机器学习驱动的基因组规模代谢模型构建与优化

吴柯; 罗家豪; 李斐然

doi:10.12211/2096-8280.2024-090

您当前的位置：

首页 >

文章列表页 >

机器学习驱动的基因组规模代谢模型构建与优化

特约评述 | 更新时间：2025-07-07

- 机器学习驱动的基因组规模代谢模型构建与优化
- Applications of machine learning in the reconstruction and curation of genome-scale metabolic models
- 合成生物学 2025年6卷第3期页码：566-584
- 作者机构：
  
  1.清华大学深圳国际研究生院，生物医药与健康工程研究院，广东深圳 518055
  2.清华大学化学工程系，工业生物催化教育部重点实验室，北京 100084
- 作者简介：
  
  [ "吴柯（2000—），男，博士研究生。研究方向为机器学习辅助基因组规模代谢模型开发。 E-mail：wk37@tju.edu.cn" ]
  [ "李斐然（1993—），女，助理教授，博士生导师。研究方向为基因组规模代谢模型开发、微生物细胞工厂设计、酶参数预测，致力于构建数字生命模型。 E-mail：feiranli@sz.tsinghua.edu.cn" ]
- 基金信息：
  
  国家自然科学基金面上项目(22478223)
- DOI：10.12211/2096-8280.2024-090
  中图分类号： Q814.9
- 收稿：2024-12-02，
  
  修回：2025-02-12，
  
  纸质出版：2025-06-30
- 稿件说明：
移动端阅览
吴柯，罗家豪，李斐然. 机器学习驱动的基因组规模代谢模型构建与优化［J］. 合成生物学， 2025， 6（3）： 566-584

WU Ke， LUO Jiahao， LI Feiran. Applications of machine learning in the reconstruction and curation of genome-scale metabolic models［J］. Synthetic Biology Journal， 2025， 6（3）： 566-584
吴柯，罗家豪，李斐然. 机器学习驱动的基因组规模代谢模型构建与优化［J］. 合成生物学， 2025， 6（3）： 566-584 DOI： 10.12211/2096-8280.2024-090.

WU Ke， LUO Jiahao， LI Feiran. Applications of machine learning in the reconstruction and curation of genome-scale metabolic models［J］. Synthetic Biology Journal， 2025， 6（3）： 566-584 DOI： 10.12211/2096-8280.2024-090.

摘要

自1999年首个基因组规模代谢模型（genome-scale metabolic model，GEM）问世以来，GEM已成为解析生物代谢的重要工具。该模型包含代谢基因、代谢物和反应，并结合化学计量矩阵与约束优化，系统描述和模拟生物体内的代谢过程。此外，GEM能够整合热力学参数、动力学参数、多组学数据及多细胞过程，构建更精细且具有更强大预测能力的多约束多过程模型。然而，先验知识的局限成为其发展的瓶颈。机器学习技术凭借强大的数据处理和模式识别能力，为进一步扩展GEM提供了新思路。本综述系统总结了传统GEM及多约束多过程模型的构建流程，并着重探讨了机器学习在其中关键步骤中的应用前景，如基因功能注释、途径解析、空缺填补和生物学参数预测。机器学习技术作为新的驱动力，有望大幅度提升GEM的规模和质量，深化对生物代谢机制的理解，并推动实现数字孪生细胞。

Abstract

Since the publication of the first genome-scale metabolic model (GEM) in 1999

GEMs have become an essential tool for analyzing metabolism. The models integrate genes

metabolites

and reactions for combining stoichiometric matrices with constraint-based optimization to systematically describe and simulate metabolic processes in organisms. The development of automated pipelines for reconstructing GEMs has expanded their applicability to organisms from all kingdoms of life. Additionally

GEMs can integrate kinetic parameters

thermodynamic parameters

multi-omics data and multi-cellular processes to reconstruct more accurate models

thereby improving prediction accuracy. However

the reconstruction of GEMs remains heavily dependent on pre-existing knowledge

inherently limiting their scope to currently available information. This dependency restricts our ability to fully unravel the complexity and dynamic nature of metabolism. Recent advances in machine learning have demonstrated extraordinary capabilities for biological tasks such as protein structure prediction

disease identification and GEM reconstruction with functional annotation and large-scale data integration

showcasing its power in identifying patterns and uncovering hidden relationships within biological systems. Machine learning provides a promising pathway to overcome the limitations of GEMs by expanding their applicability to areas previously constrained by data availability and complexity. This review summarizes the traditional reconstruction methods of GEMs and their applications in integrating multi-dimensional data to build multi-constraint and multi-process models. The review also focuses on key applications of machine learning in gene function annotation

pathway analysis

gap-filling prediction in the reconstruction of GEMs. Additionally

the potential of machine learning in predicting kinetic

thermodynamic

and other key biochemical parameters in the reconstruction of multi-constraint and multi-process models is discussed. By combining GEMs with machine learning innovations

researchers can improve model accuracy

enhance scalability

and gain new insights into previously elusive metabolic mechanisms

bridging gaps in metabolic knowledge

and underscoring its importance as a cornerstone for future development in systems biology and biotechnology.

