解码蛋白质结合可塑性通过集成的深层核糖体显示和深度学习
Mengtong Tang1,2,3, Jiawei Li1,2,3, Zhixi Li1,2
1Frontiers Science Center for Synthetic Biology (Ministry of Education), School of Synthetic Biology and Biomanufacturing, Tianjin University, Tianjin, China.
Communications biology
|December 2, 2025
概括
这项研究介绍了一种人工智能驱动的平台,用于探索蛋白质相互作用的可塑性,解码数百万个的结合活性. 该框架允许以数据为导向的新型合成的设计,具有量身定制的结合特性.
科学领域:
- 生物化学和分子生物学
- 人工智能在生物学中的应用
- 蛋白质工程是指蛋白质工程.
背景情况:
- 蛋白质相互作用可塑性对于生物网络和蛋白质设计至关重要.
- 探索蛋白质相互作用的序列空间是具有挑战性的,因为它的广的维度.
研究的目的:
- 开发一个平台,整合深度实验选和深度学习来解码蛋白质相互作用可塑性.
- 为了使蛋白质相互作用的系统探索和合成的数据驱动设计.
主要方法:
- 开发了一种新的核糖体显示系统,以生成4780万个独特的数据集.
- 采用深度学习架构,培训了序列上下文,丰富动态和后续丰富性.
- 使用了序列维度减小和图案诱导技术.
主要成果:
- 通过深度学习,在预测斯特雷普塔克丁结合活性方面取得了高准确性 (皮尔森r=0.902).
- 识别了799个具有正典动机的强结合序列.
- 发现了219个序列,具有新的图案和不同的对接构造.
结论:
- 集成的实验AI框架有效地解码了蛋白质相互作用可塑性.
- 这些发现揭示了蛋白质结合可塑性的深度和广度.
- 该平台促进了蛋白质相互作用的探索和合成的设计.
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