Molecular Dynamics Simulation of a tRNA-Leucine Dimer with an A3243G Heteroplasmy Mutation in Human Mitochondria Using a Secondary Structure Prediction Approach

https://doi.org/10.22146/ijc.72774

Iman Permana Maksum(1*), Ahmad Fariz Maulana(2), Muhammad Yusuf(3), Rahmaniar Mulyani(4), Wanda Destiarani(5), Rustaman Rustaman(6)

(1) Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Jl. Raya Bandung-Sumedang km 21, Jatinangor 45363, West Java, Indonesia
(2) Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Jl. Raya Bandung-Sumedang km 21, Jatinangor 45363, West Java, Indonesia
(3) Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Jl. Raya Bandung-Sumedang km 21, Jatinangor 45363, West Java, Indonesia Research Centre for Molecular Biotechnology and Bioinformatics, Universitas Padjadjaran, Jl. Raya Bandung-Sumedang km 21, Jatinangor 45363, West Java, Indonesia
(4) Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Jl. Raya Bandung-Sumedang km 21, Jatinangor 45363, West Java, Indonesia
(5) Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Jl. Raya Bandung-Sumedang km 21, Jatinangor 45363, West Java, Indonesia
(6) Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Jl. Raya Bandung-Sumedang km 21, Jatinangor 45363, West Java, Indonesia
(*) Corresponding Author

Abstract


Mitochondrial DNA mutations, such as A3243G, can affect changes in the structure of biomolecules, resulting in changes in the structure of Leucine transfer Ribose Nucleic Acid to form a dimer. Dimer structure modeling is needed to determine the properties of the structure. However, the lack of a structure template for the transfer of Ribose Nucleic Acid (tRNA) is challenging for the modeling of mutant structures of tRNA, especially mitochondrial tRNA that are susceptible to mutation. Therefore, this study predicted the structure of mitochondrial leucine tRNA and its stability through a knowledge-based method and molecular dynamics. Structural modeling and initial assessment were performed using RNAComposer and MolProbity, HNADOCK, and Discovery studios to form the dimer structure. Molecular dynamics simulations for stability analysis were performed using Amber and AmberTools20 software, showing that the conformational energy of the mutant leucine tRNA dimer structure was lower than the native structure. Moreover, the Root Mean Square Deviation (RMSD) of monomer native leucine tRNA was lower than the mutant, indicating that the dimer structure of mutant leucine tRNA is more stable than usual, and the normal leucine tRNA is more stable than the mutant.


Keywords


A3243G; molecular dynamics; mitochondrial DNA; structure prediction

Full Text:

Full Text PDF


References

[1] Nelson, D.L., and Cox, M.M., 2013, Lehninger Principles of Biochemistry, 6th Ed., W.H. Freeman & Company, New York.

[2] Li, L.H., Kang, T., Chen, L., Zhang, W., Liao, Y., Chen, J., and Shi, Y., 2014, Detection of mitochondrial DNA mutations by high-throughput sequencing in the blood of breast cancer patients, Int. J. Mol. Med., 33 (1), 77–82.

[3] Ramsay, E.P., and Vannini, A., 2017, Structural rearrangements of the RNA polymerase III machinery during tRNA transcription initiation, Biochim. Biophys. Acta, Gene Regul. Mech., 1861 (4), 285–294.

[4] Dianov, G.L., Souza-Pinto, N., Nyaga, S.G., Thybo, T., Stevnsner, T., and Bohr, V.A., 2001, Base excision repair in nuclear and mitochondrial DNA, Prog. Nucleic Acid Res. Mol. Biol., 68, 285–97.

[5] Maksum, I., Natradisastra, G., Nuswantara, S., and Ngili, Y., 2013, The effect of A3243G mutation of mitochondrial DNA to the clinical features of type-2 diabetes mellitus and cataract, Eur. J. Sci. Res., 96 (4), 591–599.

[6] Maksum, I.P., Farhani, A., Rachman, S.D., and Ngili, Y., 2013, Making of the A3243G mutant template through site directed mutagenesis as positive control in PASA-mismatch three bases, Int. J. PharmTech Res., 5 (2), 441–450.

