对依赖折叠的灵活性与人工智能蛋白质结构预测之间的关系进行系统分析
Neshatul Haque1, Jessica B Wagenknecht1, Brian D Ratnasinghe1
1Computational Structural Genomics Unit, Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, United States of America.
PloS one
|November 26, 2024
概括
像AlphaFold v2这样的人工智能 (AI) 模型可以准确地预测单个蛋白质结构. 然而,许多蛋白质表现出灵活性,其中一些折叠显示出对生物过程至关重要的显著构造变异.
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.
- 生物物理学的生物物理.
背景情况:
- 人工智能驱动的蛋白质结构预测正在彻底改变科学发现.
- 蛋白质具有基于原型折叠的模块化组织.
- 人工智能对灵活蛋白质的预测所捕获的构造状态仍然不清楚.
研究的目的:
- 调查人工智能预测的蛋白质结构是否代表单个构造或平均状态.
- 为了确定是否存在蛋白质折叠依赖的形状异质性.
- 使用实验数据分析蛋白质折叠的拓刚性与异质性.
主要方法:
- 对2878种具有多个实验结构的蛋白质进行分析.
- 实验结构与人工智能预测结构的比较 (AlphaFold v2).
- 估计蛋白质的拓刚性和结构异质性.
主要成果:
- AlphaFold v2准确地预测了大多数蛋白质折叠 (99.68%) 的单一构造.
- 很大一部分折叠 (27.70%) 的实验结构与人工智能预测偏差超过2.5 RMSD.
- 具有高形状异质性的蛋白质折叠对于免疫调节和新陈代谢等生物过程至关重要.
结论:
- 像AlphaFold v2这样的AI结构预测工具提供高度准确的单个构造,但可能无法捕捉固有的蛋白质灵活性.
- 了解蛋白质结构异质性对于解释AI衍生结构和推进生物见解至关重要.
- 该研究强调需要将蛋白质动态纳入结构预测数据库,弥合静态预测和功能灵活性之间的差距.
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