IRIS: A Machine Learning-Based Pose Reranking Tool for RNA-Ligand Docking

Given their fundamental
roles in cellular processes and
disease
pathogenesis, RNA molecules are promising therapeutic targets. Predicting
the 3D structure of RNA-ligand complexes using computational docking
is a key element of rational, structure-based inhibitor design. However,
RNA-ligand docking remains challenging, due in part to intrinsic properties
of RNA such as structural flexibility and a highly charged phosphate
backbone. rDock, a widely used RNA docking program, can generate ligand
poses close to the experimental structure, but its scoring function
frequently fails to rank these poses above less accurate alternatives.
To supplement rDock, here we introduce the Intelligent RNA Interaction
Scorer (IRIS), a regression model leveraging physicochemical and interaction-based
features and trained on the largest data set of experimental nucleic
acid–ligand complexes compiled to date for any ML-based tool
designed for RNA docking (608 structures). IRIS improves rDock RNA-ligand
pose ranking relative to the use of rDock scores alone. Using the
best-performing rDock protocol on the RNA portion of the data set,
we find that at least one of the 100 top generated poses for any given
complex is within 2.0 Å RMSD of the native pose in 86.3% of test
complexes. Of these 86.3%, the default rDock scoring function ranks
the correct pose first in 42.7% of cases. IRIS improves this latter
fraction to 59.8% and increases the success rate for selecting a near-native
pose among the top five ranked poses from 64.6% to 78.0%. IRIS thus
significantly enhances pose ranking accuracy and can be seamlessly
integrated into docking pipelines to rerank ligand poses in RNA-targeted
drug discovery.