关键词

Keywords

references

GONG Z J , CHEN J Y , JIAO X Y , et al . Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects [J ] . Biotechnology Advances , 2024 , 72 : 108319 .

ORTH J D , THIELE I , PALSSON B Ø . What is flux balance analysis? [J ] . Nature Biotechnology , 2010 , 28 ( 3 ): 245 - 248 .

WAGNER A , WANG C , FESSLER J , et al . Metabolic modeling of single Th17 cells reveals regulators of autoimmunity [J ] . Cell , 2021 , 184 ( 16 ): 4168 - 4185 . e21.

KIM B J , KIM W J , KIM D I , et al . Applications of genome-scale metabolic network model in metabolic engineering [J ] . Journal of Industrial Microbiology & Biotechnology , 2015 , 42 ( 3 ): 339 - 348 .

RYU J Y , KIM H U , LEE S Y . Reconstruction of genome-scale human metabolic models using omics data [J ] . Integrative Biology , 2015 , 7 ( 8 ): 859 - 868 .

GU C D , KIM G B , KIM W J , et al . Current status and applications of genome-scale metabolic models [J ] . Genome Biology , 2019 , 20 ( 1 ): 121 .

EDWARDS J S , PALSSON B O . Systems properties of the Haemophilus influenzae Rd metabolic genotype [J ] . Journal of Biological Chemistry , 1999 , 274 ( 25 ): 17410 - 17416 .

ABRAMSON J , ADLER J , DUNGER J , et al . Accurate structure prediction of biomolecular interactions with AlphaFold3 [J ] . Nature , 2024 , 630 ( 8016 ): 493 - 500 .

DAUPARAS J , ANISHCHENKO I , BENNETT N , et al . Robust deep learning-based protein sequence design using ProteinMPNN [J ] . Science , 2022 , 378 ( 6615 ): 49 - 56 .

JUMPER J , EVANS R , PRITZEL A , et al . Highly accurate protein structure prediction with AlphaFold [J ] . Nature , 2021 , 596 ( 7873 ): 583 - 589 .

YU T H , CUI H Y , LI J C , et al . Enzyme function prediction using contrastive learning [J ] . Science , 2023 , 379 ( 6639 ): 1358 - 1363 .

FINNIGAN W , HEPWORTH L J , FLITSCH S L , et al . RetroBioCat as a computer-aided synthesis planning tool for biocatalytic reactions and cascades [J ] . Nature Catalysis , 2021 , 4 ( 2 ): 98 - 104 .

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 .

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

SABZEVARI M , SZEDMAK S , PENTTILÄ M , et al . Strain design optimization using reinforcement learning [J ] . PLoS Computational Biology , 2022 , 18 ( 6 ): e1010177 .

禹伟 , 高教琪 , 周雍进 . 一碳生物转化合成有机酸的研究进展 [J ] . 合成生物学 , 2024 , 5 ( 5 ): 1169 - 1188 .

YU W , GAO J Q , ZHOU Y J . Bioconversion of one carbon feedstocks for producing organic acids [J ] . Synthetic Biology Journal , 2024 , 5 ( 5 ): 1169 - 1188 .

KUNDU P , BEURA S , MONDAL S , et al . Machine learning for the advancement of genome-scale metabolic modeling [J ] . Biotechnology Advances , 2024 , 74 : 108400 .

KANEHISA M , FURUMICHI M , SATO Y , et al . KEGG: biological systems database as a model of the real world [J ] . Nucleic Acids Research , 2025 , 53 ( D1 ): D672 - D677 .

The UniProt Consortium . UniProt: the universal protein knowledgebase [J ] . Nucleic Acids Research , 2018 , 46 ( 5 ): 2699 .

CHANG A , JESKE L , ULBRICH S , et al . BRENDA, the ELIXIR core data resource in 2021: new developments and updates [J ] . Nucleic Acids Research , 2021 , 49 ( D1 ): D498 - D508 .

