滑窗互动语法 (SWING):用于和蛋白相互作用的通用互动语言模型.
Alisa A Omelchenko1,2,3,4, Jane C Siwek1,2,3,4, Prabal Chhibbar1,2,5
1Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
bioRxiv : the preprint server for biology
|May 15, 2024
概括
我们开发了一种称为SWING的交互语言模型 (iLM),用于预测蛋白质-蛋白质相互作用 (PPI). SWING只使用序列数据准确地预测了PPI上的:MHC相互作用和突变效应.
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.
- 在基因组学中的机器学习.
背景情况:
- 由于序列数据的增加,蛋白质语言模型 (pLMs) 正在迅速发展.
- 目前用于预测蛋白-蛋白或-蛋白相互作用 (PPI) 的方法通常涉及共同嵌入或连接序列.
- 现有的任何方法都不使用对交互本身的语言表示.
研究的目的:
- 开发一种新的相互作用语言模型 (iLM),用于预测蛋白质和相互作用.
- 引入滑窗交互语法 (SWING) 来从氨基酸特性生成交互词汇.
- 评估SWING对:MHC (pMHC) 和突变诱导的PPI破坏预测的性能.
主要方法:
- 开发了一个交互LM (iLM),使用一种新的语言来表示交互.
- 使用滑动窗口交互语法 (SWING) 来创建基于氨基酸特性的交互词汇.
- 应用LM与SWING表示作为监督预测任务的特征.
主要成果:
- SWING实现了I类和II类:MHC (pMHC) 相互作用的最先进的预测性能.
- 一个独特的混合类模型共同预测了pMHC类,而I类培训预测了II类相互作用.
- 在小鼠模型中,SWING准确地预测了II类pMHC相互作用,并通过预测PPI中的突变特异性干扰来证明了概括性.
结论:
- SWING 是一流的,可泛化的零射击iLM,学习PPI的语言.
- 该模型只使用序列信息准确地预测误解突变引起的相互作用特异性干扰.
- SWING通过提供一种新的方法来理解和预测复杂的生物相互作用来推动该领域的进步.
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