An intelligent and parameter-efficient fine-tuning of protein language model for dephosphorylation site prediction

Protein dephosphorylation site prediction is important for uncovering regulatory processes inside cellular signalling pathways. Experimental approaches like mutagenesis and mass spectrometry, however, are time-consuming and expensive, whereas conventional computational models struggle to model long-range dependencies and context information. To address these limitations, this study introduces a new Convolutional Bobcat Dephosphorylation Prediction Framework (CBDPF). The architecture starts by retrieving protein sequences from biological databases and using a sliding-window approach to obtain residue-centered segments. The contextual embeddings are calculated with the ProtT5 protein language model, and parameter-efficient fine-tuning (PEFT) is obtained using LoRA. The optimized convolutional network in CBDPF efficiently extracts and processes significant contextual representations from ProtT5 embeddings, thus improving classification performance. Comparative tests with current methods illustrate that CBDPF attains higher accuracy, stability, and generalizability in identifying dephosphorylation sites. This method provides a stable computational tool for large-scale analysis of protein dephosphorylation and makes a positive contribution to the broader research on protein post-translational modification.