ArticleBioinformatics (Oxford, England)2024
PHIStruct: improving phage-host interaction prediction at low sequence similarity settings using structure-aware protein embeddings.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 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
- Innovative Applications of Artificial Intelligence in Bacteriophage Research: A New Chapter in Future Medicine.Microorganisms · 2026Review
- Phage bioinformatics tools: a review of computational approaches for bacteriophage research.Briefings in bioinformatics · 2026Review
- PhageMind: generalized strain-level phage host range prediction via meta-learning.Bioinformatics (Oxford, England) · 2026Article
- Artificial Intelligence in Bacteriophage Science: A Comprehensive Narrative Review of Applications, Challenges, and Translational Opportunities.Antibiotics (Basel, Switzerland) · 2026Review
- PhageCGRNet: Integrating Chaos Game Representation of Genomes with Convolutional Neural Network for accurate phage host classification prediction.PLoS computational biology · 2026Article
- Bacteriophage Therapy AgainstAntibiotics (Basel, Switzerland) · 2026Review
- From structure to design: experimental and AI-driven approaches in receptor-binding protein engineering for reprogramming phage host range.Archives of microbiology · 2026Review
- Protein language models enable accurate viral host range prediction.Scientific reports · 2026Article
- Phage-Based Approaches to ChronicAntibiotics (Basel, Switzerland) · 2026Review
- Towards accurate artificial intelligence models for strain-level phage-host prediction.Briefings in bioinformatics · 2026Article
- The Role of Genomics in Advancing and Standardising Bacteriophage Therapy.Antibiotics (Basel, Switzerland) · 2026Review
- MVPHI: a multi-view learning framework for predicting complex microbial interactions.Scientific reports · 2025Article
- Phage Therapy as a Novel Alternative to Antibiotics Through Adaptive Evolution and Fitness Trade-Offs.Antibiotics (Basel, Switzerland) · 2025Review
- Microbial Technologies Enhanced by Artificial Intelligence for Healthcare Applications.Microbial biotechnology · 2025Review
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Authors and funding
3 authors.
Funding
Abstract
motivationRecent computational approaches for predicting phage-host interaction have explored the use of sequence-only protein language models to produce embeddings of phage proteins without manual feature engineering. However, these embeddings do not directly capture protein structure information and structure-informed signals related to host specificity.
resultsWe present PHIStruct, a multilayer perceptron that takes in structure-aware embeddings of receptor-binding proteins, generated via the structure-aware protein language model SaProt, and then predicts the host from among the ESKAPEE genera. Compared against recent tools, PHIStruct exhibits the best balance of precision and recall, with the highest and most stable F1 score across a wide range of confidence thresholds and sequence similarity settings. The margin in performance is most pronounced when the sequence similarity between the training and test sets drops below 40%, wherein, at a relatively high-confidence threshold of above 50%, PHIStruct presents a 7%-9% increase in class-averaged F1 over machine learning tools that do not directly incorporate structure information, as well as a 5%-6% increase over BLASTp. AVAILABILITY AND IMPLEMENTATION: The data and source code for our experiments and analyses are available at https://github.com/bioinfodlsu/PHIStruct.
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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.