRYSON, from the ATSAS suite, version 2.7.5 (Svergun et al., 1998). Based on the distribution of fitness for all structures in each of the runs, we set loose cut-offs, which excluded only structures beyond the smooth, linearly increasing portion of the distribution curve. For the substrate D’-loaded complex, we selected structures within the top 90% ranking by ²H-RNA fitness χ² <1.4 χ²_min in the [off,off]-state, χ² <2.2 χ²_min in the [on,off]-state), the top 50% by ²H-L7Ae fitness (χ² < 1.3 χ²_min in the [off,off]-state, χ² <1.3 χ²_min in the [on,off]-state), and the top 80% by ²H-Nop5 fitness (χ² < 6.1 χ²_min in the [off,off]-state, χ² <6.8 χ²_min in the [on,off]-state); for the substrate D-loaded complex, we selected structures within the top 90% ranking by ²H-RNA fitness (χ² < 2.5 χ²_min in the [off,off]-state, χ² <3.4 χ²_min in the [on,off]-state), the top 80% by ²H-L7Ae fitness (χ² < 2.0 χ²_min in the [off,off]-state, χ² <1.8 χ²_min in the [on,off]-state) and the top 90% by ²H-Nop5 fitness (χ² < 6.5 χ²_min in the [off,off]-state, χ² <5.3 χ²_min in the [on,off]-state). The average pair-wise RMSD of the structures of each ensemble, calculated over the Cα and P atoms of the protein and RNA structured domains, including the fibrillarin units not bound to the RNA, was below 5 and 7 Å for the [on,off] and [off,off] conformers, respectively, with a maximum RMSD value of less than 10 Å in all cases.

Among the selected structures of each of the four runs ([on,off]- and [off,off]-states of both substrate D- and substrate D’-loaded sRNPs), the one with the lowest restraint-violation energy that maintained the correct RNA topology was chosen as the starting point for refinement in Cartesian space. The four refinement runs comprised 1500 structures each spanning up to 10 Å RMSD of Cα and P atoms relative to the starting structure (number calculated for the substrate D’-loaded [on,off]-state). At the end of the refinement, we applied stringent selection criteria with respect to the SAS curves and loose criteria with respect to the energy. The cut-offs for the SAS data were set upon visual inspection of the χ² distributions for each run and curve, whereby we allowed more structures to be selected when the χ² distribution was flat.

For the substrate D’-loaded sRNP the cut-offs are as follows: top 33% of restraint-violation, van der Waals and total energy; top 83% for ²H-RNA (χ² < 1.3 χ²_min for the [off,off]-state, χ² <2.0 χ²_min for the [on,off]-state); top 67% for ²H-L7Ae (χ² < 1.8 χ²_min for the [off,off]-state, χ² <1.1 χ²_min for the [on,off]-state); top 33% for ²H-Nop5 (χ² < 2.7 χ²_min for the [off,off]-state, χ² <3.4 χ²_min for the [on,off]-state); top 10% for ²H(70%)-Nop5-RNA (χ² < 6.1 χ²_min for the [off,off]-state, χ² <3.3 χ²_min for the [on,off]-state). Applying these criteria we selected 1 structure for the substrate D’-loaded [off,off]-state and 12 structures for the [on,off]-state. The [on,off]-state structures displayed an average RMSD of 2.4 Å, calculated on all Cα and P atoms (Figure 3) excluding the fully flexible regions, namely the free guide region of the RNA (nucleotides 51–62), the loops connecting the Nop5-NTD to the coiled-coil domain (residues 116–122), and the loops connecting the coiled-coil domain to the Nop5-CTD (residues 249–251).

For the substrate D-loaded sRNP the cut-offs are as follows: top 33% of restraint-violation, van der Waals and total energy; top 83% for ²H-RNA (χ² < 2.1 χ²_min for the [off,off]-state, χ² <2.2 χ²_min for the [on,off]-state); 66% for ²H-L7Ae (χ² < 2.7 χ²_min for the [off,off]-state, χ² <1.2 χ²_min for the [on,off]-state); 33% for ²H-Nop5 (χ² < 3.0 χ²_min for the [off,off]-state, χ² <1.9 χ²_min for the [on,off]-state); 10% for ²H(70%)-Nop5-RNA (χ² < 3.2 χ²_min for the [off,off]-state, χ² <2.8 χ²_min for the [on,off]-state). The final ensembles for the substrate D-loaded [off,off]- and [on,off]-states consist of 3 and 20 structures, respectively, with a Cα and P RMSDs of 4.6 and 2.4 Å, respectively.

