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

Autor: Lv H; Key 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 China.; Department of Molecular Life Sciences University of Zurich Zurich Switzerland., Dao FY; Key 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 China.; School of Biological Sciences Nanyang Technological University Singapore Singapore., Lin H; Key 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 China.
Jazyk: angličtina
Zdroj: IMeta [Imeta] 2022 Mar 15; Vol. 1 (1), pp. e11. Date of Electronic Publication: 2022 Mar 15 (Print Publication: 2022).
DOI: 10.1002/imt2.11
Abstrakt: 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.
Competing Interests: The authors declare that there are no conflicts of interest.
(© 2022 The Authors. iMeta published by John Wiley & Sons Australia, Ltd on behalf of iMeta Science.)
Databáze: MEDLINE
Externí odkaz: