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For efficient structure-guided drug design, it is important to
have an excellent understanding of the quality of interactions between
the target receptor and bound ligands. Identification and characterization
of poor intermolecular contacts offers the possibility to focus design
efforts directly on ligand regions with suboptimal molecular recognition.
To enable a more straightforward identification of these in a structural
model, we use a suitably enhanced version of our previously introduced
statistical ratio of frequencies (R
F)
approach. This allows us to highlight protein–ligand interactions
and geometries that occur much less often in the Protein Data Bank
than would be expected from the exposed surface areas of the interacting
atoms. We provide a comprehensive overview of such noncompetitive
interactions and geometries for a set of common ligand substituents.
Through retrospective case studies on congeneric series and single-point
mutations for several pharmaceutical targets, we illustrate how knowledge
of noncompetitive interactions could be exploited in the drug design
process.
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