Machine Learning Applications in Genome-scale Metabolic Model Reconstruction and Curation

doi:10.12211/2096-8280.2024-090

Abstract

Abstract:

Since the publication of first genome-scale metabolic model (GEM) in 1999, GEM has become an essential tool for analyzing biological metabolism. The model integrates metabolic genes, metabolites, and reactions, and combines stoichiometric matrices with constraint-based optimization to systematically describe and simulate metabolic processes in organisms. The development of automated pipelines for GEM reconstruction has expanded its applicability to organisms from all kingdoms of life. Additionally, GEM 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 GEM remains heavily dependent on pre-existing knowledge, inherently limiting its 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 in biological tasks like protein structure prediction, disease identification and GEM reconstruction related tasks such as 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 GEM by expanding its applicability to areas previously constrained by data availability and complexity. This review summarizes the traditional reconstruction methods of GEM and their applications in integrating multi-dimensional data to build multi-constraint and multi-process model. The review also focuses on key applications of machine learning in gene function annotation, pathway analysis, gap-filling prediction in the reconstruction of GEM. Additionally, the potential of machine learning in predicting kinetic, thermodynamic, and other key biochemical parameters in the reconstruction of multi-constraint and multi-process model is discussed.By combining GEM 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 developments in systems biology and biotechnology.

Key words: genome-scale metabolic model, machine learning, synthetic biology, metabolic modeling, multi-constraint and multi-process model

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，系统地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，从而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。

关键词: 基因组规模代谢模型, 机器学习, 合成生物学, 代谢建模, 多约束多过程模型

CLC Number:

Q814.9

Ke WU, Jiahao LUO, Feiran LI. Machine Learning Applications in Genome-scale Metabolic Model Reconstruction and Curation[J]. Synthetic Biology Journal, DOI: 10.12211/2096-8280.2024-090.

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, DOI: 10.12211/2096-8280.2024-090.

Figures/Tables 5

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模型应用	方法	模型框架	输入	输出	特点
辅助基因注释	DeepEC^[71]	CNN	氨基酸序列	EC编号	可区分酶与非酶，无法进行多功能注释
	CLEAN^[11]	预训练蛋白质大语言模型(ESM-1b)，对比学习	氨基酸序列	EC编号	无法区分酶与非酶，可进行多功能注释，可用于数据极少的EC编号
	DeepECtransformer^[72]	预训练蛋白质大语言模型(ProtBert)，Transformer	氨基酸序列	EC编号	可区分酶与非酶，可进行多功能注释，不可用于数据极少的EC编号
	ECRECer^[73]	预训练蛋白质大语言模型(ESM-1b) ，GRU	氨基酸序列	EC编号	可区分酶与非酶，可进行多功能注释
	ECPICK^[74]	One-hot，CNN	氨基酸序列	EC编号	无法区分酶与非酶，不可进行多功能注释，可输出预测EC编号的置信度
	EnzBert^[75]	Transformer	氨基酸序列	EC编号	无法区分酶与非酶，不可进行多功能注释，可推测关键残基
	EnzymeNet^[76]	CNN，ResNet	氨基酸序列	EC编号	可区分酶与非酶，无法进行多功能注释
	GraphEC^[77]	预训练蛋白质大语言模型（ProtTrans），ESMFold	氨基酸序列和蛋白质三维结构	EC编号	无法区分酶与非酶，可进行多功能注释，计算资源需求高
辅助途径解析-反应预测	RetroPath RL^[78]	基于蒙特卡洛树搜索的强化学习方法	化合物 SMILES	合成途径	缓解组合爆炸问题，允许探索深度是只基于模板版本的两倍以上
	ASKCOS^[79]	FNN	化合物 SMILES	合成途径	缓解组合爆炸问题，适用于化学合成途径设计
	chemoenzymatic- ASKCOS^[80]	DNN	化合物 SMILES	合成途径	缓解组合爆炸问题，可进行化学合成与生物合成混合途径设计
	RetroBioCat^[12]	DNN	化合物 SMILES	合成途径	缓解组合爆炸问题, 处理大分子化合物存在挑战
	Kreutter et al^[81]	Transformer	化合物 SMILES和酶功能描述	反应产物	预测单步反应，无法给出完整的反应，无法推荐酶功能
	Probst et al^[82]	Transformer	化合物 SMILES和EC编号	反应产物	预测单步反应，无法给出完整的反应，无法推荐EC编号
	BioNavi-NP^[83]	Transformer	天然产品的SMILES	合成途径	针对天然产物及类似物，可预测多步途径
	BioNavi^[84]	Transformer	化合物 SMILES	合成途径	可进行化学合成与生物合成混合途径设计
辅助途径解析-酶挖掘	ESP^[85]	预训练蛋白质大语言模型(ESM-1b)，GNN，XGBoost	氨基酸序列和分子指纹	酶-底物结合可能性	对于训练集中没出现代谢物的预测性能会有明显下降
	EnzRank^[86]	分子指纹，CNN	氨基酸序列和分子指纹	酶-底物结合可能性	对于天然底物及其相似物具有良好的区分能力
	PU-EPP^[87]	GNN，正样本和无标签学习	氨基酸序列和化合物SMILES	酶-底物结合可能性	鲁棒性强，可鉴定酶和底物的关键位点
	MEI	预训练蛋白质大语言模型 (ESM-1b)，GNN，DNN	氨基酸序列和化合物SMILES	酶-底物结合可能性	可利用专业数据集微调进行特定任务预测
	REME^[88]	集成ESP、DLKcat/TurNuP、DeepET等模型	反应SMILES	酶列表	多维度评价与筛选有效提高了推荐酶列表的可信度
	SPEPP^[89]	Word2Vec，Transformer	底物、反应物和酶	酶催化底物- 产物反应的可能性	计算效率相较基于相似性的方法显著提高，可大规模使用，需要提供候选酶集合
辅助空缺填补	BoostGAPFILL^[90]	基于矩阵分解的推荐系统技术	代谢模型	填补反应	融合了拓扑和约束方法，能够识别代谢网络中的潜在模式
	CHESHIRE^[91]	GCN	代谢模型	填补反应	计算效率高，可解释性强，可能引入假阳性反应，缺少反应方向性信息
	DSHCNet^[92]	GCN，MLP	代谢模型	填补反应	对反应数据依赖较强，在适应不同反应数据库中存在挑战
	DNNGIOR^[93]	CNN	代谢模型	填补反应	预测性能受系统发育距离影响

