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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.16405 |
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| _version_ | 1866917904056844288 |
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| author | Wright, George Michniewski, Slawomir Jameson, Eleanor Minhas, Fayyaz ul Amir Afsar |
| author_facet | Wright, George Michniewski, Slawomir Jameson, Eleanor Minhas, Fayyaz ul Amir Afsar |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16405 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DepoRanker: A Web Tool to predict Klebsiella Depolymerases using Machine Learning Wright, George Michniewski, Slawomir Jameson, Eleanor Minhas, Fayyaz ul Amir Afsar Genomics Machine Learning 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 |
| title | DepoRanker: A Web Tool to predict Klebsiella Depolymerases using Machine Learning |
| topic | Genomics Machine Learning |
| url | https://arxiv.org/abs/2501.16405 |