GGN-GO:通过多尺度结构特征预测蛋白质功能的几何图网络。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-09-23 DOI:10.1093/bib/bbae559
Jia Mi, Han Wang, Jing Li, Jinghong Sun, Chang Li, Jing Wan, Yuan Zeng, Jingyang Gao
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引用次数: 0

摘要

高通量测序技术的最新进展导致基因组和转录组数据激增,提供了大量蛋白质序列信息。然而,大多数蛋白质的功能仍未标注。传统的蛋白质功能注释实验方法成本高、耗时长。目前的深度学习方法通常依靠图卷积网络在蛋白质残基之间传播特征。然而,这些方法无法捕捉到精细的原子级几何结构特征,而且在传递特征时无法直接计算或传播结构特征(如距离、方向和角度),往往将其简化为标量。此外,难以捕捉长程依赖关系也限制了模型识别关键节点(残基)的能力。为了应对这些挑战,我们提出了一种预测蛋白质功能的几何图网络(GGN-GO),它通过捕捉原子和残基层面的多尺度几何结构特征来丰富特征提取。我们使用几何矢量感知器将这些特征转换为矢量表示,并将它们与节点特征聚合在一起,以便在网络中更好地理解和传播。此外,我们还引入了图形注意力汇集层,通过自适应地汇集局部功能图案来捕捉关键节点信息,而对比学习则通过随机噪声和不同视图来增强图形表征的可辨别性。实验结果表明,GGN-GO 在实验验证和预测蛋白质结构标签最多的任务中的表现优于六种比较方法。此外,GGN-GO 还能识别出与实验证实的功能残基相对应的功能残基,从而展示了其可解释性和精确定位关键蛋白质区域的能力。代码和数据可在以下网址获取:https://github.com/MiJia-ID/GGN-GO。
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GGN-GO: geometric graph networks for predicting protein function by multi-scale structure features.

Recent advances in high-throughput sequencing have led to an explosion of genomic and transcriptomic data, offering a wealth of protein sequence information. However, the functions of most proteins remain unannotated. Traditional experimental methods for annotation of protein functions are costly and time-consuming. Current deep learning methods typically rely on Graph Convolutional Networks to propagate features between protein residues. However, these methods fail to capture fine atomic-level geometric structural features and cannot directly compute or propagate structural features (such as distances, directions, and angles) when transmitting features, often simplifying them to scalars. Additionally, difficulties in capturing long-range dependencies limit the model's ability to identify key nodes (residues). To address these challenges, we propose a geometric graph network (GGN-GO) for predicting protein function that enriches feature extraction by capturing multi-scale geometric structural features at the atomic and residue levels. We use a geometric vector perceptron to convert these features into vector representations and aggregate them with node features for better understanding and propagation in the network. Moreover, we introduce a graph attention pooling layer captures key node information by adaptively aggregating local functional motifs, while contrastive learning enhances graph representation discriminability through random noise and different views. The experimental results show that GGN-GO outperforms six comparative methods in tasks with the most labels for both experimentally validated and predicted protein structures. Furthermore, GGN-GO identifies functional residues corresponding to those experimentally confirmed, showcasing its interpretability and the ability to pinpoint key protein regions. The code and data are available at: https://github.com/MiJia-ID/GGN-GO.

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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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