RT Journal Article
SR Electronic
T1 An ensemble method for designing phage-based therapy against bacterial infections
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.06.01.494305
DO 10.1101/2022.06.01.494305
A1 Suchet Aggarwal
A1 Anjali Dhall
A1 Sumeet Patiyal
A1 Shubham Choudhury
A1 Akanksha Arora
A1 Gajendra P.S. Raghava
YR 2022
UL http://biorxiv.org/content/early/2022/06/02/2022.06.01.494305.abstract
AB 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 phage to treat a bacterial infection. In this study, an attempt has been made to predict phage-host interaction with high accuracy to identify the best virus for treating a bacterial infection. All models have been developed on a training dataset containing 826 phage host-interactions, whereas models 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 at five taxonomic levels. Secondly, alignment free models have been developed using machine learning techniques. Thirdly, hybrid models have been developed by integrating alignment-free models and similarity-score where we achieved maximum performance of (60.6%-93.5%). Finally, an ensemble model has been developed that combines hybrid and alignment based model. Our ensemble model achieved highest accuracy of 67.9%, 80.6%, 85.5%, 90%, 93.5% at Genus, Family, Order, Class and Phylum levels, which is better than existing methods. In order to serve the scientific community we have developed a webserver named PhageTB and standalone software package (https://webs.iiitd.edu.in/raghava/phagetb/).Key PointsPhage therapy provides an alternative to mange drug resistant strains of bacteriaPrediction bacterial strains that can be treated by a given phageAlignment-based, alignment-free and ensemble models have been developed.Prediction of appropriate phage/virus that can lyse a given strain of bacteria.Webserver and standalone package provided to predict phage-host interactions.Competing Interest StatementThe authors have declared no competing interest.