具有对比学习的等级图形变压器用于蛋白质功能预测
Zhonghui Gu1, Xiao Luo2, Jiaxiao Chen3
1Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
Bioinformatics (Oxford, England)
|June 27, 2023
概括
一个新的深度学习模型,分层图形转换器与对比学习 (HEAL),准确地预测蛋白质功能. HEAL利用结构信息并优于现有方法,即使使用预测的蛋白质结构.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 结构生物学是结构生物学.
背景情况:
- 高通量测序提供了大量的蛋白质数据,但功能性注释是缓慢和昂贵的.
- 计算模型对于加速蛋白质功能预测至关重要.
- 现有的图形神经网络在长距离的结构相关性和确定关键残余方面扎.
研究的目的:
- 引入一种新的深度学习模型,分层图形转换器与对比学习 (HEAL),用于增强蛋白质功能的预测.
- 解决捕捉结构语义和蛋白质图中长距离依赖性的局限性.
- 为了提高计算蛋白质功能预测的准确性和效率.
主要方法:
- 开发了HEAL,一个分层图形变压器模型,包含超级节点来模仿功能动图.
- 采用图形对比学习来优化网络和改进图形表示.
- 集成的AlphaFold2预测了结构,以增强缺乏实验数据的蛋白质的预测.
主要成果:
- 在训练数据较少的情况下,HEAL-PDB实现了与最先进的方法可比的性能.
- 在使用AlphaFold2预测结构时,HEAL在PDBch测试集上的表现明显优于DeepFRI.
- 与使用预测结构的DeepFRI和DeepGOPlus相比,HEAL在AFch测试集上表现优异.
- 类激活映射使HEAL能够识别蛋白质中的功能位点.
结论:
- HEAL为蛋白质功能预测提供了一种强大而准确的方法,特别是当实验结构不可用时.
- 该模型捕捉层次结构语义和利用预测结构的能力代表了显著的进步.
- 在后基因组时代,HEAL为加速功能注释提供了一个有价值的工具.
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