Prokaryotic virus host prediction with graph contrastive augmentaion.

Prokaryotic viruses, also known as bacteriophages, play crucial roles in regulating microbial communities and have the potential for phage therapy applications. Accurate prediction of phage-host interactions is essential for understanding the dynamics of these viruses and their impacts on bacterial populations. Numerous computational methods have been developed to tackle this challenging task. However, most existing prediction models can be constrained due to the substantial number of unknown interactions in comparison to the constrained diversity of available training data. To solve the problem, we introduce a model for prokaryotic virus host prediction with graph contrastive augmentation (PHPGCA). Specifically, we construct a comprehensive heterogeneous graph by integrating virus-virus protein similarity and virus-host DNA sequence similarity information. As the backbone encoder for learning node representations in the virus-prokaryote graph, we employ LGCN, a state-of-the-art graph embedding technique. Additionally, we apply graph contrastive learning to augment the node representations without the need for additional labels. We further conducted two case studies aimed at predicting the host range of multi-species phages, helping to understand the phage ecology and evolution. Author summary: Prokaryotic viruses, which specifically target and infect bacteria, are highly prevalent in various ecosystems, establishing a dynamic relationship with their bacterial hosts. As the most abundant biological entities on Earth, phages are present wherever bacteria coexist. Therefore, accurately predicting phage-host interactions is of great significance. However, most existing prediction approaches primarily focus on learning sequence-based features for host prediction, disregarding the valuable information inherent in virus-virus and virus-prokaryote interactions. In this study, we propose a novel method, enabling accurate prediction of phage hosts. The model surpasses state-of-the-art methods in terms of host prediction performance across three benchmark datasets. Furthermore, it demonstrates the capability to predict the host range of multi-species phages, thus facilitating their practical application in various domains. [ABSTRACT FROM AUTHOR]