bioRxivBiochemistry
A Multi-pronged Screening Approach Targeting Inhibition of ETV6 PNT Domain Polymerization
C. A. N. Gerak, S. M. Zhang, A. D. Balgi, I. J. Sadowski, R. B. Sessions, L. P. McIntosh, M. Roberge
Abstract
ETV6 is an ETS family transcriptional repressor for which head-to-tail polymerization of its PNT domain facilitates cooperative binding to DNA by its ETS domain. Chromosomal translocations frequently fuse the ETV6 PNT domain to one of several protein tyrosine kinases. The resulting chimeric oncoproteins undergo ligand-independent self-association, autophosphorylation, and aberrant stimulation of downstream signaling pathways leading to a variety of cancers. Currently, no small molecules inhibitors of ETV6 PNT domain polymerization are known and no assays targeting PNT domain polymerization have been described. In this study, we developed complementary experimental and computational approaches for identifying such inhibitory compounds. One mammalian cellular approach utilized a mutant PNT domain heterodimer system covalently attached to split Gaussia luciferase fragments. In this protein fragment complementation assay, inhibition of PNT domain heterodimerization reduces luminescence. A yeast assay took advantage of activation of the reporter HIS3 gene upon heterodimerization of mutant PNT domains fused to DNA-binding and transactivation domains. In this two-hybrid screen, inhibition of PNT domain heterodimerization prevents cell growth in medium lacking histidine. The Bristol University Docking Engine (BUDE) was used to identify virtual ligands from the ZINC8 library predicted to bind the PNT domain polymerization interfaces. Over 75 hits from these three assays were tested by NMR spectroscopy for binding to the purified ETV6 PNT domain. Although none were found to bind, lessons learned from this study may facilitate future approaches for developing therapeutics that act against ETV6 oncoproteins by disrupting PNT domain polymerization.
- assay: proteomics
- research focus: drug response / drug discovery
- approach: experimental assay
- approach: statistical model
- approach: machine learning (classical)
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