Predicting the Strength of Stacking Interactions between Heterocycles and Aromatic Amino Acid Side Chains

Despite
the ubiquity of stacking interactions between heterocycles
and aromatic amino acids in biological systems, our ability to predict
their strength, even qualitatively, is limited. On the basis of rigorous
ab initio data, we developed simple predictive models of the strength
of stacking interactions between heterocycles commonly found in biologically
active molecules and the amino acid side chains Phe, Tyr, and Trp.
These models provide reliable predictions of the stacking ability
of a given heterocycle based on readily computed heterocycle descriptors,
eliminating the need for quantum chemical computations of stacked
dimers. We show that the values of these descriptors, and therefore
the strength of stacking interactions with aromatic amino acid side
chains, follow predictable trends and can be modulated by changing
the number and distribution of heteroatoms within the heterocycle.
This provides a simple conceptual means for understanding stacking
interactions in protein binding sites and tuning their strength in
the context of drug design.