将蛋白质序列和结构与变换器和等价图形神经网络相结合,以预测蛋白质功能
Frimpong Boadu1, Hongyuan Cao2, Jianlin Cheng1
1Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, United States.
通过计算预测蛋白质功能至关重要. TransFun使用先进的人工智能模型集成蛋白质序列和结构,显著提高功能预测的准确性,并弥合了序列-功能差距.
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.
- 结构生物学 结构生物学
背景情况:
- 数以百万计的蛋白质序列可用,但实验性功能确定是缓慢和昂贵的.
- 已知蛋白序列数量与其实验确定功能的数量之间存在很大的差距.
- 需要计算方法来准确有效地预测蛋白质的功能.
研究的目的:
- 开发一种用于蛋白质功能预测的新型计算方法.
- 为了利用蛋白质序列和结构信息来提高预测准确度.
- 为了解决仅序列方法在蛋白质功能预测中的局限性.
主要方法:
- 开发了TransFun,该方法结合了基于变压器的蛋白质语言模型和3D等效图形神经网络.
- 使用预先训练的蛋白质语言模型 (例如,ESM) 进行序列特征提取.
- 由AlphaFold2预测的集成3D蛋白质结构与等价图神经网络.
主要成果:
- 与基准数据集 (CAFA3和新数据集) 上的最先进方法相比,TransFun表现优越.
- 序列和结构信息的整合显著改善了蛋白质功能预测.
- 将TransFun的预测与序列相似性结合起来,进一步提高了预测的准确性.
结论:
- TransFun有效地利用蛋白质序列和结构进行准确的功能预测.
- 基于变压器的语言模型和3D等效图形神经网络是这项任务的强大工具.
- 开发的方法有助于弥合蛋白质序列功能差距,促进生物研究.
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