The initiation of bacterial translation involves the tightly regulated joining of the 50S ribosomal subunit to an initiator transfer RNA (fMet-tRNAfMet)-containing 30S ribosomal initiation complex to form a 70S initiation complex, which subsequently matures into a 70S elongation-competent complex. Rapid and accurate formation of the 70S initiation complex is promoted by initiation factors, which must dissociate from the 30S initiation complex before the resulting 70S elongation-competent complex can begin the elongation of translation1. Although comparisons of the structures of the 30S2,3,4,5 and 70S4,6,7,8 initiation complexes have revealed that the ribosome, initiation factors and fMet-tRNAfMet can acquire different conformations in these complexes, the timing of conformational changes during formation of the 70S initiation complex, the structures of any intermediates formed during these rearrangements, and the contributions that these dynamics might make to the mechanism and regulation of initiation remain unknown. Moreover, the absence of a structure of the 70S elongation-competent complex formed via an initiation-factor-catalysed reaction has precluded an understanding of the rearrangements to the ribosome, initiation factors and fMet-tRNAfMet that occur during maturation of a 70S initiation complex into a 70S elongation-competent complex. Here, using time-resolved cryogenic electron microscopy9, we report the near-atomic-resolution view of how a time-ordered series of conformational changes drive and regulate subunit joining, initiation factor dissociation and fMet-tRNAfMet positioning during formation of the 70S elongation-competent complex. Our results demonstrate the power of time-resolved cryogenic electron microscopy to determine how a time-ordered series of conformational changes contribute to the mechanism and regulation of one of the most fundamental processes in biology.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
The cryo-EM reconstruction maps have been deposited in the Electron Microscopy Data Bank (EMDB) server under the accession codes EMD-0643 (30S IC), EMD-0662 (70S IC) and EMD-0661 (70S EC). The structural models obtained by MDFF have been deposited in the Protein Data Bank (PDB) server under accession codes 6O7K (30S IC) and 6O9K (70S IC). The structural model obtained by rigid-body fitting has been deposited in the PDB server under accession code 6O9J (70S EC).
A pseudocode describing the control actions of the software synchronizing time-resolved cryo-EM apparatus is available upon request.
Antoun, A., Pavlov, M. Y., Andersson, K., Tenson, T. & Ehrenberg, M. The roles of initiation factor 2 and guanosine triphosphate in initiation of protein synthesis. EMBO J. 22, 5593–5601 (2003).
Hussain, T., Llacer, J. L., Wimberly, B. T., Kieft, J. S. & Ramakrishnan, V. Large-scale movements of IF3 and tRNA during bacterial translation initiation. Cell 167, 133–144.e13 (2016).
Julián, P. et al. The Cryo-EM structure of a complete 30S translation initiation complex from Escherichia coli. PLoS Biol. 9, e1001095 (2011).
Simonetti, A. et al. Involvement of protein IF2 N domain in ribosomal subunit joining revealed from architecture and function of the full-length initiation factor. Proc. Natl Acad. Sci. USA 110, 15656–15661 (2013).
Simonetti, A. et al. Structure of the 30S translation initiation complex. Nature 455, 416–420 (2008).
Allen, G. S., Zavialov, A., Gursky, R., Ehrenberg, M. & Frank, J. The cryo-EM structure of a translation initiation complex from Escherichia coli. Cell 121, 703–712 (2005).
Myasnikov, A. G. et al. Conformational transition of initiation factor 2 from the GTP- to GDP-bound state visualized on the ribosome. Nat. Struct. Mol. Biol. 12, 1145–1149 (2005).
Sprink, T. et al. Structures of ribosome-bound initiation factor 2 reveal the mechanism of subunit association. Sci. Adv. 2, e1501502 (2016).
Frank, J. Time-resolved cryo-electron microscopy: Recent progress. J. Struct. Biol. 200, 303–306 (2017).
Antoun, A., Pavlov, M. Y., Lovmar, M. & Ehrenberg, M. How initiation factors maximize the accuracy of tRNA selection in initiation of bacterial protein synthesis. Mol. Cell 23, 183–193 (2006).
