SSE-Net: A novel network based on sequence spatial equation for Camellia sinensis lysine acetylation identification

Lysine acetylation (Kace) is one of the most important post-translational modifications. It is key to identify Kace sites for understanding regulation mechanisms in Camellia sinensis. In this study, we defined a mathematical formula, named sequence spatial equation (SSE), which could give each amino acid coordinate in 3-D space by rotating and translating. Based on SSE, an optional network SSE-Net was constructed for representing spatial structure information. Centrality metrics of SSE-Net were used to design structure feature vectors for reflecting the importance of sites. The optimal features were fed into classifier to construct model SSE-ET. The results showed that SSE-ET outperformed the other classifiers. Meanwhile, all MCC results were higher than 0.7 for different machine learning, which indicated that SSE-Net was effective for representing Kace sites in Camellia sinensis. Moreover, we implemented the other models on our dataset. The results of comparison showed that SSE-ET was much more powerful than the others. Specifically, the result of SN was nearly 20 % higher than the other models. These results showed that the proposed SSE was a valuable mathematics concept for reflecting 3-D space Kace site information in Camellia sinensis, and SSE-Net may be an essential complementary for biology and bioinformatics research.