AUTHOR=Zhao Jiaojiao , Jiang Haoqiang , Zou Guoyang , Lin Qian , Wang Qiang , Liu Jia , Ma Leina 

TITLE=CNNArginineMe: A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence

JOURNAL=Frontiers in Genetics

VOLUME=13

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.1036862

DOI=10.3389/fgene.2022.1036862

ISSN=1664-8021

ABSTRACT=<p>Protein arginine methylation (PRme), as one post-translational modification, plays a critical role in numerous cellular processes and regulates critical cellular functions. Though several <italic>in silico</italic> models for predicting PRme sites have been reported, new models may be required to develop due to the significant increase of identified PRme sites. In this study, we constructed multiple machine-learning and deep-learning models. The deep-learning model CNN combined with the One-Hot coding showed the best performance, dubbed CNNArginineMe. CNNArginineMe performed best in AUC scoring metrics in comparisons with several reported predictors. Additionally, we employed CNNArginineMe to predict arginine methylation proteome and performed functional analysis. The arginine methylated proteome is significantly enriched in the amyotrophic lateral sclerosis (ALS) pathway. CNNArginineMe is freely available at <ext-link ext-link-type="uri" xlink:href="https://github.com/guoyangzou/CNNArginineMe" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/guoyangzou/CNNArginineMe</ext-link>.</p>