Caban, K. & Gonzalez, R. L. Jr. The emerging role of rectified thermal fluctuations in initiator aa-tRNA- and start codon selection during translation initiation. Biochimie 114, 30–38 (2015).
Milon, P., Konevega, A. L., Gualerzi, C. O. & Rodnina, M. V. Kinetic checkpoint at a late step in translation initiation. Mol. Cell 30, 712–720 (2008).
Milón, P. & Rodnina, M. V. Kinetic control of translation initiation in bacteria. Crit. Rev. Biochem. Mol. Biol. 47, 334–348 (2012).
Grigoriadou, C., Marzi, S., Kirillov, S., Gualerzi, C. O. & Cooperman, B. S. A quantitative kinetic scheme for 70 S translation initiation complex formation. J. Mol. Biol. 373, 562–572 (2007).
MacDougall, D. D. & Gonzalez, R. L. Jr. Translation initiation factor 3 regulates switching between different modes of ribosomal subunit joining. J. Mol. Biol. 427, 1801–1818 (2015).
Gualerzi, C. O. & Pon, C. L. Initiation of mRNA translation in bacteria: structural and dynamic aspects. Cell. Mol. Life Sci. 72, 4341–4367 (2015).
Wilson, D. N. Ribosome-targeting antibiotics and mechanisms of bacterial resistance. Nat. Rev. Microbiol. 12, 35–48 (2014).
López-Alonso, J. P. et al. Structure of a 30S pre-initiation complex stalled by GE81112 reveals structural parallels in bacterial and eukaryotic protein synthesis initiation pathways. Nucleic Acids Res. 45, 2179–2187 (2017).
Goyal, A., Belardinelli, R., Maracci, C., Milón, P. & Rodnina, M. V. Directional transition from initiation to elongation in bacterial translation. Nucleic Acids Res. 43, 10700–10712 (2015).
Huang, C., Mandava, C. S. & Sanyal, S. The ribosomal stalk plays a key role in IF2-mediated association of the ribosomal subunits. J. Mol. Biol. 399, 145–153 (2010).
Tomsic, J. et al. Late events of translation initiation in bacteria: a kinetic analysis. EMBO J. 19, 2127–2136 (2000).
Ling, C. & Ermolenko, D. N. Initiation factor 2 stabilizes the ribosome in a semirotated conformation. Proc. Natl Acad. Sci. USA 112, 15874–15879 (2015).
Marshall, R. A., Aitken, C. E. & Puglisi, J. D. GTP hydrolysis by IF2 guides progression of the ribosome into elongation. Mol. Cell 35, 37–47 (2009).
La Teana, A., Pon, C. L. & Gualerzi, C. O. Late events in translation initiation. Adjustment of fMet-tRNA in the ribosomal P-site. J. Mol. Biol. 256, 667–675 (1996).
Chen, B. et al. Structural dynamics of ribosome subunit association studied by mixing-spraying time-resolved cryogenic electron microscopy. Structure 23, 1097–1105 (2015).
Fu, Z. et al. Key intermediates in ribosome recycling visualized by time-resolved cryoelectron microscopy. Structure 24, 2092–2101 (2016).
Lu, Z. et al. Monolithic microfluidic mixing-spraying devices for time-resolved cryo-electron microscopy. J. Struct. Biol. 168, 388–395 (2009).
Chen, S. et al. High-resolution noise substitution to measure overfitting and validate resolution in 3D structure determination by single particle electron cryomicroscopy. Ultramicroscopy 135, 24–35 (2013).
Trabuco, L. G., Villa, E., Mitra, K., Frank, J. & Schulten, K. Flexible fitting of atomic structures into electron microscopy maps using molecular dynamics. Structure 16, 673–683 (2008).
Wang, J., Liu, Z., Frank, J. & Moore, P. B. Identification of ions in experimental electrostatic potential maps. IUCrJ 5, 375–381 (2018).
