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Understanding the Binding Affinity and Specificity of miRNAs: A Molecular Dynamics Study

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Advances in Computational Modeling and Simulation

Abstract

MicroRNAs (miRNA) are endogenously produced small (21–27 nucleotides long) non-coding RNA molecules that can play post-transcriptional regulation of gene expression either by cleavage of the mRNA strand or by translational repression. However, recognizing mRNA targets by miRNAs is not clearly understood. It is observed that one miRNA molecule can target hundreds of different mRNA sites and different miRNAs can target a single mRNA site. The majority of miRNA target prediction algorithms mainly consider the complementarity on the seed region of miRNA for their target selection. To explore the role of different regions of miRNA for target selection, explicit solvent unrestrained molecular dynamics simulation has been performed on miRNA-RNA duplex. Our studies revealed that seed region complementary base pairing is sufficient to maintain the integrity of the duplex, whereas bulge and 3′ end regions are highly flexible which can interact with target mRNA through non-canonical hydrogen bonds and regulate translational repression.

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References

  1. O’Brien J, Hayder H, Zayed Y, Peng C (2018) Overview of microRNA biogenesis, mechanisms of actions, and circulation. Front Endocrinol (Lausanne) 9:1–12. https://doi.org/10.3389/fendo.2018.00402

  2. Winter J, Jung S, Keller S, Gregory RI, Diederichs S (2009) Many roads to maturity: MicroRNA biogenesis pathways and their regulation. Nat Cell Biol 11(3):228–234. https://doi.org/10.1038/ncb0309-228

    Article  Google Scholar 

  3. Peterson SM, Thompson JA, Ufkin ML, Sathyanarayana P, Liaw L, Congdon CB (2014) Common features of microRNA target prediction tools. Front Genet 5. https://doi.org/10.3389/fgene.2014.00023

  4. Riolo G, Cantara S, Marzocchi C, Ricci C (2021) miRNA targets: from prediction tools to experimental validation. Methods Protoc 4(1):1–20. https://doi.org/10.3390/mps4010001

    Article  Google Scholar 

  5. Brancati G, Großhans H (2018) An interplay of miRNA abundance and target site architecture determines miRNA activity and specificity. Nucleic Acids Res 46(7):3259–3269. https://doi.org/10.1093/nar/gky201

    Article  Google Scholar 

  6. Ameres SL, Martinez J, Schroeder R (2007) Molecular basis for target RNA recognition and cleavage by human RISC. Cell 130(1):101–112. https://doi.org/10.1016/j.cell.2007.04.037

    Article  Google Scholar 

  7. Broughton JP, Lovci MT, Huang JL, Yeo GW, Pasquinelli AE (2016) Pairing beyond the seed supports MicroRNA targeting specificity. Mol Cell 64(2):320–333. https://doi.org/10.1016/j.molcel.2016.09.004

    Article  Google Scholar 

  8. Hausser J, Zavolan M (2014) Identification and consequences of miRNA-target interactions-beyond repression of gene expression. Nat Rev Genet 15(9):599–612. https://doi.org/10.1038/nrg3765

    Article  Google Scholar 

  9. Lai EC (2003) MicroRNAs: runts of the genome assert themselves. Curr Biol 13(23):925–936. https://doi.org/10.1016/j.cub.2003.11.017

    Article  Google Scholar 

  10. Alvarez Garcia I, Miska EA (2007) The microRNAs of C elegans. MicroRNAs from basic. Sci Dis Biol 7–21. https://doi.org/10.1017/CBO9780511541766.004

  11. Villa A, Wöhnert J, Stock G (2009) Molecular dynamics simulation study of the binding of purine bases to the aptamer domain of the guanine sensing riboswitch. Nucleic Acids Res 37(14):4774–4786. https://doi.org/10.1093/nar/gkp486

    Article  Google Scholar 

  12. Wang Y, Li Y, Ma Z, Yang W, Ai C (2010) Mechanism of microRNA-target interaction: Molecular dynamics simulations and thermodynamics analysis. PLoS Comput Biol 6(7):5. https://doi.org/10.1371/journal.pcbi.1000866

    Article  Google Scholar 

  13. Minchington TG, Griffiths-Jones S, Papalopulu N (2020) Dynamical gene regulatory networks are tuned by transcriptional autoregulation with microRNA feedback. Sci Rep 10(1):1–13. https://doi.org/10.1038/s41598-020-69791-5

