DepoRanker: A Web Tool to predict Klebsiella Depolymerases using Machine Learning
This provides a tool to expedite depolymerase discovery for phage therapy against antibiotic-resistant Klebsiella, but it is incremental as it applies existing ML methods to a specific domain problem.
The researchers tackled the problem of identifying phage depolymerases for Klebsiella infections by developing DepoRanker, a machine learning model that outperformed BLAST in predicting these proteins, as validated on 5 newly characterized proteins.
Background: Phage therapy shows promise for treating antibiotic-resistant Klebsiella infections. Identifying phage depolymerases that target Klebsiella capsular polysaccharides is crucial, as these capsules contribute to biofilm formation and virulence. However, homology-based searches have limitations in novel depolymerase discovery. Objective: To develop a machine learning model for identifying and ranking potential phage depolymerases targeting Klebsiella. Methods: We developed DepoRanker, a machine learning algorithm to rank proteins by their likelihood of being depolymerases. The model was experimentally validated on 5 newly characterized proteins and compared to BLAST. Results: DepoRanker demonstrated superior performance to BLAST in identifying potential depolymerases. Experimental validation confirmed its predictive ability on novel proteins. Conclusions: DepoRanker provides an accurate and functional tool to expedite depolymerase discovery for phage therapy against Klebsiella. It is available as a webserver and open-source software. Availability: Webserver: https://deporanker.dcs.warwick.ac.uk/ Source code: https://github.com/wgrgwrght/deporanker