PLM-互动:扩展蛋白质语言模型来预测蛋白质-蛋白质相互作用
Dan Liu1, Francesca Young1, Kieran D Lamb1
1MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom.
Nature communications
|October 28, 2025
概括
蛋白质语言模型 (PLM) 现在可以通过共同编码蛋白质对来预测蛋白质-蛋白质相互作用. 这种新的方法,PLM-互动,实现了跨物种和突变效应的最先进的结果.
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.
- 机器学习在生物学中的应用
背景情况:
- 从序列中预测蛋白质结构是准确的,但蛋白质与蛋白质相互作用 (PPI) 的预测仍然具有挑战性.
- 使用蛋白语言模型 (PLM) 的现有方法往往忽视了物理相互作用的背景.
- 需要先进的计算方法来准确预测复杂的生物分子关系.
研究的目的:
- 评估和调整蛋白质语言模型 (PLMs) 以预测蛋白质-蛋白质相互作用.
- 开发一种新的方法,PLM-interact,共同编码相互作用的蛋白质对.
- 评估模型在跨物种相互作用预测,突变效应和病毒与宿主相互作用方面的表现.
主要方法:
- 专门用于蛋白质-蛋白质相互作用预测的蛋白质语言模型 (PLMs) 的重新训练.
- 开发PLM-interact,一种联合编码蛋白质对的方法,灵感来自自然语言处理的下一个句子预测.
- 微调 PLM 相互作用,以预测突变对蛋白质相互作用的影响.
- 在跨物种基准数据集上评估性能和病毒与宿主相互作用预测.
主要成果:
- 在跨物种蛋白质-蛋白质相互作用预测基准上,PLM-interact实现了最先进的性能.
- 当该模型在人类数据上训练并测试各种物种 (如老鼠,,虫,大肠杆菌和酵母菌) 时,该模型显示出高准确性.
- 一种微调方法成功地检测出蛋白相互作用的突变效应.
- 该方法在预测病毒与宿主蛋白相互作用方面优于现有的方法.
结论:
- 蛋白质语言模型可以有效地扩展到学习复杂的生物分子关系,包括蛋白质-蛋白质相互作用,直接从序列.
- PLM-interact在预测蛋白质-蛋白质相互作用和相关生物现象方面取得了重大进展.
- 这项工作突出了大型语言模型在解读生物学中复杂的分子相互作用方面的潜力.
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