模型应用	方法	模型框架	输入	输出	特点
辅助基因注释	DeepEC^[71]	CNN	氨基酸序列	EC编号	可区分酶与非酶，无法进行多功能注释
	CLEAN^[11]	预训练蛋白质大语言模型(ESM-1b)，对比学习	氨基酸序列	EC编号	无法区分酶与非酶，可进行多功能注释，可用于数据极少的EC编号
	DeepECtransformer^[72]	预训练蛋白质大语言模型(ProtBert)，Transformer	氨基酸序列	EC编号	可区分酶与非酶，可进行多功能注释，不可用于数据极少的EC编号
	ECRECer^[73]	预训练蛋白质大语言模型(ESM-1b) ，GRU	氨基酸序列	EC编号	可区分酶与非酶，可进行多功能注释
	ECPICK^[74]	One-hot，CNN	氨基酸序列	EC编号	无法区分酶与非酶，不可进行多功能注释，可输出预测EC编号的置信度
	EnzBert^[75]	Transformer	氨基酸序列	EC编号	无法区分酶与非酶，不可进行多功能注释，可推测关键残基
	EnzymeNet^[76]	CNN，ResNet	氨基酸序列	EC编号	可区分酶与非酶，无法进行多功能注释
	GraphEC^[77]	预训练蛋白质大语言模型（ProtTrans），ESMFold	氨基酸序列和蛋白质三维结构	EC编号	无法区分酶与非酶，可进行多功能注释，计算资源需求高
辅助途径解析-反应预测	RetroPath RL^[78]	基于蒙特卡洛树搜索的强化学习方法	化合物 SMILES	合成途径	缓解组合爆炸问题，允许探索深度是只基于模板版本的两倍以上
	ASKCOS^[79]	FNN	化合物 SMILES	合成途径	缓解组合爆炸问题，适用于化学合成途径设计
	chemoenzymatic- ASKCOS^[80]	DNN	化合物 SMILES	合成途径	缓解组合爆炸问题，可进行化学合成与生物合成混合途径设计
	RetroBioCat^[12]	DNN	化合物 SMILES	合成途径	缓解组合爆炸问题, 处理大分子化合物存在挑战
	Kreutter et al^[81]	Transformer	化合物 SMILES和酶功能描述	反应产物	预测单步反应，无法给出完整的反应，无法推荐酶功能
	Probst et al^[82]	Transformer	化合物 SMILES和EC编号	反应产物	预测单步反应，无法给出完整的反应，无法推荐EC编号
	BioNavi-NP^[83]	Transformer	天然产品的SMILES	合成途径	针对天然产物及类似物，可预测多步途径
	BioNavi^[84]	Transformer	化合物 SMILES	合成途径	可进行化学合成与生物合成混合途径设计
辅助途径解析-酶挖掘	ESP^[85]	预训练蛋白质大语言模型(ESM-1b)，GNN，XGBoost	氨基酸序列和分子指纹	酶-底物结合可能性	对于训练集中没出现代谢物的预测性能会有明显下降
	EnzRank^[86]	分子指纹，CNN	氨基酸序列和分子指纹	酶-底物结合可能性	对于天然底物及其相似物具有良好的区分能力
	PU-EPP^[87]	GNN，正样本和无标签学习	氨基酸序列和化合物SMILES	酶-底物结合可能性	鲁棒性强，可鉴定酶和底物的关键位点
	MEI	预训练蛋白质大语言模型 (ESM-1b)，GNN，DNN	氨基酸序列和化合物SMILES	酶-底物结合可能性	可利用专业数据集微调进行特定任务预测
	REME^[88]	集成ESP、DLKcat/TurNuP、DeepET等模型	反应SMILES	酶列表	多维度评价与筛选有效提高了推荐酶列表的可信度
	SPEPP^[89]	Word2Vec，Transformer	底物、反应物和酶	酶催化底物- 产物反应的可能性	计算效率相较基于相似性的方法显著提高，可大规模使用，需要提供候选酶集合
辅助空缺填补	BoostGAPFILL^[90]	基于矩阵分解的推荐系统技术	代谢模型	填补反应	融合了拓扑和约束方法，能够识别代谢网络中的潜在模式
	CHESHIRE^[91]	GCN	代谢模型	填补反应	计算效率高，可解释性强，可能引入假阳性反应，缺少反应方向性信息
	DSHCNet^[92]	GCN，MLP	代谢模型	填补反应	对反应数据依赖较强，在适应不同反应数据库中存在挑战
	DNNGIOR^[93]	CNN	代谢模型	填补反应	预测性能受系统发育距离影响

