Identification of Phage Receptor-Binding Protein Sequences with Hidden Markov Models and an Extreme Gradient Boosting Classifier

Prediction of Klebsiella phage-host specificity at the strain level

Phages are increasingly considered promising alternatives to target drug-resistant bacterial pathogens. However, their often-narrow host range can make it challenging to find matching phages against bacteria of interest. Current computational tools do not accurately predict interactions at the strain level in a way that is relevant and properly evaluated for practical use. We present PhageHostLearn, a machine learning system that predicts strain-level interactions between receptor-binding proteins and bacterial receptors for Klebsiella phage-bacteria pairs. We evaluate this system both in silico and in the laboratory, in the clinically relevant setting of finding matching phages against bacterial strains. PhageHostLearn reaches a cross-validated ROC AUC of up to 81.8% in silico and maintains this performance in laboratory validation. Our approach provides a framework for developing and evaluating phage-host prediction methods that are useful in practice, which we believe to be a meaningful contribution to the machine-learning-guided development of phage therapeutics and diagnostics.

Phage therapy as a glimmer of hope in the fight against the recurrence or emergence of surgical site bacterial infections

PURPOSE

Over the last decade, surgery rates have risen alarmingly, and surgical-site infections are expanding these concerns. In spite of advances in infection control practices, surgical infections continue to be a significant cause of death, prolonged hospitalization, and morbidity. As well as the presence of bacterial infections and their antibiotic resistance, biofilm formation is one of the challenges in the treatment of surgical wounds.

METHODS

This review article was based on published studies on inpatients and laboratory animals receiving phage therapy for surgical wounds, phage therapy for tissue and bone infections treated with surgery to prevent recurrence, antibiotic-resistant wound infections treated with phage therapy, and biofilm-involved surgical wounds treated with phage therapy which were searched without date restrictions.

RESULTS

It has been shown in this review article that phage therapy can be used to treat surgical-site infections in patients and animals, eliminate biofilms at the surgical site, prevent infection recurrence in wounds that have been operated on, and eradicate antibiotic-resistant infections in surgical wounds, including multi-drug resistance (MDR), extensively drug resistance (XDR), and pan-drug resistance (PDR). A cocktail of phages and antibiotics can also reduce surgical-site infections more effectively than phages alone.

CONCLUSION

In light of these encouraging results, clinical trials and research with phages will continue in the near future to treat surgical-site infections, biofilm removal, and antibiotic-resistant wounds, all of which could be used to prescribe phages as an alternative to antibiotics.

Flagellotropic phages: common yet diverse host interaction strategies

Many bacteriophages (phages) interact with flagella and rely on bacterial motility for successful infection of their hosts. Yet, limited information is available on how phages have evolved to recognize and bind both flagella and subsequent surface receptors for phage DNA injection. Here, we present an update on the current knowledge of flagellotropic phages using a few well-studied phages as examples to unravel the molecular details of bacterial host recognition. We discuss the recent advances in the role of globular exposed flagellin domains and flagella glycosylation in phage binding to the flagella. In addition, we present diverse types of surface receptors and phage components responsible for the interaction with the host. Finally, we point to questions remaining to be answered and new approaches to study this unique group of phages.

Biological and bioinformatic tools for the discovery of unknown phage-host combinations

The field of microbial ecology has been transformed by metagenomics in recent decades and has culminated in vast datasets that facilitate the bioinformatic dissection of complex microbial communities. Recently, attention has turned from defining the microbiota composition to the interactions and relationships that occur between members of the microbiota. Within complex microbiota, the identification of bacteriophage-host combinations has been a major challenge. Recent developments in artificial intelligence tools to predict protein structure and function as well as the relationships between bacteria and their infecting bacteriophages allow a strategic approach to identifying and validating phage-host relationships. However, biological validation of these predictions remains essential and will serve to improve the existing predictive tools. In this review, I provide an overview of the most recent developments in both bioinformatic and experimental approaches to predicting and experimentally validating unknown phage-host combinations.

