Optimization and Validation of Transformer-Based PTM Site Prediction Model for Paeonia lactiflora

Post-translational modifications (PTMs) significantly regulate peony’s growth, stress resistance, and biosynthesis of active pharmaceutical ingredients. However, traditional experimental methods for peony PTM site identification are cumbersome and inefficient; existing computational models are further limited by reliance on manual features, single modification type support, and poor interpretability—failing to meet precise identification needs.To address this, we first built a peony PTM site dataset: retrieving peony proteins from TCMSP, truncating sequences via sliding window to generate 1080 positive samples and 1976 negative sample, with sequence lengths of 3–41 amino acid residues.We then used the Transformer model for prediction: it fuses word vectors and position vectors for initial sequence representation, while its multi-head self-attention captures long-range residue interactions to explore PTM site patterns.10-fold cross-validation showed optimal performance at a sliding window length of 31; key metrics (accuracy, MCC, F1) significantly outperformed existing models, validating the approach’s effectiveness for peony PTM site identification.