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Abstract：
Inhibition of the proteolytic processing of hepatocyte growth factor (HGF) and macrophage stimulating protein (MSP) is an attractive approach for the drug discovery of novel anticancer therapeutics which prevent tumor progression and metastasis. Here, we utilized an improved and expanded version of positional scanning of substrate combinatorial libraries (PS-SCL) technique called HyCoSuL to optimize peptidomimetic inhibitors of the HGF/MSP activating serine proteases, HGFA, matriptase, and hepsin. These inhibitors have an electrophilic ketone serine trapping warhead and thus form a reversible covalent bond to the protease. We demonstrate that by varying the P2, P3, and P4 positions of the inhibitor with unnatural amino acids based on the protease substrate preferences learned from HyCoSuL, we can predictably modify the potency and selectivity of the inhibitor. We identified the tetrapeptide JH-1144 (8) as a single digit nM inhibitor of HGFA, matriptase and hepsin with excellent selectivity over Factor Xa and thrombin. These unnatural peptides have increased metabolic stability relative to natural peptides of similar structure. The tripeptide inhibitor PK-1-89 (2) has excellent pharmacokinetics in mice with good compound exposure out to 24 h. In addition, we obtained an X-ray structure of the inhibitor MM1132 (15) bound to matriptase revealing an interesting binding conformation useful for future inhibitor design.
摘要：
抑制肝细胞生长因子（HGF）和巨噬细胞刺激蛋白（MSP）的蛋白水解加工是一种有吸引力的方法，用于发现预防肿瘤进展和转移的新型抗癌治疗剂。这里,我们利用了一种称为HyCoSuL的底物组合文库（PS-SCL）技术的改进和扩展版本来优化HGF/MSP激活丝氨酸蛋白酶的拟肽抑制剂，HGFA,间质蛋白酶，和hepsin。这些抑制剂具有亲电子酮丝氨酸捕获弹头，因此与蛋白酶形成可逆的共价键。我们证明，根据从HyCoSuL学习的蛋白酶底物偏好，通过使用非天然氨基酸改变抑制剂的P2，P3和P4位置，我们可以预测地改变抑制剂的效力和选择性。我们鉴定了四肽JH-1144（8）作为HGFA的单位数nM抑制剂，matriptase和hepsin对因子Xa和凝血酶具有优异的选择性。这些非天然肽相对于类似结构的天然肽具有增加的代谢稳定性。三肽抑制剂PK-1-89（2）在24小时内具有良好的化合物暴露的小鼠中具有优异的药代动力学。此外，我们获得了与matriptase结合的抑制剂MM1132(15)的X射线结构,揭示了一种可用于未来抑制剂设计的有趣的结合构象.
