Investigation of Phe-tRNA interaction with EF-Tu in GDP/GTP nucleotide bound states: a molecular dynamics simulation study
DOI:
https://doi.org/10.34117/bjdv10n3-060Abstract
Elongation factor Tu (EF-Tu) is an important class of translational GTPases (trGTPases) involved in the elongation process. EF-Tu functions by forming the tight complex with the aminoacyl-tRNA (aa-tRNA: transfer RNA carrying an amino acid) in the GTP-bound active state and subsequently transports the aa-tRNA to the A-site (aminoacyl-site) of the ribosome. The correct interaction between mRNA's codon and aa-tRNA anticodon triggers GTP hydrolysis, resulting in the transition of EF-Tu from the active state to the inactive state (EF-Tu bound to GDP). This, in turn, results in the dissociation of EF-Tu:GDP from the tRNA and ribosome. However, the conformational changes and interactions responsible for the tight complex formation between EF-Tu and aa-tRNA in the GTP-bound state and the disruption of the interaction between EF-Tu and aa-tRNA in the GDP-bound state are not well understood. Therefore, to explore the conformational changes in the EF-Tu:Phe-tRNA complex and to get insight into the interaction between Phe-tRNA (tRNA carrying Phenylalanine) and EF-Tu in GDP/GTP nucleotide bound state, 200 ns MD (molecular dynamics) simulation have been carried out. The RMSD, RMSF, cluster, and DSSP analyses suggest that the GTP-bound state attains a more favorable conformation, which may facilitate EF-Tu’s interactions with the tRNA and ribosome. Further investigation of the non-bonded interaction energy calculation revealed the significance of domainII in interaction with the Phe-tRNA in a GTP-bound state. In addition, the H-bonds calculated between the tRNA’s Phe (Phenylalanine attached to the tRNA is considered) and domainII highlights the contribution of Val285 in recognizing tRNA’s Phe by forming an H-bond throughout the simulation time in the GTP bound state. Overall these results provide insight into how GTP nucleotide influences the interaction between EF-Tu and Phe-tRNA.
References
Achila, D., Gulati, M., Jain, N., Britton, R. A. Biochemical Characterization of Ribosome Assembly GTPase RbgA in Bacillus subtilis*. Journal of Biological Chemistry 287, 8417–8423 (2012). https://doi.org/10.1074/jbc.M111.331322
Daumke, O., Praefcke, G. J. Mechanisms of GTP hydrolysis and conformational transitions in the dynamin superfamily. Biopolymers 109, (2018).
Upendra, N., Krishnaveni, S. Molecular dynamics simulation study on Thermotoga maritima EngA: GTP/GDP bound state of the second G-domain influences the domain-domain interface interactions. Journal of Biomolecular Structure and Dynamics 5, 1–13 (2020).
Agirrezabala, X., Frank, J. Elongation in translation as a dynamic interaction among the ribosome, tRNA, and elongation factors EF-G and EF-Tu. Quarterly Reviews of Biophysics 42, 159–200 (2009).
Britton, R. A. Role of GTPases in bacterial ribosome assembly. Annual Review of Microbiology 63, 155–176 (2009).
Van Aelst, L, and C D'Souza-Schorey. Rho GTPases and signaling networks. Genes & development 11, 2295-322 (1997).
Upendra, N., Kavya, K. M., Krishnaveni, S. Molecular dynamics simulation study on Bacillus subtilis EngA: the presence of Mg2+ at the active-sites promotes the functionally important conformation. Journal of Biomolecular Structure and Dynamics 41, 9219–9231 (2023). https://doi.org/10.1080/07391102.2022.2151513
Verstraeten, N., Fauvart, M., Versées, W., Michiels, J. The Universally Conserved Prokaryotic GTPases. Microbiology and Molecular Reviews (2011). https://doi.org/10.1128/MMBR.00009-11
Ramakrishnan, V. Ribosome structure and the mechanism of translation. Cell 108, 557–572 (2002).
Maracci, C., Rodnina, M. V. translational GTPases. Biopolymers 105, 463–475 (2016).
Kulczycka, K., Dlugosz, M., Trylska, J. Molecular dynamics of ribosomal elongation factors G and Tu. European Biophysics Journal 40, 289–303 (2011).
