SPRankA Knowledge-Based Scoring Function for RNA-Ligand Pose Prediction and Virtual Screening

The growing interest in RNA-targeted drugs underscores
the need
for computational modeling of interactions between RNA molecules and
small compounds. Having a reliable scoring function for RNA-ligand
interactions is essential for effective computational drug screening.
An ideal scoring function should not only predict the native pose
for ligand binding but also rank the affinity of the binding for different
ligands. However, existing scoring functions are primarily designed
to predict the native binding modes for a given RNA-ligand pair and
have not been thoroughly assessed for virtual screening purposes.
In this paper, we introduce SPRank, a combination of machine-learning
and knowledge-based scoring functions developed through a weighted
iterative approach, specifically designed to tackle both binding mode
prediction and virtual screening challenges. Our approach incorporates
third-party docking software, such as rDock and AutoDock Vina, to
sample flexible ligands against an ensemble of RNA structures, capturing
the conformational flexibility of both the RNA and the ligand. Through
rigorous testing, SPRank demonstrates improved performance compared
to the tested scoring functions across four test sets comprising 122,
42, 55, and 71 nucleic acid-ligand complexes. Furthermore, SPRank
exhibits improved performance in virtual screening tests targeting
the HIV-1 TAR ensemble, which highlights its advantage in drug discovery.
These results underscore the advantages of SPRank as a potentially
promising tool for the RNA-targeted drug design. The source code of
SPRank and the data sets are freely accessible at https://github.com/Vfold-RNA/SPRank.