Representative structures in the final ensembles were minimized in explicit water using Amber 14 and the corresponding Amber99SB force field (Hornak et al., 2006).

The PRE data and the SAS data indicated the presence of a conformational equilibrium between the [on,off]- and [off,off]-states, as discussed in the main text. The ²H-fibrillarin, ²H-fibrillarin/²H-RNA SANS and SAXS curves were therefore fitted to a mixture of structures in the [on,off]- and [off,off]-states.

In order to address the flexibility of the Nop5-NTD–fibrillarin modules not in contact with the RNA, we sought to generate ensembles containing different orientations of these modules that would improve the fit to the SAS curves. This conformational diversity is in addition to the equilibrium between the [on,off]- and [off,off]-states, resulting in a pool of structures containing both [on,off]- and [off,off]- states and multiple conformations of Nop5-NTD–fibrillarin modules in each state.

To generate these ensembles we proceeded as follows. Starting from the representative structure of each ensemble of Figure 3, corresponding to the structure closest to the mean of the ensemble, we performed a further simulated-annealing step, where the loops connecting the Nop5-NTD–fibrillarin modules to the rest of the Box C/D particle were allowed to adopt random orientations, while the rest of the particle was kept rigid. At this stage, we generated 4000 structures with randomised Nop5-NTD–fibrillarin positions, from which we removed structures containing steric clashes. The structures also contained all spin-labels, which were left flexible, in order to allow back-calculation of PREs (see below).

In a separate run comprised of 300 structures, the template structures were kept entirely rigid while the spin-label side-chains were allowed to rotate in order to generate different orientations, as multiple conformations of the spin-label have been demonstrated to fit the PRE data more accurately than a single conformation (Iwahara et al., 2004).

Ensemble scoring was carried out for substrate D’- and substrate D-loaded sRNPs via the pseudo-genetic algorithm shown in Figure 3—figure supplement 1b. First, we grouped the structures into four pools, containing 3500, 3500, 300 and 300 structures: [on,off]-state with randomised Nop5-NTD−fibrillarin positions, [off,off]-state with randomised Nop5-NTD−fibrillarin positions, [on,off]-state with randomised spin-label orientations and [off,off]-state with randomised spin-label orientations. The algorithm generated four ‘parent’ ensembles, each comprising of 2–10 conformers randomly chosen from the pools. These ensembles were merged and sub-sampled, yielding 20 ‘children’ sub-ensembles ranging from 3 to 10 conformers in size. Each sub-sampling event had a 30% probability of duplicating a conformer or replacing one with another from the main pool. The process of parent selection, sub-sampling and scoring was repeated 250 times.

The theoretical scattering curve of the ensemble was computed as the linear combination of the scattering curves of each individual conformer (scaling the populations to represent molar fractions rather than volume fractions, which is the standard ATSAS output). The χ² value with respect to the experimental data was calculated by OLIGOMER (Konarev et al., 2003). The normalization of χ² of all sub-sampled ensembles and across iterations was done according to Equation 6 (Karaca et al., 2017):

where χ²_ensemble is the fitness of an individual ensemble, and χ²_min and χ²_max are the respective minimum and maximum values across the iterations or sub-ensembles being considered. Five SAS curves were used for scoring: ²H-Nop5, ²H-Fib, ²H-Fib/²H-RNA, ²H(70%)-Nop5/²H-RNA and SAXS. The normalized χ² values for each curve were then summed and renormalized into a single value, obtained with the same Equation 6, which then represented the overall SAS-fitness.

