Data-driven prediction and design for enzymatic reactions

doi:10.12211/2096-8280.2022-066

Abstract

Abstract:

Enzymes are efficient catalysts with substrate specificity and stereo- and regioselectivity, which are widely used in producing chemicals, drugs and materials. Enzymes are cores for biocatalysis, and thus prediction on their functions and design of enzymatic reactions are driving forces for intelligent biomanufacturing through biocatalysis. So far limited understanding on enzymatic catalysis hinders the exploration of enzymatic reactions for industrial applications. For example, it is difficult to predict enzymatic activities on unreported substrates, to elucidate synthetic routes for newly found structures of enzymes, and to redesign enzymes for specific scenarios. In the era of big data, data-driven approaches have exhibited powerful capabilities for exploring enzymatic reactions, by filling gap between the large corpora of enzymatic data and limited understanding on functions of the enzymes. Recently, computational tools and platforms have greatly accelerated experimental research, and improved the design-build-test-learn cycle. Herein we review progress in computational tools for enzymatic reaction prediction and design, focusing on the application of deep learning methods in this field. Referring to key elements (substrate, product and enzyme) for enzymatic reactions, related databases are summarized. Then, the data-driven approaches for forward and backward prediction of enzymatic reaction routes and functions of enzymes, their design and theoretical calculation for enzymatic catalysis are addressed. Finally, the status and prospective of data-driven approaches for enzymatic catalysis prediction and design, including the data, model, algorithm and platform, are discussed.

Key words: big data, machine learning, enzymatic catalysis, enzyme design, biosynthesis

摘要：

酶催化已经在日用化学品、药物和功能材料等生产中得到越来越广泛的应用。酶，作为生物制造业的核心“芯片”，其催化反应的预测与设计是推动传统生物制造走向生物智造发展的核心驱动力之一。然而目前我们对大自然酶催化的了解仍然非常有限，这严重阻碍了我们对酶催化空间的探索和利用。随着大数据时代的到来，数据驱动的计算模拟已经成为酶催化新空间的挖掘及其功能优化设计的重要手段。各种计算工具和平台的开发正极大地加速并赋能于酶学相关领域的各类实验研究。本文针对酶催化过程中底物、产物和酶的预测及设计方法进行了综述，概述了近年来酶反应相关的数据库，汇总比较了数据驱动的酶反应设计工具，着重介绍了深度学习在该领域的应用，并从数据、模型、算法、平台等多方面展望和探讨了数据驱动型计算方法在酶反应预测与设计领域的发展前景。

关键词: 大数据, 机器学习, 酶催化, 酶设计, 生物合成

CLC Number:

Q814.9

Tao ZENG, Ruibo WU. Data-driven prediction and design for enzymatic reactions[J]. Synthetic Biology Journal, 2023, 4(3): 535-550.

曾涛, 巫瑞波. 数据驱动的酶反应预测与设计[J]. 合成生物学, 2023, 4(3): 535-550.

Figures/Tables 6

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数据库	特点	网址
KEGG^[23]	具有物种、基因组、酶等多水平注释的合成（代谢）反应数据库	https://www.kegg.jp/kegg
MetaCyc^[24]	以全面的初级/次级代谢产物合成途径对反应进行注释	https://metacyc.org
Rhea^[25]	全面的生物酶反应数据库，与Uniprot高度关联	https://www.rhea-db.org
BRENDA^[26]	对酶的各项信息（如分类、反应、参数等）进行详细注释	https://www.brenda-enzymes.org
SABIO-RK^[27]	包含酶反应的动力学参数、反应条件等信息	https://sabiork.h-its.org
Reactome^[28]	综合的生物通路数据库，包括代谢、信号调控等通路数据	https://reactome.org
PathBank^[29]	以常见模式物种为基础的代谢、调控通路数据库	http://www.pathbank.org
HMDB^[30]	人体小分子代谢数据库，包含反应、MS、NMR谱图等信息	https://hmdb.ca
MetaNetX^[31]	整合了多个来源的生化反应数据库用于代谢网络模型构建	https://www.metanetx.org
Reaxys^[32]	从专利和文献搜集和整理的大量有机反应和酶反应路线(商业非开源)	https://www.reaxys.com

