通过扰乱生物网络响应功能来优先考虑药物目标
Matthew C Perrone1, Michael G Lerner2, Matthew Dunworth3
1Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.
新的网络扰动理论,NetPert,确定了可以药物治疗的中间体,用于无法药物治疗的癌症标. 这种方法通过优先考虑治疗干预的关键信号蛋白来增强药物发现.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 在生物系统中,许多关键的基因调节驱动因素和下游效应因子是传统的小分子药物无法治疗的.
- 识别可用药的信号和监管中间体对于扩大治疗点选项至关重要.
研究的目的:
- 开发和验证一个网络干扰理论 (NetPert),用于识别和优先考虑可用药物的中间体.
- 提高在复杂的生物网络中针对目前无法药物处理的蛋白质的能力.
主要方法:
- 开发了NetPert,这是一个基于生物网络网络扰动理论的数学框架.
- 将NetPert应用于转移性乳腺癌的器官模型,以识别和优先考虑可用药物的中间体.
- 对比NetPert的表现与中间中心性和图形扩散方法之间的表现.
主要成果:
- NetPert有效地识别和优先考虑干扰驱动基因对响应基因的信号传递的可药物介质.
- 与其他方法相比,NetPert排名与湿实验室分析的相关性更强,并且对杂的网络数据更具稳定性.
- 针对NetPert识别的中间体的验证药物在湿实验室分析中抑制了额外的转移性表型.
结论:
- NetPert提供了一种优越的方法来识别可药物介质,扩大了向治疗的范围.
- 这种计算框架具有很大的潜力,可以促进对具有无法治疗的目标疾病的药物发现.
- 这种方法即使对于未直接差异化表达的目标也是有效的,提供了新的治疗途径.
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