Phosphorylation is central to numerous fundamental cellular processes, influencing the onset and progression of a variety of diseases. The correct identification of these phosphorylation sites is of great importance to unravel the 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 of phosphorylation sites. PTransIPs treat amino acids within protein sequences as words, extracting unique encodings based on their type and sequential position. The model also incorporates embeddings from large pretrained protein models as additional data inputs. PTransIPS is further trained on a combination model of convolutional neural network with residual connections and Transformer model equipped with multi-head attention mechanisms. At last, the model outputs classification results through a fully connected layer. The results of independent testing reveal that PTransIPs outperforms existing state-of-the-art(SOTA) methods, achieving AUROCs of 0.9232 and 0.9660 for identifying phosphorylated S/T and Y sites respectively. In addition, ablation studies prove that pretrained model embeddings contribute to the performance of PTransIPs. Furthermore, PTransIPs has interpretable amino acid preference, visible training process and shows generalizability on other bioactivity classification tasks. To facilitate usage, our code and data are publicly accessible at \url{https://github.com/StatXzy7/PTransIPs}.

翻译：磷酸化是众多基础细胞过程的核心环节，影响多种疾病的发生与发展。准确识别这些磷酸化位点对于揭示细胞内部及病毒感染过程中的复杂分子机制至关重要，并可能推动新治疗靶点的发现。本研究提出PTransIPs——一种用于磷酸化位点识别的新型深度学习模型。PTransIPs将蛋白质序列中的氨基酸视为词语，根据其类型和序列位置提取独特编码。该模型同时整合大型蛋白质预训练模型的嵌入向量作为额外数据输入。通过采用融合残差连接的卷积神经网络与配备多头注意力机制的Transformer的组合模型进行训练，最终经由全连接层输出分类结果。独立测试结果表明，PTransIPs在识别磷酸化丝氨酸/苏氨酸(S/T)位点和酪氨酸(Y)位点方面分别取得了0.9232和0.9660的AUROC值，优于现有最优方法。此外，消融实验证明预训练模型嵌入向量对PTransIPs性能具有贡献。PTransIPs还展现出可解释的氨基酸偏好性、可视化训练过程，并在其他生物活性分类任务中具有可迁移性。为方便使用，我们的代码和数据已在\url{https://github.com/StatXzy7/PTransIPs}公开。