C Muramatsu (@1.3) vs S Guo (@3.2)
13-08-2019

Our Prediction:

C Muramatsu will win

C Muramatsu – S Guo Match Prediction | 13-08-2019

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coli) and is involved in reactions catalyzed by ECs 3.5.1.6 (-ureidopropionase) and 3.5.2.2 (dihydropyrimidinase). There is one reaction (KEGG R02970) catalyzed by EC 2.6.1.47 (l-alanine:oxomalonate aminotransferase) that produces aminomalonate; but it is not a redox reaction and is associated with rat and silkworm, not E. The third reaction in this category is the hydrolytic decarboxylation reaction between 3-ureidopropionate and N-carbamoyl-l-aspartate also catalyzed by EC 4.1.1.36 (PPC decarboxylase). The first reaction (Fig.5a) is the oxidoreductive interconversion between aminomalonate and l-serine by EC 1.1.1.23 (histidinol dehydrogenase). 3-Ureidopropionate is present in eukaryotes and bacteria (but not E. coli [51]. The last reaction is the transformation between d-gluconic acid and d-galactarate by EC 1.1.1.23. The product, N-acetylputrescine, is involved in a number of enzymatic reactionsECs 1.4.3.4 (monoamine oxidase), 2.3.1.57 (spermidine acetyltransferase), and 3.5.1.62 (acetylputrescine deacetylase)in many organisms that include both eukaryotes and bacteria [16]. d-Galactarate is involved in reactions catalyzed by 4.2.1.158 that is present in Oceanobacillus iheyensis [52]. The second is a hydrolytic decarboxylation reaction between N-acetylputrescine and N-acetylornithine (Fig.5b) predicted to be catalyzed by EC 4.1.1.36 (PPC decarboxylase). C4 consists of four predicted reactions that are not currently catalogued in KEGG for any organism (Fig.5).

The key in the lookup table consisted of the R and M atom(s) in the reactant, while the value is the R and D atom(s) in the product. All atoms are labelled using KEGG atom types [54]. (ii) Difference Region (D) atoms are adjacent to the R atom and are distinct between substrate and product. The outcome of this step is a list of predicted products due to putative enzymatic activity. EMMA used PROXIMAL to predict putative products that can be added to the model. PROXIMAL utilizes RDM patterns [40] specific to the models reactions to create lookup tables that map reaction centers to structural transformation patterns. An RDM pattern specifies local regions of structural similarities/differences for reactantproduct pairs based on a given biochemical reaction. An RDM pattern consists of three parts: (i) A Reaction Center (R) atom exists in both the substrate and reactant molecule and is the center of the molecular transformation. PROXIMAL constructs a lookup table of all possible biotransformations that can occur due to promiscuous activity of enzymes based on the RDM patterns of reactions catalyzed by enzymes associated with genes in the iML1515 gene list. (iii) Matched Region (M) atoms are adjacent to the R atom but remain unmodified by the transformation. The biotransformation operators in the lookup table were then applied to model metabolites.

Round 1

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

The outcomes were divided into four categories. This reflected a curation problem where some reactions were not included in the iML1515 model. C3 reactions were not in EcoCyc but documented in KEGG for other organisms. These reactions reflect promiscuous activity that enabled the same biotransformation catalyzed by a different gene in the model. C1 reactions consisted of metabolites predicted by PROXIMAL that are already in iML1515 but catalyzed by different enzymes than the ones already listed in the model. These reactions were thus novel reactions that have not been reported in the literature. C2 reactions already existed in EcoCyc and/or KEGG but not in iML1515. C4 reactions did not exist in either EcoCyc nor in KEGG.

We predict hundreds of new metabolites that can be used to augment iML1515. 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. Our workflow utilizes PROXIMALa tool that uses reactantproduct transformation patterns from the KEGG databaseto predict putative structural modifications due to promiscuous enzymes. We then validated our method by comparing predicted metabolites with the Escherichia coli Metabolome Database (ECMDB).

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

D. Lao v H. GuoH2H statistics

coli [51]. C4 consists of four predicted reactions that are not currently catalogued in KEGG for any organism (Fig.5). The product, N-acetylputrescine, is involved in a number of enzymatic reactionsECs 1.4.3.4 (monoamine oxidase), 2.3.1.57 (spermidine acetyltransferase), and 3.5.1.62 (acetylputrescine deacetylase)in many organisms that include both eukaryotes and bacteria [16]. The last reaction is the transformation between d-gluconic acid and d-galactarate by EC 1.1.1.23. There is one reaction (KEGG R02970) catalyzed by EC 2.6.1.47 (l-alanine:oxomalonate aminotransferase) that produces aminomalonate; but it is not a redox reaction and is associated with rat and silkworm, not E. The second is a hydrolytic decarboxylation reaction between N-acetylputrescine and N-acetylornithine (Fig.5b) predicted to be catalyzed by EC 4.1.1.36 (PPC decarboxylase). 3-Ureidopropionate is present in eukaryotes and bacteria (but not E. The first reaction (Fig.5a) is the oxidoreductive interconversion between aminomalonate and l-serine by EC 1.1.1.23 (histidinol dehydrogenase). coli) and is involved in reactions catalyzed by ECs 3.5.1.6 (-ureidopropionase) and 3.5.2.2 (dihydropyrimidinase). The third reaction in this category is the hydrolytic decarboxylation reaction between 3-ureidopropionate and N-carbamoyl-l-aspartate also catalyzed by EC 4.1.1.36 (PPC decarboxylase). d-Galactarate is involved in reactions catalyzed by 4.2.1.158 that is present in Oceanobacillus iheyensis [52].