参数类型	方法	模型框架	输入	输出	特点
动力学参数	Heckmann et al^[123]	随机森林，MLP	GEM/蛋白质结构/EC号首位/pH等	k_cat	可预测体内k_cat，适用于大肠杆菌
	DLKcat^[124]	GNN，CNN	氨基酸序列和化合物SMILES	k_cat	预测k_cat（R²=0.44），内置于GECKO 3.0
	TurNuP^[125]	预训练蛋白质大语言模型（ESM-1b），XGBoost	氨基酸序列和反应指纹	k_cat	预测k_cat（R²=0.44），无法区分多底物反应中不同底物的k_cat
	DLTKcat^[126]	GNN，CNN	氨基酸序列，化合物SMILES和温度	k_cat	预测不同温度下的k_cat（R²=0.66）
	DeepEnzyme ^[127]	GCN	氨基酸序列，化合物SMILES和蛋白质三维结构	k_cat	预测k_cat（R²=0.58），预测突变型需要具备蛋白质结构预测能力
	Kroll et al ^[128]	预训练蛋白质大语言模型（ESM-1b）	氨基酸序列和分子指纹	Km	预测Km（R²=0.53）
	GraphKM ^[129]	预训练蛋白质大语言模型（ESM-2），GNN	氨基酸序列和化合物SMILES	Km	预测Km（R²=0.62），模型的预测性能受限于训练数据集的规模和质量
	MLAGO ^[130]	随机森林	EC 编号, KEGG ID和物种编号	Km	预测Km（R²=0.53），泛化能力受限于EC 编号, KEGG ID, 物种编号信息
	MPEK ^[131]	预训练蛋白质大语言模型（ProtT5），预训练小分子大语言模型（Mole-BERT），多任务学习	氨基酸序列和化合物SMILES	k_cat和Km	支持同时预测k_cat（R²=0.64）和Km（R²=0.60）
	UniKP ^[132]	预训练蛋白质大语言模型（UniRef50），预训练小分子大语言模型（SMILES Transformer），极度随机树	氨基酸序列和化合物SMILES	k_cat、Km和k_cat/ Km	支持分别预测k_cat（R²=0.67）、 Km（R²=0.60）和 k_cat/Km（R²=0.56），鲁棒性强，支持温度和pH 输入
	EITLEM- Kinetics ^[133]	预训练蛋白质大语言模型（ESM-1v），迁移学习	氨基酸序列和化合物SMILES	k_cat、Km和k_cat/Km	支持分别k_cat（R²=0.72），Km（R²=0.69）和 k_cat/Km（R²=0.68）预测，蛋白突变体的k_cat预测性能优异
热力学参数	dGPredictor^[134]	线性回归模型	分子指纹	Δ_rG′^o	不适用于异构化反应与涉及金属或聚合物结构的反应
热力学参数	Alazmi et al^[135]	线性回归模型	分子指纹	Δ_rG′ ^o	特征提取方式通用性强，可用于非4天然反应
温度相关参数	DeepSTABp ^[136]	预训练蛋白质大语言模型（ProtTrans），MLP	氨基酸序列、生物体生长温度和实验条件	T_m	预测能力对点突变不敏感
	DeepTM^[137]	GCN	氨基酸序列	T_m	特征提取复杂，训练数据未考虑其它对蛋白熔解温度影响的因素
	Tome^[138]	SVR，随机森林	氨基酸序列，OGT	T_opt	训练集高于85°C的T_opt值占比不足5%，限制了Tome对高温稳定性酶的预测能力
	TOMER^[139]	集成学习	氨基酸序列，OGT	T_opt	重采样缓解了数据分布不平衡，对高于85°C的T_opt 值预测性能显著提升
	DeepET^[140]	ResNet，迁移学习	氨基酸序列	T_m 和T_opt	类似于Tome，数据分布不均衡会限制其对极端温度蛋白质的预测性能
	Preoptem^[141]	One-hot，CNN	氨基酸序列	T_opt	Pearson 相关系数 r=0.58，适用于嗜热蛋白

