卡姆纳:基于预训练的大型蛋白质语言模型的基酶活性分类和回归模型
Anqiang Ye1,2, Ji-Yun Zhang1,2, Qian Xu1,2
1Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, 185 Donghu Road, Wuchang District, Wuhan 430071, China.
Briefings in bioinformatics
|April 24, 2025
概括
机器学习模型通过分析酶序列和基因特征来预测酶活性. 这项研究提高了对固定的理解,并有助于生物肥料的开发.
科学领域:
- 生物化学 生物化学
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
背景情况:
- 固定的微生物对全球循环至关重要.
- 酶 (EC 1.18.6.1) 将大气中的转化为氨.
- 了解酶活性调节对于农业应用至关重要.
研究的目的:
- 开发用于分类和预测酶活性的机器学习模型 (Carmna).
- 确定影响酶活性的关键特征.
- 为开发高效的生物肥料作出贡献.
主要方法:
- 用了六个机器学习算法来进行分类和回归任务.
- 使用 ProtT5 来从酶序列中提取特征.
- 纳入基因表达和代码子偏好数据用于模型培训.
主要成果:
- XGBoost模型实现了0.9365的AUC进行分类.
- 使用支向量回归的堆叠模型为回归产生了0.5572的R2.
- 确定了氨基酸比例,代码偏好,基因表达和基因距离作为影响活动的因素.
结论:
- 机器学习有效地模拟了酶活动.
- 多个基因组和表达特征与酶功能有关.
- 这些发现支持开发增强的固生物肥料.
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