DUVAUD S , GABELLA C , LISACEK F , et al . Expasy, the Swiss bioinformatics resource portal, as designed by its users [J ] . Nucleic Acids Research , 2021 , 49 ( W1 ): W216 - W227 .

WITTIG U , REY M , WEIDEMANN A , et al . SABIO-RK: an updated resource for manually curated biochemical reaction kinetics [J ] . Nucleic Acids Research , 2018 , 46 ( D1 ): D656 - D660 .

BANSAL P , MORGAT A , AXELSEN K B , et al . Rhea, the reaction knowledgebase in 2022 [J ] . Nucleic Acids Research , 2022 , 50 ( D1 ): D693 - D700 .

KARP P D , BILLINGTON R , CASPI R , et al . The BioCyc collection of microbial genomes and metabolic pathways [J ] . Briefings in Bioinformatics , 2019 , 20 ( 4 ): 1085 - 1093 .

MORETTI S , TRAN V D T , MEHL F , et al . MetaNetX/MNXref: unified namespace for metabolites and biochemical reactions in the context of metabolic models [J ] . Nucleic Acids Research , 2021 , 49 ( D1 ): D570 - D574 .

KING Z A , LU J , DRÄGER A , et al . BiGG Models: a platform for integrating, standardizing and sharing genome-scale models [J ] . Nucleic Acids Research , 2016 , 44 ( D1 ): D515 - D522 .

MALIK-SHERIFF R S , GLONT M , NGUYEN T V N , et al . BioModels-15 years of sharing computational models in life science [J ] . Nucleic Acids Research , 2020 , 48 ( D1 ): D407 - D415 .

ARKIN A P , COTTINGHAM R W , HENRY C S , et al . KBase: the United States Department of Energy systems biology knowledgebase [J ] . Nature Biotechnology , 2018 , 36 ( 7 ): 566 - 569 .

LIEVEN C , BEBER M E , OLIVIER B G , et al . Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing [J ] . Nature Biotechnology , 2020 , 38 ( 4 ): 504 .

THIELE I , PALSSON B Ø . A protocol for generating a high-quality genome-scale metabolic reconstruction [J ] . Nature Protocols , 2010 , 5 ( 1 ): 93 - 121 .

HEIRENDT L , ARRECKX S , PFAU T , et al . Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0 [J ] . Nature Protocols , 2019 , 14 ( 3 ): 639 - 702 .

WANG H , MARCIŠAUSKAS S , SÁNCHEZ B J , et al . RAVEN 2.0: a versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor [J ] . PLoS Computational Biology , 2018 , 14 ( 10 ): e1006541 .

DEVOID S , OVERBEEK R , DEJONGH M , et al . Automated genome annotation and metabolic model reconstruction in the SEED and Model SEED [J ] . Methods in Molecular Biology , 2013 , 985 : 17 - 45 .

MACHADO D , ANDREJEV S , TRAMONTANO M , et al . Fast automated reconstruction of genome-scale metabolic models for microbial species and communities [J ] . Nucleic Acids Research , 2018 , 46 ( 15 ): 7542 - 7553 .

CAPELA J , LAGOA D , RODRIGUES R , et al . Merlin, an improved framework for the reconstruction of high-quality genome-scale metabolic models [J ] . Nucleic Acids Research , 2022 , 50 ( 11 ): 6052 - 6066 .

ZIMMERMANN J , KALETA C , WASCHINA S . Gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models [J ] . Genome Biology , 2021 , 22 ( 1 ): 81 .

HEINKEN A , HERTEL J , ACHARYA G , et al . Genome-scale metabolic reconstruction of 7302 human microorganisms for personalized medicine [J ] . Nature Biotechnology , 2023 , 41 ( 9 ): 1320 - 1331 .

FANG X , LLOYD C J , PALSSON B O . Reconstructing organisms in silico : genome-scale models and their emerging applications [J ] . Nature Reviews Microbiology , 2020 , 18 ( 12 ): 731 - 743 .

LU H Z , XIAO L C , LIAO W B , et al . Cell factory design with advanced metabolic modelling empowered by artificial intelligence [J ] . Metabolic Engineering , 2024 , 85 : 61 - 72 .

CARRASCO MURIEL J , LONG C , SONNENSCHEIN N . Simultaneous application of enzyme and thermodynamic constraints to metabolic models using an updated Python implementation of GECKO [J ] . Microbiology Spectrum , 2023 , 11 ( 6 ): e0170523 .

