An Improved Prediction Model for Phage-host Interactions Based on Fusing Global and Local Semantics of RBP Information

Yifei Chen, Weizhong Zhao, Xinhui Tu, Tingting He

Abstract: Accurate prediction of phage-host interactions (PHIs) is crucial for phage therapy and antimicrobial substitution research. Although many methods have been proposed for the task of PHIs prediction, most of them fail to employ the biological knowledge in phage-host interaction, such as the receptor-binding protein (RBP) of phages plays an essential role in the binding mechanism between phages and hosts, leading to undesirable prediction performance. In this study, we propose a novel PHIs prediction model in which the RBP data is explicitly employed. Specifically, RBP information and whole protein sequence for each phage information are integrated by the cross-attention mechanism, by which global and local semantics contained in phages are effectively captured. For hosts, the protein sequences are used to extract the features accordingly. Then, the final representation is obtained by combining the phage features and host features, on which a MLP is utilized to derive the predicted probability of interaction between the input phage-host pair. Comprehensive experiments are conducted and the results demonstrate the effectiveness of the proposed method.