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MECE:一种基于深度神经网络和分子演变的催化效率提升的方法
Hanqing Liu1, Feifei Guan2, Tuoyu Liu2
1Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Science bulletin
|October 22, 2023
概括
一个新的深度学习平台,MECE (增强催化效率的方法),准确地预测突变以改善甘氨酸化酶 (GHs). 这种人工智能方法显著提高了酶催化效率,为工业应用提供了一个新的工具.
科学领域:
- 酶工程是什么?酶工程是什么?
- 生物信息学是一种生物信息学.
- 计算生物学是一种计算生物学.
背景情况:
- 高效的甘氨酸化酶 (GHs) 对于各种工业应用至关重要.
- 通过突变预测来提高酶催化效率仍然是生物技术的一个重大挑战.
研究的目的:
- 引入和验证基于深度学习的平台 (MECE),用于预测增强GHs催化效率的突变.
- 证明DeepGH模型在识别GH家族和功能残留物的能力.
主要方法:
- 开发了DeepGH,这是一个基于来自CAZy数据库的119个GH家族序列的深度学习模型.
- 使用梯度加权类激活映射 (Grad-CAM) 来理解模型分类和指导突变设计.
- 通过创建和表征一种新型酶突变物CHIS1754-MUT7.7,验证MECE平台的有效性.
主要成果:
- 在经过十倍的交叉验证后,DeepGH模型实现了96.73%的预测准确度.
- 工程突变CHIS1754-MUT7,有七种氨基酸替代,与野生类型相比,kcat/Km增加了2353倍.
- MECE平台展示了高计算效率和低实验成本.
结论:
- 由深度学习驱动的MECE平台为酶催化效率的智能设计提供了一种新且有效的方法.
- 这种方法在加速酶工程和扩大生物技术和工业应用方面具有重大前景.
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