DFN-kcr: a dual-branch deep learning model with attention-guided fusion for predicting lysine crotonylation sites in human non-histone proteins

Abstract

Introduction Lysine crotonylation (Kcr) is extensively present in human non-histone proteins and plays a critical regulatory role in essential biological processes, including cell signaling and metabolic regulation. However, conventional wet-lab approaches for Kcr site identification are costly, time-consuming, and ill-suited for large-scale profiling. Although computational prediction methods have garnered increasing attention in recent years, there remains a notable lack of efficient and specialized tools tailored specifically for Kcr site prediction in human non-histone proteins. Methods To address this gap, we propose DFN-Kcr, a dual-branch deep learning model explicitly designed for human non-histone Kcr site prediction. DFN-Kcr employs a dual-input strategy combining ProteinBERT embeddings and integer-encoded amino acid sequences, leveraging residual convolutional networks to capture local sequence motifs and a Transformer architecture to model long-range contextual dependencies. A branch-level gating attention fusion mechanism is further introduced to effectively integrate complementary features from both branches. Results Extensive experiments demonstrate that DFN-Kcr significantly outperforms state-of-the-art methods, with its sensitivity (Sn), specificity (Sp), accuracy (Acc), and Matthews correlation coefficient (MCC) being 0.8244, 0.7679, 0.7961, and 0.5932, respectively. Discussion This model offers a reliable solution for high-throughput identification of Kcr sites in non-histone proteins. To facilitate community access, we have deployed a user-friendly web server at http://www.lzzzlab.top/dfnkcr/.