一个蛋白学数据资源,用于系统级模拟酶信号网络
Song Feng1, James A Sanford1, Thomas Weber1
1Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352 USA.
bioRxiv : the preprint server for biology
|August 14, 2023
概括
这项研究开发了一种深度蛋白学工作流程,用于绘制MCF10A细胞中的EGFR-MAPK通路图,揭示双相信号传递和识别关键介质. 这些数据有助于构建这个关键信号通路的机械模型.
科学领域:
- 蜂信号传输是如何进行的
- 蛋白质组学是指蛋白质组学.
- 分子生物学分子生物学
背景情况:
- 激酶驱动的信号通路对于细胞功能至关重要.
- 蛋白质酸化的定量测量对于机械模型是必不可少的.
- 传统技术缺乏对综合性蛋白组学的敏感性.
研究的目的:
- 开发一种敏感的蛋白学工作流程,用于对EGFR-MAPK通路进行深度分析.
- 在各种生理条件下量化测量蛋白质酸化.
- 为建立EGFR-MAPK信号传输的机械模型生成数据资源.
主要方法:
- 多重复合的深层蛋白质形工作流.
- 改进了酸盐测绘技术.
- 与15个公共蛋白质数据库的集成.
主要成果:
- 在6600种蛋白质上确定了超过46,000个酸化位,其中4500个位由EGF显著增加.
- 揭示了双相EGFR信号与不同的基质反应.
- 通过至少3条并行途径发现了RAS激活,其中两条涉及PTPN11.
- 在RAS和RAF激活之间发现了~4分钟的延迟.
结论:
- 深度蛋白组学数据为EGFR-MAPK信号传递的机制建模提供了宝贵的资源.
- EGFR信号表现出复杂的,双相调节,对各种细胞过程产生下游影响.
- 了解这些酸化动态是理解细胞反应和疾病机制的关键.
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