Caban, K., Pavlov, M., Ehrenberg, M. & Gonzalez, R. L. Jr. A conformational switch in initiation factor 2 controls the fidelity of translation initiation in bacteria. Nat. Commun. 8, 1475 (2017).
Fei, J. et al. A highly purified, fluorescently labeled in vitro translation system for single-molecule studies of protein synthesis. Methods Enzymol. 472, 221–259 (2010).
Wang, J., Caban, K. & Gonzalez, R. L. Jr. Ribosomal initiation complex-driven changes in the stability and dynamics of initiation factor 2 regulate the fidelity of translation initiation. J. Mol. Biol. 427, 1819–1834 (2015).
Elvekrog, M. M. & Gonzalez, R. L. Jr. Conformational selection of translation initiation factor 3 signals proper substrate selection. Nat. Struct. Mol. Biol. 20, 628–633 (2013).
Guenneugues, M. et al. Mapping the fMet-tRNAf Met binding site of initiation factor IF2. EMBO J. 19, 5233–5240 (2000).
Antoun, A., Pavlov, M. Y., Lovmar, M. & Ehrenberg, M. How initiation factors tune the rate of initiation of protein synthesis in bacteria. EMBO J. 25, 2539–2550 (2006).
Grigoriadou, C., Marzi, S., Pan, D., Gualerzi, C. O. & Cooperman, B. S. The translational fidelity function of IF3 during transition from the 30 S initiation complex to the 70 S initiation complex. J. Mol. Biol. 373, 551–561 (2007).
Fabbretti, A. et al. The real-time path of translation factor IF3 onto and off the ribosome. Mol. Cell 25, 285–296 (2007).
Russo, C. J. & Passmore, L. A. Electron microscopy: ultrastable gold substrates for electron cryomicroscopy. Science 346, 1377–1380 (2014).
Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).
Rohou, A. & Grigorieff, N. CTFFIND4: fast and accurate defocus estimation from electron micrographs. J. Struct. Biol. 192, 216–221 (2015).
Scheres, S. H. W. RELION: implementation of a Bayesian approach to cryo-EM structure determination. J. Struct. Biol. 180, 519–530 (2012).
Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).
Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D 60, 2126–2132 (2004).
Kaledhonkar, S., Fu, Z., White, H. & Frank, J. in Protein Complex Assembly: Methods and Protocols (ed. Marsh, J. A.) 59–71 (Humana, 2018).
Tan, Y.Z., Baldwin, P.R., Davis, J.H., Williamson, J.R., Potter, C.S., Carragher, B. & Lyumkis, D. Addressing preferred specimen orientation in single-particle cryo-EM through tilting. Nat. Methods 14, 793–796 (2017).
Raw, A. S., Coleman, D. E., Gilman, A. G. & Sprang, S. R. Structural and biochemical characterization of the GTPγS-, GDP·Pi-, and GDP-bound forms of a GTPase-deficient Gly42 → Val mutant of Giα1. Biochemistry 36, 15660–15669 (1997).
This work was supported by funds to J.F. from the National Institutes of Health (R01 GM 55440 and GM 29169) and to R.L.G. from the National Institutes of Health (R01 GM 084288). K.C. was supported by an American Cancer Society Postdoctoral Fellowship (125201).
