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. Electronic supplementary material The online version of this article (10.1186/s12934-019-1156-3) contains supplementary material, which is available to authorized users.

BackgroundIncreasing understanding of metabolic and regulatory networks underlying microbial physiology has enabled creation of progressively more complex synthetic biological systems for biochemical, biomedical, agricultural, and environmental applications. However, despite best efforts, confounding phenotypes still emerge from unforeseen interplay between biological parts, and the design of robust and modular biological systems remains elusive. Such interactions are difficult to predict when designing synthetic systems and may manifest during experimental testing as inefficiencies that need to be overcome. Despite advances in tools and methodologies for strain engineering, there remains a lack of tools that can systematically identify incompatibilities between the native metabolism of the host and its engineered modifications.ResultsTransforming organisms such as Escherichia coli into microbial factories is achieved via a number of engineering strategies, used individually or in combination, with the goal of maximizing the production of chosen target compounds. One technique relies on suppressing or overexpressing selected genes; another involves on introducing heterologous enzymes into a microbial host. These modifications steer mass flux towards the set of desired metabolites but may create unexpected interactions. In this work, we develop a computational method, termed Metabolic Disruption Workflow (MDFlow), for discovering interactions and network disruption arising from enzyme promiscuity – the ability of enzymes to act on a wide range of molecules that are structurally similar to their native substrates. We apply MDFlow to two experimentally verified cases where strains with essential genes knocked out are rescued by interactions resulting from overexpression of one or more other genes. We then apply MDFlow to predict and evaluate a number of putative promiscuous reactions that can interfere with two heterologous pathways designed for 3-hydroxypropic acid (3-HP) production.ConclusionsUsing MDFlow, we can identify putative enzyme promiscuity and the subsequent formation of unintended and undesirable byproducts that are not only disruptive to the host metabolism but also to the intended end-objective of high biosynthetic productivity and yield. In addition, we show how enzyme promiscuity can potentially be responsible for the adaptability of cells to the disruption of essential pathways in terms of biomass growth.

Increasing understanding of metabolic and regulatory networks underlying microbial physiology has enabled creation of progressively more complex synthetic biological systems for biochemical, biomedical, agricultural, and environmental applications. However, despite best efforts, confounding phenotypes still emerge from unforeseen interplay between biological parts, and the design of robust and modular biological systems remains elusive. Such interactions are difficult to predict when designing synthetic systems and may manifest during experimental testing as inefficiencies that need to be overcome. Transforming organisms such as Escherichia coli into microbial factories is achieved via several engineering strategies, used individually or in combination, with the goal of maximizing the production of chosen target compounds. One technique relies on suppressing or overexpressing selected genes; another involves introducing heterologous enzymes into a microbial host. These modifications steer mass flux towards the set of desired metabolites but may create unexpected interactions. In this work, we develop a computational method, termed M etabolic D isruption Work flow ( MDFlow ), for discovering interactions and network disruptions arising from enzyme promiscuity – the ability of enzymes to act on a wide range of molecules that are structurally similar to their native substrates. We apply MDFlow to two experimentally verified cases where strains with essential genes knocked out are rescued by interactions resulting from overexpression of one or more other genes. We demonstrate how enzyme promiscuity may aid cells in adapting to disruptions of essential metabolic functions. We then apply MDFlow to predict and evaluate a number of putative promiscuous reactions that can interfere with two heterologous pathways designed for 3-hydroxypropionic acid (3-HP) production. Using MDFlow , we can identify putative enzyme promiscuity and the subsequent formation of unintended and undesirable byproducts that are not only disruptive to the host metabolism but also to the intended end-objective of high biosynthetic productivity and yield. As we demonstrate, MDFlow provides an innovative workflow to systematically identify incompatibilities between the native metabolism of the host and its engineered modifications due to enzyme promiscuity.

Current pathway synthesis tools identify possible pathways that can be added to a host to produce a desired target molecule through the exploration of abstract metabolic and reaction network space. However, not many of these tools do explore gene-level information required to physically realize the identified synthesis pathways, and none explore enzyme-host compatibility. Developing tools that address this disconnect between abstract reactions/metabolic design space and physical genetic sequence design space will enable expedited experimental efforts that avoid exploring unprofitable synthesis pathways. This work describes a workflow, termed Probabilistic Pathway Assembly with Solubility Scores (ProPASS), which links synthesis pathway construction with the exploration of the physical design space as imposed by the availability of enzymes with characterized activities within the host. Predicted protein solubility propensity scores are used as a confidence level to quantify the compatibility of each pathway enzyme with the host (E. coli). This work also presents a database, termed Protein Solubility Database (ProSol DB), which provides solubility confidence scores in E. coli for 240,016 characterized enzymes obtained from UniProtKB/Swiss-Prot. The utility of ProPASS is demonstrated by generating genetic implementations of heterologous synthesis pathways in E. coli that target several commercially useful biomolecules.

1Background 2 Metabolic models are indispensable in guiding cellular engineering and in advancing our 3 understanding of systems biology. As not all enzymatic activities are fully known and/or 4 annotated, metabolic models remain incomplete, resulting in suboptimal computational analysis 5 and leading to unexpected experimental results. We posit that one major source of unaccounted 6 metabolism is promiscuous enzymatic activity. It is now well-accepted that most, if not all, 7 enzymes are promiscuous -i.e., they transform substrates other than their primary substrate. 8However, there have been no systematic analyses of genome-scale metabolic models to predict 9 putative reactions and/or metabolites that arise from enzyme promiscuity. 10 Results 11Our workflow utilizes PROXIMAL -a tool that uses reactant-product transformation patterns 12 from the KEGG database -to predict putative structural modifications due to promiscuous 13 enzymes. Using iML1515 as a model system, we first utilized a computational workflow, 14 referred to as Extended Metabolite Model Annotation (EMMA), to predict promiscuous 15 reactions catalyzed, and metabolites produced, by natively encoded enzymes in E. coli. We 16 predict hundreds of new metabolites that can be used to augment iML1515. We then validated 17 our method by comparing predicted metabolites with the Escherichia coli Metabolome Database 18 (ECMDB). 19 Conclusions 20We utilized EMMA to augment the iML1515 metabolic model to more fully reflect cellular 21 metabolic activity. This workflow uses enzyme promiscuity as basis to predict hundreds of 22 reactions and metabolites that may exist in E. coli but may have not been documented in 23 3 iML1515 or other databases. We provide detailed analysis of 23 predicted reactions and 16 1 associated metabolites. Interestingly, nine of these metabolites, which are in ECMDB, have not 2 previously been documented in any other E. coli databases. Four of the predicted reactions 3 provide putative transformations parallel to those already in iML1515. We suggest adding 4 predicted metabolites and reactions to iML1515 to create an Extended Metabolic Model (EMM) 5 for E. coli. 6 7 Keywords 8 Metabolic engineering, enzyme promiscuity, extended metabolic model, systems biology, 9 enzyme activity prediction 10 11 Background 12The engineering of metabolic networks has enabled the production of high-volume commodity 13 chemicals such as biopolymers and fuels, therapeutics, and specialty products [1][2][3][4][5]. Producing 14 such compounds requires transforming microorganisms into efficient cellular factories [6][7][8][9]. 15 Biological engineering has been aided via computational tools for constructing synthesis 16 pathways, strain optimization, elementary flux mode analysis, discovery of hierarchical 17 networked modules that elucidate function and cellular organization, and many others (e.g., [10-18 14]). These design tools rely on organism-specific metabolic models that represent cellular 19 reactions and their substrates and products. Model recons...