Phosphorylation is central to numerous fundamental cellular processes,
influencing the onset and progression of a variety of diseases. Identification
of phosphorylation sites is thus an important step for understanding the
molecular mechanisms of cells and virus infection, which potentially leads to
new therapeutic targets. In this study, we present PTransIPs, a novel deep
learning model for the identification of phosphorylation sites. PTransIPs
treats amino acids in protein sequences as words in natural language,
extracting unique encodings based on the types along with position of amino
acids in the sequence. It also incorporates embeddings from large pre-trained
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 methodologies, 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 https://github.com/StatXzy7/PTransIPs.

PTransIPs 是一个新颖的深度学习模型，用于识别磷酸化位点，通过在蛋白质序列中提取氨基酸的唯一编码，并结合预训练的大规模蛋白质模型的嵌入作为附加数据输入，PTransIPs 采用卷积神经网络与残差连接以及带有多头注意机制的 Transformer 模型组合进行训练，取得了比现有方法更好的分类结果，对于识别磷酸化 S/T 和 Y 位点的 AUROC 分别为 0.9232 和 0.9660，并且证实了预训练模型嵌入对于 PTransIPs 性能的贡献。