一个配对序列语言模型用于蛋白质-蛋白质相互作用建模
Jun Liu1, Hungyu Chen2, Yang Zhang3,4,5
1Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
Nature communications
|March 11, 2026
概括
一个新的蛋白质对语言模型 (PPLM) 通过分析配对序列来学习蛋白质-蛋白质相互作用 (PPI). 这种方法推进了PPI预测,结合亲和力和接触预测,优于现有的方法.
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.
- 蛋白质的结构和功能.
背景情况:
- 蛋白与蛋白相互作用 (PPI) 是细胞机制和药物开发的基础.
- 目前的蛋白质语言模型主要关注单个蛋白质序列,限制它们捕捉相互作用动态的能力.
- 需要模型,可以有效地从配对的蛋白质序列中学习,以理解PPI.
研究的目的:
- 引入一种新的蛋白质对语言模型 (PPLM),能够共同编码配对的蛋白质序列.
- 开发基于PPLM的专门工具 (PPLM-PPI,PPLM-Affinity,PPLM-Contact) 用于预测二进制相互作用,结合亲和关系和接口联系.
- 为了证明PPLM在各种PPI预测任务中的卓越性能.
主要方法:
- 开发一种用于联合序列编码的蛋白质对语言模型 (PPLM).
- 应用PPLM来创建二进制相互作用 (PPLM-PPI),结合亲和力 (PPLM-Affinity) 和接触预测 (PPLM-Contact) 的模型.
- 对不同物种和复杂的生物系统的大规模数据集进行广泛的实验验证.
主要成果:
- PPLM-PPI在跨物种的二元蛋白-蛋白相互作用预测方面取得了最先进的结果.
- 与ESM2和基于结构的方法相比,PLM-Affinity的结合亲和力预测优越,特别是对于抗体-抗原和TCR-pMHC复合体.
- 在蛋白质间接触预测和接口残留识别方面,PPLM-Contact的表现优于现有的预测指标.
结论:
- 共同代表的语言模型,如PPLM,为蛋白质-蛋白质相互作用的计算建模提供了一个强大的新范式.
- 该PPLM框架显著提高了PPI的各个方面的预测准确性,包括相互作用,亲和力和联系.
- 这项工作为更好地了解细胞过程和通过增强PPI预测加速治疗发现铺平了道路.
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