Res-GCN: Identification of protein phosphorylation sites using graph convolutional network and residual network

IF 2.6 4区 生物学 Q2 BIOLOGY Computational Biology and Chemistry Pub Date : 2024-08-24 DOI:10.1016/j.compbiolchem.2024.108183
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Abstract

An essential post-translational modification, phosphorylation is intimately related with a wide range of biological activities. The advancement of effective computational methods for correctly recognizing phosphorylation sites is important for in-depth understanding of various physiological phenomena. However, the traditional method of identifying phosphorylation sites experimentally is time-consuming and laborious, which makes it difficult to meet the processing demands of today's big data. This research proposes the use of a novel model, Res-GCN, to recognize the phosphorylation sites of SARS-CoV-2. Firstly, eight feature extraction strategies are utilized to digitize the protein sequence from multiple viewpoints, including amino acid property encodings (AAindex), pseudo-amino acid composition (PseAAC), adapted normal distribution bi-profile Bayes (ANBPB), dipeptide composition (DC), binary encoding (BE), enhanced amino acid composition (EAAC), Word2Vec, and BLOSUM62 matrices. Secondly, elastic net is utilized to eliminate redundant data in the fused matrix. Finally, a combination of graph convolutional network (GCN) and residual network (ResNet) is used to classify the phosphorylated sites and output predictions using a fully connected layer (FC). The performance of Res-GCN is tested by 5-fold cross-validation and independent testing, and excellent results are obtained on S/T and Y datasets. This demonstrates that the Res-GCN model exhibits exceptional predictive performance and generalizability.

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Res-GCN:利用图卷积网络和残差网络识别蛋白质磷酸化位点
磷酸化是一种重要的翻译后修饰,与多种生物活动密切相关。发展有效的计算方法来正确识别磷酸化位点,对于深入了解各种生理现象非常重要。然而,传统的磷酸化位点实验识别方法费时费力,难以满足当今大数据的处理需求。本研究提出使用新型模型 Res-GCN 来识别 SARS-CoV-2 的磷酸化位点。首先,利用氨基酸属性编码(AAindex)、伪氨基酸组成(PseAAC)、适应正态分布双轮廓贝叶斯(ANBPB)、二肽组成(DC)、二进制编码(BE)、增强氨基酸组成(EAAC)、Word2Vec 和 BLOSUM62 矩阵等八种特征提取策略,从多个角度对蛋白质序列进行数字化处理。其次,利用弹性网消除融合矩阵中的冗余数据。最后,结合图卷积网络(GCN)和残差网络(ResNet)对磷酸化位点进行分类,并使用全连接层(FC)输出预测结果。Res-GCN 的性能通过 5 倍交叉验证和独立测试进行了检验,并在 S/T 和 Y 数据集上取得了优异的结果。这表明 Res-GCN 模型具有卓越的预测性能和普适性。
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来源期刊
Computational Biology and Chemistry
Computational Biology and Chemistry 生物-计算机:跨学科应用
CiteScore
6.10
自引率
3.20%
发文量
142
审稿时长
24 days
期刊介绍: Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.
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