Macrocyclic peptides can disrupt previously intractable protein–protein interactions (PPIs) relevant to oncology targets such as KRAS. Early hits often lack cellular activity and require meticulous improvement of affinity, permeability, and metabolic stability to become viable leads. We have validated the use of the Automated Ligand Identification System (ALIS) to screen oncogenic KRAS^G12D (GDP) against mass-encoded mini-libraries of macrocyclic peptides and accelerate our structure–activity relationship (SAR) exploration. These mixture libraries were generated by premixing various unnatural amino acids without the need for the laborious purification of individual peptides. The affinity ranking of the peptide sequences provided SAR-rich data sets that led to the selection of novel potency-enhancing substitutions in our subsequent designs. Additional stability and permeability optimization resulted in the identification of peptide 7 that inhibited pERK activity in a pancreatic cancer cell line. More broadly, this methodology offers an efficient alternative to accelerate the fastidious hit-to-lead optimization of PPI peptide inhibitors.

中文翻译：

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使用混合文库和自动配体识别系统 (ALIS) 技术加速识别细胞活性 KRAS 抑制性大环肽

大环肽可以破坏与 KRAS 等肿瘤靶点相关的先前难以处理的蛋白质-蛋白质相互作用 (PPI)。早期命中通常缺乏细胞活性，需要精心改善亲和力、渗透性和代谢稳定性才能成为可行的线索。我们已经验证了使用自动配体识别系统 (ALIS) 来筛选致癌 KRAS ^G12D（GDP）针对大规模编码的大环肽小型库，并加速我们的构效关系（SAR）探索。这些混合物文库是通过预混合各种非天然氨基酸而产生的，无需费力地纯化单个肽。肽序列的亲和力排名提供了富含 SAR 的数据集，导致我们在后续设计中选择了新的效力增强替代。额外的稳定性和渗透性优化导致识别出抑制胰腺癌细胞系中 pERK 活性的肽7 。更广泛地说，这种方法提供了一种有效的替代方案，可以加速 PPI 肽抑制剂的严格的先导优化。