DeepKla: An attention mechanism‐based deep neural network for protein lysine lactylation site prediction
Abstract As a newly discovered protein posttranslational modification, lysine lactylation (Kla) plays a pivotal role in various cellular processes. High throughput mass spectrometry is the primary approach for the detection of Kla sites. However, experimental approaches for identifying Kla sites are...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-03-01
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| Series: | iMeta |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/imt2.11 |
| _version_ | 1811318929049518080 |
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| author | Hao Lv Fu‐Ying Dao Hao Lin |
| author_facet | Hao Lv Fu‐Ying Dao Hao Lin |
| author_sort | Hao Lv |
| collection | DOAJ |
| description | Abstract As a newly discovered protein posttranslational modification, lysine lactylation (Kla) plays a pivotal role in various cellular processes. High throughput mass spectrometry is the primary approach for the detection of Kla sites. However, experimental approaches for identifying Kla sites are often time‐consuming and labor‐intensive when compared to computational methods. Therefore, it is desirable to develop a powerful tool for identifying Kla sites. For this purpose, we presented the first computational framework termed as DeepKla for Kla sites prediction in rice by combining supervised embedding layer, convolutional neural network, bidirectional gated recurrent units, and attention mechanism layer. Comprehensive experiment results demonstrated the excellent predictive power and robustness of DeepKla. Based on the proposed model, a web‐server called DeepKla was established and is freely accessible at http://lin-group.cn/server/DeepKla. The source code of DeepKla is freely available at the repository https://github.com/linDing-group/DeepKla. |
| first_indexed | 2024-04-13T12:33:46Z |
| format | Article |
| id | doaj.art-96cd26b1bd204ad4bab9ba846c4fee06 |
| institution | Directory Open Access Journal |
| issn | 2770-596X |
| language | English |
| last_indexed | 2024-04-13T12:33:46Z |
| publishDate | 2022-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | iMeta |
| spelling | doaj.art-96cd26b1bd204ad4bab9ba846c4fee062022-12-22T02:46:43ZengWileyiMeta2770-596X2022-03-0111n/an/a10.1002/imt2.11DeepKla: An attention mechanism‐based deep neural network for protein lysine lactylation site predictionHao Lv0Fu‐Ying Dao1Hao Lin2Key Laboratory for Neuro‐Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology University of Electronic Science and Technology of China Chengdu Sichuan ChinaKey Laboratory for Neuro‐Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology University of Electronic Science and Technology of China Chengdu Sichuan ChinaKey Laboratory for Neuro‐Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology University of Electronic Science and Technology of China Chengdu Sichuan ChinaAbstract As a newly discovered protein posttranslational modification, lysine lactylation (Kla) plays a pivotal role in various cellular processes. High throughput mass spectrometry is the primary approach for the detection of Kla sites. However, experimental approaches for identifying Kla sites are often time‐consuming and labor‐intensive when compared to computational methods. Therefore, it is desirable to develop a powerful tool for identifying Kla sites. For this purpose, we presented the first computational framework termed as DeepKla for Kla sites prediction in rice by combining supervised embedding layer, convolutional neural network, bidirectional gated recurrent units, and attention mechanism layer. Comprehensive experiment results demonstrated the excellent predictive power and robustness of DeepKla. Based on the proposed model, a web‐server called DeepKla was established and is freely accessible at http://lin-group.cn/server/DeepKla. The source code of DeepKla is freely available at the repository https://github.com/linDing-group/DeepKla.https://doi.org/10.1002/imt2.11attention mechanismbidirectional gated recurrent unitsconvolutional neural networkembedding layerlactylation |
| spellingShingle | Hao Lv Fu‐Ying Dao Hao Lin DeepKla: An attention mechanism‐based deep neural network for protein lysine lactylation site prediction iMeta attention mechanism bidirectional gated recurrent units convolutional neural network embedding layer lactylation |
| title | DeepKla: An attention mechanism‐based deep neural network for protein lysine lactylation site prediction |
| title_full | DeepKla: An attention mechanism‐based deep neural network for protein lysine lactylation site prediction |
| title_fullStr | DeepKla: An attention mechanism‐based deep neural network for protein lysine lactylation site prediction |
| title_full_unstemmed | DeepKla: An attention mechanism‐based deep neural network for protein lysine lactylation site prediction |
| title_short | DeepKla: An attention mechanism‐based deep neural network for protein lysine lactylation site prediction |
| title_sort | deepkla an attention mechanism based deep neural network for protein lysine lactylation site prediction |
| topic | attention mechanism bidirectional gated recurrent units convolutional neural network embedding layer lactylation |
| url | https://doi.org/10.1002/imt2.11 |
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