A Novel Spatial Feature For Predicting Lysine Malonylation Sites Using Machine Learning

Lysine malonylation is a recently identified post-translation modification type with known effects on type 2 diabetes. Several machine learning algorithms have been used to predict lysine malonylation sites using various features of protein. We proposed a novel spatial feature that captures rich structure information in a succinct form. The dimension of this feature is much lower than that of other sequence and structural features that were used in previous studies. When the proposed feature was used to predict lysine malonylation sites, it achieved performance comparable to other state-of- the-art methods that had much higher dimension. The low dimensionality of the proposed feature would be very helpful for building interpretable predictors for various applications involving protein structures.