<p>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. Based on rigorous <i>ab initio</i> data, we have devised a simple predictive model of the
strength of stacking interactions between heterocycles commonly found in biologically
active molecules and the amino acid side chains Phe, Tyr, and Trp. This model provides rapid predictions of the
stacking ability of a given heterocycle based on readily-computed heterocycle descriptors. We show that the values of these descriptors,
and therefore the strength of stacking interactions with aromatic amino acid
side chains, follow simple predictable trends and can be modulated by changing the
number and distribution of heteroatoms within the heterocycle. This provides a simple
conceptual model for understanding stacking interactions in protein binding
sites and optimizing inhibitor binding in drug design.</p>
Studies on Aromatic Amino Acid Uptake by Rat Brain in Vivo
