在 silico 方法发现微生物抗病毒防御系统的方法
Lixu Jiang1, Yansheng Li2, Baocai Xie2
1Department of Critical Care Medicine, Intensive Care Unit, Shenzhen Key Laboratory of Microbiology in Genomic Modification & Editing and Application, Shenzhen Institute of Translational Medicine, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, 3002 Sungang West Road, Futian District, Shenzhen 518060, China.
计算方法对于发现各种 prokaryotic 抗病毒防御系统至关重要. 本综述分类了四种策略,以加强新型微生物免疫系统的识别,用于菌体治疗等应用.
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.
- 免疫学 免疫学 免疫学
背景情况:
- Prokaryotes 使用广泛的抗病毒防御系统来抵抗菌体掠食.
- 发现这些系统的挑战包括横向基因转移,序列多样性和快速进化.
- 高通量测序产生了大量的基因组数据,需要计算发现工具.
研究的目的:
- 审查和分类用于发现核细胞抗病毒防御系统的计算策略.
- 为系统地探索微生物免疫系统提供框架.
- 引导菌体治疗,微生物组工程和合成生物学中的应用.
主要方法:
- 将计算方法分类为四种策略:序列同质,结构引导,基因组上下文和人工智能.
- 讨论新兴工具,如保存基因集群发现和基因组基础模型.
- 方法原则,优势和局限性的比较.
主要成果:
- 序列同类学方法可靠地识别已知的系统,但错过了不同的系统.
- 结构导向的方法可以检测远程同类,但在计算上很昂贵.
- 基因组背景策略揭示了多基因集群和新型模块.
- 由人工智能驱动的方法整合了序列和上下文,用于大规模发现新系统.
结论:
- 计算策略对于有效和全面发现微生物防御系统是不可或缺的.
- 新兴的工具和人工智能为识别新系统和设计合成模块提供了强大的功能.
- 本综述为推进研究 prokaryotic 免疫及其应用提供了实际框架.
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