ProtFun:使用图形注意力网络与蛋白质大语言模型的蛋白质功能预测模型
Muhammed Talo1,2,3, Serdar Bozdag1,2,3,4
1Department of Computer Science and Engineering, University of North Texas, Denton, TX 76207, USA.
bioRxiv : the preprint server for biology
|June 4, 2025
概括
计算预测蛋白质功能对于疾病研究至关重要. 我们的新型ProtFun模型,使用深度学习和蛋白质嵌入,准确地预测蛋白质功能,优于现有方法.
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
- 基因组学就是基因组学.
背景情况:
- 实验性蛋白质功能的确定是昂贵和耗时的.
- 高通量技术产生了大量的蛋白质序列数据.
- 准确的蛋白质功能预测对于疾病研究和药物发现至关重要.
研究的目的:
- 开发一种先进的计算方法,用于自动预测蛋白质功能.
- 介绍ProtFun,一个多模态深度学习架构,用于增强蛋白质功能预测.
主要方法:
- 综合蛋白质大语言模型 (LLM) 嵌入到蛋白质家族网络中.
- 使用图表注意力网络 (GAT) 来学习蛋白质嵌入.
- 结合LLM嵌入与InterPro签名用于函数预测模型培训.
主要成果:
- 与基准数据集上最先进的方法相比,ProtFun表现出更高的性能.
- 一项废除研究证实了ProtFun架构中的单个组件的意义.
- 该模型在预测蛋白质功能方面取得了很高的准确性.
结论:
- ProtFun为蛋白质功能预测提供了一种强大而高效的方法.
- 开发的方法有助于了解疾病机制和确定治疗点.
- 开源数据和代码有助于进一步的研究和应用.
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