How Good Are Current Docking Programs at Nucleic Acid–Ligand Docking? A Comprehensive Evaluation

Nucleic
acid (NA)–ligand interactions are of paramount importance
in a variety of biological processes, including cellular reproduction
and protein biosynthesis, and therefore, NAs have been broadly recognized
as potential drug targets. Understanding NA–ligand interactions
at the atomic scale is essential for investigating the molecular mechanism
and further assisting in NA-targeted drug discovery. Molecular docking
is one of the predominant computational approaches for predicting
the interactions between NAs and small molecules. Despite the availability
of versatile docking programs, their performance profiles for NA–ligand
complexes have not been thoroughly characterized. In this study, we
first compiled the largest structure-based NA–ligand binding
data set to date, containing 800 noncovalent NA–ligand complexes
with clearly identified ligands. Based on this extensive data set,
eight frequently used docking programs, including six protein–ligand
docking programs (LeDock, Surflex-Dock, UCSF Dock6, AutoDock, AutoDock
Vina, and PLANTS) and two specific NA–ligand docking programs
(rDock and RLDOCK), were systematically evaluated in terms of binding
pose and binding affinity predictions. The results demonstrated that
some protein–ligand docking programs, specifically PLANTS and
LeDock, produced more promising or comparable results compared with
the specialized NA–ligand docking programs. Among the programs
evaluated, PLANTS, rDock, and LeDock showed the highest performance
in binding pose prediction, and their top-1 and best root-mean-square
deviation (rmsd) success rates were as follows: PLANTS (35.93 and
76.05%), rDock (27.25 and 72.16%), and LeDock (27.40 and 64.37%).
Compared with the moderate level of binding pose prediction, few programs
were successful in binding affinity prediction, and the best correlation
(Rp = −0.461) was observed with
PLANTS. Finally, further comparison with the latest NA–ligand
docking program (NLDock) on four well-established data sets revealed
that PLANTS and LeDock outperformed NLDock in terms of binding pose
prediction on all data sets, demonstrating their significant potential
for NA–ligand docking. To the best of our knowledge, this study
is the most comprehensive evaluation of popular molecular docking
programs for NA–ligand systems.