一个可扩展的等价图形网络框架,用于精确的蛋白质功能预测
Zixu Ran1, Xudong Guo1,2, Tong Pan2,3
1College of Information Engineering, Northwest A&F University, Yangling, 712100, China.
Genome biology
|November 29, 2025
概括
我们开发了ENGINE,这是一种用于蛋白质功能预测的深度学习框架. 它使用3D结构和序列数据准确预测蛋白质功能,提供生物学见解.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物信息学 结构生物信息学
- 在生命科学领域的机器学习.
背景情况:
- 了解复杂的细胞过程依赖于蛋白质功能研究.
- 蛋白质序列数据的快速增长需要有效的计算方法进行注释.
- 蛋白质结构和功能复杂性对准确的预测构成挑战.
研究的目的:
- 推出ENGINE,一种用于蛋白质功能预测的新型多道深度学习框架.
- 为了提高计算蛋白质注释的准确性和稳定性.
- 为蛋白质功能预测提供生物解释性.
主要方法:
- 使用等价图卷积网络来检测蛋白质3D结构的几何特征.
- 采用ESM-C大型语言模型用于进化和序列信息.
- 整合一个创新的3D序列表示,结合空间和序列信号.
主要成果:
- 在各种蛋白质功能预测基准中,ENGINE的性能优于最先进的方法.
- 该框架表现出强大的概括性和高预测准确性.
- 引擎提供了对关键序列特征和结构动机的可解释的见解,识别了功能重要残留物.
结论:
- ENGINE通过可靠,可解释的预测,增强了对细胞过程和疾病机制的研究.
- 该模型有助于更深入地理解蛋白质功能的机制.
- ENGINE是公开的,作为科学界的一个工具.
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