GASIDN:通过多尺度特征融合识别亚高尔基体蛋白。

IF 3.5 2区 生物学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Jianan Sui, Jiazi Chen, Yuehui Chen, Naoki Iwamori, Jin Sun
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引用次数: 0

摘要

高尔基体是真核细胞内膜系统的重要组成部分,在蛋白质的生物合成中发挥着核心作用。高尔基体功能障碍与神经退行性疾病有关。因此,准确鉴定高尔基体下蛋白质类型对于开发治疗此类疾病的有效方法至关重要。由于鉴定高尔基体下蛋白类型的实验方法既昂贵又耗时,人们开发了各种计算方法作为鉴定工具。然而,这些方法大多只依赖于蛋白质序列中的邻近特征,而忽略了蛋白质的关键空间结构信息。为了探索准确识别亚高尔基体蛋白质的替代方法,我们开发了一种名为 GASIDN 的模型。GASIDN 模型利用蛋白质序列上的一维卷积模块和 AlphaFold2 构建的接触图上的图学习模块提取多维特征。在独立测试和十倍交叉验证中,GASIDN 的准确率分别达到了 98.4% 和 96.4%,优于之前的大多数预测器。据我们所知,这是第一种利用多尺度特征融合来识别和定位亚高尔基体蛋白质的方法。为了评估我们的模型的通用性和可扩展性,我们进行了实验,将其应用于识别其他细胞器的蛋白质,包括植物液泡和过氧物酶体。这些实验结果表明,我们的模型具有良好的有效性和通用性。源代码和数据集可从 https://github.com/SJNNNN/GASIDN 获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GASIDN: identification of sub-Golgi proteins with multi-scale feature fusion.

The Golgi apparatus is a crucial component of the inner membrane system in eukaryotic cells, playing a central role in protein biosynthesis. Dysfunction of the Golgi apparatus has been linked to neurodegenerative diseases. Accurate identification of sub-Golgi protein types is therefore essential for developing effective treatments for such diseases. Due to the expensive and time-consuming nature of experimental methods for identifying sub-Golgi protein types, various computational methods have been developed as identification tools. However, the majority of these methods rely solely on neighboring features in the protein sequence and neglect the crucial spatial structure information of the protein.To discover alternative methods for accurately identifying sub-Golgi proteins, we have developed a model called GASIDN. The GASIDN model extracts multi-dimension features by utilizing a 1D convolution module on protein sequences and a graph learning module on contact maps constructed from AlphaFold2.The model utilizes the deep representation learning model SeqVec to initialize protein sequences. GASIDN achieved accuracy values of 98.4% and 96.4% in independent testing and ten-fold cross-validation, respectively, outperforming the majority of previous predictors. To the best of our knowledge, this is the first method that utilizes multi-scale feature fusion to identify and locate sub-Golgi proteins. In order to assess the generalizability and scalability of our model, we conducted experiments to apply it in the identification of proteins from other organelles, including plant vacuoles and peroxisomes. The results obtained from these experiments demonstrated promising outcomes, indicating the effectiveness and versatility of our model. The source code and datasets can be accessed at https://github.com/SJNNNN/GASIDN .

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来源期刊
BMC Genomics
BMC Genomics 生物-生物工程与应用微生物
CiteScore
7.40
自引率
4.50%
发文量
769
审稿时长
6.4 months
期刊介绍: BMC Genomics is an open access, peer-reviewed journal that considers articles on all aspects of genome-scale analysis, functional genomics, and proteomics. BMC Genomics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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