An ensemble method for prediction of phage-based therapy against bacterial infections - Aggarwal, Suchet; Dhall, Anjali; Patiyal, Sumeet; Choudhury, Shubham; Arora, Akanksha; Raghava, Gajendra P. S. (Frontiers in Microbiology, 2023)
Phage therapy is a viable alternative to antibiotics for treating microbial infections, particularly managing drug-resistant strains of bacteria. One of the major challenges in designing phage-based therapy is to identify the most appropriate potential phage candidate to treat bacterial infections. In this study, an attempt has been made to predict phage-host interactions with high accuracy to identify the potential bacteriophage that can be used for treating a bacterial infection. The developed models have been created using a training dataset containing 826 phage- host interactions, and have been evaluated on a validation dataset comprising 1201 phage-host interactions. Firstly, alignment-based models have been developed using similarity between phage-phage (BLASTPhage), host-host (BLASTHost) and phage-CRISPR (CRISPRPred), where we achieved accuracy between 42.4%-66.2% for BLASTPhage, 55%-78.4% for BLASTHost, and 43.7%-80.2% for CRISPRPred across five taxonomic levels. Secondly, alignment free models have been developed using machine learning techniques. Thirdly, hybrid models have been developed by integrating the alignment-free models and the similarity-scores where we achieved maximum performance of (60.6%-93.5%). Finally, an ensemble model has been developed that combines the hybrid and alignment-based models. Our ensemble model achieved highest accuracy of 67.9%, 80.6%, 85.5%, 90%, and 93.5% at Genus, Family, Order, Class, and Phylum levels on validation dataset. In order to serve the scientific community, we have also developed a webserver named PhageTB and provided a standalone software package (https://webs.iiitd.edu.in/raghava/phagetb/ ) for the same.