学习一个通用的图形变压器,用于在不相似的序列中预测蛋白质功能
Yiwei Fu1, Zhonghui Gu2, Xiao Luo3
1School of Mathematical Sciences, Peking University, Beijing 100871, China.
GigaScience
|December 10, 2024
概括
我们开发了GALA (Graph Adversarial Learning with Alignment),这是一种用于准确预测蛋白质功能的新型深度学习方法. 通过学习域不变表示,GALA增强了对新蛋白质的概括性,提高了生物洞察力.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 高通量测序产生了大量数据,超过了实验验证.
- 深度学习为快速的蛋白质功能预测提供了有希望的解决方案.
- 当前的深度学习模型可能会与远离训练数据的新型蛋白质作斗争.
研究的目的:
- 引入图形对抗式学习与对齐 (GALA),一种用于蛋白质功能预测的通用深度学习方法.
- 提高蛋白质功能预测模型对新型非同类蛋白质的概括性.
- 为了提高蛋白质功能预测模型的解释性.
主要方法:
- GALA集成了图形变压器架构和注意力聚合,用于统一的蛋白质序列和结构表示学习.
- 具有域区分器的对抗性学习确保了域不变的蛋白质表示.
- 标签嵌入生成并在隐藏空间中对齐以优化标签信息.
主要成果:
- 在PDB和Swiss-Prot数据集上,GALA的性能与最先进的方法相美.
- 该模型通过使用类激活映射识别关键功能残留物来证明生物可解释性.
- 在未见的序列空间中预测蛋白质的功能时,GALA显示出极好的概括性.
结论:
- GALA的对抗性学习和标签嵌入对齐产生域不变表示,提高了概括性.
- 将AlphaFold2结构与GALA集成显示了对新发现的蛋白质序列进行注释的潜力.
- GALA的实施是公开可用于研究的.
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