coli or other organisms, suggesting novel undocumented promiscuous transformations, while five other reactions are catalogued for species other than E. We identify 23 new reactions and 16 new metabolites that we recommend adding to the E. The putative products are then compared to measured metabolites as reported in Escherichia coli Metabolome Database, ECMDB [42, 43]. While initially developed to investigate products of Cytochrome P450 (CYP) enzymes, highly promiscuous enzymes utilized for detoxification, the PROXIMAL method is generic. Four of these reactions have not been catalogued prior for E. coli. To create an EMM for a known metabolic model, PROXIMAL generates biotransformation operators for each reaction in the model and then applies the operators to known metabolites within the model. coli. EcoCyc [44], and KEGG), but not in iML1515. EMMA utilizes PROXIMAL [39], a method for creating biotransformation operators from KEGG reactions IDs using RDM (Reaction Center, Difference Region, and Matched Region) patterns [40], and then applying the operators to given molecules. coli databases (e.g. coli model iML1515. Additionally, there were four new reactions that present putative transformation routes that are in parallel to existing reactions in E. Further, there were ten reactions that were cataloged in other E. EMMA predicts hundreds of putative reactions and their products due to promiscuous activities in E. No new metabolites are added due to these four reactions. We refer to the augmented models as extended metabolic models (EMMs), and to the workflow to create them as EMMA (EMM annotation). Each reaction, and thus transformation, is assumed to be reversible unless indicated otherwise. These 19 reactions led to the addition of the 16 metabolites that are new to iML1515. We describe in this paper a computational workflow that aims to extend preexisting models with reactions catalyzed by promiscuous native enzymes and validate the outcomes using published metabolomics datasets. coli. The outcome of our workflow is a list of putative metabolites due to promiscuous enzymatic activity and their catalyzing enzymes and reactions. In this work, we apply EMMA to iML1515, a genome-scale model of Escherichia coli MG1655 [41]. Each metabolic model is assumed to have a set of reactions and their compounds and KEGG reaction IDs.

We focus in this paper on another major source of uncatalogued cellular activitypromiscuous enzymatic activity, which has recently been referred to as underground metabolism [23,24,25]. While enzymes have widely been held as highly-specific catalysts that only transform their annotated substrate to product, recent studies show that enzymatic promiscuityenzymes catalyzing reactions other than their main reactionsis not an exception but can be a secondary task for enzymes [26,27,28,29,30,31]. The identified reactions can then be used to complete existing metabolic models. More than two-fifths (44%) of KEGG enzymes are associated with more than one reaction [32]. Outside of in vitro biochemical characterization studies to predict promiscuous activities, there are few resources that record details about promiscuous enzymes such as MINEs Database [33], and ATLAS [34]. Promiscuous activities however are not easily detectable in vivo since, (i) metabolites produced due to enzyme promiscuity may be unknown, (ii) product concentration due to promiscuous activity may be low, (iii) there is no high-throughput way to relate formed products to specific enzymes, and (iv) it is difficult to identify potentially unknown metabolites in complex biological samples. Advances in computing and the ability to collect large sets of metabolomics data through untargeted metabolomics provide an exciting opportunity to develop methods to identify promiscuous reactions, their catalyzing enzymes, and their products that are specific to the sample under study. Despite the current wide-spread acceptance of enzyme promiscuity, and its prominent utilization to engineer catalyzing enzymes in metabolic engineering practice [35,36,37,38], promiscuous enzymatic activity is not currently fully documented in metabolic models.

We focused on these metabolites as the assumption is that high concentration metabolites are more likely to undergo transformation by promiscuous enzymatic activity and form detectible derivatives. After manual curation (per Step 1 in the Methods section), our workflow recommended 16 new metabolites and 23 reactions that can be used to augment the iML1515 model. When applied to this set, the operators predicted the formation of 1423 known (with PubChem IDs) metabolites of which 57 were identified to exist in E. Results of flux balance analysis and flux variability analysis for the added EMMA reactions are reported in Additional file 2. Our workflow predicted the formation of 3694 known (with PubChem IDs) metabolites. One set consisted of 106 iML1515 metabolites with predicted or measured concentration values above 1M [45]. The operators were applied on two sets of metabolites. coli per ECMDB. For the remainder of the Results section, we focus on detailed analysis of derivative products due to high-concentration metabolites. We provide a listing of all derivatives in Additional file 1. The application of PROXIMAL to iML1515 yielded a lookup table with 1875 biotransformation operator entries. Out of the predicted metabolites of the second set 210 derivatives are found in ECMDB. The second set of metabolites consisted of the non-high concentration metabolites in iML1515.