Prediction of Lysine Succinylation Sites by SVR and Weighted Down-sampling

Abstract

Succinylation is a post-translational modification (PTM), which changes the chemical structure of lysine and results in significant changes in the structure and function of proteins. Lysine succinylation plays an important role in coordinating various biological processes, and it isalso associated with some diseases. Accurately identifying the lysine succinylation sites in proteins is of significant importance for basic research and drug development. Lysine succinylation sites prediction is a typical imbalanced and fragmentary learning problem. Directly applyingthe traditional machine learning approach for this task is not suitable. To circumvent this problem, based on extracting the features of protein sequences by sliding window and mirror-effect, weighted under-sampling is developed to make samples complete and balanced. Finally based on SVR prediction model and the corresponding suitable threshold, comparing with several state-of-art related methods, the effectiveness of the proposed method was validated by the experimental results.