AGRAMP:用于预测抗微生物对植物病原菌的机器学习模型
Jonathan Shao1,2, Yan Zhao3, Wei Wei3
1Statistics and Bioinformatics Group - Northeast Area, U.S. Department of Agriculture, Agricultural Research Service, Beltsville, MD, United States.
Frontiers in microbiology
|March 22, 2024
概括
本研究引入了一种机器学习方法,用于识别用于对抗植物病原体的抗微生物 (AMP). 开发的生物信息学模型准确地从类基因组中预测AMP,为农业应用提供了比实验查更快的替代方案.
科学领域:
- 生物信息学和计算生物学
- 农业科学 农业科学
- 微生物学 微生物学
背景情况:
- 抗微生物 (AMP) 是传统抗生素的替代品,可以控制植物病原体.
- 实验性识别AMP是耗时和资源密集的.
- 开发有效的AMP发现方法对于可持续农业至关重要.
研究的目的:
- 开发和验证机器学习模型,用于预测对植物病原细菌有活性的抗微生物 (AMP).
- 通过生物信息学方法从类植物基因组中识别潜在的AMP.
- 为加速农业应用新型APP的发现提供一个工具.
主要方法:
- 使用了减少氨基酸字母的序列的N-gram表示.
- 使用机器学习模型进行训练和评估,使用5倍交叉验证.
- 应用模型来预测来自类基因间区域和小型开放阅读框架 (ORF) 的AMP.
主要成果:
- 机器学习模型的预测准确度从0.72到0.91.91不等.
- 大约7%的来自基因间区域和全基因组数据集的被预测为可能的AMP.
- 实验验证证证实了一些预测的AMP对Spiroplasma citri的抗菌活性.
结论:
- 机器学习模型有效地预测了针对植物病原体的AMP,显示出农业用途的巨大潜力.
- 对于AMP预测的关键特征包括疏水性和正电荷的氨基酸残留物,以及聚合倾向.
- 开发的模型可以通过AGRAMP服务器访问,有助于开发基于AMP的植物疾病管理策略.
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