AbbVie (United States)
Publishes on Computational Drug Discovery Methods, Protein Structure and Dynamics, Chemical Synthesis and Analysis. 121 papers and 16.9k citations.
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A nuclear magnetic resonance (NMR)-based method is described in which small organic molecules that bind to proximal subsites of a protein are identified, optimized, and linked together to produce high-affinity ligands. The approach is called "SAR by NMR" because structure-activity relationships (SAR) are obtained from NMR. With this technique, compounds with nanomolar affinities for the FK506 binding protein were rapidly discovered by tethering two ligands with micromolar affinities. The method reduces the amount of chemical synthesis and time required for the discovery of high-affinity ligands and appears particularly useful in target-directed drug research.
An analysis of heteronuclear-NMR-based screening data is used to derive relationships between the ability of small molecules to bind to a protein and various parameters that describe the protein binding site. It is found that a simple model including terms for polar and apolar surface area, surface complexity, and pocket dimensions accurately predicts the experimental screening hit rates with an R(2) of 0.72, an adjusted R(2) of 0.65, and a leave-one-out Q(2) of 0.56. Application of the model to predict the druggability of protein targets not used in the training set correctly classified 94% of the proteins for which high-affinity, noncovalent, druglike leads have been reported. In addition to understanding the pocket characteristics that contribute to high-affinity binding, the relationships that have been defined allow for quantitative comparative analyses of protein binding sites for use in target assessment and validation, virtual ligand screening, and structure-based drug design.