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Non-Nucleotide Hint1-Labile Phosphoramidates for the Interrogation of Pin1 Biology

Abstract

The peptidyl-prolyl isomerase Pin1 is the only known enzyme capable of isomerizing the peptide bond between pThr/pSer and Pro residues. This bond is extremely rigid and exists in distinct cis and trans conformations absent the activity of this designated molecular chaperone. Through decades of genetic experiments, Pin1’s essentiality in regulating cell cycle progression, apoptosis, and differentiation has been discovered and validated. Interestingly, potent and selective inhibitors of Pin1 were intractable using traditional structure-guided medicinal chemistry as these approaches were capable of yielding only potent or biologically active molecules and failed at finding high-affinity molecules which were able to permeate the cell membrane. This is due to the requirement for phosphates for high-affinity reversible binding to the Pin1 catalytic domain. Recently, a handful of efforts using non-traditional approaches to inhibitor design have discovered potent and biologically active molecules. Herein, the design, discovery, validation, and use of the most potent biologically-active Pin1 inhibitor to date is discussed. In this approach, a potent in vitro inhibitor of Pin1 discovered by Pfizer is converted to a phosphoramidate-based prodrug to overcome permeability challenges. This approach not only yielded a potent Pin1 inhibitor which could be used in a variety of biological experiments to probe Pin1 biology, but also expanded the understanding of this prodrugging approach. Specifically, the aryloxy phosphoramidate approach has only been described in mechanistic detail with nucleotide phosphoramidates. These proof-of-concept molecules not only show that highly ligand efficient molecules are amenable to this approach but demonstrates that they are liberated through the canonical enzymatic processing. Further, since the parent molecule used is one in a large series of analogs, there is significant SAR around the scaffold which will be useful in future optimization.

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