Identification of small molecule inhibitors of USP15

USP15小分子抑制剂的鉴定

• 批准号: 10011400
• 负责人: Anton Simeonov
• 金额: $ 13.27万
• 依托单位: NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10011400
• 关键词: Biological Assay; Biological Process; Computer Simulation; Development; Disease; Eukaryota; Excision; Family; Genes; Glioblastoma; Human; Inflammatory; Knockout Mice; Lead; Machine Learning; Malignant Neoplasms; Malignant neoplasm of ovary; NF-kappa B; Neurodegenerative Disorders; Pathway interactions; Play; Post-Translational Protein Processing; Proteins; Role; Signal Pathway; Structure; T-Cell Activation; TP53 gene; Testing; Transforming Growth Factor beta; Ubiquitin; Ubiquitination; Virus Diseases; Work; base; beta catenin; cancer immunotherapy; cancer type; colon cancer cell line; high throughput screening; human disease; inhibitor/antagonist; interest; malignant breast neoplasm; melanoma; mouse model; overexpression; prospective; screening; small molecule; small molecule inhibitor; tumorigenesis; ubiquitin-specific protease

Given USP15s role in tumorigenesis and T cell activation, USP15 is a promising target for cancer and cancer immunotherapy. Identification of USP15-specific small-molecule inhibitors through a quantitative high-throughput screening and further development of the lead compound could not only open new strategies to tackle cancer, but allow to better understand biological functions of USP15. During this period, the project team worked to validate small molecule hits from the previously completed high-throughput screen. Using activity profiles from the orthogonal assays tested against the screening hits, the team employed a machine learning approach to conduct an in silico screen against an additional 100,000 small molecules to extend the number of prospective chemotypes with inhibitory activity against USP15.

鉴于USP15在肿瘤发生和T细胞激活中的作用，USP15是癌症和癌症免疫治疗的一个有前途的靶点。通过定量的高通量筛选和先导化合物的进一步开发来鉴定USP15特异的小分子抑制剂，不仅可以开辟抗癌的新策略，而且可以更好地了解USP15的生物学功能。 在此期间，项目团队致力于验证来自之前完成的高通量筛查的小分子点击。使用针对筛选命中进行测试的正交分析的活性图谱，该团队采用了一种机器学习方法，对另外100,000个小分子进行了计算机筛选，以扩大对USP15具有抑制活性的预期化学类型的数量。