This project focuses on optimising small molecules that stabilize the FGE protein, as pharmacological agents for therapeutic intervention of MSD. It has previously been shown that MSD is caused by SUMF1 missense mutations, resulting in unstable FGE with low catalytic functionality. Our therapeutic strategy is based on the rationale that small molecules potently binding at the surface of FGE mutants will act as pharmacological chaperones, stabilising the protein and rescuing its catalytic activity towards sulfatase substrates.
In earlier work (unpublished) we have identified/validated dozens of unoptimized ligands of FGE using 1) biophysical and crystallographic screening of ~ 3500 fragments; 2) DNA encoded libraries technology (DELT) for screening ~ 11 billion lead size compounds (Roche, Basel, Switzerland).
Here, we are delineating our strategy for optimising these hits into highly potent ligands that could be developed into sought drugs. A key focus is on medicinal chemistry, building on biophysical and structural data accumulated in recent years. This will involve 1) the structure- based design of novel high-affinity molecules, by exploring “growing”, “merging” and “linking” approaches on structurally validated (i.e. crystal structures of protein-ligand complexes) hits from past fragment and DELT screens, supported by computational modelling; 2) the chemical synthesis and purification of the designed libraries of molecules; 3) assessment of their binding potency (Kd) and ability to stabilise FGE using in vitro biophysical/biochemical assays; and 4) evaluating the therapeutic effect of most promising in vitro candidates in MSD-derived cells.
This project brings together the synergistic expertise of the applicants spanning across complementary disciplines, including synthetic medicinal chemistry, biophysics, biochemistry, computational studies, molecular/structural/cell biology and clinical sciences. In a long- standing and well-established collaboration we showed that FGE is potentially druggable and successfully identified many promising hits. Reaping the benefits of our previous work, we will now optimize our hits into drug-like leads.