ArticlePloS one2023
Protein embeddings improve phage-host interaction prediction.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 4 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Precision phage therapy in the AI/ML era: a systematic review of discovery-to-clinical translation evidence.Frontiers in microbiology · 2026Pooled it
- From genomic signals to prediction tools: a critical feature analysis and rigorous benchmark for phage-host prediction.Briefings in bioinformatics · 2025Pooled it
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled 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
- Bacteriophage-Based Therapeutic, Diagnostic, and Biocontrol Platforms: Engineering, Evidence, and Translational Challenges.International journal of molecular sciences · 2026Review
- PEPE: scalable extraction of multi-modal protein language model representations.Bioinformatics (Oxford, England) · 2026Article
- Artificial Intelligence in Bacteriophage Science: A Comprehensive Narrative Review of Applications, Challenges, and Translational Opportunities.Antibiotics (Basel, Switzerland) · 2026Review
- Bacteriophage Therapy AgainstAntibiotics (Basel, Switzerland) · 2026Review
- High-resolution phage-host assignment through key proteins using large language models.Nature communications · 2026Article
- Protein language models enable accurate viral host range prediction.Scientific reports · 2026Article
- Artificial Intelligence Applications in Bacteriophage Genomics and Host Interaction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Unlocking data in Klebsiella lysogens to predict capsular type-specificity of phage depolymerases.Nature communications · 2025Article
- PHIStruct: improving phage-host interaction prediction at low sequence similarity settings using structure-aware protein embeddings.Bioinformatics (Oxford, England) · 2024Article
- Renewed insights into Ackermannviridae phage biology and applications.Npj viruses · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the growing interest in using phages to combat antimicrobial resistance, computational methods for predicting phage-host interactions have been explored to help shortlist candidate phages. Most existing models consider entire proteomes and rely on manual feature engineering, which poses difficulty in selecting the most informative sequence properties to serve as input to the model. In this paper, we framed phage-host interaction prediction as a multiclass classification problem that takes as input the embeddings of a phage's receptor-binding proteins, which are known to be the key machinery for host recognition, and predicts the host genus. We explored different protein language models to automatically encode these protein sequences into dense embeddings without the need for additional alignment or structural information. We show that the use of embeddings of receptor-binding proteins presents improvements over handcrafted genomic and protein sequence features. The highest performance was obtained using the transformer-based protein language model ProtT5, resulting in a 3% to 4% increase in weighted F1 and recall scores across different prediction confidence thresholds, compared to using selected handcrafted sequence features.
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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.