用机器学习快速预测能攻击肺炎克雷伯菌的噬菌体酶，提升新药研发效率。
DepoRanker: A Web Tool to predict Klebsiella Depolymerases using Machine Learning
- 基于机器学习构建蛋白排序模型，识别靶向克雷伯菌的噬菌体酶
- 在5个新蛋白上验证准确率优于传统比对工具BLAST
- 提供开源网页工具，适合抗生素耐药研究者使用
背景：噬菌体疗法有望治疗耐药性肺炎克雷伯菌感染。识别靶向其荚膜多糖的噬菌体解聚酶至关重要，因荚膜与生物膜形成和致病性相关。但同源搜索在发现新型解聚酶时存在局限。目标：开发一种机器学习模型，用于识别并排序潜在的靶向肺炎克雷伯菌的噬菌体解聚酶。方法：我们构建了DepoRanker，一种机器学习算法，按蛋白质成为解聚酶的可能性进行排序。该模型在5个新鉴定的蛋白上进行了实验验证，并与BLAST对比。结果：DepoRanker在识别潜在解聚酶方面表现优于BLAST。实验验证确认了其对新蛋白的预测能力。结论：DepoRanker为加速针对肺炎克雷伯菌的噬菌体疗法中解聚酶的发现提供了准确且功能性的工具。该工具已作为网页服务器和开源软件发布。可用性：网页版：https://deporanker.dcs.warwick.ac.uk/ 源代码：https://github.com/wgrgwrght/deporanker
原文摘要 · Abstract (English)
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
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