DLBWE-Cys: a deep-learning-based tool for identifying cysteine S-carboxyethylation sites using binary-weight encoding
Cysteine S-carboxyethylation, a novel post-translational modification (PTM), plays a critical role in the pathogenesis of autoimmune diseases, particularly ankylosing spondylitis. Accurate identification of S-carboxyethylation modification sites is essential for elucidating their functional mechanis...
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| Format: | Article |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2024.1464976/full |
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| author | Zhengtao Luo Zhengtao Luo Zhengtao Luo Qingyong Wang Qingyong Wang Qingyong Wang Yingchun Xia Yingchun Xia Yingchun Xia Xiaolei Zhu Xiaolei Zhu Xiaolei Zhu Shuai Yang Shuai Yang Shuai Yang Zhaochun Xu Zhaochun Xu Lichuan Gu Lichuan Gu Lichuan Gu |
| author_facet | Zhengtao Luo Zhengtao Luo Zhengtao Luo Qingyong Wang Qingyong Wang Qingyong Wang Yingchun Xia Yingchun Xia Yingchun Xia Xiaolei Zhu Xiaolei Zhu Xiaolei Zhu Shuai Yang Shuai Yang Shuai Yang Zhaochun Xu Zhaochun Xu Lichuan Gu Lichuan Gu Lichuan Gu |
| author_sort | Zhengtao Luo |
| collection | DOAJ |
| description | Cysteine S-carboxyethylation, a novel post-translational modification (PTM), plays a critical role in the pathogenesis of autoimmune diseases, particularly ankylosing spondylitis. Accurate identification of S-carboxyethylation modification sites is essential for elucidating their functional mechanisms. Unfortunately, there are currently no computational tools that can accurately predict these sites, posing a significant challenge to this area of research. In this study, we developed a new deep learning model, DLBWE-Cys, which integrates CNN, BiLSTM, Bahdanau attention mechanisms, and a fully connected neural network (FNN), using Binary-Weight encoding specifically designed for the accurate identification of cysteine S-carboxyethylation sites. Our experimental results show that our model architecture outperforms other machine learning and deep learning models in 5-fold cross-validation and independent testing. Feature comparison experiments confirmed the superiority of our proposed Binary-Weight encoding method over other encoding techniques. t-SNE visualization further validated the model’s effective classification capabilities. Additionally, we confirmed the similarity between the distribution of positional weights in our Binary-Weight encoding and the allocation of weights in attentional mechanisms. Further experiments proved the effectiveness of our Binary-Weight encoding approach. Thus, this model paves the way for predicting cysteine S-carboxyethylation modification sites in protein sequences. The source code of DLBWE-Cys and experiments data are available at: https://github.com/ztLuo-bioinfo/DLBWE-Cys. |
| format | Article |
| id | doaj-art-3f55bddbda274fbdb6becfdf5d7c7e95 |
| institution | OA Journals |
| issn | 1664-8021 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj-art-3f55bddbda274fbdb6becfdf5d7c7e952025-08-20T02:26:41ZengFrontiers Media S.A.Frontiers in Genetics1664-80212025-01-011510.3389/fgene.2024.14649761464976DLBWE-Cys: a deep-learning-based tool for identifying cysteine S-carboxyethylation sites using binary-weight encodingZhengtao Luo0Zhengtao Luo1Zhengtao Luo2Qingyong Wang3Qingyong Wang4Qingyong Wang5Yingchun Xia6Yingchun Xia7Yingchun Xia8Xiaolei Zhu9Xiaolei Zhu10Xiaolei Zhu11Shuai Yang12Shuai Yang13Shuai Yang14Zhaochun Xu15Zhaochun Xu16Lichuan Gu17Lichuan Gu18Lichuan Gu19School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, ChinaAnhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Hefei, Anhui, ChinaAnhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing, Anhui Agricultural University, Hefei, Anhui, ChinaSchool of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, ChinaAnhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Hefei, Anhui, ChinaAnhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing, Anhui Agricultural University, Hefei, Anhui, ChinaSchool of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, ChinaAnhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Hefei, Anhui, ChinaAnhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing, Anhui Agricultural University, Hefei, Anhui, ChinaSchool