一个结构意识的生成性AI框架,用于揭示蛋白质家族中的功能关系
Divyanshu Shukla1, Jonathan Martin2, Faruck Morcos2,3,4
1Bioinformatics and Computational Biology Program, Iowa State University.
bioRxiv : the preprint server for biology
|December 25, 2025
概括
这项研究引入了一个使用3D几何和共进化的新框架来分析蛋白质序列. 它通过整合结构信息来改进同质检测和蛋白质设计.
科学领域:
- 计算生物学 计算生物学
- 结构生物信息学 结构生物信息学
- 蛋白质科学 蛋白质科学
背景情况:
- 蛋白质序列数据库的增长速度快于实验性结构确定,许多序列没有注释.
- 蛋白质折叠比序列更保守和更有信息,提供了强大的分析工具.
研究的目的:
- 为绘制,聚类和设计蛋白质序列开发一个生成的,结构意识的框架.
- 为了利用几何编码和共同进化的约束来增强蛋白质分析.
主要方法:
- 采用3D交互 (3Di) 字母来对蛋白质局部结构进行离散的几何表示.
- 使用 ProstT5 进行氨基酸序列和3Di表示之间的双向翻译.
- 在潜在的生成景观中,集成3Di对齐与直接合分析 (DCA) 和变量自动编码器 (VAE).
主要成果:
- 实现了敏感的同质检测和结构引导的蛋白质序列生成.
- 加强同进化信号的检测和结构变异的合理采样.
- 证明了在各种蛋白质家族中改进了接触预测,同质推断和序列生成.
结论:
- 整合性框架提供了蛋白质结构空间的定量,生成视图.
- 通过结合结构信息来推进蛋白质进化和设计研究.
- 允许敏感的分析和设计,即使对于具有低序列身份的远程同类.
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