基于多任务协作训练的蛋白质多标签亚细胞定位和功能预测的深度学习模型
Peihao Bai1, Guanghui Li1, Jiawei Luo2
1School of Information and Software Engineering, East China Jiaotong University, No. 808 Shuanggang East Road, Nanchang 330013, China.
Briefings in bioinformatics
|November 3, 2024
概括
DeepMTC是一种新的深度学习模型,可以准确地预测蛋白质细胞下定位和功能,而不需要依赖基因本体学数据库. 这一进步有助于了解疾病机制,并为新发现的蛋白质发现药物.
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.
- 在蛋白质科学中的深度学习应用.
背景情况:
- 蛋白质功能性研究对于理解病变发生,药物开发和目标发现至关重要.
- 目前用于细胞下定位的计算模型存在局限性,包括对基因本体学 (GO) 数据库的依赖以及忽视GO-蛋白定位关系.
研究的目的:
- 开发一个先进的计算模型,DeepMTC,用于准确的蛋白质细胞下定位和功能预测.
- 通过整合蛋白质功能和局部化而克服现有模型的局限性,而不依赖已知的GO注释.
- 为了能够预测新发现的缺乏先前功能数据的蛋白质.
主要方法:
- 开发了DeepMTC,这是一个端到端的深度学习多任务协作培训模型.
- 利用预训练的语言模型提取蛋白质的3D结构和序列特征.
- 采用图形变压器模块来编码蛋白质序列特征并解决远程依赖.
- 集成了一个功能性交叉注意力机制,以结合学习的功能特征以进行亚细胞局部化.
主要成果:
- 与最先进的模型相比,DeepMTC在蛋白质功能预测和亚细胞局部化方面都表现出优异的性能.
- 该模型成功地预测了蛋白质的亚细胞定位和功能,而没有先前的GO注释.
- 可解释性实验证实了DeepMTC识别关键蛋白质残留物和功能域的能力.
结论:
- DeepMTC为蛋白质功能研究提供了一种强大而通用的工具,特别是用于新型蛋白质.
- 该模型能够整合功能和本地化预测的能力增强了生物洞察力.
- 开发的方法通过减少对广泛的注释数据库的依赖来推进计算生物学.
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