BI X Y , CHENG Y , XU X H , et al . etiBsu1209: a comprehensive multiscale metabolic model for Bacillus subtilis [J ] . Biotechnology and Bioengineering , 2023 , 120 ( 6 ): 1623 - 1639 .

SCHROEDER W L , SUTHERS P F , WILLIS T C , et al . Current state, challenges, and opportunities in genome-scale resource allocation models: a mathematical perspective [J ] . Metabolites , 2024 , 14 ( 7 ): 365 .

HENRY C S , JANKOWSKI M D , BROADBELT L J , et al . Genome-scale thermodynamic analysis of Escherichia coli metabolism [J ] . Biophysical Journal , 2006 , 90 ( 4 ): 1453 - 1461 .

HENRY C S , BROADBELT L J , HATZIMANIKATIS V . Thermodynamics-based metabolic flux analysis [J ] . Biophysical Journal , 2007 , 92 ( 5 ): 1792 - 1805 .

DASH S , OLSON D G , JOSHUA CHAN S H , et al . Thermodynamic analysis of the pathway for ethanol production from cellobiose in Clostridium thermocellum [J ] . Metabolic Engineering , 2019 , 55 : 161 - 169 .

NOOR E , BAR-EVEN A , FLAMHOLZ A , et al . Pathway thermodynamics highlights kinetic obstacles in central metabolism [J ] . PLoS Computational Biology , 2014 , 10 ( 2 ): e1003483 .

HÄDICKE O , VON KAMP A , AYDOGAN T , et al . OptMDFpathway: identification of metabolic pathways with maximal thermodynamic driving force and its application for analyzing the endogenous CO 2 fixation potential of Escherichia coli [J ] . PLoS Computational Biology , 2018 , 14 ( 9 ): e1006492 .

FLAMHOLZ A , NOOR E , BAR-EVEN A , et al . Glycolytic strategy as a tradeoff between energy yield and protein cost [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2013 , 110 ( 24 ): 10039 - 10044 .

BEKIARIS P S , KLAMT S . Automatic construction of metabolic models with enzyme constraints [J ] . BMC Bioinformatics , 2020 , 21 ( 1 ): 19 .

BEG Q K , VAZQUEZ A , ERNST J , et al . Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2007 , 104 ( 31 ): 12663 - 12668 .

ADADI R , VOLKMER B , MILO R , et al . Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters [J ] . PLoS Computational Biology , 2012 , 8 ( 7 ): e1002575 .

MAO Z T , ZHAO X , YANG X , et al . ECMpy, a simplified workflow for constructing enzymatic constrained metabolic network model [J ] . Biomolecules , 2022 , 12 ( 1 ): 65 .

MAO Z T , NIU J H , ZHAO J X , et al . ECMpy 2.0: a Python package for automated construction and analysis of enzyme-constrained models [J ] . Synthetic and Systems Biotechnology , 2024 , 9 ( 3 ): 494 - 502 .

SÁNCHEZ B J , ZHANG C , NILSSON A , et al . Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints [J ] . Molecular Systems Biology , 2017 , 13 ( 8 ): 935 .

DOMENZAIN I , SÁNCHEZ B , ANTON M , et al . Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0 [J ] . Nature Communications , 2022 , 13 ( 1 ): 3766 .

CHEN Y , GUSTAFSSON J , TAFUR RANGEL A , et al . Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0 [J ] . Nature Protocols , 2024 , 19 ( 3 ): 629 - 667 .

KERKHOVEN E J . Advances in constraint-based models: methods for improved predictive power based on resource allocation constraints [J ] . Current Opinion in Microbiology , 2022 , 68 : 102168 .

YANG X , MAO Z T , ZHAO X , et al . Integrating thermodynamic and enzymatic constraints into genome-scale metabolic models [J ] . Metabolic Engineering , 2021 , 67 : 133 - 144 .

LI G , HU Y T , ZRIMEC J , et al . Bayesian genome scale modelling identifies thermal determinants of yeast metabolism [J ] . Nature Communications , 2021 , 12 ( 1 ): 190 .

O’BRIEN E J , LERMAN J A , CHANG R L , et al . Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction [J ] . Molecular Systems Biology , 2013 , 9 : 693 .

ALSIYABI A , CHOWDHURY N B , LONG D N , et al . Enhancing in silico strain design predictions through next generation metabolic modeling approaches [J ] . Biotechnology Advances , 2022 , 54 : 107806 .

CHEN K , GAO Y , MIH N , et al . Thermosensitivity of growth is determined by chaperone-mediated proteome reallocation [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2017 , 114 ( 43 ): 11548 - 11553 .

