DepoRanker: A Web Tool to predict Klebsiella Depolymerases using Machine Learning
Journal:
arXiv
Published Date:
Jan 27, 2025
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
