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.

到目前为止，发现的脯氨酸tRNA合成酶抑制剂都是基于知识的，采样的化学性质空间非常小，这种方法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，项目组对选定的小分子文库进行了定量高通量筛选(QHTS)，并对HIT化合物进行了验证。 机器学习方法被用来构建电子定量结构-活性关系(QSAR)模型，以实现针对更大的NCATS分子集合的化合物的虚拟筛选。正在进行的工作是筛选更大的化合物集合，以识别具有更高效力的化合物，并使QSAR模型能够得到完善。