Predicting lysine‐malonylation sites of proteins using sequence and predicted structural features

Malonylation is a recently discovered post‐translational modification (PTM) in which a malonyl group attaches to a lysine (K) amino acid residue of a protein. In this work, a novel machine learning model, SPRINT‐Mal, is developed to predict malonylation sites by employing sequence and predicted structural features. Evolutionary information and physicochemical properties are found to be the two most discriminative features whereas a structural feature called half‐sphere exposure provides additional improvement to the prediction performance. SPRINT‐Mal trained on mouse data yields robust performance for 10‐fold cross validation and independent test set with Area Under the Curve (AUC) values of 0.74 and 0.76 and Matthews’ Correlation Coefficient (MCC) of 0.213 and 0.20, respectively. Moreover, SPRINT‐Mal achieved comparable performance when testing on H. sapiens proteins without species‐specific training but not in bacterium S. erythraea. This suggests similar underlying physicochemical mechanisms between mouse and human but not between mouse and bacterium. SPRINT‐Mal is freely available as an online server at: http://sparks-lab.org/server/SPRINT-Mal/. © 2018 Wiley Periodicals, Inc. Malonylation is a recently discovered post‐translational modification in which a malonyl group attaches to a lysine amino acid of a protein. Identifying malonylation sites in a protein sequence is the first step toward discovering its biological function. Thus, making “educated” computational prediction prior to experimental studies is necessary. All existing prediction tools infer malonylation sites from sequence‐derived features of proteins. Here, we developed a method employing sequence and predicted structural features of proteins which outperforms existing methods significantly.