De novo engineering of protein agonists and antagonists

描述(申请人提供)：除了它们在正常生理中的重要作用外，重组产生的细胞因子和抗体构成了用于治疗癌症、艾滋病毒和一系列血液和免疫疾病的一大类关键药物。然而，这些分子往往难以大量生产，由于二硫键的存在而折叠和聚集较差，作为治疗制剂相对不稳定。最近，具有优于细胞因子和抗体的生物物理特性的替代支架已被用于定向进化实验，以设计与感兴趣的靶的高亲和力结合。然而，细胞因子和抗体治疗药物既可以作为激动剂，也可以作为拮抗剂，目前的定向进化方法只选择靶标结合，而不是随之而来的激动剂或拮抗剂。在这里，我们建议利用另一种支架的模块折叠-设计的锚蛋白重复蛋白-开发新的定向进化方法来分离新的激动剂和拮抗剂。这个R21提案的目标是：1)设计Anykrin激动剂，模拟白细胞介素12(一种强大的抗肿瘤细胞因子)，以及2)创造位置特异性的Mattritase，一种与多种上皮源性肿瘤有关的细胞表面蛋白水解酶。更广泛地说，这些方法不仅能够产生新的治疗方法，而且还将提供对特定的、高亲和力蛋白质识别的基本要求的独特见解。

DESCRIPTION (provided by applicant): In addition to their vital roles in normal physiology, recombinantly produced cytokines and antibodies constitute a large, critical family of drugs used in the treatments for cancer, HIV, and a host of hematological and immune disorders. However, these molecules are often difficult to produce in mass quantities, suffer from poor folding and aggregation due to the presence of disulfide bonds, and are relatively unstable as therapeutic formulations. Recently, alternative scaffolds with superior biophysical properties to cytokines and antibodies have been used in directed evolution experiments to engineer high-affinity binders to targets of interest. However, cytokine and antibody therapeutics can serve as either agonists or antagonists, and current directed evolution methodologies only select for target binding, not consequent agonism or antagonism. Here, we propose to exploit the modular fold of an alternative scaffold - the designed ankyrin repeat protein - to develop novel directed evolution approaches for isolating new agonists and antagonists. The goals of this R21 proposal are to: 1) engineer anykrin agonists that are mimetics of interleukin-12, a potent anti-tumor cytokine, and 2) create site-specific ankyrin inhibitors of matriptase, a cell-surface protease tha is implicated in a variety of epithelial-derived tumors. More generally, these methods will not only enable the production of new therapeutics, but will also provide unique insights into the basic requirements for specific, high-affinity protein recognition.