DPPBP: Dual-stream Protein-peptide Binding Sites Prediction Based on Region Detection

Prediction of protein-peptide binding sites plays a critical role in the regulation of cellular functions and the targeted drug discovery. Recently, sequence-based prediction methods have been widely used due to their simplicity, effectiveness, and low cost of data collection. However, these methods rely on the binary classification of individual amino acids within the protein sequence, which often overlooks the dependencies between binding amino acids in the training labels. To address this issue, we propose a novel Dual-stream Protein-Peptide Binding sites Prediction method DPPBP based on region detection and protein language model. For the first-stream, we group successive binding sites into a single region to capture the relationships between binding amino acids and highlight the binding region of the entire sequence. Then, we use a fixed small set of learned target queries to reason about the relationships between the target regions and the global sequence information of the protein, generating the final predictions in parallel. For the second-stream, we continue to use a binary classification to discriminate each individual amino acid at a fine-grained level, and the final prediction is obtained by combining the results of both streams. Extensive experiments show that our DPPBP method outperforms the existing state-of-the-art sequence-based methods on the two benchmark datasets. Datasets and codes can be found at https: github.com 22Donkey DPPBP.