蛋白MAE:用于蛋白质表面自我监督学习的蒙面自编码器
Mingzhi Yuan1,2, Ao Shen1,2, Kexue Fu1,2
1Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China.
Bioinformatics (Oxford, England)
|November 29, 2023
概括
蛋白MAE是一个自我监督的框架,通过利用未标记的数据来克服标签稀缺性来增强蛋白质表面表示. 这种方法可以提高各种任务的性能,并大大降低计算成本.
科学领域:
- 计算生物学 计算生物学
- 结构生物信息学 结构生物信息学
- 机器学习 机器学习
背景情况:
- 蛋白质功能是由表面特性决定的,对于蛋白质设计和相互作用预测等任务至关重要.
- 目前用于蛋白质表面分析的深度学习方法受到有限的实验数据 (标签稀缺) 的阻碍.
- 自主监督学习 (SSL) 在克服其他领域的数据限制方面表现有前途.
研究的目的:
- 介绍ProteinMAE,一种用于蛋白质表面表示的新型自我监督框架.
- 解决基于学习的蛋白质表面分析中标签稀缺的挑战.
- 开发一种计算效率高的方法,用于预训练蛋白质表面模型.
主要方法:
- 为蛋白质表面表示开发了一个高效的网络架构.
- 利用大量未标记的蛋白质数据进行自我监督预训 (ProteinMAE).
- 在下游任务上微调预训练模型:结合位点识别,蛋白质口袋分类和蛋白质-蛋白质相互作用预测.
主要成果:
- 在所有评估的下游任务中,ProteinMAE显著提高了性能.
- 与最先进的方法相比,该方法取得了具有竞争力的结果.
- 蛋白MAE网络展示了实质性的计算优势,需要以前方法内存成本的不到10%.
结论:
- 蛋白MAE通过自我监督学习有效地减轻了蛋白质表面表示的标签稀缺性.
- 该框架为分析蛋白质表面提供了一种强大且计算效率高的替代方案.
- 这种方法具有很大的潜力,可以在结构生物学和药物发现中推进各种应用.
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In this pathway, the target proteins are first tagged with small proteins called ubiquitin. This involves participation of a series of enzymes including— E1 (ubiquitin-activating enzyme), E2 (ubiquitin-conjugating enzyme), and E3 (ubiquitin...
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