Journal of Chemical Information and ModelingBiophysicsPeer-reviewed journal article
Investigating Ligand-Mediated Conformational Dynamics of Pre-miR21: A Machine-Learning-Aided Enhanced Sampling Study
Simone AureliiD, Francesco Bellina, Valerio RizziiD, Francesco Luigi GervasioiD
Abstract
MicroRNAs (miRNAs) are short, non-coding RNA strands that regulate the activity of messenger RNAs (mRNAs) by affecting the repression of protein translation, and their dysregulation has been implicated in several pathologies. miR21 in particular has been implicated in tumourigenesis and anticancer drug resistance, making it a critical target for drug design. miR21 biogenesis involves precise biochemical pathways, including the cleavage of its precursor, pre-miR21, by the enzyme Dicer. The present work investigates the conformational dynamics of pre-miR21, focusing on the role of adenine29 in switching between Dicer-binding-prone and inactive states. We also investigated the effect of L50, a cyclic peptide binder of pre-miR21 and a weak inhibitor of its processing. Using time series data and our novel collective variable-based enhanced sampling technique OneOPES, we simulated these conformational changes and assessed the effect of L50 on the conformational plasticity of pre-miR21. Our results provide insight into peptide-induced conformational changes and pave the way for the development of a computational platform for the screening of inhibitors of pre-miR21 processing that considers RNA flexibility, a stepping stone for an effective structure-based drug design, with potentially broad applications in drug discovery. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/601024v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@188857borg.highwire.dtl.DTLVardef@1f37d2forg.highwire.dtl.DTLVardef@9fc9dforg.highwire.dtl.DTLVardef@7e8629_HPS_FORMAT_FIGEXP M_FIG C_FIG
- research focus: drug response / drug discovery
- research focus: protein folding & dynamics
- approach: machine learning (classical)
- Citations
- 7
- FWCI
- 0.81
- Field percentile
- top 32%
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