Abstract
Viral sequences in diverse environments remain largely uncharacterized, impeding our comprehension of their genetic makeup, biological interactions, and potential applications. This underscores an urgent need for innovative analytical methods. Here, we present the VirHost Hunter framework, which employs phage tails and lysins, bypassing the requirement for full genomes, for efficient and high-resolution host assignment. By harnessing Protein Language Models and Vision Transformers, VirHost Hunter captures protein functional homology despite sequence dissimilarity, significantly boosting prediction accuracy. In the scenario of disease-associated gut bacteria, the calibrated VirHost Hunter surpasses existing methods, doubling phage host assignments, expanding taxonomic reach, and revealing previously uncharacterized phages targeting gut bacteria, including Akkermansia and Prevotella. Therefore, we establish a gut phage lysin database, enabling the synthesis of a lysin that effectively and specifically targets an obesity-promoting bacterium. VirHost Hunter’s precision and scalability mark a significant leap forward in virome research and present a promising avenue for microbiome therapies.
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Data availability
The Gut Phage Lysin Database (GPLD) protein sequence data generated in this study have been deposited in the CNGB Sequence Archive (CNSA)111 of the China National GeneBank DataBase (CNGBdb)112 with accession number CNP0005794. The benchmark datasets, derived training and testing data generated in this study, are available in the Zenodo repository under https://doi.org/10.5281/zenodo.17340915. All accession codes are from previously published datasets and are provided in Supplementary Data 5. Source data supporting the findings of this study are provided in the Source Data. All these bacterial strains and plasmids can be requested by direct correspondence with the lead contact, Dr. Minfeng Xiao (xiaominfeng@genomics.cn). These resources are strictly limited to academic research use, and this publication must be cited accordingly. Source data are provided with this paper.
Code availability
The models constructed in this study, together with the corresponding scripts, are publicly available on GitHub at https://github.com/YuehuaOu/Viral-Host-Hunter and have been archived on Zenodo under https://doi.org/10.5281/zenodo.18399248. The pre-trained model weights generated in this study are available in the Zenodo repository under https://doi.org/10.5281/zenodo.17340381.
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Acknowledgments
This work was supported by the National Key R&D Program of China (Grant No. 2020YFA0908700) to M.X., Z.D., Junhua L., and Jianqiang L., and in part by the National Natural Science Fund for Distinguished Young Scholars (Grant No. 62325307) to Jiangqiang L. We sincerely thank the China National GeneBank Database (CNGB) for providing valuable data support and computational resources.
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M.X. conceived the study. Z.D., K.L., and Y.O. developed the tool. M.L. and B.X. compiled the training, validation, and test sets. K.L., M.L., B.X., and M.X. analyzed the viral dark matter. K.L., M.L., B.X., and Y.O. drafted the manuscript and made the figures. Z.L. and M.L. maintained and improved the code. Z.D., M.X., and Junhua L. revised the manuscript. W.S., J.C. and Jianqiang L. provided consultation. B.X., Y.O., and Z.L. contributed equally to this work. All authors read, edited, and approved the final manuscript.
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Authors affiliated with BGI Research (M. L., M. X., and Junhua. L) and authors affiliated with Shenzhen University (Z. D., K. L., and Jiangqiang. L) have filed a patent application titled “ Method and System for Predicting Host Range of Bacteriophages, and Corresponding Computer Device or Medium”(Application No. PCT/CN2025/075560). It describes predicting algorithm in this study that predicts host specificity of phages based on tail and lysin proteins. Other authors declare no competing interests.
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Du, Z., Li, M., Lin, K. et al. High-resolution phage-host assignment through key proteins using large language models. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70613-x
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DOI: https://doi.org/10.1038/s41467-026-70613-x
