标题： DepoRanker：一种使用机器学习预测克雷伯氏菌解聚酶的网络工具 显示英文标题

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

摘要： 背景：噬菌体疗法在治疗耐抗生素的克雷伯菌感染方面显示出潜力。识别针对克雷伯菌荚膜多糖的噬菌体去聚糖酶至关重要，因为这些荚膜有助于生物膜形成和毒力。然而，基于同源性的搜索在新去聚糖酶的发现中存在局限性。目的：开发一种机器学习模型，以识别和排序针对克雷伯菌的噬菌体去聚糖酶。方法：我们开发了DepoRanker，这是一种机器学习算法，用于根据蛋白质成为去聚糖酶的可能性对其进行排序。该模型对5种新表征的蛋白质进行了实验验证，并与BLAST进行了比较。结果：DepoRanker在识别潜在去聚糖酶方面表现出优于BLAST的性能。实验验证证实了其在新蛋白质上的预测能力。结论：DepoRanker提供了一个准确且功能强大的工具，以加速针对克雷伯菌的噬菌体疗法中的去聚糖酶发现。它作为一个网络服务器和开源软件可用。可用性：网络服务器：https://deporanker.dcs.warwick.ac.uk/ 源代码：https://github.com/wgrgwrght/deporanker 显示英文摘要

摘要： 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