RMSxAI: Arginine methylation sites prediction from protein sequencesusing Machine Learning algorithms and explainable ArtificialIntelligence

Protein methylation is a vital regulator of many biological processes at the post-translational level,and accurate prediction of protein methylation sites is essential for research and drug discovery.In this paper, we present a new method, namely RMSxAI, to predict the arginine methylationsites from primary sequences using machine learning algorithms and describe the predictions usingexplainable artificial intelligence (XAI) techniques. Leveraging experimentally validated methylatedand unmethylated protein sequences from diverse organisms, we deduced several sequence features,encompassing physicochemical properties, amino acid composition, and evolutionary insights. Ourresults show that the proposed RMSxAI can predict protein methylation sites with high accuracy,bringing the F1 score up to 0.88 and overall accuracy up to 88.4%. We use various XAI methods toexplain the forecast. These include key features, partial occupancy maps, and local variation modelsthat provide insight into key features and interactions that lead to forecasts. Overall, our approach isrelevant to research and drug discovery, and our results demonstrate the potential of machine learningalgorithms and XAI methods to provide accurate and meaningful prediction of arginine methylationsites.