Abstract
During protein synthesis, tRNAs move from the ribosome's aminoacyl to peptidyl to exit sites. Here we investigate conformational motions during spontaneous translocation, using molecular dynamics simulations of 13 intermediate-translocation-state models obtained by combining Escherichia coli ribosome crystal structures with cryo-EM data. Resolving fast transitions between states, we find that tRNA motions govern the transition rates within the pre- and post-translocation states. Intersubunit rotations and L1-stalk motion exhibit fast intrinsic submicrosecond dynamics. The L1 stalk drives the tRNA from the peptidyl site and links intersubunit rotation to translocation. Displacement of tRNAs is controlled by 'sliding' and 'stepping' mechanisms involving conserved L16, L5 and L1 residues, thus ensuring binding to the ribosome despite large-scale tRNA movement. Our results complement structural data with a time axis, intrinsic transition rates and molecular forces, revealing correlated functional motions inaccessible by other means.
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Acknowledgements
We thank B. de Groot, U. Zachariae, C. Kutzner, R. Jahn, G. Hummer, B. Roux, W. Wintermeyer and C. Rotte for discussions and critical reading of the manuscript. M.V.R. and H.S. acknowledge financial support of the Deutsche Forschungsgemeinschaft (DFG) (Sonderforschungsbereich grant 860 and DFG-Forschergruppe 1805). Financial support for L.V.B., C.B., A.C.V. and H.G. comes from the Max Planck Society, International Max Planck Research School for Physics of Biological and Complex Systems and DFG-Forschergruppe 1805. We thank the computer center Garching (RZG) and the Gesellschaft fuer wissenschaftliche Datenverarbeitung Goettingen (GWDG) for technical assistance; computer time has been provided by the RZG.
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Contributions
L.V.B. and A.C.V. prepared the ribosome model; G.F.S. refined the model against the cryo-EM maps; L.V.B. performed the molecular dynamics simulations; L.V.B. and C.B. analyzed the data with mentoring by A.C.V. and H.G.; C.B., L.V.B., A.C.V. and H.G. developed and implemented analysis methods for rate estimation; I.I.D. carried out the sequence-conservation analysis; N.F. performed the cryo-EM experiments and analysis; H.S., H.G. and M.V.R. conceived of the project. All authors discussed the results and wrote the manuscript.
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Integrated supplementary information
Supplementary Figure 1 All-atom models of pre1a-post4 states obtained from refinement of atomic models against cryo-EM maps.
For each state, the refined structure and an isosurface of the cryo-EM map (grey surface) are shown. The ribosomal subunits (50S and 30S) are shown in ribbon representation; tRNAfMet and tRNAVal atoms are depicted by magenta and green spheres, respectively.
Supplementary Figure 2 Validation of models.
(a) Comparison of tRNA positions between models and crystal structures in the P-site and P/E hybrid state. The tRNAs from crystal structures32 and our models (left: pre1a, right: pre4) are shown as red and green ribbons, respectively, after rigid-body fitting of the binding region only (grey ribbons). Cα and P atoms used for fitting are depicted as grey spheres and CCA-tail and acceptor stem regions are indicated by black and blue circles, respectively. (b) Structural deviations during the simulations. For each ribosome simulation, started either from the model refined against the cryo-EM map or from the PE-model, the RMSD relative to the starting structure is shown for the different simulation steps (red, green, blue, and magenta curves), and relative to the structure at 20 ns (cyan curve).
Supplementary Figure 3 Estimation of transition rates.
(a) Attempt rate and free energy calibration factor. The upper panel shows an excerpt of the normalized distance between the ensembles for each pair of states versus the uncalibrated free energy estimate. This is done for each of the ribosome components (colored circles). A barrier between two states is considered crossed if this distance is smaller than one. The lower panel shows the frequency of barrier crossings pA→Bsim = (nA→B)/n calculated for free energy intervals of 1 kbT (colored lines). The probability of barrier crossing pA→B fitted to pA→Bsim is shown as a black line. (b) Statistical uncertainty of the attempt rate of the movement of individual ribosome components. Shown are the medium value of the distribution of the attempt rates A (circles) and standard deviation (bars). The overall attempt rate is shown as reference (black line).
Supplementary Figure 4 Quality of tRNA-mRNA base-paring.
For each state, histograms of the distances between codon residues of the mRNA and the corresponding anticodon residues of the two tRNAs are shown.
Supplementary Figure 5 Fast relaxation motions of the ribosome after tRNA removal during the simulations.
Shown are time-traces of 30S head tilting, head swiveling, and body rotation angles (left panel), as well as of interaction enthalpies (right panel) for intersubunit bridge B1b, derived from four independent simulations. Blue curves refer to the two simulations started from the refined structure of the pre5b state with bound tRNAs, the green ones refer to simulations started from the same structure after removal of the tRNAs.
Supplementary Figure 6 Transition rates.
(a) Schematic representation of the translocation intermediate states as a Markov model. Circles denote states, connecting lines encode the transition time estimates for L1-stalk, tRNAfMet, tRNAVal motion as well as body and head rotation. We thank Benoit Roux for providing the idea. (b) Fastest progression sequences of translocation intermediate states ranked according to similarity to the sequence proposed by Fischer et al.10 For all 31 possible combinations of ribosome components (top, color scheme as in Fig. 1a,d), the fastest progression sequence was determined as in 2.9. The similarity of each of the identified sequences (mid, columns) to the sequence given by Fischer et al.10 was described using the absolute Kendall rank correlation coefficient T (bottom). As a reference the mean T value for random sequences (0.23) and their probability distribution p(T) is shown.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–6, Supplementary Tables 1–4 and Supplementary Notes 1–3 (PDF 5311 kb)
25 near-atomic models obtained from refinement against cryo-EM maps, sorted according to structural similarity
The ribosomal subunits (50S and 30S) are shown in ribbon representation, tRNAVal and tRNAfMet are depicted by magenta and green spheres, respectively. The sequence of models is repeated four times. (MOV 3450 kb)
Simulation trajectory of the ribosome in the post1 state after 20-ns equilibration.
The ribosome is shown in ribbon representation, tRNA atoms are shown as spheres. Water molecules and solvent ions are omitted for clarity. (MOV 10751 kb)
Visualization of ribosomal translocation as a Brownian machine.
The movie shows the interplay of different time scales during translocation, by combining molecular dynamics simulations (fast nanosecond time scales) with stochastic transitions governed by slower (microseconds) rates estimated from the simulations. A possible translocation pathway is represented by concatenating individual frames from the MD-simulations. The sequence of frames is determined by a simulation of the stochastic dynamics of the underlying Markov process. For this, transition probabilities between states were determined using the lowest rate of the ribosome components and were scaled logarithmically in order to compensate for the exponential timescales involved. Large- scale transitions, not accessible to the simulations (exceeding the microsecond timescale), were introduced at random to allow the system to access all states. These transitions are represented by grey bars. Each frame is represented by a ribosome shown in ribbon representation with tRNA atoms depicted as spheres (top). The time trace obtained is shown below. (MOV 47280 kb)
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Bock, L., Blau, C., Schröder, G. et al. Energy barriers and driving forces in tRNA translocation through the ribosome. Nat Struct Mol Biol 20, 1390–1396 (2013). https://doi.org/10.1038/nsmb.2690
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DOI: https://doi.org/10.1038/nsmb.2690
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