iNucRes‐ASSH: Identifying nucleic acid‐binding residues in proteins by using self‐attention‐based structure‐sequence hybrid neural network

Abstract

AbstractInteraction between proteins and nucleic acids is crucial to many cellular activities. Accurately detecting nucleic acid‐binding residues (NABRs) in proteins can help researchers better understand the interaction mechanism between proteins and nucleic acids. Structure‐based methods can generally make more accurate predictions than sequence‐based methods. However, the existing structure‐based methods are sensitive to protein conformational changes, causing limited generalizability. More effective and robust approaches should be further explored. In this study, we propose iNucRes‐ASSH to identify nucleic acid‐binding residues with a self‐attention‐based structure‐sequence hybrid neural network. It improves the generalizability and robustness of NABR prediction from two levels: residue representation and prediction model. Experimental results show that iNucRes‐ASSH can predict the nucleic acid‐binding residues even when the experimentally validated structures are unavailable and outperforms five competing methods on a recent benchmark dataset and a widely used test dataset.