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revised on 01.05.2019 and posted on 02.05.2019by Andrea
N. Bootsma, Analise C. Doney, Steven Wheeler
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 ab initio 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 reliable predictions of
the stacking ability of a given heterocycle based on readily-computed
heterocycle descriptors, obviating 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 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 tuning the strength of stacking interactions in drug design.