CNNArginineMe: A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence - Zhao, Jiaojiao; Jiang, Haoqiang; Zou, Guoyang; Lin, Qian; Wang, Qiang; Liu, Jia; Ma, Leina (Frontiers in Genetics, 2022)
Methylation is one of the most prominent post-translational modifications on proteins, and protein arginine methylation plays a critical role in numerous cellular processes and regulates many critical cellular functions. Though several model-based arginine methylation site predictors have been reported， the model needs further optimization. In this paper, we propose an approach based on deep learning to predict arginine methylation sites and compare it with other models based on machine learning. Importantly, in side-by-side comparisons with other machines learning arginine methylation site predictors, the deep learning method performs on par or better in AUC (Sn (Sp=0.9) Sn (Sp=0.95)) scoring metrics tested. We obtained 10,923 proteins through model prediction and performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of related genes. In the KEGG analysis, it was found that the arginine methylated protein was significantly enriched in the ALS pathway. CNNArginineMe is freely available at https://github.com/guoyangzou/DeepRme.