Naeglearia fowleri proline tRNA synthetase inhibitors

福氏耐格里霉脯氨酸 tRNA 合成酶抑制剂

• 批准号: 10691134
• 负责人: Alexey Zakharov
• 金额: $ 14.62万
• 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/10691134
• 关键词: Amino Acyl-tRNA Synthetases; Amoeba genus; Brain; Chemicals; Clinical; Collection; Communicable Diseases; Drug Targeting; Eating; Fresh Water; Hot Springs; Infection; Machine Learning; Microscopic; Modeling; Naegleria fowleri; National Center for Advancing Translational Sciences; Pathogenicity; Protozoa; Quantitative Structure-Activity Relationship; Rivers; Sampling; Soil; Work; chemical property; high throughput screening; in silico; inhibitor; knowledge base; primary amebic meningoencephalitis; proline-tRNA; screening; small molecule libraries; virtual screening

Proline tRNA synthetase inhibitors discovered to date have all been knowledge-based and sampled an extremely small portion of chemical property space and compounds identified this way have had negligible clinical impact against infectious diseases. During this period, the project team conducted quantitative high-throughput screening (qHTS) on select small molecule libraries, and hit compounds were validated. Machine learning approaches were utilized to construct in silico quantitative structure-activity relationship (QSAR) models to enable virtual screening of compounds against larger collections of NCATS molecules. Work is ongoing screening larger compound collections to identify compounds with increased potency and enable refinement of the QSAR model.

迄今为止发现的脯氨酸tRNA合成酶抑制剂都是基于知识的，并且只采样了化学性质空间的极小部分，并且以这种方式鉴定的化合物对感染性疾病的临床影响可以忽略不计。在此期间，项目团队对选定的小分子文库进行了定量高通量筛选（qHTS），并对命中化合物进行了验证。 利用机器学习方法来构建计算机定量结构-活性关系（QSAR）模型，以使得能够针对更大的NCATS分子集合虚拟筛选化合物。目前正在筛选更大的化合物集合，以确定具有更高效力的化合物，并改进QSAR模型。