Abstract: The deeper principles of molecular life can be revealed through an in-depth study of protein-protein interaction sites(PPIS).However,existing methods for identifying PPIS are complex and time-consuming,and more accurate models are needed for PPIS prediction.Although deep learning techniques based on attention mechanisms and convolutional neural networks(CNNs) have made progress in PPIS prediction,they still face limitations in capturing amino acid features.To effectively capture long-range dependencies in protein sequences and accurately characterize amino acid properties,this paper proposes a multi-feature hybrid network(MFH),PPIS-MFH,for predicting protein-protein interaction sites.Protein-protein interaction sites are predicted by combining both global and local sequence features.For local sequence features,the PPIS-MFH model incorporates a Vision Transformer(ViT) module,which captures long-range dependencies and extracts local features from protein sequences.For global sequence features,the model employs a bidirectional gated recurrent neural network to discern intrinsic connections between amino acids in protein sequences.This is achieved through a feature crossover network that combines a text convolutional neural network(TextCNN) with an attention mechanism,specifically a text recurrent neural network(TextRNN-Attention).In this study,the PPIS-MFH model was evaluated on four datasets and compared with eight similar methods.The experimental results show that,on most metrics,the proposed method outperforms other similar methods.

Key words: Protein-protein interaction site, Attention mechanism, Text convolutional neural network, Bidirectional gated recurrent neural network, Feature crosses network