Identification of Protein-Nucleotide Binding Residues With Deep Multi-Task and Multi-Scale Learning.

Abstract

Accurate identification of protein-nucleotide binding residues is essential for protein functional annotation and drug discovery. Advancements in computational methods for predicting binding residues from protein sequences have significantly improved predictive accuracy. However, it remains a challenge for current methodologies to extract discriminative features and assimilate heterogeneous data from different nucleotide binding residues. To address this, we introduce NucMoMTL, a novel predictor specifically designed for identifying protein-nucleotide binding residues. Specifically, NucMoMTL leverages a pre-trained language model for robust sequence embedding and utilizes deep multi-task and multi-scale learning within parameter-based orthogonal constraints to extract shared representations, capitalizing on auxiliary information from diverse nucleotides binding residues. Evaluation of NucMoMTL on the benchmark datasets demonstrates that it outperforms state-of-the-art methods, achieving an average AUROC and AUPRC of 0.961 and 0.566, respectively. NucMoMTL can be explored as a reliable computational tool for identifying protein-nucleotide binding residues and facilitating drug discovery.