    Article  Google Scholar 

  14. Bochicchio A, Krepl M, Yang F, Varani G, Sponer J, Carloni P (2018) Molecular basis for the increased affinity of an RNA recognition motif with re-engineered specificity: a molecular dynamics and enhanced sampling simulations study. PLoS Comput Biol 14(12):1–27. https://doi.org/10.1371/journal.pcbi.1006642

    Article  Google Scholar 

  15. Yue K, Wang X, Wu Y, Zhou X, He Q, Duan Y (2016) MicroRNA-7 regulates cell growth, migration and invasion via direct targeting of PAK1 in thyroid cancer. Mol Med Rep 14(3):2127–2134. https://doi.org/10.3892/mmr.2016.5477

    Article  Google Scholar 

  16. Gajda E, Grzanka M, Godlewska M, Gawel D (2021) The role of miRNA-7 in the biology of cancer and modulation of drug resistance. Pharmaceuticals 14(2):1–24. https://doi.org/10.3390/ph14020149

    Article  Google Scholar 

  17. Saydam et al O (2012) NIH Public Access, vol 71, no 3, pp 852–861 https://doi.org/10.1158/0008-5472.CAN-10-1219.miRNA-7

  18. Huang HY et al (2020) MiRTarBase 2020: updates to the experimentally validated microRNA-target interaction database. Nucleic Acids Res 48(D1):D148–D154. https://doi.org/10.1093/nar/gkz896

    Article  Google Scholar 

  19. Popenda M et al (2012) Automated 3D structure composition for large RNAs. Nucleic Acids Res 40(14):1–12. https://doi.org/10.1093/nar/gks339

    Article  Google Scholar 

  20. Case DA et al (2018) Amber 2018. The University of California, San Francisco 2018:1–923

    Google Scholar 

  21. Zgarbová M et al (2011) Refinement of the Cornell et al. Nucleic acids force field based on reference quantum chemical calculations of glycosidic torsion profiles. J Chem Theory Comput 7(9):2886–2902. https://doi.org/10.1021/ct200162x

  22. Ryckaert JP, Ciccotti G, Berendsen HJC (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23(3):327–341. https://doi.org/10.1016/0021-9991(77)90098-5

    Article  Google Scholar 

  23. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14:33–38. https://www.tapbiosystems.com/tap/products/index.htm

  24. Bansal M, Bhattacharyya D, Ravi B (1995) Acid Structures. Cabios 11(3):281–287

    Google Scholar 

  25. Miller BR, Mcgee TD, Swails JM, Homeyer N, Gohlke H, Roitberg AE (2012) MMPBSA . py : an efficient program for end-state free energy calculations

    Google Scholar 

  26. Ragan C, Zuker M, Ragan MA (2011) Quantitative prediction of miRNA-mRNA interaction based on equilibrium concentrations. PLoS Comput Biol 7(2). https://doi.org/10.1371/journal.pcbi.1001090.

  27. Arnott S, Hukins DWL, Dover SD, Fuller W, Hodgson AR (1973) Structures of synthetic polynucleotides in the A-RNA and A′-RNA conformations: X-ray diffraction analyses of the molecular conformations of polyadenylic acid polyuridylic acid and polyinosinic acid polycytidylic acid. J Mol Biol 81(2):107–122. https://doi.org/10.1016/0022-2836(73)90183-6

    Article  Google Scholar 

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Acknowledgements

We would like to acknowledge the HPC facilities of BITS-Pilani, Pilani campus.

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Correspondence to Shibasish Chowdhury .

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This research did not receive any specific grant from funding agencies.

Author Contributions

SC conceived, designed, and supervised the overall project; AM carried out initial modeling work; and SK, AM, and SC performed data analysis and wrote the manuscript. All authors have approved the final version of the manuscript.

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All the authors declare no conflict of interest.

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Kushwaha, S., Mandloi, A., Chowdhury, S. (2022). Understanding the Binding Affinity and Specificity of miRNAs: A Molecular Dynamics Study. In: Srinivas, R., Kumar, R., Dutta, M. (eds) Advances in Computational Modeling and Simulation. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-7857-8_19

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  • DOI: https://doi.org/10.1007/978-981-16-7857-8_19

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