Abstract
The use of fragments to biophysically characterize a protein binding pocket and determine the strengths of certain interactions is a computationally and experimentally commonly applied approach. Almost all drug like molecules contain at least one aromatic moiety forming stacking interactions in the binding pocket. In computational drug design, the strength of stacking and the resulting optimization of the aromatic core or moiety is usually calculated using high level quantum mechanical approaches. However, as these calculations are performed in a vacuum, solvation properties are neglected. We close this gap by using Grid Inhomogeneous Solvation Theory (GIST) to describe the properties of individual heteroaromatics and complexes and thereby estimate the desolvation penalty. In our study, we investigated the solvation free energies of heteroaromatics frequently occurring in drug design projects in complex with truncated side chains of phenylalanine, tyrosine, and tryptophan. Furthermore, we investigated the properties of drug-fragments crystallized in a fragment-based lead optimization approach investigating PDE-10-A. We do not only find good correlation for the estimated desolvation penalty and the experimental binding free energy, but our calculations also allow us to predict prominent interaction sites. We highlight the importance of including the desolvation penalty of the respective heteroaromatics in stacked complexes to explain the gain or loss in affinity of potential lead compounds.
Highlights
Molecular recognition in biological systems strongly depends on specific interactions between two molecules
An attempt to characterize this druglikeness was proposed with the Lipinsky rule of 5, describing molecular size and hydrophilicity as primary risk factors in drug design.[6] π-stacking interactions between aromatic rings play a central role in medicinal chemistry as an important contribution to ligand binding.[1,7]
We performed calculations on 18 monocycles frequently occurring in computational drug design, which have been recently investigated in a study focusing on π-stacking of heteroaromatics[17] (Figure 1)
Summary
Molecular recognition in biological systems strongly depends on specific interactions between two molecules. In structure-based drug design these structure−activity relationships between ligands and their target molecules are rationalized and optimized and provide additional valuable information for the drug discovery process.[1,2] the number of possible and favorable interaction types, which have to be considered in the drug design process, increased significantly over the past decades.[3] Various options of possible interactions exist in a protein−ligand binding site. Water molecules within the active site of a protein play a crucial role and have to be considered in structure-based drug
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