通过过度波因卡雷图嵌入和大规模蛋白质语言技术预测菌体与宿主相互作用
Jie Pan1, Rui Wang2, Wenjing Liu1
1Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China.
iScience
|January 27, 2025
概括
通过机器学习,GE-PHI准确地预测了菌体与宿主相互作用,为抗生素耐药性感染推进了菌体治疗方法. 该工具集成了图形嵌入和蛋白质语言模型,以改进菌体发现和诊断.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 菌体 (菌体) 显示出治疗抗生素耐药细菌感染的前景.
- 鉴定针对性治疗的特定菌体是具有挑战性的,因为不同的宿主范围.
- 目前的计算方法在预测细菌物种间的菌体与宿主相互作用方面缺乏准确性.
研究的目的:
- 开发GE-PHI,一种新的机器学习模型,用于准确预测菌体与宿主相互作用 (PHI).
- 整合知识图嵌入和蛋白质语言模型,以提高PHI预测.
- 为促进微生物工程中的菌体治疗和诊断提供一个工具.
主要方法:
- 构建了菌-宿主异质关联网络 (PHAN),包括菌-菌和宿主-宿主相似性.
- 应用多关联的庞卡雷图嵌入 (MuRP) 来提取网络拓模式.
- 利用ESM-2蛋白语言模型从菌体和宿主蛋白质获取进化信息.
主要成果:
- GE-PHI在形上实现了0.9453的曲线下的交叉验证面积 (AUC).
- 该模型在案例研究中表现出强的性能,验证了其预测准确性.
- 集成的网络拓和蛋白质进化信息,用于卓越的PHI预测.
结论:
- GE-PHI在预测菌体与宿主相互作用方面取得了重大进展.
- 该模型支持开发机器学习引导的菌体治疗方法.
- GE-PHI有助于菌体诊断和微生物工程应用.
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