DeepKla: An attention mechanism‐based deep neural network for protein lysine lactylation site prediction

• Published in: iMeta Vol. 1; no. 1; pp. e11 - n/a
• Main Authors: Lv, Hao, Dao, Fu‐Ying, Lin, Hao
• Format: Journal Article
• Language: English
• Published: Australia John Wiley & Sons, Inc 01.03.2022; John Wiley and Sons Inc; Wiley
• Subjects: Accuracy; attention mechanism; bidirectional gated recurrent units; convolutional neural network; Datasets; Deep learning; embedding layer; lactylation; Neural networks; Performance evaluation; Proteins; Short Communication; Short Communications; Writing; embedding layer; attention mechanism; bidirectional gated recurrent units; lactylation; convolutional neural network
• ISSN: 2770-596X; 2770-5986; 2770-596X
• DOI: 10.1002/imt2.11

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. We presented the first computational tool, termed DeepKla, to identify Kla sites in rice. Supervised embedding layer, convolutional neural network, bidirectional gated recurrent units, and attention mechanism layer were applied to train the model. A robust, generalized, and convenient web‐server of DeepKla was established at http://lin-group.cn/server/DeepKla. Highlights We presented the first computational tool, termed DeepKla, to identify Kla sites in rice. Supervized embedding layer, convolutional neural network, bidirectional gated recurrent units, and attention mechanism layer were applied to train the model. A robust, generalized, and convenient web‐server of DeepKla was established at http://lin-group.cn/server/DeepKla.