From Molecules to Mechanisms: Integrating MD and Stochastic Modeling to Decipher RXR-RAR Gene Regulation

This study integrates atomistic molecular dynamics (MD) simulations with stochastic modeling to unravel the gene regulatory mechanism mediated by nuclear receptors (NRs), ligand-activated transcription factors. We specifically study the heterodimeric RXR-RAR nuclear receptor system. We use MD simulations to investigate ligand-induced allosteric communication, receptor dynamics, and DNA recognition. The results suggest that receptor dimerization precedes DNA binding, and even single-ligand occupancy induces allosteric signaling that stabilizes the complex, indicating ligand binding as a modulator of gene expression. In contrast, we propose a ligand-induced downregulation mechanism of gene expression that involves disruption of allosteric pathways, weakening of the receptor–DNA interface, and promotion of complex dissociation, as inferred from binding free energy calculations. To bridge the timescale gap between molecular events and gene-level regulation, we integrate MD-derived data into a stochastic modeling framework to construct an NR-mediated gene regulatory network. This analysis reveals that ligand-specific allosteric activation predominantly drives gene expression. The results further demonstrate that the system maintains an ordered transcriptional response despite noisy intermediate dynamics, ensuring minimal mRNA production through a balance between synthesis and degradation. This integrated approach connects molecular-scale interactions with long-timescale regulatory dynamics, providing mechanistic insights into NR-mediated regulation. Using RXR–RAR as a representative system, we establish a computational framework to explore the principles of NR function and dysfunction that are often inaccessible through experiments or conventional MD simulations.