穆兰:用于编码序列和结构的多模式蛋白质语言模型
Daria Frolova1,2, Marina Pak1,3, Anna Litvin1,3,4
1Ligand Pro, Moscow 121205, Russia.
Bioinformatics advances
|September 24, 2025
概括
穆兰是一个多式蛋白质语言模型 (PLM),通过高效地结合序列和结构数据来增强蛋白质表示. 这种结构意识的PLM可以改善预测,特别是蛋白质-蛋白质相互作用,而无需进行广泛的再培训.
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.
- 生物学中的人工智能
背景情况:
- 蛋白质语言模型 (PLM) 主要利用蛋白质序列进行高质量的表示.
- 纳入蛋白质结构信息对于各种预测任务至关重要,这引发了对结构感知PLM的兴趣.
- 现有的结构意识的PLM通常需要从头开始进行训练或为结构编码增加大量的参数.
研究的目的:
- 引入MULAN,一种多式蛋白语言模型 (PLM),集成了基于序列和角度的结构信息.
- 开发一个参数有效的方法,将结构意识纳入预先训练的PLM中.
- 与现有模型相比,评估MULAN在各种下游任务中的表现.
主要方法:
- 开发了MULAN,这是一款多模式的PLM,具有预训练的序列编码器和参数高效的结构适配器.
- 融合了序列编码器和结构适配器,将它们一起训练用于多式模式表示学习.
- 在九个下游预测任务中对MULAN进行了评估,根据仅序列和结构意识的基线评估其性能.
主要成果:
- 与仅序列ESM2和结构感知SaProt.Prot.相比,MULAN模型在各种尺寸中表现出更好的性能.
- 在蛋白质-蛋白质相互作用预测方面观察到显著改善,AUROC增加了高达0.12.
- 穆兰通过高效微调现有PLM来提高结构意识,避免了从头开始昂贵的培训.
结论:
- 穆兰提供了一种有效且计算成本低廉的方法,以赋予蛋白质表示结构意识.
- 拟议的结构适配器提供了一个参数高效的方式,将结构信息集成到PLM中.
- 穆兰代表了结构意识蛋白质建模和预测任务的宝贵进步.
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