MSNGO:基于3D蛋白质结构和网络传播的多种蛋白质功能注释
Beibei Wang1, Boyue Cui1, Shiqu Chen1
1School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guang Dong 518055, China.
Bioinformatics (Oxford, England)
|May 6, 2025
概括
通过整合蛋白质结构和网络传播,MSNGO模型增强了多种蛋白质功能预测. 这种方法改善了对蛋白质注释有限的物种的跨物种标签传播.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 结构生物学 结构生物学
背景情况:
- 使用AlphaFold2预测结构,蛋白质功能预测的准确性已经提升,超过仅序列的方法.
- 多种蛋白质功能预测在数据集成和不同物种之间的知识转移方面面临挑战.
- 对稀有标注物种进行有效的跨物种标签传播仍然是一个关键的挑战.
研究的目的:
- 提出一种新型模型,MSNGO (多种蛋白质结构和预测GO术语的网络),用于改进多种蛋白质功能预测.
- 整合结构特征和网络传播,以解决当前多种方法的局限性.
- 为了增强具有稀疏蛋白质注释的物种的跨物种标签传播.
主要方法:
- 使用图形表示学习从蛋白质结构联系图中提取氨基酸特征.
- 使用图形卷积聚合模块来导出蛋白质水平的结构特征.
- 整合ESM-2的序列特征,并在异质网络中应用网络传播.
主要成果:
- 该MSNGO模型整合了结构和序列特征,用于增强蛋白质功能预测.
- 网络传播有效地汇总信息并更新异质网络中的节点表示.
- 与现有的多种方法相比,MSNGO表现出优异的性能,仅依赖于序列特征和蛋白质-蛋白质网络.
结论:
- 整合结构特征显著提高了多种蛋白质功能预测的准确性.
- MSNGO为跨物种标签的传播提供了一个有效的解决方案,特别是对于标注有限的物种.
- 开发的模型通过利用结构和网络信息来推进多种蛋白质功能的预测领域.
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