A high-throughput library approach for minimizing peptide off-targets

口服肽递送的最新突破极大地扩展了药物-蛋白质/蛋白质相互作用和其他具有挑战性的靶标的化学空间。在这种情况下，高通量技术，如mRNA显示是非常适合提供临床候选人。然而，与传统方法相比，这些新的肽发现技术具有更高的脱靶风险，导致药物的有效性降低，并且由于毒理学研究中潜在的不良发现而导致更高的损耗率。因此，迫切需要创新技术来应对这些挑战。在这个项目中，我们将开发一种流线型的基于mrna显示的方法来识别并随后使用光亲和标记策略最小化肽脱靶。来自下一代测序和质谱的补充数据将使hit肽序列的复杂硅优化成为可能。

Recent breakthroughs in oral peptide delivery have vastly extended the chemical space which is available to drug protein/protein interactions and other challenging targets. In this context, high throughput techniques such as mRNA display are perfectly suited to provide clinical candidates. However, in contrast to classical approaches, these de novo peptide discovery technologies carry an increased risk of engaging off-targets, leading to less efficacious drugs and higher attrition rates due to potential adverse findings in toxicology studies. Consequently, innovative technologies to address these challenges are critically needed. In this project we will develop a streamlined mRNA-display-based approach to identify and subsequently minimize peptide off-targets using a photo-affinity labelling strategy. Complementary data from next generation sequencing and mass spectrometry will enable sophisticated in silico optimization of hit peptide sequences.