School of Information and Software Engineering, East China Jiaotong University, No. 808 Shuanggang East Road, Nanchang 330013, China.
College of Computer Science and Electronic Engineering, Hunan University, No. 2 Lushan Road, Changsha 410082, China.
Brief Bioinform. 2024 Sep 23;25(6). doi: 10.1093/bib/bbae568.
The functional study of proteins is a critical task in modern biology, playing a pivotal role in understanding the mechanisms of pathogenesis, developing new drugs, and discovering novel drug targets. However, existing computational models for subcellular localization face significant challenges, such as reliance on known Gene Ontology (GO) annotation databases or overlooking the relationship between GO annotations and subcellular localization. To address these issues, we propose DeepMTC, an end-to-end deep learning-based multi-task collaborative training model. DeepMTC integrates the interrelationship between subcellular localization and the functional annotation of proteins, leveraging multi-task collaborative training to eliminate dependence on known GO databases. This strategy gives DeepMTC a distinct advantage in predicting newly discovered proteins without prior functional annotations. First, DeepMTC leverages pre-trained language model with high accuracy to obtain the 3D structure and sequence features of proteins. Additionally, it employs a graph transformer module to encode protein sequence features, addressing the problem of long-range dependencies in graph neural networks. Finally, DeepMTC uses a functional cross-attention mechanism to efficiently combine upstream learned functional features to perform the subcellular localization task. The experimental results demonstrate that DeepMTC outperforms state-of-the-art models in both protein function prediction and subcellular localization. Moreover, interpretability experiments revealed that DeepMTC can accurately identify the key residues and functional domains of proteins, confirming its superior performance. The code and dataset of DeepMTC are freely available at https://github.com/ghli16/DeepMTC.
蛋白质的功能研究是现代生物学中的一项关键任务，对于理解发病机制、开发新药和发现新的药物靶点起着至关重要的作用。然而，现有的亚细胞定位计算模型面临着重大挑战，例如依赖于已知的基因本体论（GO）注释数据库，或者忽略 GO 注释与亚细胞定位之间的关系。为了解决这些问题，我们提出了 DeepMTC，这是一个基于端到端深度学习的多任务协作训练模型。DeepMTC 整合了亚细胞定位和蛋白质功能注释之间的相互关系，利用多任务协作训练来消除对已知 GO 数据库的依赖。这种策略使 DeepMTC 在预测新发现的蛋白质时具有明显的优势，而这些蛋白质之前没有功能注释。首先，DeepMTC 利用具有高精度的预训练语言模型来获取蛋白质的 3D 结构和序列特征。此外，它采用图转换器模块对蛋白质序列特征进行编码，解决了图神经网络中长程依赖的问题。最后，DeepMTC 使用功能交叉注意机制来有效地组合上游学习到的功能特征，以执行亚细胞定位任务。实验结果表明，DeepMTC 在蛋白质功能预测和亚细胞定位方面均优于最先进的模型。此外，可解释性实验表明，DeepMTC 可以准确识别蛋白质的关键残基和功能域，证实了其卓越的性能。DeepMTC 的代码和数据集可在 https://github.com/ghli16/DeepMTC 上免费获取。
