醇脱氢酶/羰基还原酶与多底物分子适配性研究的进展★

doi:10.6023/cjoc202506006

摘要/Abstract

手性醇作为药物活性分子与精细化学品的关键手性砌块, 在医药、农药及化工等领域具有广泛应用. 然而传统合成技术存在效率低、选择性不足等瓶颈, 亟需通过新型催化体系实现高效不对称合成. 醇脱氢酶(ADHs)/羰基还原酶(CRs)作为手性醇绿色合成的核心生物催化剂, 其分子设计与工业应用已成为研究热点. 尽管这类酶具有环境友好性和催化特异性的优势, 但目前仍受限于底物宽泛性不足、底物适配性差等问题, 严重制约了其在手性醇精准合成中的规模化应用. 此综述系统阐述了DHs/CRs多底物适配度定量研究最新进展, 重点围绕酶库与多底物间构效关系的解析方法展开讨论, 并结合当前研究趋势提出未来发展方向. 期望为研究者提供理论参考, 推动多酶-多底物构效关系的深度探索, 进而建立ADHs/CRs的智能预测与设计模型, 实现手性醇合成用酶从“大海捞针式筛选”到“精准定制化开发”的技术革新.

关键词: 醇脱氢酶/羰基还原酶, 底物库设计, 酶库构建, 定量构效关系, 生物催化数据库, 深度学习

Chiral alcohols, as essential building blocks for active pharmaceutical ingredients and fine chemicals, have found broad applications in pharmaceuticals, agrochemicals, and chemical industries. However, conventional synthetic strategies often suffer from low efficiency and insufficient selectivity, highlighting the urgent need for innovative catalytic systems to achieve efficient asymmetric synthesis. Alcohol dehydrogenases (ADHs) and carbonyl reductases (CRs) are key biocatalysts enabling the green synthesis of chiral alcohols, and their molecular engineering and industrial deployment have emerged as research frontiers. Despite their intrinsic advantages of environmental compatibility and catalytic specificity, the practical application of ADHs/CRs remains hindered by their limited substrate promiscuity and poor substrate adaptability, which severely restrict large-scale implementation in the precise synthesis of chiral alcohols. This review provides a systematic overview of recent advances in quantitative multisubstrate fitness studies of ADHs/CRs, with a particular emphasis on methodologies for elucidating the quantitative structure-activity relationships (QSARs) across ADH libraries and diverse substrate panels. The perspectives presented aim to advance fundamental understanding of multi-enzyme-multi-substrate QSARs, enabling the development of intelligent design platform trained on tens of millions of QSAR data points. Such innovations promise to revolutionize alcohol dehydrogenase engineering paradigms, shifting the paradigm for optimal enzyme discovery in chiral alcohol synthesis from “needle-in-a-haystack” screening to “tailor-made” precision design.

Key words: alcohol dehydrogenase/carbonyl reductase, substrate library construction, enzyme library construction, quantitative structure-activity relationship, biocatalysis database, deep learning

引用此文

赵友学, 李兮若, 孟洛冰, 李春秀, 范贵生, 许建和. 醇脱氢酶/羰基还原酶与多底物分子适配性研究的进展^★[J]. 有机化学, 2025, 45(9): 3175-3185.

Youxue Zhao, Xiruo Li, Luobing Meng, Chunxiu Li, Guisheng Fan, Jianhe Xu. Advances in Understanding the Substrate Promiscuity of Alcohol Dehydrogenases/Carbonyl Reductases^★[J]. Chinese Journal of Organic Chemistry, 2025, 45(9): 3175-3185.

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图/表 13

图1. 包含手性醇、手性胺砌块的药物活性成分[3]

Figure 1. Various pharmaceutical active ingredients with chiral alcohols and/or chiral amine building blocks[3]

图2. 短链醇脱氢酶-NADPH (a)、中链醇脱氢酶-NADPH-Zn2＋ (b)和醛酮还原酶-NADPH (c)的结构对比

Figure 2. Structural comparison among short-chain alcohol dehydrogenase with NADPH (a), medium-chain alcohol dehydrogenase with NADPH and Zn²⁺ (b), and aldo-keto reductase with NADPH (c)

图3. cpADH5的潜在可催化的底物谱[14]

Figure 3. Potential catalytic substrate profile of cpADH5[14]

图4. 建立cpADH5潜在活性催化底物谱的流程[14]

Figure 4. A flowchart for building a potential catalytically active substrate profile for cpADH5[14]

图5. 综合比酶活和S-W指数进行的羰基还原酶高通量筛选: (A)以NADPH为辅因子时的筛选结果, (B)以NADH为辅因子时的筛选结果[15]

Figure 5. High-throughput screening of carbonyl reductase based on a combination of enzyme activity and S-W index: (A) screening results using NADPH as a cofactor, (B) screening results using NADH as a cofactor[15]

图6. 两次基因挖掘实践所建立的不同ADHs进化树分析[15-16]

Figure 6. Evolutionary tree analysis of different ADHs created by two gene mining practices[15-16] Right panel blue indicates inactive ADHs, red indicates active ADHs. Ancestral ADHs used for characterization are marked with red dots

图7. 两次酶学表征过程所用的不同羰基底物集合: 底物阵列I和底物阵列II[15-16]

Figure 7. Collection of different carbonyl substrates used in the two enzymatic characterization processes: substrate array I and substrate array II[15-16] Substrate libraries are classified into subgroups according to the type of substituent and reducing group of the substrate and are distinguished by different background colors

图8. 多底物面向的羰基还原酶资源库构建示意图[17]

Figure 8. Schematic diagram of the multifaceted construction of multiple ketoreductase libraries[17]

图9. 主成分分析(PCA)的高维聚类蛋白质多序列比对方法筛选及多底物表征流程[18]

Figure 9. Screening of high-dimensional clustered proteins for multiple sequence comparison methods and multi-substrate characterization process by principal component analysis (PCA)[18]

图10. 利用Forge软件进行酮底物反应性鉴别的流程图[19]

Figure 10. Flow chart of ketone substrate reactivity identification using Forge software[19]

图11. 一种利用机器学习算法建立不同ADH-底物关联性模型的方法[20]

Figure 11. A method for modeling different ADH-substrate correlations using machine learning algorithms[20]

图12. 基于米氏方程的动力学参数传统表征方法

Figure 12. Traditional methods for characterizing kinetic parameters based on the Michaelis-Menten equation

图13. 动力学参数表征方法的发展历程

Figure 13. Development of methods for characterization of kinetic parameters

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