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 time scale 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-time scale 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.