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用实验性NMR数据对单体AlphaFold2蛋白结构模型进行盲目评估

Ethan H Li¹, Laura E Spaman¹, Roberto Tejero¹

¹Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.

Journal of magnetic resonance (San Diego, Calif. : 1997)

|May 31, 2023

概括

使用NMR数据,AlphaFold-2 (AF2) 准确地模拟小蛋白质结构,即使对于其训练集中没有的目标. 这些AI预测通常与实验性NMR结构模型相匹配或超过.

科学领域:

结构生物学结构生物学
人工智能的人工智能
生物物理学的生物物理.

背景情况:

最近分子建模的进步,特别是AlphaFold-2 (AF2),正在改变结构生物学.
使用深度学习,AF2以高准确度预测蛋白质结构,可与X射线晶体学和冷电子显微镜 (cryoEM) 等实验方法相提并论.
对溶液核磁共振 (NMR) 数据的AF2的性能,特别是对于其训练集中缺少的蛋白质,需要进一步调查.

研究的目的:

用实验溶液NMR数据评估AlphaFold-2 (AF2) 在建模小的单体蛋白质中的准确性.
评估AF2对"盲目"目标的性能,这意味着AF2培训数据集中不包括的蛋白质,在培训时缺少蛋白质数据库中的同类结构.
为验证未来蛋白质结构预测和NMR数据分析方法提供基准NMR数据.

主要方法:

确定了九个开源蛋白质NMR数据集,用于满足"盲目的"目标标准的小单体蛋白质.
为这些9种蛋白质生成AF2预测模型.
使用已建立的NMR结构验证工具,对AF2模型与实验性NMR数据 (化学转移,FID,NOESY,剩余二极合) 进行验证.

主要成果:

针对9个小 (70-108残留) 单体蛋白质,生成了AF2预测模型.
在大多数情况下,AF2模型对实验性NMR数据具有很高的匹配度.
AF2模型与NMR数据的匹配性与之前沉积的NMR结构模型相比,在某些情况下甚至优于它们.

结论:

AlphaFold-2 (AF2) 显示了使用溶液NMR数据精确建模小蛋白质结构的巨大潜力,即使是针对新的目标.
AF2可以作为一个有价值的指导工具在分析NMR数据和在结构生物学产生假设.
该研究提供了对推进蛋白质结构预测和NMR分析技术至关重要的基准NMR数据集.

关键词:

在AlphaFold2中,我们将使用AlphaFold2.人工智能的人工智能是人工智能.深度学习是一种深度学习.模型验证模型验证关于NMR数据的数据蛋白质NMR是一种蛋白质NMR.

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