ArticleScientific reports2021
Predicting bacteriophage hosts based on sequences of annotated receptor-binding proteins.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 2 of them syntheses that pooled it.
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Who cites it
70 citing papers in PubMed, 2 syntheses or guidelines pooled it, 107 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
- Advances in phage-host interaction prediction: in silico method enhances the development of phage therapies.Briefings in bioinformatics · 2024Pooled it
- Phylogeny-agnostic strain-level prediction of phage-host interactions from genomes using machine learning.Nature microbiology · 2026Article
- Mapping structural constraints and adaptive potential in a capsule-degrading phage tailspike protein.Science advances · 2026Article
- Innovative Applications of Artificial Intelligence in Bacteriophage Research: A New Chapter in Future Medicine.Microorganisms · 2026Review
- Cross-family and phage-specific gene requirements for Klebsiella infection revealed by scalable RB-TnSeq genetic screens.PLoS genetics · 2026Article
- EAGP: an efficient generative augmentation framework for phage protein classification under severe class imbalance.Bioinformatics (Oxford, England) · 2026Article
- Genomic characterization and application on food samples of bacteriophage ETEC-S3 to control enterotoxigenic Escherichia coli.BMC research notes · 2026Article
- Analysis of a Novel T1-like Phage KanT1 Reveals a Standalone SH3 Domain as a Widespread Component ofInternational journal of molecular sciences · 2026Article
- PhageCGRNet: Integrating Chaos Game Representation of Genomes with Convolutional Neural Network for accurate phage host classification prediction.PLoS computational biology · 2026Article
- High-resolution phage-host assignment through key proteins using large language models.Nature communications · 2026Article
- From structure to design: experimental and AI-driven approaches in receptor-binding protein engineering for reprogramming phage host range.Archives of microbiology · 2026Review
- Resolution of a T1-Like Bacteriophage Outbreak by Receptor Engineering.Molecular biotechnology · 2026Article
- Prophage: agent provocateur?Archives of microbiology · 2026Review
- Gut virome dynamics: from commensal to critical player in health and disease.Nature reviews. Gastroenterology & hepatology · 2026Review
- Phage-Based Approaches to ChronicAntibiotics (Basel, Switzerland) · 2026Review
- Phage Therapy in Plant Disease Management: 110 Years of History, Current Challenges, and Future Trends.Plants (Basel, Switzerland) · 2026Review
- Bacteriophages in gut metagenomes: from analysis to application.Virology journal · 2026Review
- The antagonistic coevolution between Pseudomonas sivasensis W-6 and the cold-adapted phage VW6S.Antonie van Leeuwenhoek · 2026Article
- PBIP: a deep learning framework for predicting phage-bacterium interactions at the strain level.Briefings in bioinformatics · 2025Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays, bacteriophages are increasingly considered as an alternative treatment for a variety of bacterial infections in cases where classical antibiotics have become ineffective. However, characterizing the host specificity of phages remains a labor- and time-intensive process. In order to alleviate this burden, we have developed a new machine-learning-based pipeline to predict bacteriophage hosts based on annotated receptor-binding protein (RBP) sequence data. We focus on predicting bacterial hosts from the ESKAPE group, Escherichia coli, Salmonella enterica and Clostridium difficile. We compare the performance of our predictive model with that of the widely used Basic Local Alignment Search Tool (BLAST). Our best-performing predictive model reaches Precision-Recall Area Under the Curve (PR-AUC) scores between 73.6 and 93.8% for different levels of sequence similarity in the collected data. Our model reaches a performance comparable to that of BLASTp when sequence similarity in the data is high and starts outperforming BLASTp when sequence similarity drops below 75%. Therefore, our machine learning methods can be especially useful in settings in which sequence similarity to other known sequences is low. Predicting the hosts of novel metagenomic RBP sequences could extend our toolbox to tune the host spectrum of phages or phage tail-like bacteriocins by swapping RBPs.
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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.