MetODeep: A Deep Learning Approach for Prediction of Methionine Oxidation Sites in Proteins

After being synthesized by ribosomes in the cells, proteins can suffer from post-translational modifications (PTM) that affect their functionality. One of the most studied PTMs is phosphorylation. Mass-spectrometry methods aimed at identifying phosphorylation sites in proteins are arduous and expensive. For these reasons, numerous studies propose the use of machine leaning techniques to predict this PTM. Like phosphorylation, methionine oxidation is another important PTM. Recently, we have proposed a machine learning approach that extracts a set of features from the primary and tertiary structure of the proteins to predict methionine oxidation sites. However, this work had an important limitation that impairs feature extraction, since the 3D structure of many proteins is not fully resolved. In this study, we present MetODeep, a deep learning approach to predict methionine oxidation. Unlike phosphorylation, for which datasets with several hundred thousands samples are available to train effective predictive models, methionine oxidation counts on small datasets, which could lead a deep neural network to experiment over-fitting issues. The recently evidenced existence of a cross-talk between phosphorylation and methionine oxidation, has motivated our transfer-learning approach. Thus, on the basis of a deep convolutional neural network (CNN) pre-trained with phosphorylation data, MetODeep is fine-tuned to predict methionine oxidation. The resulting CNN architecture allows us to omit manual feature extraction, since it accepts raw protein sequences as input data. The final model gives performance results (AUC 0.8267±0.0174) that surpass state-of-art of computational models for the prediction of methionine oxidation.