参数类型	方法	模型框架	输入	输出	特点
动力学参数	Heckmann et al^[123]	随机森林，MLP	GEM/蛋白质结构/EC号首位/pH等	k_cat	可预测体内k_cat，适用于大肠杆菌
	DLKcat^[124]	GNN，CNN	氨基酸序列和化合物SMILES	k_cat	预测k_cat（R²=0.44），内置于GECKO 3.0
	TurNuP^[125]	预训练蛋白质大语言模型（ESM-1b），XGBoost	氨基酸序列和反应指纹	k_cat	预测k_cat（R²=0.44），无法区分多底物反应中不同底物的k_cat
	DLTKcat^[126]	GNN，CNN	氨基酸序列，化合物SMILES和温度	k_cat	预测不同温度下的k_cat（R²=0.66）
	DeepEnzyme ^[127]	GCN	氨基酸序列，化合物SMILES和蛋白质三维结构	k_cat	预测k_cat（R²=0.58），预测突变型需要具备蛋白质结构预测能力
	Kroll et al ^[128]	预训练蛋白质大语言模型（ESM-1b）	氨基酸序列和分子指纹	Km	预测Km（R²=0.53）
	GraphKM ^[129]	预训练蛋白质大语言模型（ESM-2），GNN	氨基酸序列和化合物SMILES	Km	预测Km（R²=0.62），模型的预测性能受限于训练数据集的规模和质量
	MLAGO ^[130]	随机森林	EC 编号, KEGG ID和物种编号	Km	预测Km（R²=0.53），泛化能力受限于EC 编号, KEGG ID, 物种编号信息
	MPEK ^[131]	预训练蛋白质大语言模型（ProtT5），预训练小分子大语言模型（Mole-BERT），多任务学习	氨基酸序列和化合物SMILES	k_cat和Km	支持同时预测k_cat（R²=0.64）和Km（R²=0.60）
	UniKP ^[132]	预训练蛋白质大语言模型（UniRef50），预训练小分子大语言模型（SMILES Transformer），极度随机树	氨基酸序列和化合物SMILES	k_cat、Km和k_cat/ Km	支持分别预测k_cat（R²=0.67）、 Km（R²=0.60）和 k_cat/Km（R²=0.56），鲁棒性强，支持温度和pH 输入
	EITLEM- Kinetics ^[133]	预训练蛋白质大语言模型（ESM-1v），迁移学习	氨基酸序列和化合物SMILES	k_cat、Km和k_cat/Km	支持分别k_cat（R²=0.72），Km（R²=0.69）和 k_cat/Km（R²=0.68）预测，蛋白突变体的k_cat预测性能优异
热力学参数	dGPredictor^[134]	线性回归模型	分子指纹	Δ_rG′^o	不适用于异构化反应与涉及金属或聚合物结构的反应
热力学参数	Alazmi et al^[135]	线性回归模型	分子指纹	Δ_rG′ ^o	特征提取方式通用性强，可用于非4天然反应
温度相关参数	DeepSTABp ^[136]	预训练蛋白质大语言模型（ProtTrans），MLP	氨基酸序列、生物体生长温度和实验条件	T_m	预测能力对点突变不敏感
	DeepTM^[137]	GCN	氨基酸序列	T_m	特征提取复杂，训练数据未考虑其它对蛋白熔解温度影响的因素
	Tome^[138]	SVR，随机森林	氨基酸序列，OGT	T_opt	训练集高于85°C的T_opt值占比不足5%，限制了Tome对高温稳定性酶的预测能力
	TOMER^[139]	集成学习	氨基酸序列，OGT	T_opt	重采样缓解了数据分布不平衡，对高于85°C的T_opt 值预测性能显著提升
	DeepET^[140]	ResNet，迁移学习	氨基酸序列	T_m 和T_opt	类似于Tome，数据分布不均衡会限制其对极端温度蛋白质的预测性能
	Preoptem^[141]	One-hot，CNN	氨基酸序列	T_opt	Pearson 相关系数 r=0.58，适用于嗜热蛋白

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