Stacking Interactions of Druglike Heterocycles with Nucleobases

Stacking interactions contribute significantly to the
interaction
of small molecules with RNA, and harnessing the power of these interactions
will likely prove important in the development of RNA-targeting inhibitors.
To this end, we present a comprehensive computational analysis of
stacking interactions between a set of 54 druglike heterocycles and
the natural nucleobases. We first show that heterocycle choice can
tune the strength of stacking interactions with nucleobases over a
large range and that heterocycles favor stacked geometries that cluster
around a discrete set of stacking loci characteristic of each nucleobase.
Symmetry-adapted perturbation theory results indicate that the strengths
of these interactions are modulated primarily by electrostatic and
dispersion effects. Based on this, we present a multivariate predictive
model of the maximum strength of stacking interactions between a given
heterocycle and nucleobase that depends on molecular descriptors derived
from the electrostatic potential. These descriptors can be readily
computed using density functional theory or predicted directly from
atom connectivity (e.g., SMILES). This model is used to predict the
maximum possible stacking interactions of a set of 1854 druglike heterocycles
with the natural nucleobases. Finally, we show that trivial modifications
of standard (fixed-charge) molecular mechanics force fields reduce
errors in predicted stacking interaction energies from around 2 kcal/mol
to below 1 kcal/mol, providing a pragmatic means of predicting more
reliable stacking interaction energies using existing computational
workflows. We also analyze the stacking interactions between ribocil
and a bacterial riboswitch, showing that two of the three aromatic
heterocyclic components engage in near-optimal stacking interactions
with binding site nucleobases.