DU B , YANG L , LLOYD C J , et al . Genome-scale model of metabolism and gene expression provides a multi-scale description of acid stress responses in Escherichia coli [J ] . PLoS Computational Biology , 2019 , 15 ( 12 ): e1007525 .

YANG L , MIH N , ANAND A , et al . Cellular responses to reactive oxygen species are predicted from molecular mechanisms [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2019 , 116 ( 28 ): 14368 - 14373 .

ZHAO J , CHEN K , PALSSON B O , et al . StressME: unified computing framework of Escherichia coli metabolism, gene expression, and stress responses [J ] . PLoS Computational Biology , 2024 , 20 ( 2 ): e1011865 .

CHEN Y , VAN PELT-KLEINJAN E , VAN OLST B , et al . Proteome constraints reveal targets for improving microbial fitness in nutrient-rich environments [J ] . Molecular Systems Biology , 2021 , 17 ( 4 ): e10093 .

ELSEMMAN I E , RODRIGUEZ PRADO A , GRIGAITIS P , et al . Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies [J ] . Nature Communications , 2022 , 13 ( 1 ): 801 .

LI F R , CHEN Y , QI Q , et al . Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints [J ] . Nature Communications , 2022 , 13 ( 1 ): 2969 .

CHEN Y , LI F R , MAO J W , et al . Yeast optimizes metal utilization based on metabolic network and enzyme kinetics [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2021 , 118 ( 12 ): e2020154118 .

SALVY P , HATZIMANIKATIS V . The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models [J ] . Nature Communications , 2020 , 11 ( 1 ): 30 .

OFTADEH O , SALVY P , MASID M , et al . A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reacti on thermodynamics [J ] . Nature Communications , 2021 , 12 ( 1 ): 4790 .

RYU J Y , KIM H U , LEE S Y . Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2019 , 116 ( 28 ): 13996 - 14001 .

KIM G B , KIM J Y , LEE J A , et al . Functional annotation of enzyme-encoding genes using deep learning with transformer layers [J ] . Nature Communications , 2023 , 14 ( 1 ): 7370 .

SHI Z K , DENG R , YUAN Q Q , et al . Enzyme commission number prediction and benchmarking with hierarchical dual-core multitask learning framework [J ] . Research , 2023 , 6 : 0153 .

HAN S R , PARK M , KOSARAJU S , et al . Evidential deep learning for trustworthy prediction of enzyme commission number [J ] . Briefings in Bioinformatics , 2023 , 25 ( 1 ): bbad401 .

BUTON N , COSTE F , LE CUNFF Y . Predicting enzymatic function of protein sequences with attention [J ] . Bioinformatics , 2023 , 39 ( 10 ): btad620 .

WATANABE N , YAMAMOTO M , MURATA M , et al . EnzymeNet: residual neural networks model for Enzyme Commission number prediction [J ] . Bioinformatics Advances , 2023 , 3 ( 1 ): vbad173 .

SONG Y D , YUAN Q M , CHEN S , et al . Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures [J ] . Nature Communications , 2024 , 15 ( 1 ): 8180 .

KOCH M , DUIGOU T , FAULON J L . Reinforcement learning for bioretrosynthesis [J ] . ACS Synthetic Biology , 2020 , 9 ( 1 ): 157 - 168 .

COLEY C W , THOMAS D A , LUMMISS J A M , et al . A robotic platform for flow synthesis of organic compounds informed by AI planning [J ] . Science , 2019 , 365 ( 6453 ): eaax1566 .

LEVIN I , LIU M J , VOIGT C A , et al . Merging enzymatic and synthetic chemistry with computational synthesis planning [J ] . Nature Communications , 2022 , 13 ( 1 ): 7747 .

KREUTTER D , SCHWALLER P , REYMOND J L . Predicting enzymatic reactions with a molecular transformer [J ] . Chemical Science , 2021 , 12 ( 25 ): 8648 - 8659 .

PROBST D , MANICA M , NANA TEUKAM Y G , et al . Biocatalysed synthesis planning using data-driven learning [J ] . Nature Communications , 2022 , 13 ( 1 ): 964 .

ZHENG S J , ZENG T , LI C T , et al . Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP [J ] . Nature Communications , 2022 , 13 ( 1 ): 3342 .

ZENG T , JIN Z H , ZHENG S J , et al . Developing BioNavi for hybrid retrosynthesis planning [J ] . JACS Au , 2024 , 4 ( 7 ): 2492 - 2502 .

