使用融合图卷积网络和改进的注意力网络与alphaFold3衍生特征的蛋白质溶解性预测
Guanjie Song1, Zhenjie Luo1, Aoyun Geng1
1School of Computer Science and Technology, Hainan University, Haikou 570228, China.
Journal of chemical information and modeling
|January 8, 2026
概括
一个新的模型,FGNNSol,通过整合3D结构和全球特征来增强蛋白质可溶性预测. 它的性能优于现有方法,在生物制药和工业应用中提高了准确性.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 结构生物学 结构生物学
背景情况:
- 蛋白质溶解性对于工业生产,生物制药和食品工业至关重要.
- 现有的预测模型通常依赖于有限的数据,例如1D序列或3D联系信息,忽视了全面的结构和物理化学特性.
- 蛋白质语言模型 (例如ESM-C) 和结构预测 (例如AlphaFold3) 的最新进展为改善溶解性预测提供了增强的数据.
研究的目的:
- 开发一种新且有效的模型来预测蛋白质溶解度.
- 利用先进的蛋白质结构预测和语言模型进行更准确的特征提取.
- 通过结合各种结构和物理化学数据来改进现有的蛋白质溶解性预测方法.
主要方法:
- 利用AlphaFold3来预测蛋白质的三维结构,生成用于图形构建的结构信息.
- 集成ESM-C嵌入和残留水平属性作为节点特征,与蛋白质水平的全球特征一起,以创建全面的蛋白质图表表示.
- 采用混合图形神经网络架构,结合GPSol (改进的图形注意网络) 和图形卷积网络,然后使用多层感知器进行预测.
主要成果:
- 在FGNNSol模型中,在*Escherichia coli* eSOL和*Saccharomyces cerevisiae*数据集上,分别获得了0.577和0.469的R2值.
- 与现有模型相比,FGNNSol在蛋白质溶解性的回归和二进制分类任务中表现优越.
- 在对外部数据集进行评估时,该模型表现出强大的概括能力.
结论:
- FGNNSol有效地整合了各种蛋白质特征,包括3D结构和全球物理化学性质,以准确地预测溶解度.
- 拟议的模型显著优于现有方法,突出了结合先进结构和序列信息的好处.
- FGNNSol为生物制药开发和其他依赖蛋白质溶解度的行业的应用提供了一个有前途的工具.
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