IPs-GRUAtt: An attention-based bidirectional gated recurrent unit network for predicting phosphorylation sites of SARS-CoV-2 infection

Gao, Shan; Tang, Qiang; Feng, Pengmian; Chen, Wei

doi:10.1016/j.omtn.2023.02.027

Cited by 12 publications

(2 citation statements)

References 34 publications

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“…Notably, tree-based classifiers contributed significantly to the top-performing models regardless of datasets. Although most existing methods rely on deep learning-based algorithms [ 18 , 41 ] and often use word-embedding vectors as features, our study is the first examination of such an extensive range of feature descriptors and classifiers for phosphorylation site prediction. We assessed the discriminative ability of each feature descriptor and the capacity of the classifiers at predicting on different datasets, laying the groundwork for future studies when larger datasets become available.…”

Section: Resultsmentioning

confidence: 99%

Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach

Pham,

Phan,

Seo

et al. 2023

Briefings in Bioinformatics

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The worldwide appearance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has generated significant concern and posed a considerable challenge to global health. Phosphorylation is a common post-translational modification that affects many vital cellular functions and is closely associated with SARS-CoV-2 infection. Precise identification of phosphorylation sites could provide more in-depth insight into the processes underlying SARS-CoV-2 infection and help alleviate the continuing COVID-19 crisis. Currently, available computational tools for predicting these sites lack accuracy and effectiveness. In this study, we designed an innovative meta-learning model, Meta-Learning for Serine/Threonine Phosphorylation (MeL-STPhos), to precisely identify protein phosphorylation sites. We initially performed a comprehensive assessment of 29 unique sequence-derived features, establishing prediction models for each using 14 renowned machine learning methods, ranging from traditional classifiers to advanced deep learning algorithms. We then selected the most effective model for each feature by integrating the predicted values. Rigorous feature selection strategies were employed to identify the optimal base models and classifier(s) for each cell-specific dataset. To the best of our knowledge, this is the first study to report two cell-specific models and a generic model for phosphorylation site prediction by utilizing an extensive range of sequence-derived features and machine learning algorithms. Extensive cross-validation and independent testing revealed that MeL-STPhos surpasses existing state-of-the-art tools for phosphorylation site prediction. We also developed a publicly accessible platform at https://balalab-skku.org/MeL-STPhos. We believe that MeL-STPhos will serve as a valuable tool for accelerating the discovery of serine/threonine phosphorylation sites and elucidating their role in post-translational regulation.

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Section: Resultsmentioning

confidence: 99%

Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach

Pham,

Phan,

Seo

et al. 2023

Briefings in Bioinformatics

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“…Wet-lab experimental approaches combined with high-throughput sequencing techniques have offered experimentally validated m 5 U sites in multiple species (Xuan et al, 2018 ; Carter et al, 2019 ). However, wet-lab approaches can be a costly and time-consuming process; thus, an increasing number of computational efforts have been made, targeting different aspects of biological problems, including phosphorylation prediction (Zhang G. et al, 2023 ), protein structure prediction (Jumper et al, 2021 ), drug discovery (Chen et al, 2023 ), and microbiome studies (Goodswen et al, 2021 ; Jiang et al, 2022 ; Yuan et al, 2023 ). For epitranscriptomic field, a number of bioinformatics databases (Boccaletto et al, 2018 ; Luo et al, 2021 ; Song et al, 2021 , 2023 ; Bao et al, 2023 ; Liang et al, 2023 ) and in silico prediction frameworks (Qiu et al, 2017 ; Zhai et al, 2018 ; Chen et al, 2019 ; Körtel et al, 2021 ; Xiong et al, 2021 ; Liang et al, 2022 ; Song et al, 2022 ; Yao et al, 2023 ) have been widely applied.…”

Section: Introductionmentioning

confidence: 99%

m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features

Xu,

Wang,

Meng

et al. 2023

Front. Microbiol.

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5-Methyluridine (m5U) is one of the most common post-transcriptional RNA modifications, which is involved in a variety of important biological processes and disease development. The precise identification of the m5U sites allows for a better understanding of the biological processes of RNA and contributes to the discovery of new RNA functional and therapeutic targets. Here, we present m5U-GEPred, a prediction framework, to combine sequence characteristics and graph embedding-based information for m5U identification. The graph embedding approach was introduced to extract the global information of training data that complemented the local information represented by conventional sequence features, thereby enhancing the prediction performance of m5U identification. m5U-GEPred outperformed the state-of-the-art m5U predictors built on two independent species, with an average AUROC of 0.984 and 0.985 tested on human and yeast transcriptomes, respectively. To further validate the performance of our newly proposed framework, the experimentally validated m5U sites identified from Oxford Nanopore Technology (ONT) were collected as independent testing data, and in this project, m5U-GEPred achieved reasonable prediction performance with ACC of 91.84%. We hope that m5U-GEPred should make a useful computational alternative for m5U identification.

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AI-Assisted Methods for Protein Structure Prediction and Analysis

Goel,

Kumar,

Kumar

2024

Microorganisms for Sustainability

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IPs-GRUAtt: An attention-based bidirectional gated recurrent unit network for predicting phosphorylation sites of SARS-CoV-2 infection

Cited by 12 publications

References 34 publications

Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach

Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach

m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features

AI-Assisted Methods for Protein Structure Prediction and Analysis

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