Large-scale genomic survey with deep learning-based method reveals strain-level phage specificity determinants

BACKGROUND

Phage 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.

RESULTS

In 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.

CONCLUSIONS

Our 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.

Knowing and Naming: Phage Annotation and Nomenclature for Phage Therapy

Bacteriophages, or phages, are viruses that infect bacteria shaping microbial communities and ecosystems. They have gained attention as potential agents against antibiotic resistance. In phage therapy, lytic phages are preferred for their bacteria killing ability, while temperate phages, which can transfer antibiotic resistance or toxin genes, are avoided. Selection relies on plaque morphology and genome sequencing. This review outlines annotating genomes, identifying critical genomic features, and assigning functional labels to protein-coding sequences. These annotations prevent the transfer of unwanted genes, such as antimicrobial resistance or toxin genes, during phage therapy. Additionally, it covers International Committee on Taxonomy of Viruses (ICTV)-an established phage nomenclature system for simplified classification and communication. Accurate phage genome annotation and nomenclature provide insights into phage-host interactions, replication strategies, and evolution, accelerating our understanding of the diversity and evolution of phages and facilitating the development of phage-based therapies.

Protein embeddings improve phage-host interaction prediction

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.

DePolymerase Predictor (DePP): a machine learning tool for the targeted identification of phage depolymerases

Biofilm production plays a clinically significant role in the pathogenicity of many bacteria, limiting our ability to apply antimicrobial agents and contributing in particular to the pathogenesis of chronic infections. Bacteriophage depolymerases, leveraged by these viruses to circumvent biofilm mediated resistance, represent a potentially powerful weapon in the fight against antibiotic resistant bacteria. Such enzymes are able to degrade the extracellular matrix that is integral to the formation of all biofilms and as such would allow complementary therapies or disinfection procedures to be successfully applied. In this manuscript, we describe the development and application of a machine learning based approach towards the identification of phage depolymerases. We demonstrate that on the basis of a relatively limited number of experimentally proven enzymes and using an amino acid derived feature vector that the development of a powerful model with an accuracy on the order of 90% is possible, showing the value of such approaches in protein functional annotation and the discovery of novel therapeutic agents.

Current Clinical Landscape and Global Potential of Bacteriophage Therapy

In response to the global spread of antimicrobial resistance, there is an increased demand for novel and innovative antimicrobials. Bacteriophages have been known for their potential clinical utility in lysing bacteria for almost a century. Social pressures and the concomitant introduction of antibiotics in the mid-1900s hindered the widespread adoption of these naturally occurring bactericides. Recently, however, phage therapy has re-emerged as a promising strategy for combatting antimicrobial resistance. A unique mechanism of action and cost-effective production promotes phages as an ideal solution for addressing antibiotic-resistant bacterial infections, particularly in lower- and middle-income countries. As the number of phage-related research labs worldwide continues to grow, it will be increasingly important to encourage the expansion of well-developed clinical trials, the standardization of the production and storage of phage cocktails, and the advancement of international collaboration. In this review, we discuss the history, benefits, and limitations of bacteriophage research and its current role in the setting of addressing antimicrobial resistance with a specific focus on active clinical trials and case reports of phage therapy administration.

Genetic determinants of host tropism in Klebsiella phages

Bacteriophages play key roles in bacterial ecology and evolution and are potential antimicrobials. However, the determinants of phage-host specificity remain elusive. Here, we isolate 46 phages to challenge 138 representative clinical isolates of Klebsiella pneumoniae, a widespread opportunistic pathogen. Spot tests show a narrow host range for most phages, with <2% of 6,319 phage-host combinations tested yielding detectable interactions. Bacterial capsule diversity is the main factor restricting phage host range. Consequently, phage-encoded depolymerases are key determinants of host tropism, and depolymerase sequence types are associated with the ability to infect specific capsular types across phage families. However, all phages with a broader host range found do not encode canonical depolymerases, suggesting alternative modes of entry. These findings expand our knowledge of the complex interactions between bacteria and their viruses and point out the feasibility of predicting the first steps of phage infection using bacterial and phage genome sequences.