ARES-Kcr: A New Network Model Utilizing Attention Mechanism and Residual Structure for the Prediction of Lysine Crotonylation Sites

Lysine crotonylation (Kcr), as a significant post-translational modification of protein, exists in the core histones and some non histones of many organisms, and plays a crucial regulatory role in many biological processes such as gene expression, cell development, and disease treatment. Due to the high cost, time-consuming and labor-intensive nature of traditional biological experimental methods, it is necessary to develop efficient, low-cost and accurate calculation methods for identifying crotonylation sites. Therefore, we propose a new network model called ARES-Kcr, which extracts three types of features from different perspectives and integrates convolutional modules, attention mechanisms, and residual modules for feature fusion to improve prediction ability in this paper. Our model performs significantly better than other models on the benchmark dataset, with an average AUC of 92% in the independent test set, demonstrating its excellent predictive ability.