Abstract:

Structured RNA elements, such as CUG repeat expansions characterized by UU internal loop motifs, are high-value but challenging targets for small molecule recognition. A significant barrier in RNA-targeted drug discovery is the efficient identification of ligands that can bind to dynamic RNA loop motifs. To address this, we developed two dynamic docking approaches, DynaD and DynaD/Auto—computational tools designed to rapidly identify small molecule binding properties to RNA targets. These methods predict global minimum and local minima bound states along with their binding energies, elucidating the binding energy landscape not accessible by current experimental techniques.

DynaD is a physics-based computational method that predicts initial ligand-bound states to RNA loops where binding pockets are known but structural data is missing. It simulates the binding process using molecular dynamics (MD), guided by a distance-based reaction coordinate and force-field interactions, rather than empirical scoring functions. This enables exploration of complex RNA-ligand energy landscapes and identification of energetically favorable binding modes. Binding free energies are calculated using MM/PBSA and MM/3D-RISM to compare modes and determine the global minimum.

To address conformational sampling limitations in flexible systems, we developed DynaD/Auto, a hybrid approach incorporating umbrella sampling data from UU internal loops and AutoDock predictions. This workflow improves sampling and prediction accuracy by integrating prior structural insights. Umbrella sampling trajectories enable sampling of RNA loop conformational ensembles, identifying biologically relevant global and local minima. Simulation predictions were validated against experimental data, showing strong positive correlations. Dendrogram analysis highlighted distinct binding modes based on RMSD clustering.

DynaD offers forced targeting capabilities when structural data is lacking, while DynaD/Auto leverages known free energy landscapes to enhance prediction accuracy. Together, these approaches provide a powerful framework for reliable identification of RNA-ligand interactions, particularly in dynamic and structurally complex RNA targets like CUG repeats.

Speaker:

Nakul Balaji, Florida Atlantic University

Nakul Balaji is an undergraduate researcher in computational biophysics at Florida Atlantic University’s Wilkes Honors College, where he is pursuing a concentration in Data Analytics. His research focuses on molecular docking and small-molecule drug discovery.