iPalmT: a new paradigm for palmitoyltransferase discovery via end-to-end deep learning

Protein S-palmitoylation, a dynamic lipid modification, is essential for protein stability, trafficking, and signaling; dysregulated palmitoyltransferases drive cancer, yet systematic discovery of palmitoyltransferases remains hindered by labor-intensive, motif-dependent assays. We present iPalmT, an end-to-end deep learning framework that identifies palmitoyltransferases directly from primary amino acid sequence without handcrafted features or prior domain annotations. The model combines convolutional layers and squeeze-and-excitation mechanisms to capture local sequence signals and long-range dependencies. On an independent test set, iPalmT achieved 0.99 accuracy, 0.98 precision, 0.97 recall, and 0.98 F1 score. Integrated Gradients attribution emphasized the canonical DHHC motif and highlighted additional putative functional domains, despite receiving no motif supervision. Proteome-scale application to human sequences yielded unreviewed candidates; two (A0A0D9SEX5 and A0A1W2PRJ8) underwent structural analysis and experimental validation, which supported the predictions. We further release a large predicted palmitoyltransferase resource comprising 10,365,644 sequences identified from 147,847,003 proteins across 33,285 species-level groups to support large-scale exploration and cross-species analyses. iPalmT is available as a standalone program ( https://github.com/Tengda-Li-Lab/iPalmT.git ), offering a scalable, sequence-only route to discover noncanonical, evolutionarily divergent palmitoyltransferases.