数据库	特点	网址
KEGG^[23]	具有物种、基因组、酶等多水平注释的合成（代谢）反应数据库	https://www.kegg.jp/kegg
MetaCyc^[24]	以全面的初级/次级代谢产物合成途径对反应进行注释	https://metacyc.org
Rhea^[25]	全面的生物酶反应数据库，与Uniprot高度关联	https://www.rhea-db.org
BRENDA^[26]	对酶的各项信息（如分类、反应、参数等）进行详细注释	https://www.brenda-enzymes.org
SABIO-RK^[27]	包含酶反应的动力学参数、反应条件等信息	https://sabiork.h-its.org
Reactome^[28]	综合的生物通路数据库，包括代谢、信号调控等通路数据	https://reactome.org
PathBank^[29]	以常见模式物种为基础的代谢、调控通路数据库	http://www.pathbank.org
HMDB^[30]	人体小分子代谢数据库，包含反应、MS、NMR谱图等信息	https://hmdb.ca
MetaNetX^[31]	整合了多个来源的生化反应数据库用于代谢网络模型构建	https://www.metanetx.org
Reaxys^[32]	从专利和文献搜集和整理的大量有机反应和酶反应路线(商业非开源)	https://www.reaxys.com

反应预测与酶设计工具
		基于相似性	基于反应规则	基于机器学习
正向反应预测		BioSynther^[44] （http://www.rxnfinder.org/）	ATLASx^[40] （https://lcsb-databases.epfl.ch/Atlas2） BCSExplorer^[43] （http://www.rxnfinder.org/）	Reymond等^[45] （https://github.com/reymondgroup/OpenNMT-py） Kavraki等^[46] （https:// github.com/KavrakiLab/MetaTrans）
逆合成预测		PrecursorFinder^[47] （http://www.rxnfinder.org/）	RetroPath^[48] （https://github.com/brsynth/RetroPathRL） RetroBioCat^[49] （https://retrobiocat.com）	BioNavi-NP^[50] （http://biopathnavi.qmclab.com/） Probst等^[51] （https://github.com/rxn4chemistry/biocatalysis-model）
酶搜索和设计		EC-BLAST^[52] （https://www.ebi.ac.uk/）	Selenzyme^[53] （http://selenzyme.synbiochem.co.uk/） BridgIT^[54] （https://lcsb-databases.epfl.ch/Atlas2） E-zyme2^[55] （https://www.genome.jp/tools/e-zyme2/）	Faulon等^[56] （tool not available） Ranganathan等^[57] （https://github.com/ranganathanlab/bmDCA）
酶功能与性质预测工具
酶功能预测	功能分类	DeepEC^[58]（https://bitbucket.org/kaistsystemsbiology/deepec） Araki等^[59]（https://github.com/nwatanbe/SVM_E_model） MTDNN^[60]（http://bioinf.cs.ucl.ac.uk/downloads/mtdnn）
酶功能预测	功能优化	ECNet^[61]（https://github.com/luoyunan/ECNet ） Gitter等^[62]（https://github.com/gitter-lab/nn4dms）
酶反应性质预测		Lercher等^[63]（https://github.com/AlexanderKroll/KM_prediction） Palsson等^[64]（tool not available） DLKcat^[65]（https://github.com/SysBioChalmers/DLKcat）

反应预测与酶设计工具
		基于相似性	基于反应规则	基于机器学习
正向反应预测		BioSynther^[44] （http://www.rxnfinder.org/）	ATLASx^[40] （https://lcsb-databases.epfl.ch/Atlas2） BCSExplorer^[43] （http://www.rxnfinder.org/）	Reymond等^[45] （https://github.com/reymondgroup/OpenNMT-py） Kavraki等^[46] （https:// github.com/KavrakiLab/MetaTrans）
逆合成预测		PrecursorFinder^[47] （http://www.rxnfinder.org/）	RetroPath^[48] （https://github.com/brsynth/RetroPathRL） RetroBioCat^[49] （https://retrobiocat.com）	BioNavi-NP^[50] （http://biopathnavi.qmclab.com/） Probst等^[51] （https://github.com/rxn4chemistry/biocatalysis-model）
酶搜索和设计		EC-BLAST^[52] （https://www.ebi.ac.uk/）	Selenzyme^[53] （http://selenzyme.synbiochem.co.uk/） BridgIT^[54] （https://lcsb-databases.epfl.ch/Atlas2） E-zyme2^[55] （https://www.genome.jp/tools/e-zyme2/）	Faulon等^[56] （tool not available） Ranganathan等^[57] （https://github.com/ranganathanlab/bmDCA）
酶功能与性质预测工具
酶功能预测	功能分类	DeepEC^[58]（https://bitbucket.org/kaistsystemsbiology/deepec） Araki等^[59]（https://github.com/nwatanbe/SVM_E_model） MTDNN^[60]（http://bioinf.cs.ucl.ac.uk/downloads/mtdnn）
酶功能预测	功能优化	ECNet^[61]（https://github.com/luoyunan/ECNet ） Gitter等^[62]（https://github.com/gitter-lab/nn4dms）
酶反应性质预测		Lercher等^[63]（https://github.com/AlexanderKroll/KM_prediction） Palsson等^[64]（tool not available） DLKcat^[65]（https://github.com/SysBioChalmers/DLKcat）

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