Computational modeling of AGO-mediated molecular inhibition of ARF6 by miR-145

https://doi.org/10.22146/ijbiotech.55631

Jeremias Ivan(1), Rizky Nurdiansyah(2), Arli Aditya Parikesit(3*)

(1) Department of Bioinformatics, School of Life Sciences, Indonesia International Institute for Life Sciences, Pulomas Barat Kav 88, Jakarta Timur 13210
(2) Department of Bioinformatics, School of Life Sciences, Indonesia International Institute for Life Sciences, Pulomas Barat Kav 88, Jakarta Timur 13210
(3) Department of Bioinformatics, School of Life Sciences, Indonesia International Institute for Life Sciences, Pulomas Barat Kav 88, Jakarta Timur 13210
(*) Corresponding Author

Abstract


Inhibition of ADP-ribosylation factor 6 messenger RNA (ARF6 mRNA) by microRNA-145 (miR-145), mediated by Argonaute (AGO) protein, has been found to play essential roles in several types of cancer and cellular processes. This study aimed to model the molecular interaction between miR-145 and ARF6 mRNA with AGO protein. The sequences of miR-145 and the 3’ untranslated region (UTR) of ARF6 mRNA were retrieved from miRTarBase, followed by miRNA target-site and structure predictions were done using RNAhybrid, RNAfold, and simRNAweb, respectively. The interaction between the miRNA-mRNA duplex and AGO was further assessed via molecular docking, interaction analysis, and dynamics, using PatchDock Server, PLIP, and VMD/NAMD, respectively. The models between miR-145, predicted target site of ARF6 mRNA, and AGO protein returned stable thermodynamic variables with negative free energy. Specifically, the RNA duplex had an energy of -19.80 kcal/mol, while the docking had -84.58 atomic contact energy supported by 70 hydrogen bonds and 14 hydrophobic interactions. However, the stability of the RMSD plot was still unclear due to limited computational resources. Nevertheless, these results computationally confirm favorable interaction of the three molecules, which can be utilized for further transcriptomics-based drugs or treatments.


Keywords


Gene silencing; in silico; molecular simulation; transcriptomics; tumorigenesis

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DOI: https://doi.org/10.22146/ijbiotech.55631

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