Exploring Protein-DNA Binding Residue Prediction and Consistent Interpretability Analysis Using Deep Learning

Abstract

AO_SCPLOWBSTRACTC_SCPLOWAccurately identifying DNA-binding residues is a crucial step in developing computational tools to model DNA-protein binding properties, which is essential for binding pocket discovery and related drug design. Although several tools have been developed to predict DNA-binding residues based on protein sequences and structures, their performance remains limited, and proteins with crystal structures still represent only a small fraction of DNA-binding proteins. Additionally, the process of extracting handcrafted features for protein representation is labor-intensive. In this study, we combined the strengths of pre-trained protein language models and attention mechanisms to propose a sequence-based method: an attention-based deep learning approach for accurately predicting DNA-binding residues, incorporating a contrastive learning module. Our method outperformed all other sequence-based models across two prevalent benchmark datasets. Furthermore, we developed a structure-based graph neural network (GNN) model to demonstrate the impact of the contrastive module. A common limitation of existing models is their lack of interpretability, which hinders our ability to understand what these models have learned. To address this, we introduced a novel perspective for interpreting our sequence-based model by analyzing the consistency between attention scores and the edge weights generated by the GNN model. Interestingly, our results show that large-scale pre-trained protein language models, together with attention mechanisms, can effectively capture structural information solely from protein sequence inputs.