SOPE-MsL: Synergy-Optimized Protein Language Model Embeddings with Multiscale Learning for Interpretable Protein–Small-Molecule Binding-Site Prediction

Protein-small-molecule interactions are fundamental to cellular regulation and represent critical targets for therapeutic intervention. Accurate identification of binding residues is essential for elucidating molecular recognition mechanisms and guiding the rational drug design. Experimental approaches, however, are often costly, time-consuming, and limited in scalability, while existing computational methods that rely on handcrafted features or single Protein Language Model (PLM) embeddings fail to capture comprehensive residue-level representations and overlook the potential synergistic effects among diverse PLMs. Here, we present SOPE-MsL, a synergy-optimized approach that integrates PLM embedding fusion with multiscale learning for binding-site prediction. Through a systematic evaluation of representative state-of-the-art PLMs from the ProtTrans, ESM, and Ankh families, we identified ProstT5 and Ankh as the most effective embedding pair. The fused embeddings are then processed by a network that combines multiscale convolutional operations with attention mechanisms, enabling the concurrent modeling of intricate local patterns and long-range dependencies. To address the pronounced class imbalance between binding and nonbinding residues, a weighted focal loss is employed. Beyond predictive performance, t-SNE and SHAP analyses further confirm the advantages of synergistic embedding fusion over single-model representations, providing residue-level interpretability. Extensive experiments across multiple benchmark data sets demonstrate that SOPE-MsL achieves competitive performance and provides a robust and interpretable tool for structure-aware sequence analysis and the identification of protein-small-molecule interaction sites.