PLM-ARG:使用预训练的蛋白质语言模型识别抗生素耐药性基因
Jun Wu1, Jian Ouyang1, Haipeng Qin1
1Center for Bioinformatics and Computational Biology, and The Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China.
Bioinformatics (Oxford, England)
|November 23, 2023
概括
一个新的AI框架,PLM-ARG,准确地识别了抗生素耐药性基因 (ARG) 和它们的类别. 该工具改进了现有方法,有助于评估公共卫生和环境研究中的抗生素耐药性威胁.
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.
- 人工智能的人工智能
背景情况:
- 抗生素耐药性是一个关键的全球健康和环境问题.
- 目前用于识别抗生素耐药性基因 (ARG) 的方法与新奇或不相似的序列作斗争.
- 这种缺乏认可对评估抗生素耐药性的全部范围构成了挑战.
研究的目的:
- 开发一个人工智能支持的框架,用于同时识别和分类ARG.
- 解决ARG检测中的基于序列相似性的方法的局限性.
- 为分析不同生物环境中的抗生素耐药性提供一个强大的工具.
主要方法:
- 使用预训练的大型蛋白质语言模型 (PLM).
- 在一个包含超过28000个ARG和29个抗性类别的综合数据集上训练模型.
- 采用5倍交叉验证策略和独立验证套件进行绩效评估.
主要成果:
- 在交叉验证过程中,PLM-ARG框架实现了高精度,马修的相关系数 (MCC) 为0.983±0.001.
- 在独立验证集上获得0.838的MCC,显著优于现有的ARG预测工具 (51.8%-107.9%的改进).
- 通过在UniProt数据库中注释耐药性和评估ARG对环境微生物群的影响来证明其实用性.
结论:
- PLM-ARG提供了一种强大而准确的解决方案,用于识别和分类ARG.
- 该框架提高了检测和理解抗生素耐药性的能力.
- PLM-ARG通过GitHub和一个用于学术用途的网络服务器提供.
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