保护AutoNet:与机器人和机器学习的高通量联合解蛋白质分析.
Mengge Lyu1,2,3,4, Pingping Hu2,3,4, Guangmei Zhang2,3,4
1School of Basic Medical Science, Fudan University, Shanghai, China.
Nature communications
|January 22, 2026
概括
通过使用机器人平台和机器学习,ProteoAutoNet增强了蛋白质与蛋白质相互作用分析. 这种高通量系统提高了数据质量和稳定性,用于发现新的生物相互作用.
科学领域:
- 生物化学 生物化学
- 蛋白质组学是指蛋白质组学.
- 计算生物学 计算生物学
背景情况:
- 同分离质谱法 (CF-MS) 对于绘制蛋白质-蛋白质相互作用至关重要.
- 目前的CF-MS方法的吞吐量很低,限制了预测模型训练数据.
- 高质量数据的稀缺性和有限多样性阻碍了对蛋白质相互作用的强大预测模型的开发.
研究的目的:
- 开发ProteoAutoNet,一个机器人平台和计算工作流程,用于高通量CF-MS分析.
- 提高蛋白质-蛋白质相互作用研究的吞吐量和数据质量.
- 提高机器学习模型对蛋白质相互作用的稳定性和预测能力.
主要方法:
- 实现了一个机器人实验平台,与CF-MS的计算工作流集成.
- 在机器学习模型中利用有针对性的数据增强来扩展和多样化蛋白质相互作用数据.
- 应用了ProteoAutoNet系统来分析三种甲状腺细胞系.
主要成果:
- 从蛋白质复合体到的样本处理吞吐量增加了两倍.
- 预测有25173个共化蛋白质,接收器运行特征曲线 (AUROC) 下的面积为0.78.
- 在转移性甲状腺癌细胞系中鉴定出显著上调的蛋白酶体和前叠蛋白复合体,以及TGM2和HK1.1.之间的新奇相互作用.
结论:
- ProteoAutoNet为高通量蛋白质-蛋白质相互作用研究提供了一个改进的框架.
- 该平台增强了生物相关蛋白相互作用的发现.
- 这种方法促进了对细胞机制和蛋白质相互作用网络中与疾病相关的变化的理解.
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