Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN.

Area of Science:

• Computational chemistry
• Structural biology
• Drug discovery

Background:

• Targeting RNA with small molecules is crucial for drug development but hindered by limited understanding of RNA's dynamic structure.
• Existing computational tools often use static RNA structures, failing to capture the dynamic nature essential for accurate binding site identification.

Purpose of the Study:

• To introduce SHAMAN, a novel computational technique for identifying potential small-molecule binding sites within RNA structural ensembles.
• To overcome the limitations of static structure-based methods in RNA drug design.

Main Methods:

• SHAMAN utilizes atomistic molecular dynamics simulations to explore RNA's conformational landscape.
• It employs probes and enhanced-sampling techniques for efficient identification of RNA pockets within these dynamic ensembles.

Main Results:

• SHAMAN successfully identified all experimentally resolved binding pockets in benchmark RNA molecules, including riboswitches and viral RNAs.
• The method ranked identified pockets favorably among probe hotspots, demonstrating its efficacy.

Conclusions:

• SHAMAN provides a robust computational foundation for small-molecule drug design targeting RNA.
• This technique effectively addresses the long-standing challenge of accounting for RNA dynamics in drug discovery efforts.