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利用酶多功能性预测和代谢组学数据为大肠杆菌创建扩展代谢模型 (EMM)。

Towards creating an extended metabolic model (EMM) for E. coli using enzyme promiscuity prediction and metabolomics data.

机构信息

Department of Computer Science, Tufts University, Medford, MA, USA.

Department of Biology, University of North Carolina, Chapel Hill, NC, USA.

出版信息

Microb Cell Fact. 2019 Jun 13;18(1):109. doi: 10.1186/s12934-019-1156-3.

Abstract

BACKGROUND

Metabolic models are indispensable in guiding cellular engineering and in advancing our understanding of systems biology. As not all enzymatic activities are fully known and/or annotated, metabolic models remain incomplete, resulting in suboptimal computational analysis and leading to unexpected experimental results. We posit that one major source of unaccounted metabolism is promiscuous enzymatic activity. It is now well-accepted that most, if not all, enzymes are promiscuous-i.e., they transform substrates other than their primary substrate. However, there have been no systematic analyses of genome-scale metabolic models to predict putative reactions and/or metabolites that arise from enzyme promiscuity.

RESULTS

Our workflow utilizes PROXIMAL-a tool that uses reactant-product transformation patterns from the KEGG database-to predict putative structural modifications due to promiscuous enzymes. Using iML1515 as a model system, we first utilized a computational workflow, referred to as Extended Metabolite Model Annotation (EMMA), to predict promiscuous reactions catalyzed, and metabolites produced, by natively encoded enzymes in Escherichia coli. We predict hundreds of new metabolites that can be used to augment iML1515. We then validated our method by comparing predicted metabolites with the Escherichia coli Metabolome Database (ECMDB).

CONCLUSIONS

We utilized EMMA to augment the iML1515 metabolic model to more fully reflect cellular metabolic activity. This workflow uses enzyme promiscuity as basis to predict hundreds of reactions and metabolites that may exist in E. coli but may have not been documented in iML1515 or other databases. We provide detailed analysis of 23 predicted reactions and 16 associated metabolites. Interestingly, nine of these metabolites, which are in ECMDB, have not previously been documented in any other E. coli databases. Four of the predicted reactions provide putative transformations parallel to those already in iML1515. We suggest adding predicted metabolites and reactions to iML1515 to create an extended metabolic model (EMM) for E. coli.

摘要

背景

代谢模型在指导细胞工程和推进系统生物学理解方面不可或缺。由于并非所有酶活性都被完全了解和/或注释,代谢模型仍然不完整,导致计算分析不理想,并导致意外的实验结果。我们假设未被考虑的代谢的一个主要来源是混杂酶活性。现在人们普遍认为,即使不是所有的酶,也是混杂的,即它们转化的底物不是它们的主要底物。然而,还没有对基因组规模的代谢模型进行系统分析,以预测由酶混杂性引起的潜在反应和/或代谢物。

结果

我们的工作流程利用 PROXIMAL——一种利用来自 KEGG 数据库的反应物-产物转化模式来预测由于混杂酶引起的潜在结构修饰的工具。我们首先使用 iML1515 作为模型系统,利用一种称为扩展代谢物模型注释(EMMA)的计算工作流程,预测固有编码酶在大肠杆菌中催化的混杂反应和产生的代谢物。我们预测了数百种可用于增强 iML1515 的新代谢物。然后,我们通过将预测的代谢物与大肠杆菌代谢组数据库(ECMDB)进行比较来验证我们的方法。

结论

我们利用 EMMA 来增强 iML1515 代谢模型,以更全面地反映细胞代谢活性。该工作流程利用酶混杂性作为基础,预测了数百种可能存在于大肠杆菌中但可能未在 iML1515 或其他数据库中记录的反应和代谢物。我们对 23 个预测的反应和 16 个相关代谢物进行了详细分析。有趣的是,其中 9 种代谢物在 ECMDB 中,以前在任何其他大肠杆菌数据库中都没有记录过。预测的反应中有 4 种提供了与 iML1515 中已有的反应平行的潜在转化。我们建议将预测的代谢物和反应添加到 iML1515 中,以创建大肠杆菌的扩展代谢模型(EMM)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7da/6567437/3f2558db6802/12934_2019_1156_Fig1_HTML.jpg

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