基于基质-产品对的酶乱交的高通量预测
Huadong Xing1, Pengli Cai1, Dongliang Liu1
1CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Briefings in bioinformatics
|March 15, 2024
概括
我们开发了基质-产品基于对的酶放荡性预测 (SPEPP) 模型,可以在没有先前反应知识的情况下预测酶放荡性. 这个工具通过启用自定义的酶库选来帮助代谢工程和合成生物学.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 合成生物学 合成生物学
背景情况:
- 对特定的基质-产品对进行酶选对于代谢工程和合成生物学至关重要.
- 当前的方法往往需要先前的知识,并且缺乏灵活性与定制的酶库.
研究的目的:
- 开发一种用于预测酶乱交的新型模型.
- 克服现有的酶选工具的局限性,消除了对先前反应知识的需求,并允许自定义的酶库.
主要方法:
- 开发了基质-产品基于对的酶流动性预测 (SPEPP) 模型.
- 利用转移学习和变压器架构进行预测.
- 创建了EnzyPick,一个用户友好的网络服务器用于酶选.
主要成果:
- SPEPP显示出对酶乱交的强有力的预测能力.
- 该模型成功地预测了酶功能,而不依赖于先前的反应数据.
- EnzyPick为代谢工程师提供了一个可访问的平台.
结论:
- SPEPP模型增强了用于代谢工程和合成生物学的酶发现.
- EnzyPick为所有技能水平的用户提供了路径设计和优化.
- 该SPEPP方法扩大了酶查能力,用于各种应用.
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