Nature thanks A. Amunts, Simpson Joseph and the other anonymous reviewer(s) for their contribution to the peer review of this work.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, b, 3D cryo-EM-derived Coulomb potential maps30 of the 30S IC (a) and the 30S subunit + fMet-tRNAfMet complex (b) obtained from a control experiment in which the 30S IC in Tris-polymix buffer and a solution of Tris-polymix buffer lacking 50S subunits were injected into the microfluidic chip designed to give the longest reaction time (~600 ms), mixed, allowed to react, and sprayed onto an electron microscopy grid that was rapidly plunged into liquid ethane. The sizes of the resulting populations of the 30S IC and the 30S subunit + fMet-tRNAfMet complex were 75% and 25%, respectively, which demonstrates that most of the 30S ICs remain intact during the mixing-spraying process. c, Plot of the concentrations of the 50S subunit, 70S IC and 70S EC as a function of time generated by using the initial 50S subunit and 30S IC concentrations analogous to those used in our mixing-spraying microfluidic chip (that is, 0.6 µM and 1.2 µM, respectively) and modelling the kinetics of subunit joining using the kinetic scheme and set of rate constants reported previously for a subunit-joining reaction performed in the presence of IF1 and IF2, but in the absence of the IF319. A detailed description of the kinetic modelling can be found in the Methods. The plot predicts that the 70S IC population should peak within 50–250 ms after mixing of the 50S subunit and 30S IC, and that these 70S ICs should mature to a notable population of 70S ECs within the next several hundreds of milliseconds. Therefore, to ensure that we would capture formation of the 70S IC and its maturation to the 70S EC, we selected microfluidic chips designed to provide reaction times of approximately 20 ms, 80 ms, 200 ms and 600 ms. The free 50S subunit, 70S IC and 70S EC populations observed in our time-resolved cryo-EM experiments are shown as blue diamonds, light grey circles and dark grey triangles, respectively.
a, A photograph of the mixing-spraying, time-resolved cryo-EM apparatus, labelled to show all major components. The mixing-spraying microfluidic chip is mounted inside an environmentally controlled chamber. A syringe pump, which is controlled by a laboratory-written, Visual Basic and C++ software program called Howard5e45, is used to inject the reactants from inlets 1 and 2 into the microfluidic chip. Once in the microfluidic chip, the reactants are mixed and allowed to react for the reaction time specific to the microfluidic chip being used. The electron microscopy grid is held at the end of the plunger by a pair of tweezers. The Howard5e software controls and synchronizes the syringe pump as well as the plunger that holds the tweezer-mounted electron microscopy grid45. Thus, as the sprayers discharge the reaction from the microfluidic chip onto the electron microscopy grid, the plunger is activated to plunge the grid into cryogen45. b–f, Images of cryo-EM grids prepared by the mixing-spraying, time-resolved cryo-EM apparatus, going from low to high magnification. b, Grid-view depicting droplets of different sizes deposited on the grid. c, Square-view depicting droplet distribution over the holes. d, Hole-view depicting ice distribution over holes. For image acquisition, thin ice regions were selected. e, A representative micrograph showing good particle density. f, Power spectrum of the acquisition image in e. Source Data
In the first step, particles were auto-picked from the images recorded for the individual time points. Auto-picked particles were then extracted using 2× binning of the images and subjected to 2D classification to discard ice-like and/or debris-like particles and define 30S subunit-like, 50S subunit-like and 70S ribosome-like particle classes. Representative 2D classes of 30S subunit-like, 50S subunit-like and 70S ribosome-like particles are shown on the left and right of the flow chart. A detailed account of the classification scheme is provided in the Methods. In brief, following 2D classification at each time point, two particle datasets were created. The first particle dataset was composed of 170,864 30S subunit-like projections, and the second was composed of 144,504 50S subunit-like and 70S ribosome-like projections. The first particle dataset with 170,864 30S subunit-like projection classes was subjected to 3D classification, which yielded two major subclasses. The first of these contained 86,367 30S ICs and the second contained 17,686 30S subunits carrying only fMet-tRNAfMet (that is, 30S subunit + fMet-tRNAfMet complexes). The second particle dataset, containing the 144,504 50S subunit-like and 70S ribosome-like particles, was also subjected to a combination of 3D and 2D classification to separate compositional heterogeneity consisting of the 50S subunit ribosome and 70S ribosome. After performing a combination of 3D and 2D classifications, two particle datasets were created, the first containing 50,918 50S subunit particles and the second containing 80,138 70S ribosome-like particles. Further 3D classification was performed on the dataset containing 80,138 70S ribosome-like particles, which yielded 70S IC and 70S EC classes. Particles from 50S subunit, 70S IC and 70S EC were traced back to each time point, as tabulated at the bottom of the flow chart.
