Identification of Small-Molecule Inhibitors of FGF23 Signaling via In Silico Hot Spot Prediction and Molecular Docking to α-Klotho
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Fibroblast growth factor 23 (FGF23) is a therapeutic target for treating hereditary and acquired hypophosphatemic disorders, such as X-linked hypophosphatemic (XLH) rickets and tumor-induced osteomalacia (TIO), respectively. FGF23-induced hypophosphatemia is mediated by signaling through a ternary complex formed by FGF23, FGF receptor (FGFR), and alpha-Klotho. Currently, disorders of excess FGF23 are treated with an FGF23-blocking antibody, Burosumab. Small-molecule drugs that disrupt protein:protein interactions necessary for the ternary complex formation offer an alternative to disrupt FGF23 signaling. In this study, the FGF23:alpha-Klotho interface was targeted to identify small-molecule protein:protein interaction inhibitors. We computationally identified “hot spots” in the FGF23:alpha-Klotho interface of the ternary complex and performed in silico docking of ~5.5 million compounds from the ZINC database to the interface region of alpha-Klotho from the ternary crystal structure. Following docking, 23 and 18 compounds were chosen based on the lowest binding free energies to alpha-Klotho and the largest number of contacts with Tyr433, a predicted hot spot, respectively. 5 compounds available were assessed experimentally by their FGF23-mediated extracellular signal-regulated kinase (ERK) activities in vitro, and two of these reduce activities significantly. Both these compounds have a favorable predicted binding affinity, but not a large number of contacts with the hot spot residues. ZINC12409120 was found experimentally to reduce FGF23-mediated ERK activities by 70% and have a half maximal inhibitory concentration (IC50) of 5.0 ± 0.23 uM. ZINC12409120 exhibits contacts with residues on KL1 and KL2 domains and on the linker between the two domains of alpha-Klotho in in silico binding poses, thereby possibly disrupting the regular function of alpha-Klotho and impeding FGF23 binding. ZINC12409120 is a candidate for lead optimization.