Dynamic docking of small molecules targeting RNA CUG repeats causing myotonic dystrophy type 1.

Area of Science:

• Computational chemistry
• Molecular modeling
• Drug discovery

Background:

• Myotonic dystrophy type 1 (DM1) is caused by expanded RNA CUG repeats that sequester muscleblind-like 1 (MBNL1) proteins.
• Expanded RNA CUG repeats are potential drug targets due to their unique structure.
• Small molecules can be designed to target these repeats, inhibiting MBNL1 binding and mitigating DM1 symptoms.

Purpose of the Study:

• To develop and validate physics-based dynamic docking approaches for targeting expanded RNA CUG repeats.
• To assess the efficacy of DynaD and DynaD/Auto in predicting small molecule binding to RNA.
• To explore the role of solvation in RNA-small molecule binding calculations.

Main Methods:

• Development of two physics-based dynamic docking approaches: DynaD and DynaD/Auto.
• Application of these methods to nine small molecules targeting RNA CUG repeats.
• Utilizing umbrella sampling and AutoDock calculations within DynaD/Auto for enhanced energy landscape sampling.
• Comparison of computational predictions with experimental data.

Main Results:

• Both DynaD and DynaD/Auto showed positive correlations with experimental data (R=0.70 and R=0.81, respectively).
• MM/3D-RISM calculations indicated the critical importance of solvation in binding accuracy.
• DynaD/Auto outperformed DynaD due to the incorporation of prior binding site knowledge from umbrella sampling.
• Dendrograms were developed to visualize binding state connectivity.

Conclusions:

• DynaD and DynaD/Auto represent novel, physics-based computational methodologies for drug design against complex RNA targets.
• These in silico tools can accelerate the discovery of drug-like molecules for targeting RNA structures implicated in diseases like DM1.
• The study underscores the significance of solvation effects and prior structural information in accurate molecular docking simulations.