使用转移学习的蛋白质-蛋白质相互作用和位点预测
Tuoyu Liu1, Han Gao2, Xiaopu Ren1
1Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Briefings in bioinformatics
|October 23, 2023
概括
这项研究引入了MP-BERT,一种用于识别蛋白质与蛋白质相互作用 (PPI) 和相互作用部位的新模型. 该模型显示了优异的性能和跨生物体的概括性,有助于蛋白质研究.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 在蛋白质组学中的机器学习.
背景情况:
- 先进的语言模型越来越多地用于生物序列分析.
- 蛋白与蛋白相互作用 (PPI) 对细胞功能至关重要.
- 识别PPI及其相互作用部位对于理解生物过程至关重要.
研究的目的:
- 开发和评估一种新的深度学习模型,用于预测蛋白质-蛋白质相互作用 (PPI) 和相互作用地点.
- 评估模型的性能和对不同生物体的概括能力.
- 为了证明转移学习对蛋白质对任务的有效性.
主要方法:
- 从变压器 (BERT) 模型中训练一个双向编码器表示,命名为MindSpore ProteinBERT (MP-BERT),使用蛋白质对.
- 微调MP-BERT用于PPI预测 (MPB-PPI) 和相互作用地点预测 (MPB-PPISP).
- 在不同的基准数据集和多个生物体上评估模型性能.
主要成果:
- 微调的MPB-PPI模型在PPI预测中超过了最先进的方法.
- 一个合并生物模型实现了92.65%的准确性,显示出高度的概括性.
- MPB-PPISP模型在预测交互地点倾向方面表现出能力.
结论:
- MP-BERT框架有效地预测了PPI及其交互地点.
- 转移学习显著提高了蛋白质对任务的性能.
- 开发的模型为推进蛋白相互作用研究提供了强大的工具.
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