of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, ChinaAnhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Hefei, Anhui, ChinaAnhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing, Anhui Agricultural University, Hefei, Anhui, ChinaSchool of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, ChinaAnhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Hefei, Anhui, ChinaAnhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing, Anhui Agricultural University, Hefei, Anhui, ChinaComputer Department, Jingdezhen Ceramic University, Jingdezhen, ChinaSchool for Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, ChinaSchool of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, ChinaAnhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Hefei, Anhui, ChinaAnhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing, Anhui Agricultural University, Hefei, Anhui, ChinaCysteine S-carboxyethylation, a novel post-translational modification (PTM), plays a critical role in the pathogenesis of autoimmune diseases, particularly ankylosing spondylitis. Accurate identification of S-carboxyethylation modification sites is essential for elucidating their functional mechanisms. Unfortunately, there are currently no computational tools that can accurately predict these sites, posing a significant challenge to this area of research. In this study, we developed a new deep learning model, DLBWE-Cys, which integrates CNN, BiLSTM, Bahdanau attention mechanisms, and a fully connected neural network (FNN), using Binary-Weight encoding specifically designed for the accurate identification of cysteine S-carboxyethylation sites. Our experimental results show that our model architecture outperforms other machine learning and deep learning models in 5-fold cross-validation and independent testing. Feature comparison experiments confirmed the superiority of our proposed Binary-Weight encoding method over other encoding techniques. t-SNE visualization further validated the model’s effective classification capabilities. Additionally, we confirmed the similarity between the distribution of positional weights in our Binary-Weight encoding and the allocation of weights in attentional mechanisms. Further experiments proved the effectiveness of our Binary-Weight encoding approach. Thus, this model paves the way for predicting cysteine S-carboxyethylation modification sites in protein sequences. The source code of DLBWE-Cys and experiments data are available at: https://github.com/ztLuo-bioinfo/DLBWE-Cys.https://www.frontiersin.org/articles/10.3389/fgene.2024.1464976/fullS-carboxyethylationpost-translational modificationbahdanau attention mechanismbinary-weight encodingdeep learning |
| spellingShingle | Zhengtao Luo Zhengtao Luo Zhengtao Luo Qingyong Wang Qingyong Wang Qingyong Wang Yingchun Xia Yingchun Xia Yingchun Xia Xiaolei Zhu Xiaolei Zhu Xiaolei Zhu Shuai Yang Shuai Yang Shuai Yang Zhaochun Xu Zhaochun Xu Lichuan Gu Lichuan Gu Lichuan Gu DLBWE-Cys: a deep-learning-based tool for identifying cysteine S-carboxyethylation sites using binary-weight encoding Frontiers in Genetics S-carboxyethylation post-translational modification bahdanau attention mechanism binary-weight encoding deep learning |
| title | DLBWE-Cys: a deep-learning-based tool for identifying cysteine S-carboxyethylation sites using binary-weight encoding |
| title_full | DLBWE-Cys: a deep-learning-based tool for identifying cysteine S-carboxyethylation sites using binary-weight encoding |
| title_fullStr | DLBWE-Cys: a deep-learning-based tool for identifying cysteine S-carboxyethylation sites using binary-weight encoding |
| title_full_unstemmed | DLBWE-Cys: a deep-learning-based tool for identifying cysteine S-carboxyethylation sites using binary-weight encoding |
| title_short | DLBWE-Cys: a deep-learning-based tool for identifying cysteine S-carboxyethylation sites using binary-weight encoding |
| title_sort | dlbwe cys a deep learning based tool for identifying cysteine s carboxyethylation sites using binary weight encoding |
| topic | S-carboxyethylation post-translational modification bahdanau attention mechanism binary-weight encoding deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2024.1464976/full |
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