蛋白质结构的深度生成模型揭示了连续折叠空间的远距离关系
Eli J Draizen1,2, Stella Veretnik3, Cameron Mura4,5
1School of Data Science, University of Virginia, Charlottesville, VA, USA. edraizen@gmail.com.
Nature communications
|September 18, 2024
概括
深度生成模型DeepUrfold通过整合序列,结构和生物物理性质来分析蛋白质的关系. 这种方法揭示了进化上遥远的蛋白质超级家族,表明蛋白质折叠空间的连续视图.
科学领域:
- 结构生物学是结构生物学.
- 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.
背景情况:
- 目前的蛋白质折叠空间分析依赖于关于粒度和离散性的假设,可能缺少远程进化关系.
- 像CATH这样的等级分类系统是有价值的,但可以通过启发式分类来限制,阻碍细微的探索.
- 了解蛋白质结构,功能和进化需要超越离散分组的灵活方法.
研究的目的:
- 开发一个深度生成模型框架,DeepUrfold,用于大规模分析蛋白质关系.
- 创建一个结构导向的方法,整合了蛋白质表示的序列,结构和生物物理特性.
- 探索蛋白质超级家族,并揭示现有方法遗漏的远程进化联系.
主要方法:
- 开发了DeepUrfold,这是一个基于蛋白质结构的Urfold模型的深度生成建模框架.
- 利用高维潜伏空间生成蛋白质的合并表示.
- 将序列,结构和生物物理特性集成到一个统一的,以结构为指导的表示中.
- 与CATH蛋白质结构数据库一起部署了DeepUrfold.
主要成果:
- DeepUrfold学会了嵌入,通过合并序列,结构和生物物理性质来代表蛋白质.
- 该框架确定了在进化上遥远的蛋白质关系,这些关系无法通过当前的方法来检测.
- 使用CATH数据的分析揭示了新的超级家族定义.
- 这些发现支持蛋白质折叠空间的主要连续视图.
结论:
- DeepUrfold提供了一个灵活而强大的框架,用于大规模分析蛋白质关系.
- 该模型的综合方法揭示了更深层次的进化联系,超越了简单的结构相似性.
- 这项工作表明,蛋白质折叠空间在很大程度上是连续的,包括集成的序列结构功能特性.
相关概念视频
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