KROLL A , RANJAN S , ENGQVIST M K M , et al . A general model to predict small molecule substrates of enzymes based on machine and deep learning [J ] . Nature Communications , 2023 , 14 ( 1 ): 2787 .

UPADHYAY V , BOORLA V S , MARANAS C D . Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network [J ] . Metabolic Engineering , 2023 , 78 : 171 - 182 .

ZHANG D C , XING H D , LIU D L , et al . Discovery of toxin-degrading enzymes with positive unlabeled deep learning [J ] . ACS Catalysis , 2024 , 14 ( 5 ): 3336 - 3348 .

SHI Z K , WANG D H , LI Y , et al . REME: an integrated platform for reaction enzyme mining and evaluation [J ] . Nucleic Acids Research , 2024 , 52 ( W1 ): W299 - W305 .

XING H D , CAI P L , LIU D L , et al . High-throughput prediction of enzyme promiscuity based on substrate-product pairs [J ] . Briefings in Bioinformatics , 2024 , 25 ( 2 ): bbae089 .

OYETUNDE T , ZHANG M H , CHEN Y X , et al . BoostGAPFILL: improving the fidelity of metabolic network reconstructions through integrated constraint and pattern-based methods [J ] . Bioinformatics , 2017 , 33 ( 4 ): 608 - 611 .

CHEN C , LIAO C , LIU Y Y . Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning [J ] . Nature Communications , 2023 , 14 ( 1 ): 2375 .

HUANG W H , YANG F , ZHANG Q , et al . A dual-scale fused hypergraph convolution-based hyperedge prediction model for predicting missing reactions in genome-scale metabolic networks [J ] . Briefings in Bioinformatics , 2024 , 25 ( 5 ): bbae383 .

BOER M D , MELKONIAN C , ZAFEIROPOULOS H , et al . Improving genome-scale metabolic models of incomplete genomes with deep learning [J ] . iScience , 2024 , 27 ( 12 ): 111349 .

ASHBURNER M , BALL C A , BLAKE J A , et al . Gene ontology: tool for the unification of biology. The gene ontology consortium [J ] . Nature Genetics , 2000 , 25 ( 1 ): 25 - 29 .

SHEN H B , CHOU K C . EzyPred: a top-down approach for predicting enzyme functional classes and subclasses [J ] . Biochemical and Biophysical Research Communications , 2007 , 364 ( 1 ): 53 - 59 .

LI Y H , XU J Y , TAO L , et al . SVM-prot 2016: a web-server for machine learning prediction of protein functional families from sequence irrespective of similarity [J ] . PLoS One , 2016 , 11 ( 8 ): e0155290 .

LI Y , WANG S , UMAROV R , et al . DEEPre: sequence-based enzyme EC number prediction by deep learning [J ] . Bioinformatics , 2018 , 34 ( 5 ): 760 - 769 .

NURSIMULU N , XU L L , WASMUTH J D , et al . Improved enzyme annotation with EC-specific cutoffs using DETECT v2 [J ] . Bioinformatics , 2018 , 34 ( 19 ): 3393 - 3395 .

DALKIRAN A , RIFAIOGLU A S , MARTIN M J , et al . ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature [J ] . BMC Bioinformatics , 2018 , 19 ( 1 ): 334 .

RIVES A , MEIER J , SERCU T , et al . Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2021 , 118 ( 15 ): e2016239118 .

LIN Z M , AKIN H , RAO R , et al . Evolutionary-scale prediction of atomic-level protein structure with a language model [J ] . Science , 2023 , 379 ( 6637 ): 1123 - 1130 .

HADADI N , HAFNER J , SHAJKOFCI A , et al . ATLAS of biochemistry: a repository of all possible biochemical reactions for synthetic biology and metabolic engineering studies [J ] . ACS Synthetic Biology , 2016 , 5 ( 10 ): 1155 - 1166 .

KHERSONSKY O , TAWFIK D S . Enzyme promiscuity: a mechanistic and evolutionary perspective [J ] . Annual Review of Biochemistry , 2010 , 79 : 471 - 505 .

PONTRELLI S , FRICKE R C B , TEOH S T , et al . Metabolic repair through emergence of new pathways in Escherichia coli [J ] . Nature Chemical Biology , 2018 , 14 ( 11 ): 1005 - 1009 .

NI Z F , STINE A E , TYO K E J , et al . Curating a comprehensive set of enzymatic reaction rules for efficient novel biosynthetic pathway design [J ] . Metabolic Engineering , 2021 , 65 : 79 - 87 .

