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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Case DA et al (2018) Amber 2018. The University of California, San Francisco 2018:1–923
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
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
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
Bansal M, Bhattacharyya D, Ravi B (1995) Acid Structures. Cabios 11(3):281–287
Miller BR, Mcgee TD, Swails JM, Homeyer N, Gohlke H, Roitberg AE (2012) MMPBSA . py : an efficient program for end-state free energy calculations
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.
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
Acknowledgements
We would like to acknowledge the HPC facilities of BITS-Pilani, Pilani campus.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Funding Sources
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.
Conflict of Interest
All the authors declare no conflict of interest.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-16-7857-8_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7856-1
Online ISBN: 978-981-16-7857-8
eBook Packages: EngineeringEngineering (R0)