Predicting protein-protein interaction with interpretable bilinear attention network.
Journal:
Computer methods and programs in biomedicine
Published Date:
Mar 30, 2025
Abstract
BACKGROUND AND OBJECTIVE: Protein-protein interactions (PPIs) play the key roles in myriad biological processes, helping to understand the protein function and disease pathology. Identification of PPIs and their interaction types through wet experimental methods are costly and time-consuming. Therefore, some computational methods (e.g., sequence-based deep learning method) have been proposed to predict PPIs. However, these methods predominantly focus on protein sequence information, neglecting the protein structure information, while the protein structure is closely related to its function. In addition, current PPI prediction methods that introduce the protein structure information use independent encoders to learn the sequence and structure representations from protein sequences and structures, respectively, without explicitly learn the important local interaction representation of two proteins, making the prediction results hard to interpret.
