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

Area of Science:

• Molecular Biology
• Computational Biology
• Biophysics

Background:

• Nuclear receptors (NRs) are ligand-activated transcription factors crucial for gene regulation.
• Understanding the molecular mechanisms of NR-mediated gene expression is essential for deciphering cellular processes and disease states.

Purpose of the Study:

• To investigate the gene regulatory mechanisms of nuclear receptors (NRs), specifically the RXR-RAR heterodimer, using integrated computational approaches.
• To elucidate how ligand binding influences NR dynamics, DNA recognition, and subsequent gene expression modulation.

Main Methods:

• Atomistic molecular dynamics (MD) simulations to analyze ligand-induced allosteric communication, receptor dynamics, and DNA binding.
• Stochastic modeling framework integrating MD-derived data to construct NR-mediated gene regulatory networks.
• Binding free energy calculations to infer mechanisms of gene expression activation and downregulation.

Main Results:

• Receptor dimerization precedes DNA binding, and ligand occupancy stabilizes the NR-DNA complex, promoting gene expression.
• Ligand binding can also disrupt allosteric pathways and weaken the receptor-DNA interface, leading to gene downregulation.
• A balanced synthesis and degradation process ensures an ordered transcriptional response despite molecular noise.

Conclusions:

• The integrated computational approach provides mechanistic insights into NR-mediated gene regulation, linking molecular dynamics to long-timescale regulatory networks.
• Ligand-specific allosteric activation is a primary driver of gene expression.
• This framework enables exploration of NR function and dysfunction, offering insights beyond experimental limitations.