Alladi Ramakrishnan Hall

Fragment-Based Discovery of Subtype-Selective Adenosine Receptor Ligands from Homology Models

Anirudh Ranganathan

Fragment-based lead discovery (FBLD) holds great promise for drug
discovery, but applications to G protein-coupled receptors (GPCRs) have
been limited by a lack of sensitive screening techniques and scarce
structural information. If virtual screening against homology models of
GPCRs could be used to identify fragment ligands, FBLD could be extended to
numerous important drug targets and contribute to efficient lead
generation. Access to models of multiple receptors may further enable the
discovery of fragments that bind specifically to the desired target. To
investigate these questions, we used molecular docking to screen >500 000
fragments against homology models of the A3 and A1 adenosine receptors
(ARs) with the goal to discover A3AR-selective ligands. Twenty-one
fragments with predicted A3AR-specific binding were evaluated in live-cell
fluorescence-based assays; of eight verified ligands, six displayed A3/A1
selectivity, and three of these had high affinities ranging from 0.1 to 1.3
μM. Subsequently, structure-guided fragment-to-lead optimization led to the
identification of a >100-fold-selective antagonist with nanomolar affinity
from commercial libraries. These results highlight that molecular docking
screening can guide fragment-based discovery of selective ligands even if
the structures of both the target and antitarget receptors are unknown. The
same approach can be readily extended to a large number of pharmaceutically
important targets.

pubs.acs.org/doi/abs/10.1021/acs.jmedchem.5b01120