ProtAlign-ARG:抗生素耐药性基因特征集成蛋白质语言模型和基于对齐的评分
Shafayat Ahmed1, Muhit Islam Emon1, Nazifa Ahmed Moumi1
1Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, USA.
Scientific reports
|August 18, 2025
概括
一个新的混合模型,ProtAlign-ARG,增强了从DNA测序中检测抗生素耐药性基因 (ARG). 它结合了蛋白质语言模型和对齐评分,用于在识别和分类ARG时获得更高的准确性和回忆.
科学领域:
- 基因组学就是基因组学.
- 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:
- 抗生素耐药性基因 (ARG) 的进化和传播是全球卫生危机.
- 目前用于ARG检测的DNA测序方法在识别新型变异方面存在局限性.
- 蛋白质语言模型具有潜力,但需要广泛的培训数据库.
研究的目的:
- 引入ProtAlign-ARG,一种用于改进ARG检测和分类的新型混合模型.
- 利用蛋白质语言模型和对齐评分来提高ARG识别能力.
- 扩大ARG检测能力,超越现有的基于对齐和独立蛋白质语言模型方法.
主要方法:
- 开发了ProtAlign-ARG,这是一个混合模型,将预先训练的蛋白质语言模型与对齐评分方法集成在一起.
- 利用原始蛋白语言模型嵌入用于准确的ARG分类.
- 嵌入基于对齐的评分 (比特分数,e值) 在模型信心较低时进行分类.
- 扩展的ProtAlign-ARG用于预测ARG的功能和移动性.
主要成果:
- 在识别和分类ARG方面,ProtAlign-ARG的准确性很高.
- 与现有的ARG识别工具相比,该模型显示出更好的回忆能力.
- ProtAlign-ARG有效地预测了ARG的功能和移动性,展示了它的多功能性.
- 综合性比较证实了ProtAlign-ARG在独立模型中的优越性能.
结论:
- 在基因组数据的ARG检测和分类方面,ProtAlign-ARG代表了重大进展.
- 混合方法克服了现有方法的局限性,特别是对于新型ARG变体.
- 该模型增强了对抗生素耐药性传播的监测和理解.
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