RLSuccSite: succinylation sites prediction based on reinforcement learning dynamic with balanced reward mechanism and three-peaks enhanced method for physicochemical property scores

Abstract

Recent progress in computational biology has driven the development of machine learning models for predicting protein post-translational modification sites. However, challenges such as data imbalance and limited sequence-context representation continue to hinder prediction accuracy, particularly for less frequent modifications like succinylation. In this study, we propose RLSuccSite, a reinforcement learning-based framework specifically designed to predict succinylation sites by addressing the class imbalance issue via a dynamic with balanced reward mechanism. To enhance sequence feature representation, this study also introduces Three-Peaks Enhanced Method for Physicochemical Property Scores (TPEM-PPS), a physicochemical property-driven feature extraction method that incorporates position-aware scoring to reflect amino acid contributions more effectively. The code and data of RLSuccSite can be obtained from the website: https://github.com/Zhangqingchao-Ch/RLSuccSite.git . Scientific contribution This study applies reinforcement learning to protein succinylation sites prediction, introducing a dynamic with balanced reward mechanism that effectively addresses dataset imbalance. Additionally, this study proposes a novel Three-Peaks Enhanced Method for Physicochemical Scoring, which captures residue contributions with higher precision than traditional feature extraction techniques.