iLMS, Computational Identification of Lysine-Malonylation Sites by Combining Multiple Sequence Features

Abstract

Lysine malonylation is a newly discovered post-translational modification of proteins, which plays an important role in regulating many cellular functions. Several approaches are available to identify malonylation proteins and its malonylation sites, however; experimental identification of malonylation sites is often laborious and costly. Therefore, computational schemes are needed to identify potential malonylation sites prior to in vitro experimentation. In this paper, a novel computational scheme iLMS (Identification of Lysine-Malonylation Sites) has been developed by combining primary sequences and evolutionary features via a support vector machine classifier. The final iLMS scheme achieved a robust performance in cross-validation test in both human and mouse datasets. For the mouse data, the iLMS predictor outperformed other existing implementations. The iLMS is a promising computational scheme for the prediction of malonylation sites.