ArticleGigaScience2024
Large-scale genomic survey with deep learning-based method reveals strain-level phage specificity determinants.
Article in GigaScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Precision phage therapy in the AI/ML era: a systematic review of discovery-to-clinical translation evidence.Frontiers in microbiology · 2026Pooled it
- Bacterial Lysis Observation Over Time Assay (BLOTS) for High-Throughput Isolation, Sequencing, and Characterization of Bacteriophages.Current protocols · 2026Article
- Structural Receptors and Host Adaptations Affecting the Bacteriophage Targeting ofMicroorganisms · 2026Review
- Artificial Intelligence in Bacteriophage Science: A Comprehensive Narrative Review of Applications, Challenges, and Translational Opportunities.Antibiotics (Basel, Switzerland) · 2026Review
- DposFinder: an interpretable transformer model for predicting phage-derived polysaccharide depolymerases and their host capsular serotypes.Genome medicine · 2026Article
- Role of the C-terminal modules of Klebsiella phage KP32 receptor-binding protein gp38 in protein and phage functionality.PLoS pathogens · 2026Article
- Genome characterization and receptor-binding protein identification of Klebsiella phage vB_VIPKPNMC05, a member of a novel viral family Pituviridae.Archives of virology · 2026Article
- MADRe: Strain-level metagenomic classification through assembly-driven database reduction.GigaScience · 2026Article
- Towards accurate artificial intelligence models for strain-level phage-host prediction.Briefings in bioinformatics · 2026Article
- Advances in deep learning for antimicrobial research.Frontiers in microbiology · 2026Review
- A comprehensive review of AI innovations for tackling antimicrobial resistance.Le infezioni in medicina · 2026Review
- Unlocking data in Klebsiella lysogens to predict capsular type-specificity of phage depolymerases.Nature communications · 2025Article
- A Novel LyticFoods (Basel, Switzerland) · 2025Article
- Comprehensive genomic and evolutionary analysis of biofilm matrix clusters and proteins in themSystems · 2025Article
- Genomic insights into bacteriophages: a new frontier in AMR detection and phage therapy.Briefings in functional genomics · 2025Review
- Review
- Article
- PHIStruct: improving phage-host interaction prediction at low sequence similarity settings using structure-aware protein embeddings.Bioinformatics (Oxford, England) · 2024Article
- Large-scale genomic survey with deep learning-based method reveals strain-level phage specificity determinants.GigaScience · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundPhage therapy, reemerging as a promising approach to counter antimicrobial-resistant infections, relies on a comprehensive understanding of the specificity of individual phages. Yet the significant diversity within phage populations presents a considerable challenge. Currently, there is a notable lack of tools designed for large-scale characterization of phage receptor-binding proteins, which are crucial in determining the phage host range.
resultsIn this study, we present SpikeHunter, a deep learning method based on the ESM-2 protein language model. With SpikeHunter, we identified 231,965 diverse phage-encoded tailspike proteins, a crucial determinant of phage specificity that targets bacterial polysaccharide receptors, across 787,566 bacterial genomes from 5 virulent, antibiotic-resistant pathogens. Notably, 86.60% (143,200) of these proteins exhibited strong associations with specific bacterial polysaccharides. We discovered that phages with identical tailspike proteins can infect different bacterial species with similar polysaccharide receptors, underscoring the pivotal role of tailspike proteins in determining host range. The specificity is mainly attributed to the protein's C-terminal domain, which strictly correlates with host specificity during domain swapping in tailspike proteins. Importantly, our dataset-driven predictions of phage-host specificity closely match the phage-host pairs observed in real-world phage therapy cases we studied.
conclusionsOur research provides a rich resource, including both the method and a database derived from a large-scale genomics survey. This substantially enhances understanding of phage specificity determinants at the strain level and offers a valuable framework for guiding phage selection in therapeutic applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.