Integrating Biological Language Processing and Memory Attention Model for Protein-DNA Binding Residue Prediction

Proteins are among the most important substances in the human body, and identifying protein-DNA binding sites is crucial for studying their interactions. Although traditional wet-lab methods can accurately identify these sites, they are time-consuming, labor-intensive, and expensive, making it challenging to keep pace with the rapid increase in protein sequence data. In this study, we propose the Memory Attention-Based Protein-DNA Binding Sites Prediction (MAPDB) model, which leverages multi-head Memory Attention for predicting protein-DNA binding sites. Our model employs a pre-trained embedding module to generate numerical representations of protein sequences, followed by a feature extraction module that uses Memory Attention to capture both intra-sequence and inter-sequence relationships. Extensive experiments on five benchmark datasets show that our model outperforms other state-of-the-art methods, especially in improving MCC scores. These results indicate that MAPDB effectively captures complex relationships within protein sequences, leading to more accurate predictions of protein-DNA binding residues.