[7] Destiarani, W., Mulyani, R., Yusuf, M., and Maksum, I.P., 2020, Molecular dynamics simulation of T10609C and C10676G mutations of mitochondrial ND4L gene associated with proton translocation in type 2 diabetes mellitus and cataract patients, Bioinf. Biol. Insights, 14, 1177932220978672.

[8] Maksum, I.P., Saputra, S.R., Indrayati, N., Yusuf, M., and Subroto, T., 2017, Bioinformatics study of m.9053G>A mutation at the ATP6 gene in relation to type 2 diabetes mellitus and cataract diseases, Bioinform. Biol. Insights, 11, 1177932217728515.

[9] Wittenhagen, L.M., and Kelley, S.O., 2002, Dimerization of a pathogenic human mitochondrial tRNA, Nat. Struct. Biol., 9 (8), 586–590.

[10] Popenda, M., Szachniuk, M., Antczak, M., Purzycka, K.J., Lukasiak, P., Bartol, N., Blazewicz, J., and Adamiak, R.W., 2012, Automated 3D structure composition for large RNAs, Nucleic Acids Res., 40 (14), e112.

[11] Kaufmann, M., Klinger, C., and Savelsbergh, A., 2017, Functional Genomics: Methods and Protocols, 3rd Ed., Humana Press, New York, USA.

[12] Jain, S., Richardson, D.C., and Richardson, J.S., 2015, Computational methods for RNA structure validation and improvement, Methods Enzymol., 558, 181–212.

[13] Baba, N., Elmetwaly, S., Kim, N., and Schlick, T., 2016, Predicting large RNA-like topologies by a knowledge-based clustering approach, J. Mol. Biol., 428 (5 Pt A), 811–821.

[14] Antczak, M., Popenda, M., Zok, T., Sarzynska, J., Ratajczak, T., Tomczyk, K., Adamiak, R.W., and Szachniuk, M., 2016, New functionality of RNAComposer: An application to shape the axis of miR160 precursor structure, Acta Biochim. Pol., 63 (4), 737–744.

[15] Chan, P.P., and Lowe, T.M., 2019, "tRNAscan-SE: Searching for tRNA Genes in Genomic Sequences" in Gene Prediction. Methods in Molecular Biology, vol 1962, Eds. Kollmar, M., Humana Press, New York, USA, 1–14.

[16] Stasiewicz, J., Mukherjee, S., Nithin, C., and Bujnicki, J.M., 2019, QRNAS: Software tool for refinement of nucleic acid structures, BMC Struct. Biol., 19 (1), 5.

[17] He, J., Wang, J., Tao, H., Xiao, Y., and Huang, S.Y., 2019, HNADOCK: A nucleic acid docking server for modeling RNA/DNA-RNA/DNA 3D complex structures, Nucleic Acids Res., 47 (W1), W35–W42.

[18] Lorenz, C., Lünse, C.E., and Mörl, M., 2017, tRNA modifications: Impact on structure and thermal adaptation, Biomolecules, 7 (2), 35.

[19] Turner, P., McLennan, A., Bates, A., and Mike, W., 2018, Molecular Biology, 3rd Ed., Academic Cell, Cambridge Massachusetts, USA.

[20] Williams, C.J., Headd, J.J., Moriarty, N.W., Prisant, M.G., Videau, L.L., Deis, L.N., Verma, V., Keedy, D.A., Hintze, B.J., Chen, V.B., Jain, S., Lewis, S.M., Arendall, W.B., Snoeyink, J., Adams, P.D., Lovell, S.C., Richardson, J.S., and Richardson, D.C., 2018, MolProbity: More and better reference data for improved all-atom structure validation, Protein Sci., 27 (1), 293–315.

[21] Sargsyan, K., Grauffel, C., and Lim, C., 2017, How molecular size impacts RMSD applications in molecular dynamics simulations, J. Chem. Theory Comput., 13 (4), 1518–1524.



DOI: https://doi.org/10.22146/ijc.72774

Article Metrics

Abstract views : 1978 | views : 1052


Copyright (c) 2022 Indonesian Journal of Chemistry

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

 


Indonesian Journal of Chemistry (ISSN 1411-9420 / 2460-1578) - Chemistry Department, Universitas Gadjah Mada, Indonesia.

Web
Analytics View The Statistics of Indones. J. Chem.