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

用于最大限度减少肽脱靶的高通量库方法

• 批准号: 2754264
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2754264
• 关键词: throughput; library; approach; minimizing; peptide

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.

口服肽递送的最新突破极大地扩展了可用于药物蛋白质/蛋白质相互作用和其他具有挑战性的靶点的化学空间。在这种情况下，高通量技术如mRNA展示非常适合于提供临床候选物。然而，与经典方法相比，这些从头肽发现技术具有更高的脱靶风险，由于毒理学研究中的潜在不良发现，导致药物效力降低和损耗率升高。因此，迫切需要创新技术来应对这些挑战。在这个项目中，我们将开发一种简化的基于mRNA展示的方法来识别并随后使用光亲和标记策略最大限度地减少肽脱靶。来自下一代测序和质谱的互补数据将使命中肽序列的复杂的计算机优化成为可能。