使用预训练的语言模型进行转移学习,用于预测Escherichia coli中的蛋白质表达水平
Chunhe Yang1,2,3, YuLing Zhao4,3, Ruoyu Wang4,3
1Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
Synthetic and systems biotechnology
|December 24, 2025
概括
预测重组蛋白表达是很困难的. 一个新的框架,TLCP-EPE,将子和蛋白质信息结合起来,以提高大肠杆菌的准确性,帮助蛋白质设计.
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:
- 由于基因调节和翻译因素,预测大肠杆菌中的重组蛋白表达是复杂的.
- 当前的计算方法通常集中在密码子使用或蛋白质序列上,限制了预测的准确性和范围.
研究的目的:
- 开发一个先进的计算框架,准确预测重组蛋白表达水平.
- 通过转移学习来整合子和蛋白质序列信息,以提高预测能力.
主要方法:
- 引入了TLCP-EPE,这是一个转移学习框架,融合了先前训练过的语言模型的子级 (CaLM) 和蛋白质级 (ProtT5).
- 用于微调模型的低级调整 (LoRA) 和用于整合嵌入的BiGRU-MLP预测器.
- 在两个独立的测试数据集上评估性能.
主要成果:
- 与最先进的方法相比,TLCP-EPE显示出更高的性能.
- 取得了强大的预测准确性,在子数据上AUC为0.835和蛋白质数据上AUC为0.713.
- 超越了传统的基于编码子的指标和现有的深度学习基线.
结论:
- 编码子和蛋白质序列的双模式建模显著提高了表达水平预测的准确性和概括性.
- TLCP-EPE框架为合理的蛋白质设计和生物制造提供了一个强大的工具.
- 这种方法推进了计算蛋白质工程领域的发展.
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