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Phosphorylation is central to numerous fundamental cellular processes,influencing the onset and progression of a variety of diseases. The correctidentification of these phosphorylation sites is of great importance to unravelthe intricate molecular mechanisms within cells and during viral infections,potentially leading to the discovery of new therapeutic targets. In this study,we introduce PTransIPs, a novel deep learning model for the identification ofphosphorylation sites. PTransIPs treat amino acids within protein sequences aswords, extracting unique encodings based on their type and sequential position.The model also incorporates embeddings from large pretrained protein models asadditional data inputs. PTransIPS is further trained on a combination model ofconvolutional neural network with residual connections and Transformer modelequipped with multi-head attention mechanisms. At last, the model outputsclassification results through a fully connected layer. The results ofindependent testing reveal that PTransIPs outperforms existingstate-of-the-art(SOTA) methods, achieving AUROCs of 0.9232 and 0.9660 foridentifying phosphorylated S/T and Y sites respectively. In addition, ablationstudies prove that pretrained model embeddings contribute to the performance ofPTransIPs. Furthermore, PTransIPs has interpretable amino acid preference,visible training process and shows generalizability on other bioactivityclassification tasks. To facilitate usage, our code and data are publiclyaccessible at \url{https://github.com/StatXzy7/PTransIPs}.

PTransIPs: Identification of phosphorylation sites based on protein pretrained language model and Transformer