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

TL;DR

DepoRanker, a machine learning algorithm to rank proteins by their likelihood of being depolymerases, provides an accurate and functional tool to expedite depolymerase discovery for phage therapy against Klebsiella.

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

Figures (3)

• Figure 1: Comparison between different models using percentiles of Rank of First Positive Prediction (RFPP). The curves show RFPP percentiles for the proposed model, a random baseline, and BLAST.
• Figure 2: The top 5 mean absolute SHAP values for each feature for scores from cross-validation.
• Figure 3: Receiver operating characteristic (ROC) curves and showing the performance for our ranking model (blue) and BLAST (red).