The calculation of the theoretical I_para/I_dia ratios from mixed [on,off]- and [off,off]-state ensembles requires an estimation of the timescale of the exchange rate k_ex (k_ex = k₁ + k_-1) between the [on,off]- and [off,off]-conformers. This can be easily done by inspecting the ILV-methyl ¹H-¹³C spectra of fibrillarin: in the case of slow conformational exchange, the methyl groups in the fibrillarin copy sampling the on- and off-states should each yield two separate NMR peaks, while for fast conformational exchange these methyl groups should each show only a single peak, at a position corresponding to the population-weighted average of the positions corresponding to the on- and off-states. To investigate this, we used the spectrum of the RNP assembled with ssR26 and loaded with substrate RNA as a reference for the slow-exchange situation: in this complex, two of the four fibrillarin copies adopt a stable on-state, while the other two are in the off-state, and a subset of the fibrillarin methyl groups show separate and resolvable peaks corresponding to the two states. In the spectra of the half-loaded st-sR26 RNPs we did not detect any peak at the positions corresponding to RNA-bound fibrillarin in the holo ssR26 RNP spectra, indicating that in the half-loaded mono-RNP, either the k_ex is faster than the differences in the resonance frequencies of the fibrillarin methyl groups in the on- and off-states (~40–100 Hz), or the population of the on-state is too small to be detected. In the second case, one would expect no CSPs upon substrate RNA binding, which does not correspond with the observed spectra (Figure 2a, right panel), Thus, we back-calculated the PREs for the fibrillarin copy that can be in contact with the substrate–guide duplex using <r⁻⁶>ensemble averaged distances over the [on,off]- and [off,off]-states, as appropriate for the fast exchange regime.

Each methyl group of each fibrillarin or L7Ae copy is influenced by two PRE tags (SL1 and SL2). The resulting Γ₂ values for the methyl groups of the two copies are given by:

where Methyl1 and Methyl2 refer to the two copies of L7Ae or fibrillarin. Because Methyl1 and Methyl2 have almost indistinguishable chemical shifts, the resulting I_para/I_dia ratios for Methyl1 and Methyl2, calculated from Equations (2) and (3), were averaged before comparison to the experimental data. The PRE fitness was quantified using χ² to all experimental PRE values using Equation (4). Distances were computed from the PDB files using the Biopython Bio.PDB module (Cock et al., 2009). The fitness of PRE data was normalized using Equation (6) and summed with the SAS-fitness score, to yield a consensus PRE-SAS score for each ensemble within the 20 sub-sampling events, and across the 250 iterations.

Three independent runs of the scoring algorithm were performed for substrate D’- and substrate D-loaded sRNPs, with the top scoring ensemble, judged by the consensus PRE-SAS score, displayed in Figure 4.

After this selection, the conformations of each individual tag were refined by generating additional 3000 conformers per tag and by using the same pseudo-genetic algorithm to select the ensembles of tag conformations that best fitted each individual PRE dataset. During this refinement step the positions of all proteins and RNA, as well as the populations of fibrillarin conformers in the ensemble, were left invariant, in order not to alter the fit to the SAS data.

Molecular dynamics simulations of the substrate D’- and substrate D-bound structures representing the [on,off]-states were carried out in AMBER 2018 (Case et al., 2018). The simulations were carried out in explicit TIP3P water using a cubic box with a 14 Å water layer and the ff14SB parameter set. The system was subjected to 20,000 cycles of solvent minimization with positional restraints on the complex (NPT), followed by heating to 328 K (NVT). The complete system was subjected to an additional 20,000 cycles of energy minimisation, and then allowed to relax, keeping restraints on the proteins and heavy atoms (NPT at 328 K, 0.5 ns). Subsequently, the two structures were subjected to a 150-ns molecular dynamics. Contacts were extracted using CPPTRAJ (Roe and Cheatham, 2013).

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

The authors thank Dr. Artem Feoktystov (MLZ Munich) for assistance with recording and processing SANS data at KWS-1; Dr. Roman Lichtenecker (University of Vienna), for kindly providing the leucine methyl labelling precursor sodium [3-²H₂,4-²H, 5-¹³C, 5’-² _H3]-α-ketoiso-caproate; Dr. Pawel Masiewicz (EMBL Heidelberg) and Susanne Zur Lage (HZI Braunschweig) for RNA production and Dr. Bernd Simon (EMBL Heidelberg) for assistance with structure calculations.

© 2020, Graziadei et al.

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