NAM H , LEWIS N E , LERMAN J A , et al . Network context and selection in the evolution to enzyme specificity [J ] . Science , 2012 , 337 ( 6098 ): 1101 - 1104 .

MORIYA Y , SHIGEMIZU D , HATTORI M , et al . PathPred: an enzyme-catalyzed metabolic pathway prediction server [J ] . Nucleic Acids Research , 2010 , 38 ( S2 ): W138 - W143 .

DELÉPINE B , DUIGOU T , CARBONELL P , et al . RetroPath2.0: a retrosynthesis workflow for metabolic engineers [J ] . Metabolic Engineering , 2018 , 45 : 158 - 170 .

DING S Z , TIAN Y , CAI P L , et al . novoPathFinder: a webserver of designing novel-pathway with integrating GEM-model [J ] . Nucleic Acids Research , 2020 , 48 ( W1 ): W477 - W487 .

GENHEDEN S , THAKKAR A , CHADIMOVÁ V , et al . AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning [J ] . Journal of Cheminformatics , 2020 , 12 ( 1 ): 70 .

GRICOURT G , MEYER P , DUIGOU T , et al . Artificial intelligence methods and models for retro-biosynthesis: a scoping review [J ] . ACS Synthetic Biology , 2024 , 13 ( 8 ): 2276 - 2294 .

COLEY C W , ROGERS L , GREEN W H , et al . SCScore: synthetic complexity learned from a reaction corpus [J ] . Journal of Chemical Information and Modeling , 2018 , 58 ( 2 ): 252 - 261 .

CARBONELL P , WONG J , SWAINSTON N , et al . Selenzyme: enzyme selection tool for pathway design [J ] . Bioinformatics , 2018 , 34 ( 12 ): 2153 - 2154 .

RAHMAN S A , CUESTA S M , FURNHAM N , et al . EC-BLAST: a tool to automatically search and compare enzyme reactions [J ] . Nature Methods , 2014 , 11 ( 2 ): 171 - 174 .

GIRI V , SIVAKUMAR T V , CHO K M , et al . RxnSim: a tool to compare biochemical reactions [J ] . Bioinformatics , 2015 , 31 ( 22 ): 3712 - 3714 .

STONEY R A , HANKO E K R , CARBONELL P , et al . SelenzymeRF: updated enzyme suggestion software for unbalanced biochemical reactions [J ] . Computational and Structural Biotechnology Journal , 2023 , 21 : 5868 - 5876 .

WILLETT P . Searching techniques for databases of two- and three-dimensional chemical structures [J ] . Journal of Medicinal Chemistry , 2005 , 48 ( 13 ): 4183 - 4199 .

SCHWALLER P , HOOVER B , REYMOND J L , et al . Extraction of organic chemistry grammar from unsupervised learning of chemical reactions [J ] . Science Advances , 2021 , 7 ( 15 ): eabe4166 .

QIAN W J , WANG X R , HUANG Y S , et al . Deep learning-driven insights into enzyme-substrate interaction discovery [J ] . Journal of Chemical Information and Modeling , 2025 , 65 ( 1 ): 187 - 200 .

ORTH J D , PALSSON B Ø . Systematizing the generation of missing metabolic knowledge [J ] . Biotechnology and Bioengineering , 2010 , 107 ( 3 ): 403 - 412 .

THIELE I , VLASSIS N , FLEMING R M T . fastGapFill: efficient gap filling in metabolic networks [J ] . Bioinformatics , 2014 , 30 ( 17 ): 2529 - 2531 .

KUMAR V S , DASIKA M S , MARANAS C D . Optimization based automated curation of metabolic reconstructions [J ] . BMC Bioinformatics , 2007 , 8 : 212 .

HECKMANN D , LLOYD C J , MIH N , et al . Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models [J ] . Nature Communications , 2018 , 9 ( 1 ): 5252 .

LI F R , YUAN L , LU H Z , et al . Deep learning-based k cat prediction enables improved enzyme-constrained model reconstruction [J ] . Nature Catalysis , 2022 , 5 ( 8 ): 662 - 672 .

KROLL A , ROUSSET Y , HU X P , et al . Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning [J ] . Nature Communications , 2023 , 14 ( 1 ): 4139 .

QIU S Z , ZHAO S M , YANG A D . DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates [J ] . Briefings in Bioinformatics , 2023 , 25 ( 1 ): bbad506 .

