Data-driven enzyme engineering to identify function-enhancing enzymes.
Data-driven enzyme engineering to identify function-enhancing enzymes.
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数据驱动的酶工程来识别功能增强酶。
DOI：
10.1093/protein/gzac009
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Yang,ZhongyueJ
中科院分区：
文献类型：
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作者：
Jiang,Yaoyukun;Ran,Xinchun;Yang,ZhongyueJ
识别功能增强酶变体是蛋白质科学中的“圣杯”挑战，因为它将使研究人员能够扩展生物催化工具箱，用于药物样分子的后期功能化，塑料和其他污染物的环境降解以及食物过敏的医学治疗。数据驱动的策略，包括统计建模、机器学习和深度学习，在很大程度上促进了对酶的序列-结构-功能关系的理解。它们还增强了预测和设计新酶和酶变体的能力，以催化新反应的转化。本文综述了近年来数据驱动模型在催化反应效率提高突变体识别中的应用进展。我们还讨论了社区目前面临的挑战和障碍。虽然这篇综述并不全面，但我们希望通过讨论可以让读者了解数据驱动酶工程的最新进展，激发更多的联合实验-计算努力，以开发和应用数据驱动模型来创新合成和制药应用的生物催化剂。
Identifying function-enhancing enzyme variants is a ‘holy grail’ challenge in protein science because it will allow researchers to expand the biocatalytic toolbox for late-stage functionalization of drug-like molecules, environmental degradation of plastics and other pollutants, and medical treatment of food allergies. Data-driven strategies, including statistical modeling, machine learning, and deep learning, have largely advanced the understanding of the sequence–structure–function relationships for enzymes. They have also enhanced the capability of predicting and designing new enzymes and enzyme variants for catalyzing the transformation of new-to-nature reactions. Here, we reviewed the recent progresses of data-driven models that were applied in identifying efficiency-enhancing mutants for catalytic reactions. We also discussed existing challenges and obstacles faced by the community. Although the review is by no means comprehensive, we hope that the discussion can inform the readers about the state-of-the-art in data-driven enzyme engineering, inspiring more joint experimental-computational efforts to develop and apply data-driven modeling to innovate biocatalysts for synthetic and pharmaceutical applications.