用深度学习和泛癌蛋白质组学对蛋白质与蛋白质相互作用进行背景意识的酸盐调节
Mingyue Cai1, Yuqing Xiao2,3, Jiani Zheng1
1Department of Bioinformatics and Computational Biology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou 215123, China.
Journal of chemical information and modeling
|October 10, 2025
概括
我们开发了PhosPPI-SEQ,这是一种深度学习工具,用于识别调节蛋白相互作用的酸盐. 这种方法揭示了新的癌症标,如小细胞肺癌中的HDAC1酸化,从而导致了新的治疗策略.
科学领域:
- 分子生物学分子生物学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 酸化对于通过蛋白质-蛋白质相互作用 (PPI) 进行细胞信号传递至关重要.
- 不调节的酸化有助于癌症和神经退行性疾病.
- 在PPI中识别功能性酸盐对于了解疾病机制至关重要.
研究的目的:
- 介绍PhosPPI-SEQ,这是一个深度学习框架,用于识别调节PPI的酸盐.
- 整合多组学数据来绘制癌症中的特定背景酸盐的地图.
- 通过实验验证发现并探索治疗含义.
主要方法:
- 使用预训练的变压器架构,并对酸盐识别进行交叉注意.
- 集成的PhosPPI-SEQ与回归模型和泛癌多组学数据.
- 在表观遗传调节的背景下进行实验验证.
主要成果:
- PhosPPI-SEQ实现了高预测准确度和生物学上有意义的注意力模式.
- 在六种癌症类型中确定了调节PPI的特定环境酸盐.
- 证明在S421/S423的HDAC1高酸化增强了KDM1A的结合,与小细胞肺癌 (SCLC) 的不良结果相关.
结论:
- 在PPI法规中建立了解码酸盐的计算实验范式.
- 以酸盐生物标志物为导向的方式扩展了可向的PPI景观.
- 展示了针对SCLC中的HDAC1-KDM1A相互作用的治疗潜力.
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