BLOSSOM-Kcr: A structure-informed deep learning framework for lysine crotonylation site prediction

Lysine crotonylation (Kcr) is an important post-translational modification (PTM) involved in diverse biological processes, including chromatin regulation, protein function modulation, and cellular signaling. Although mass spectrometry-based proteomics has substantially expanded the identification of Kcr sites, experimental screening remains labor-intensive, costly, and difficult to apply at proteome scale. Computational methods provide an efficient strategy for prioritizing candidate Kcr sites. However, most existing predictors mainly rely on sequence-derived representations and insufficiently exploit protein structural context. In this study, we propose BLOSSOM-Kcr, a structure-informed deep learning framework for Kcr site prediction. BLOSSOM-Kcr integrates BLOSUM62-based sequence substitution features with residue-level structural descriptors, including secondary structure, solvent accessibility, backbone geometry, and spatial neighborhood information. The fused residue-level representation is further processed by residual convolutional blocks, channel attention, bidirectional long short-term memory (BiLSTM) layers, and attention pooling to capture local motif patterns, informative feature dimensions, and contextual dependencies surrounding candidate lysine residues. Fivefold cross-validation was performed for model optimization and comparative analysis, while an independent test set was used for final evaluation against existing Kcr site predictors. On the independent test set, BLOSSOM-Kcr achieved an AUC of 0.9023, an MCC of 0.6479, and an F1-score of 0.8336, outperforming representative Kcr site predictors. These results suggest that BLOSSOM-Kcr provides an effective structure-aware framework for Kcr site prediction.