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.