ULDNA: Integrating Unsupervised Multi-Source Language Models with LSTM-Attention Network for Protein-DNA Binding Site Prediction

Abstract

Accurate identification of protein-DNA interactions is critical to understand the molecular mechanisms of proteins and design new drugs. We proposed a novel deeplearning method, ULDNA, to predict DNA-binding sites from protein sequences through a LSTM-attention architecture embedded with three unsupervised language models pretrained in multiple large-scale sequence databases. The method was systematically tested on 1287 proteins with DNA-binding site annotation from Protein Data Bank. Experimental results showed that ULDNA achieved a significant increase of the DNA-binding site prediction accuracy compared to the state-of-the-art approaches. Detailed data analyses showed that the major advantage of ULDNA lies in the utilization of three pre-trained transformer language models which can extract the complementary DNA-binding patterns buried in evolution diversity-based feature embeddings in residue-level. Meanwhile, the designed LSTM-attention network could further enhance the correlation between evolution diversity and protein-DNA interaction. These results demonstrated a new avenue for high-accuracy deep-learning DNA-binding site prediction that is applicable to large-scale protein-DNA binding annotation from sequence alone.