Synthetic Biology Journal ›› 2023, Vol. 4 ›› Issue (3): 535-550.DOI: 10.12211/2096-8280.2022-066
• Invited Review • Previous Articles Next Articles
Tao ZENG, Ruibo WU
Received:
2022-11-23
Revised:
2022-12-27
Online:
2023-07-05
Published:
2023-06-30
Contact:
Ruibo WU
曾涛, 巫瑞波
通讯作者:
巫瑞波
作者简介:
基金资助:
CLC Number:
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.
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数据库 | 特点 | 网址 |
---|---|---|
KEGG[ | 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[ | 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[ | 全面的生物酶反应数据库,与Uniprot高度关联 | https://www.rhea-db.org |
BRENDA[ | 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[ | 包含酶反应的动力学参数、反应条件等信息 | https://sabiork.h-its.org |
Reactome[ | 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[ | 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[ | 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[ | 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[ | 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 |
Table 1 Databases of enzymatic reactions
数据库 | 特点 | 网址 |
---|---|---|
KEGG[ | 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[ | 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[ | 全面的生物酶反应数据库,与Uniprot高度关联 | https://www.rhea-db.org |
BRENDA[ | 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[ | 包含酶反应的动力学参数、反应条件等信息 | https://sabiork.h-its.org |
Reactome[ | 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[ | 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[ | 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[ | 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[ | 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 |
Fig. 2 Prediction of forward and backward enzymatic reactions[Prediction starts with an enzyme molecule (green node) to deduce its substrate or product (yellow nodes), the lines represent transformation reactions between two molecules, with arrow from substrate (enzyme) to product (a) and the reverse (b). A reaction network is developed after the iterative prediction in which both known (solid nodes) and unknown (hollow nodes) molecules are included. The forward prediction is generally random while a target (blue node, such as a building block) is specified in the backward prediction, and the exploration will lead to the target with the help of iterative algorithms.]
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[ (http://www.rxnfinder.org/) | ATLASx[ (https://lcsb-databases.epfl.ch/Atlas2) BCSExplorer[ (http://www.rxnfinder.org/) | Reymond等[ (https://github.com/reymondgroup/OpenNMT-py) Kavraki等[ (https:// github.com/KavrakiLab/MetaTrans) | |
逆合成预测 | PrecursorFinder[ (http://www.rxnfinder.org/) | RetroPath[ (https://github.com/brsynth/RetroPathRL) RetroBioCat[ (https://retrobiocat.com) | BioNavi-NP[ (http://biopathnavi.qmclab.com/) Probst等[ (https://github.com/rxn4chemistry/biocatalysis-model) | |
酶搜索和设计 | EC-BLAST[ (https://www.ebi.ac.uk/) | Selenzyme[ (http://selenzyme.synbiochem.co.uk/) BridgIT[ (https://lcsb-databases.epfl.ch/Atlas2) E-zyme2[ (https://www.genome.jp/tools/e-zyme2/) | Faulon等[ (tool not available) Ranganathan等[ (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[ Araki等[ MTDNN[ | ||
功能 优化 | ECNet[ Gitter等[ | |||
酶反应性质预测 | Lercher等[ Palsson等[ DLKcat[ |
Table 2 Tools for the prediction and design of enzymatic reactions
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[ (http://www.rxnfinder.org/) | ATLASx[ (https://lcsb-databases.epfl.ch/Atlas2) BCSExplorer[ (http://www.rxnfinder.org/) | Reymond等[ (https://github.com/reymondgroup/OpenNMT-py) Kavraki等[ (https:// github.com/KavrakiLab/MetaTrans) | |
逆合成预测 | PrecursorFinder[ (http://www.rxnfinder.org/) | RetroPath[ (https://github.com/brsynth/RetroPathRL) RetroBioCat[ (https://retrobiocat.com) | BioNavi-NP[ (http://biopathnavi.qmclab.com/) Probst等[ (https://github.com/rxn4chemistry/biocatalysis-model) | |
酶搜索和设计 | EC-BLAST[ (https://www.ebi.ac.uk/) | Selenzyme[ (http://selenzyme.synbiochem.co.uk/) BridgIT[ (https://lcsb-databases.epfl.ch/Atlas2) E-zyme2[ (https://www.genome.jp/tools/e-zyme2/) | Faulon等[ (tool not available) Ranganathan等[ (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[ Araki等[ MTDNN[ | ||
功能 优化 | ECNet[ Gitter等[ | |||
酶反应性质预测 | Lercher等[ Palsson等[ DLKcat[ |
Fig. 3 Models for searching and predicting enzymes[Circular and square nodes represent sequences and reactions, respectively, and yellow filling indicates known data while green filling mean objects to be predicted. Similarity search (a) is to find a similar object in known enzyme-reaction pairs (connected nodes) to predict reactions (or enzymes) for target object. Classification model (b) is trained by enzymes with known function (usually discrete), in which the classification rule (white boundary) is clarified, and then the model can be used to classify an enzyme with unknown function. Regression model (c) is adapted to draw fitness landscape to predict continues variables such as the activity or stability of enzymes, which can then be used for enzyme design.]
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