Advances in the field of phage-based therapy with special emphasis on computational resources

Abstract

In the current era, one of the major challenges is to manage the treatment of drug/antibiotic-resistant strains of bacteria. Phage therapy, a century-old technique, may serve as an alternative to antibiotics in treating bacterial infections caused by drug-resistant strains of bacteria. In this review, a systematic attempt has been made to summarize phage-based therapy in depth. This review has been divided into the following two sections: general information and computer-aided phage therapy (CAPT). In the case of general information, we cover the history of phage therapy, the mechanism of action, the status of phage-based products (approved and clinical trials) and the challenges. This review emphasizes CAPT, where we have covered primary phage-associated resources, phage prediction methods and pipelines. This review covers a wide range of databases and resources, including viral genomes and proteins, phage receptors, host genomes of phages, phage–host interactions and lytic proteins. In the post-genomic era, identifying the most suitable phage for lysing a drug-resistant strain of bacterium is crucial for developing alternate treatments for drug-resistant bacteria and this remains a challenging problem. Thus, we compile all phage-associated prediction methods that include the prediction of phages for a bacterial strain, the host for a phage and the identification of interacting phage–host pairs. Most of these methods have been developed using machine learning and deep learning techniques. This review also discussed recent advances in the field of CAPT, where we briefly describe computational tools available for predicting phage virions, the life cycle of phages and prophage identification. Finally, we describe phage-based therapy’s advantages, challenges and opportunities.

Automating Predictive Phage Therapy Pharmacology

“…A limitation for all of these approaches, including the mathematical modeling emphasized here, is that resulting predictions of subsequent phage performance during therapies will only be as useful as experimental in vitro environments are representative of subsequent in situ conditions [ 56 , 57 , 58 ]. Nonetheless, the goal with all of these methods is to gain a more robust appreciation of what at least might be achievable by a given phage during an actual treatment rather than choosing phages for phage therapy based solely on more simplistic measures of host range such as just spotting or just plaquing abilities [ 43 , 59 , 60 ], or instead relying upon genome sequence-based methods for phage host-range determination, the latter as currently are under development [ 61 , 62 ].…”

Predicting phage-bacteria interactions at the strain level from genomes

“…Recent studies have used genomic traits to decipher phage-bacteria interactions within bacterial species including Staphylococcus aureus (8 phages, 259 isolates) or Klebsiella pneumoniae (46 phages, 138 isolates) [24], [25], paving the way to predict phage-bacteria specificity from their genomes. Current algorithms typically determine which bacterial genus a given phage can infect but cannot make predictions at deeper taxonomic levels [26]- [29]. Even within the same genus, bacterial strains may exhibit important differences in their susceptibility to phages [14], [15], [17].…”