从自然多样性中引导PET水溶酶的机器学习识别
Brenna Norton-Baker1,2,3, Evan Komp1,3, Japheth E Gado1,2
1Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States.
概括
研究人员使用机器学习和高通量选发现了91种新的PET水解酶. 几种酶在晶体聚乙烯二甲 (PET) 上在低pH下表现出高活性,推动了化学回收工作.
科学领域:
- 生物技术是生物技术.
- 酵素工程是什么意思 酵素工程
- 聚合物科学 聚合物科学
背景情况:
- 聚乙烯二甲 (PET) 的酶去聚合是一种关键的化学回收策略.
- 从自然来源识别新型PET降解酶 (PET酶) 对酶进化和工程至关重要.
研究的目的:
- 使用代机器学习策略,改进用于PET回收的酶发现.
- 在工业相关条件下识别具有高活性的新型PET水解酶.
主要方法:
- 采用了一种代机器学习策略,与HMM搜索相结合,以识别假定的PET水解酶.
- 利用高通量选来表达,净化和测定200多种PET水解酶候选物.
- 在不同的pH值,温度和基质结晶度上研究了酶活性.
主要成果:
- 发现了91种以前未知的PET水解酶,其中35种在晶体PET的pH值为4.5时活跃.
- 在具有挑战性的条件下,确定了四种酶,其活性与LCC-ICCG基准值相当或超过.
- 发现了酶区域和低pH活性之间的统计相关性,提高了机器学习预测器精度高达30%.
结论:
- 代机器学习和实验选有效地发现PET回收利用的活性酶.
- 确定了适用于开发更高效的商业回收工艺的新型PET酸盐.
- 在特定条件下了解结构-活性关系可以增强酶发现和工程工作.
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