使用Seq2Symm,快速准确地预测蛋白质同类寡合体对称性
Meghana Kshirsagar1, Artur Meller2,3, Ian R Humphreys4,5
1AI for Good Research Lab, Microsoft Corporation, Redmond, WA, USA. meghana.kshirsagar@microsoft.com.
Nature communications
|February 27, 2025
概括
预测蛋白质组合对称性对于功能至关重要. Seq2Symm是一种新的机器学习模型,可以从单个序列中准确预测同类寡合体对称性,超过现有方法,并使大规模的蛋白质组分析成为可能.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物学是结构生物学.
- 机器学习是机器学习.
背景情况:
- 高阶蛋白质组合对生物功能至关重要.
- 预测这些蛋白质组合的对称性,特别是同型寡合体,仍然是当前机器学习模型面临的挑战.
- 准确预测蛋白质组合对称性对于理解蛋白质功能和设计新型蛋白质至关重要.
研究的目的:
- 开发和评估机器学习模型,从蛋白质序列中预测 homo-oligomer 对称性.
- 为了弥补蛋白质组合对称性的准确和快速预测的差距.
- 提供一种可以与结构生成方法集成的工具,用于大规模的蛋白质组分析.
主要方法:
- 微调各种蛋白质基础模型,包括ESM2,以预测同类寡合体对称性.
- 开发了一个名为Seq2Symm的模型,作为表现最好的方法.
- 评估Seq2Symm与基于模板和其他深度学习方法对持有测试集的评估.
主要成果:
- 与基于模板的搜索 (0.24-0.25) 相比,Seq2Symm在预测同类寡合体对称性方面明显优于现有的基于模板和深度学习方法,达到高AUC-PR得分 (0.47-0.49).
- Seq2Symm处理单个序列作为输入,并达到每小时约80,000个蛋白质的预测速率.
- 该模型成功应用于5个蛋白质组,分析了约350万个未标记的蛋白质序列.
结论:
- Seq2Symm代表了预测蛋白质同类寡合体对称性的重大进步.
- 该模型的速度和准确性使其适用于大规模的蛋白质组分析和与RoseTTAFold2和AlphaFold2-multimer等结构预测工具的集成.
- 代码,数据集和模型的可用性促进了结构生物学和计算蛋白质设计的进一步研究和应用.
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