病毒猎人:基于深度学习的病毒性因子预测器揭示了各种微生物环境中的病原性
Chen Chen1,2, Yong Xu1, Jian Ouyang1,3
1Center for Bioinformatics and Computational Biology, and The Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Dongchuan Road 500, Shanghai 200241, China.
Briefings in bioinformatics
|June 16, 2025
概括
深度学习工具VirulentHunter从蛋白质序列中准确地识别和分类细菌毒性因子 (VF),性能优于现有方法. 它有助于理解病原体的差异和肠道微生物群的健康.
科学领域:
- 计算生物学 计算生物学
- 细菌病原体的产生
- 微生物学中的机器学习
背景情况:
- 病毒性因子 (VFs) 是细菌致病性的关键.
- 目前基于同质学的方法与新型或分歧的VFs作斗争.
- 现有的机器学习方法往往缺乏VF的功能分类.
研究的目的:
- 引入VirulentHunter,这是一个深度学习框架,用于同时识别VF和功能分类.
- 利用微调的蛋白质语言模型进行增强的VF预测.
- 提供一种多功能工具,用于在各种生物环境中分析VF.
主要方法:
- 开发VirulentHunter,这是一个深度学习框架,利用微调的蛋白质语言模型.
- 策划一个全面的VF数据库,并扩展了类别注释.
- 与现有的 VF 识别方法进行比较.
主要成果:
- 病毒猎人表现出卓越的性能,特别是在识别没有可检测同类的VF时.
- 菌株水平分析显示了Mycobacterium结核菌和Mycobacterium avium之间不同的致病性概况.
- 对IBD患者肠道微生物群的基因组分析显示,与免疫平衡相关的VFs的枯竭.
结论:
- VirulentHunter是一个强大的,多功能工具,用于毒性因子分析.
- 该框架使细菌病原性和宿主微生物群相互作用的细微了解成为可能.
- 为更广泛的研究社区提供免费可访问的网络服务.
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