WANG T , XIANG G M , HE S W , et al . DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures [J ] . Briefings in Bioinformatics , 2024 , 25 ( 5 ): bbae409 .

KROLL A , ENGQVIST M K M , HECKMANN D , et al . Deep learning allows genome-scale prediction of Michaelis constants from structural features [J ] . PLoS Biology , 2021 , 19 ( 10 ): e3001402 .

HE X , YAN M . GraphKM: machine and deep learning for K m prediction of wildtype and mutant enzymes [J ] . BMC Bioinformatics , 2024 , 25 ( 1 ): 135 .

MAEDA K , HATAE A , SAKAI Y , et al . MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling [J ] . BMC Bioinformatics , 2022 , 23 ( 1 ): 455 .

WANG J J , YANG Z J , CHEN C , et al . MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction [J ] . Briefings in Bioinformatics , 2024 , 25 ( 5 ): bbae387 .

YU H , DENG H X , HE J H , et al . UniKP: a unified framework for the prediction of enzyme kinetic parameters [J ] . Nature Communications , 2023 , 14 ( 1 ): 8211 .

SHEN X W , CUI Z H , LONG J Y , et al . EITLEM-Kinetics: a deep-learning framework for kinetic parameter prediction of mutant enzymes [J ] . Chem Catalysis , 2024 , 4 ( 9 ): 101094 .

WANG L , UPADHYAY V , MARANAS C D . dGPredictor: automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design [J ] . PLoS Computational Biology , 2021 , 17 ( 9 ): e1009448 .

ALAZMI M , KUWAHARA H , SOUFAN O , et al . Systematic selection of chemical fingerprint features improves the Gibbs energy prediction of biochemical reactions [J ] . Bioinformatics , 2019 , 35 ( 15 ): 2634 - 2643 .

JUNG F , FREY K , ZIMMER D , et al . DeepSTABp: a deep learning approach for the prediction of thermal protein stability [J ] . International Journal of Molecular Sciences , 2023 , 24 ( 8 ): 7444 .

LI M Y , WANG H Z , YANG Z W , et al . DeepTM: a deep learning algorithm for prediction of melting temperature of thermophilic proteins directly from sequences [J ] . Computational and Structural Biotechnology Journal , 2023 , 21 : 5544 - 5560 .

LI G , RABE K S , NIELSEN J , et al . Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima [J ] . ACS Synthetic Biology , 2019 , 8 ( 6 ): 1411 - 1420 .

GADO J E , BECKHAM G T , PAYNE C M . Improving enzyme optimum temperature prediction with resampling strategies and ensemble learning [J ] . Journal of Chemical Information and Modeling , 2020 , 60 ( 8 ): 4098 - 4107 .

LI G , BURIC F , ZRIMEC J , et al . Learning deep representations of enzyme thermal adaptation [J ] . Protein Science , 2022 , 31 ( 12 ): e4480 .

ZHANG Y , GUAN F F , XU G S , et al . A novel thermophilic chitinase directly mined from the marine metagenome using the deep learning tool Preoptem [J ] . Bioresources and Bioprocessing , 2022 , 9 ( 1 ): 54 .

NILSSON A , NIELSEN J , PALSSON B O . Metabolic models of protein allocation call for the kinetome [J ] . Cell Systems , 2017 , 5 ( 6 ): 538 - 541 .

SUTHERS P F , FOSTER C J , SARKAR D , et al . Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms [J ] . Metabolic Engineering , 2021 , 63 : 13 - 33 .

JANKOWSKI M D , HENRY C S , BROADBELT L J , et al . Group contribution method for thermodynamic analysis of complex metabolic networks [J ] . Biophysical Journal , 2008 , 95 ( 3 ): 1487 - 1499 .

NOOR E , HARALDSDÓTTIR H S , MILO R , et al . Consistent estimation of Gibbs energy using component contributions [J ] . PLoS Computational Biology , 2013 , 9 ( 7 ): e1003098 .

BEBER M E , GOLLUB M G , MOZAFFARI D , et al . eQuilibrator 3.0: a database solution for thermodynamic constant estimation [J ] . Nucleic Acids Research , 2022 , 50 ( D1 ): D603 - D609 .

LEUENBERGER P , GANSCHA S , KAHRAMAN A , et al . Cell-wide analysis of protein thermal unfolding reveals determinants of thermostability [J ] . Science , 2017 , 355 ( 6327 ): eaai7825 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

AI+定向进化赋能蛋白改造及优化

功能肽合成和挖掘策略研究进展

合成生物学赋能细胞膜纳米颗粒的精准诊疗