Extended Data Fig. 4 Masked classification scheme to look for rare conformations of the 70S IC and 70S EC.
a–c, The mask (grey) covering densities of IF1 (magenta), IF2 (purple), P/P-tRNA (orange) and P/E-tRNA is shown in different views. The views depict the position of the mask with respect to the 30S subunit (pale yellow) and the 50S subunit (blue). d, For the masked 3D classification scheme, this mask was applied to the dataset of refined 80,138 70S particles, which yielded mostly three types of class. The first class encompasses 44% of the particles with density for IF2 (purple) and tRNA in the P/I position (green), the second class encompasses 48% with density for tRNA in the P/P position (orange), and the third class encompasses approximately 8% without any density in the masked region. The 3D refinement of the first, second and third types of class yielded cryo-EM maps of 70S IC, 70S EC and low-resolution 70S EC, respectively.
Extended Data Fig. 5 Selection of noise particles and angular coverage of 70S IC with addition of noisy particles.
a, Noise particles selected from the gain-corrected micrograph. The noise particles that did not exhibit any ribosome particle-like features were selected from the background (green circles). b, The angular coverage of the 70S IC with respect to the view depicted in the centre panel, as a function of the level of added noise.
Extended Data Fig. 6 Fourier shell curves and cryo-EM reconstructions for the 30S IC, 70S IC and 70S EC.
a–c, Fourier shell curves (FSC) for the 30S IC (a), 70S IC (b) and 70S EC (c). The resolutions of these structures were estimated using a resolution-estimating protocol that avoids overfitting and uses the FSC and 0.143 criterion28. d–f, Cryo-EM reconstructions of 30S IC (d), 70S IC (e) and 70S EC (f). g–i, Angular orientation coverage of 30S IC (g), 70S IC (h), and 70S EC (i), presented corresponding to the views depicted in d, e and f, respectively. j–l, Directional FSC plots46 of the cryo-EM reconstructions of the 30S IC (j), 70S IC (k) and 70S EC (l).
a, The positions of IF2 (dark purple), 70S P/I fMet-tRNAfMet (green), uS12 (yellow), and H69, H71, H80, H89 and H95 (SRL; all shown in blue) within the cryo-EM reconstruction of the 70S IC (transparent grey) were obtained using the MDFF method. b, A magnified view of the structure shown in a, highlighting the interactions that IF2 makes with the 70S ribosome in the 70S IC.
Extended Data Fig. 8 Portion of the Coulomb potential map corresponding to the guanosine nucleotide obtained from the 4 Å resolution, cryo-EM reconstruction of the 70S IC.
a, b, Rigid-body fitting was used to position either GTP (a) or GDP·Pi (b) into the Coulomb potential map. The Coulomb potential map is shown as a blue mesh. The initial position of Pi relative to GDP for the rigid-body fitting was taken from the structure of the GDP·Pi-form of the G protein Giα1 (PDB code 1AS2)47. Source Data
Extended Data Fig. 9 Views of the major components of the 30S IC and 70S IC after structural modelling of the 30S IC and 70S IC using the MDFF method.
a, fMet-tRNAfMet (orange) in its 30S P/I configuration in the 30S IC. b, IF1 (magenta) in the 30S IC. c, IF2 (light purple) in the 30S IC. d, fMet-tRNAfMet (green) in its 70S P/I configuration in the 70S IC. e, IF2 (dark purple) in the 70S IC. For each component, the reconstructed Coulomb potential map is represented by the mesh and the structural model is represented by secondary structure cartoons.
About this article
Cite this article
Kaledhonkar, S., Fu, Z., Caban, K. et al. Late steps in bacterial translation initiation visualized using time-resolved cryo-EM. Nature 570, 400–404 (2019). https://doi.org/10.1038/s41586-019-1249-5
A Time‐Resolved Cryo‐EM Study of Saccharomyces cerevisiae 80S Ribosome Protein Composition in Response to a Change in Carbon Source
A mutation in the ribosomal protein uS12 reveals novel functions of its universally conserved PNSA loop
Molecular Microbiology (2021)
Nature Methods (2021)
Nature Communications (2021)
Nucleic Acids Research (2021)