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Identification of methylation binding sites facilitates a better understanding of the molecular function of proteins. Recent developments in the field of deep neural networks have led to a proliferation of deep learning-based methylation identification studies because of their fast and accurate prediction. In this paper, we propose DeepGpgs, an advanced deep learning model incorporating Gaussian prior and gated attention mechanism. We introduce a residual network channel to extract the evolutionary information of proteins. Then we combine the adaptive embedding with bidirectional long short-term memory networks to form a context-shared encoder layer. A gated multi-head attention mechanism is followed to obtain the global information about the sequence. A Gaussian prior is injected into the sequence to assist in predicting PTMs. We also propose a weighted joint loss function to alleviate the false negative problem. We empirically show that DeepGpgs improves Matthews correlation coefficient by 6.3% on the arginine methylation independent test set compared with the existing state-of-the-art methylation site prediction methods. Furthermore, DeepGpgs has good robustness in phosphorylation site prediction of SARS-CoV-2, which indicates that DeepGpgs has good transferability and the potential to be extended to other modification sites prediction. The open-source code and data of the DeepGpgs can be obtained from https:\/\/github.com\/saizhou1\/DeepGpgs.<\/jats:p>","DOI":"10.1093\/bib\/bbad018","type":"journal-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T08:02:37Z","timestamp":1672905757000},"source":"Crossref","is-referenced-by-count":11,"title":["DeepGpgs: a novel deep learning framework for predicting arginine methylation sites combined with Gaussian prior and gated self-attention mechanism"],"prefix":"10.1093","volume":"24","author":[{"given":"Haiwei","family":"Zhou","sequence":"first","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University , Shanghai 200433 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxi","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Fudan University , Shanghai 200433 , 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