DeepDA-Ace: A Novel Domain Adaptation Method for Species-Specific Acetylation Site Prediction

Yu Liu, Qiang Wang and Jianing Xi
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Yu Liu: School of Integrated Circuits, Anhui University, 111 JiuLong Road, Hefei 230601, China
Qiang Wang: School of Integrated Circuits, Anhui University, 111 JiuLong Road, Hefei 230601, China
Jianing Xi: School of Biomedical Engineering, Guangzhou Medical University, Guangzhou 510182, China

Mathematics, 2022, vol. 10, issue 14, 1-17

Abstract: Protein lysine acetylation is an important type of post-translational modification (PTM), and it plays a crucial role in various cellular processes. Recently, although many researchers have focused on developing tools for acetylation site prediction based on computational methods, most of these tools are based on traditional machine learning algorithms for acetylation site prediction without species specificity, still maintained as a single prediction model. Recent studies have shown that the acetylation sites of distinct species have evident location-specific differences; however, there is currently no integrated prediction model that can effectively predict acetylation sites cross all species. Therefore, to enhance the scope of species-specific level, it is necessary to establish a framework for species-specific acetylation site prediction. In this work, we propose a domain adaptation framework DeepDA-Ace for species-specific acetylation site prediction, including Rattus norvegicus, Schistosoma japonicum, Arabidopsis thaliana, and other types of species. In DeepDA-Ace, an attention based densely connected convolutional neural network is designed to capture sequence features, and the semantic adversarial learning strategy is proposed to align features of different species so as to achieve knowledge transfer. The DeepDA-Ace outperformed both the general prediction model and fine-tuning based species-specific model across most types of species. The experiment results have demonstrated that DeepDA-Ace is superior to the general and fine-tuning methods, and its precision exceeds 0.75 on most species. In addition, our method achieves at least 5% improvement over the existing acetylation prediction tools.

Keywords: domain adaptation; post-translational modification; acetylation; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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