使用机器学习对酸盐的共同调节来解读黑暗癌症蛋白质组
Wen Jiang1,2, Eric J Jaehnig1,2, Yuxing Liao1,2
1Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA.
Nature communications
|March 21, 2025
概括
我们开发了CoPheeMap和CoPheeKSA,这两种机器学习工具整合了蛋白组数据,绘制了酸盐联合调节和预测酶-基质关联,揭示了新的癌症信号洞察力.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白组学提供了对蛋白质酸化的广泛观点,但对酸盐调节和功能的理解仍然有限.
- 这种知识差距阻碍了从蛋白质组数据中提取可操作的生物学见解.
- 识别酶-基质关联对于理解细胞信号来说至关重要.
研究的目的:
- 开发一个计算框架来绘制酸盐协调和预测酶-基质关联.
- 利用机器学习和蛋白质组数据来发现新的生物学见解.
- 通过分析失调的信号通路来确定人类癌症中的潜在治疗点.
主要方法:
- 整合机器学习与来自11种癌症类型的1195个瘤样本的基蛋白质数据.
- 构建了CoPheeMap,这是一个对26,280个酸盐的网络映射共同调节.
- 开发了CoPheeKSA,这是第二个机器学习模型,结合了CoPheeMap功能来预测酶-基质关联.
主要成果:
- CoPheeMap成功地绘制了26,280个酸盐的共同调节.
- CoPheeKSA预测了24015个酶基质关联,其中包括9399个酸盐和104个酶.
- 使用实验数据验证了预测,突出了未注释的酸盐和未研究的激酶.
- 对癌症酸盐的应用揭示了失调的信号通路和潜在的治疗点.
结论:
- CoPheeMap和CoPheeKSA提供了强大的工具来分析蛋白质组数据和预测酶-基质相互作用.
- 这些工具提高了我们对酸盐功能和癌症调节的理解.
- 鉴定到的酶-基质关联和失调的途径为癌症治疗研究提供了新的途径.
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