Deep learning model for protein multi-label subcellular localization and function prediction based on multi-task collaborative training.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Peihao Bai, Guanghui Li, Jiawei Luo, Cheng Liang
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Abstract

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.

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深度学习模型。
蛋白质的功能研究是现代生物学的一项关键任务,在了解发病机制、开发新药和发现新的药物靶点方面发挥着举足轻重的作用。然而,现有的亚细胞定位计算模型面临着巨大的挑战,例如依赖于已知的基因本体(GO)注释数据库,或者忽视了 GO 注释与亚细胞定位之间的关系。为了解决这些问题,我们提出了基于深度学习的端到端多任务协作训练模型 DeepMTC。DeepMTC 整合了亚细胞定位与蛋白质功能注释之间的相互关系,利用多任务协作训练消除了对已知 GO 数据库的依赖。这一策略使 DeepMTC 在预测没有预先功能注释的新发现蛋白质时具有明显优势。首先,DeepMTC 利用预先训练的高精度语言模型来获取蛋白质的三维结构和序列特征。此外,它还采用了图转换器模块来编码蛋白质序列特征,从而解决了图神经网络中的长程依赖性问题。最后,DeepMTC 利用功能交叉注意机制,有效地结合上游学习到的功能特征来完成亚细胞定位任务。实验结果表明,DeepMTC 在蛋白质功能预测和亚细胞定位方面都优于最先进的模型。此外,可解释性实验表明,DeepMTC 能准确识别蛋白质的关键残基和功能域,从而证实了其卓越的性能。DeepMTC 的代码和数据集可在 https://github.com/ghli16/DeepMTC 免费获取。
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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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