葡萄Pi:基于图形的神经网络,用于增强蛋白质识别在蛋白质组学管道中的蛋白质
Chunhui Gu1,2, Seyyed Mahmood Ghasemi1,2, Yining Cai2
1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.
Bioinformatics advances
|May 23, 2025
概括
一个新的图形神经网络模型,Grape-Pi,通过使用蛋白质-蛋白质相互作用数据,在质谱学中增强了蛋白质识别. 这种方法提高了准确性,并识别了传统方法遗漏的关键蛋白质.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 质谱 (MS) 是非向蛋白质识别的关键,但面临着数据复杂性和错误发现率 (FDR) 的挑战.
- 准确的蛋白质鉴定对于理解生物过程和疾病机制至关重要.
研究的目的:
- 开发一种先进的计算模型,以提高质谱中的蛋白质识别精度.
- 利用蛋白质与蛋白质相互作用 (PPI) 数据来提高蛋白质组学管道的性能.
主要方法:
- 开发了一个基于图形神经网络 (GNN) 的模型,命名为Grape-Pi (使用蛋白质-蛋白质相互作用增强蛋白质识别的图形神经网络).
- 综合PPI数据使用两种类型的消息传递层来结合目标蛋白及其相互作用者的证据.
- 将模型应用于各种蛋白质组学数据集,包括酵母和胃样本.
主要成果:
- 与传统方法相比,葡萄Pi显著改善了用于蛋白质识别的接收器操作特征曲线 (AUC) 下的面积,在酵母样本中显示18%和7%的增长,在胃样本中显示9%的增长.
- 在胃样本中由Grape-Pi识别的蛋白质与mRNA数据高度相关.
- 该模型成功地识别了关键的胃癌蛋白质,如MAP4K4,这些蛋白质被传统技术遗漏了.
结论:
- 葡萄Pi提供了一种强大的新方法,以提高基于质谱的蛋白质组学中的蛋白质识别精度.
- 该模型集成PPI数据的能力提供了更全面的分析,从而改善了对生物相关蛋白质的检测.
- 葡萄Pi在各种蛋白质组学管道中适用,并表明在癌症等疾病中发现生物标志物的潜力.
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