  Phosphorylation is pivotal in numerous fundamental cellular processes and
plays a significant role in the onset and progression of various diseases. The
accurate identification of these phosphorylation sites is crucial for
unraveling the molecular mechanisms within cells and during viral infections,
potentially leading to the discovery of novel therapeutic targets. In this
study, we develop PTransIPs, a new deep learning framework for the
identification of phosphorylation sites. Independent testing results
demonstrate that PTransIPs outperforms existing state-of-the-art (SOTA)
methods, achieving AUCs of 0.9232 and 0.9660 for the identification of
phosphorylated S/T and Y sites, respectively. PTransIPs contributes from three
aspects. 1) PTransIPs is the first to apply protein pre-trained language model
(PLM) embeddings to this task. It utilizes ProtTrans and EMBER2 to extract
sequence and structure embeddings, respectively, as additional inputs into the
model, effectively addressing issues of dataset size and overfitting, thus
enhancing model performance; 2) PTransIPs is based on Transformer architecture,
optimized through the integration of convolutional neural networks and TIM loss
function, providing practical insights for model design and training; 3) The
encoding of amino acids in PTransIPs enables it to serve as a universal
framework for other peptide bioactivity tasks, with its excellent performance
shown in extended experiments of this paper. Our code, data and models are
publicly available at https://github.com/StatXzy7/PTransIPs.
