ATP-Pred: Prediction of Protein-ATP Binding Residues via Fusion of Residue-Level Embeddings and Kolmogorov–Arnold Network

Accurately identifying protein-ATP binding residues is essential for understanding biological processes and designing drugs. However, current sequence-based methods have limitations, such as difficulties in extracting discriminative features and the need for more efficient algorithms. Additionally, methods based on multiple sequence alignments often face challenges in handling large-scale predictions. To address these issues, we developed ATP-Pred, a sequence-based method for predicting ATP-binding residues in proteins. This model applies transfer learning by using two recently developed pretrain protein language models, Ankh and ProstT5, to extract residue-level embeddings that capture protein functionality. ATP-Pred also integrates a CNN-BiLSTM network and a Kolmogorov-Arnold network to build the prediction model. To handle data imbalance, we introduced a weighted focal loss function. Experimental results on three independent test data sets showed that ATP-Pred outperforms most existing methods. Its generalizability was further validated on four protein-mononucleotide binding residue data sets, where it delivered promising results. These findings suggest that ATP-Pred is a robust and reliable predictor.