DeepMBEnzy:一个人工智能驱动的菌毒素生物转化酶数据库
Pengli Cai1, Dongliang Liu1, Huadong Xing1
1Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Journal of agricultural and food chemistry
|May 16, 2025
概括
研究人员开发了一种深度学习模型,以预测菌毒素生物转化酶. 这个名为DeepMBEnzy的工具有助于识别真菌毒素的新解毒酶.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 菌类学 菌类学是指菌类学.
背景情况:
- 菌毒素是有毒的真菌代谢物,具有健康风险.
- 酶生物转化为真菌毒素排毒和代谢研究提供了一种策略.
- 对菌毒素生物转化酶的有限鉴定阻碍了研究.
研究的目的:
- 开发一种用于识别能够进行菌毒素生物转化的酶的预测模型.
- 创建一个全面的数据库预测菌毒素生物转化酶.
主要方法:
- 使用冷蛋白数据分割方法微调预训练模型.
- 开发一种用于真菌毒素生物转化 (EPP-MB) 模型的酶乱交预测模型.
- 利用深度学习进行酶预测,并将其应用于4000多种真菌毒素.
主要成果:
- 在预测菌毒素生物转化酶方面,EPP-MB模型实现了79%的验证准确性.
- 编译了DeepMBEnzy数据库,其中包含4000多种真菌毒素的预测酶.
- 为数据库建立了一个用户友好的,公开可访问的界面.
结论:
- EPP-MB模型有效地预测了菌毒素生物转化酶.
- DeepMBEnzy数据库有助于探索和利用用于真菌毒素排毒的酶候选者.
- 这项工作支持了真菌毒素排毒研究和应用方面的进展.
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