DepoRanker: A Web Tool to predict Klebsiella Depolymerases using Machine Learning

TL;DR

DepoRanker is a machine learning-based web tool that effectively predicts and ranks potential Klebsiella phage depolymerases, outperforming traditional homology searches and aiding phage therapy development.

Contribution

The paper introduces DepoRanker, a novel machine learning model that improves the identification of phage depolymerases targeting Klebsiella, validated through experimental testing.

Findings

DepoRanker outperforms BLAST in predicting depolymerases.

Experimental validation confirms the model's accuracy on novel proteins.

The tool is available as a webserver and open-source software.

Abstract

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