Prediction of Residue-Level Base Preference for DNA-Binding Proteins Using a Multimodal Learning Framework

Abstract

Specific recognition between proteins and DNA governs essential cellular functions, yet the binding specificity of most DNA-binding proteins (DBPs) remains experimentally uncharacterized and high-throughput experimental methods still lag far behind the ever-growing protein data set. Here, we present 3M-BSIP (3-modal base-specific interaction predictor), a multimodal deep learning framework capable of predicting the DNA-binding interface and residue-level base preference for DBPs. 3M-BSIP constructs a holistic protein representation by integrating three distinct modalities: a protein language model capturing evolutionary conservation; a graph neural network encoding 3D structural features; and a hierarchical encoder characterizing physicochemical properties of protein surface pointclouds. Comprehensive benchmarking demonstrates that 3M-BSIP achieves SOTA performance on both interface-residue identification and base-preference predictions. Crucially, the model maintains robust performance on computationally constructed protein structures and is also tolerant toward those undergoing profound structural rearrangements upon DNA binding. Two real-world applications of 3M-BSIP demonstrate that our model not only can accurately predict the specific binding motif of structurally unknown transcription factors but also, more importantly, can guide rapid reprogramming of the base preference of DBPs based on a purely computational pipeline. Therefore, 3M-BSIP holds great potential in annotating the binding motifs of all DBPs in a high-throughput manner and designing new DBPs with desired base specificities.