Jacquet, E., Parmeggiani, A. Structure-function relationships in the GTP binding domain of EF-Tu: mutation of Val20, the residue homologous to position 12 in p21. EMBO J 7, 2861–2867 (1988).
Zhan, B., Gao, Y., Gao, W., Li, Y., Li, Z., Qi, Q., et al. Structural insights of the elongation factor EF-Tu complexes in protein translation of Mycobacterium tuberculosis. Commun Biol 5, 1052 (2022). https://doi.org/10.1038/s42003-022-04019-y
Warias, M., Grubmüller, H., Bock, L. V. tRNA Dissociation from EF-Tu after GTP Hydrolysis: Primary Steps and Antibiotic Inhibition. Biophys J 118, 151–161 (2020). https://doi.org/10.1016/j.bpj.2019.10.028
Voorhees, R. M., Ramakrishnan, V. Structural basis of the translational elongation cycle. Annual Review of Biochemistry 82, 203–236 (2013). https://doi.org/10.1146/annurev-biochem-113009-092313
Lai, J., Ghaemi, Z., Luthey-Schulten, Z. The Conformational Change in Elongation Factor Tu Involves Separation of Its Domains. Biochemistry 56, 5972–5979 (2017). https://doi.org/10.1021/acs.biochem.7b00591
Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., et al. UCSF Chimera—a visualization system for exploratory research and analysis. Journal of Computational Chemistry 25, 1605–1612 (2004).
Páll, S., Zhmurov, A., Bauer, P., Abraham, M., Lundborg, M., Gray, A., et al. Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS. The Journal of Chemical Physics 153, (2020).
Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1, 19–25 (2015).
Páll, S., Abraham, M. J., Kutzner, C., Hess, B., Lindahl, E. Tackling exascale software challenges in molecular dynamics simulations with GROMACS. in Solving Software Challenges for Exascale: International Conference on Exascale Applications and Software, EASC 2014, Stockholm, Sweden, April 2-3, 2014, Revised Selected Papers 2 pp. 3–27 (Springer, 2015).
Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., et al. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29, 845–854 (2013).
Hess, B., Kutzner, C., Van Der Spoel, D., Lindahl, E. GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. Journal of Chemical Theory and Computation 4, 435–447 (2008).
Van Der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E., Berendsen, H. J. GROMACS: fast, flexible, and free. Journal of Computational Chemistry 26, 1701–1718 (2005).
Lindahl, E., Hess, B., Van Der Spoel, D. GROMACS 3.0: a package for molecular simulation and trajectory analysis. Molecular Modeling Annual 7, 306–317 (2001).
Berendsen, H. J., van der Spoel, D., van Drunen, R. GROMACS: a message-passing parallel molecular dynamics implementation. Computer Physics Communications 91, 43–56 (1995).
Lindorff-Larsen, K., Piana, S., Palmo, K., Maragakis, P., Klepeis, J. L., Dror, R. O., et al. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 78, 1950–1958 (2010). https://doi.org/10.1002/prot.22711
Götz, A. W., Williamson, M. J., Xu, D., Poole, D., Le Grand, S., Walker, R. C. Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 1. Generalized Born. J. Chem. Theory Comput. 8, 1542–1555 (2012). https://doi.org/10.1021/ct200909j
Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., Klein, M. L. Comparison of simple potential functions for simulating liquid water. The Journal of Chemical Physics 79, 926–935 (1983).
Darden, T., York, D., Pedersen, L. Particle mesh Ewald: An N⋅ log (N) method for Ewald sums in large systems. The Journal of Chemical Physics 98, 10089–10092 (1993).
Essmann, U., Perera, L., Berkowitz, M. L., Darden, T., Lee, H., Pedersen, L. G. A smooth particle mesh Ewald method. The Journal of Chemical Physics 103, 8577–8593 (1995).
Hess, B., Bekker, H., Berendsen, H. J., Fraaije, J. G. LINCS: a linear constraint solver for molecular simulations. Journal of Computational Chemistry 18, 1463–1472 (1997).
Upendra, N., Krishnaveni, S. Conformational exploration of RbgA using molecular dynamics: Possible implications in ribosome maturation and GTPase activity in different nucleotide bound states. Journal of Molecular Graphics and Modelling 111, 108087 (2022). https://doi.org/10.1016/j.jmgm.2021.108087
