ExplorEnz: a MySQL database of the IUBMB enzyme nomenclature

McDonald, Andrew G.; Boyce, Sinéad; Moss, G. P.; Dixon, H. B. F.; Tipton, Keith F.

doi:10.1186/1471-2091-8-14

Cited by 29 publications

(23 citation statements)

References 6 publications

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“…The first three levels (Class, Sub-class and Sub-subclass) broadly define the overall chemistry involved, and the serial number (the fourth level) generally defines the substrate specificity. Whilst there are over 4000 EC serial numbers, 3,4 there is only a relatively small number (229) of sub-subclasses, of which only 188 are associated with known protein 3D structures. MACiE includes approximately 86% of these subsubclasses, but coverage at the serial number level is only 16%.…”

Section: Introductionmentioning

confidence: 99%

“…16, 17 and 19. ) All enzymes characterised to date have an Enzyme Commission (EC) number; 3,4 this code is a four-level description that has long been used to classify enzymes with respect to the overall transformation of substrate into product. The first three levels (Class, Sub-class and Sub-subclass) broadly define the overall chemistry involved, and the serial number (the fourth level) generally defines the substrate specificity.…”

Section: Introductionmentioning

confidence: 99%

“…These databases, focusing either solely on enzymes or on proteins in general, have been developed from the information found in the primary literature (UniProt, 1 wwPDB, 2 IntEnz, 3 ExplorEnz, 4 pFam, 5 CSA, 6,7 BRENDA, 8 PLD, 9 KEGG, 10 BioPath, 11 Promise 12 and MDB 13 ). With the advent of structure determination methods for proteins, and by using ingenious chemical and biochemical experimental designs, scientists have been able to propose catalytic mechanisms for many different enzymes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Understanding the Functional Roles of Amino Acid Residues in Enzyme Catalysis

Holliday

Mitchell

Thornton

2009

Journal of Molecular Biology

135

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Understanding the Functional Roles of Amino Acid Residues in Enzyme Catalysis

Holliday

Mitchell

Thornton

2009

Journal of Molecular Biology

135

View full text Add to dashboard Cite

“…Several resources were used during the manual curation phase, such as primary literature and the databases KEGG, EMBL-EBI, ExplorEnz, BIGG, BRENDA, MetaCyc, and SwissProt (McDonald et al, 2007;Schellenberger et al, 2010;Scheer et al, 2011;McWilliam et al, 2013;Caspi et al, 2014;Kanehisa et al, 2014;UniProt Consortium, 2015). All the resources used during the reconstruction process are highlighted in Table I.…”

Section: Draft Generationmentioning

confidence: 99%

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions

Zuñiga

Huelsman

et al. 2016

Plant Physiol.

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The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.

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“…Then, also from SGD, we obtained the protein interactions for the proteins encoded by these genes. To obtain the reactions from the EC numbers, we used the Explore Enz database [30]: For each pair of EC numbers x and y, we checked whether there is at least one product of x that is a substrate of y. If this were the case, then the arc (g x , g y ) was added to D, where g x and g y are the gene identifiers associated with the EC numbers x and y, respectively.…”

Section: Observation 2 If V /mentioning

confidence: 99%

Finding Supported Paths in Heterogeneous Networks

et al. 2015

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Subnetwork mining is an essential issue in the analysis of biological, social and communication networks. Recent applications require the simultaneous mining of several networks on the same or a similar vertex set. That is, one searches for subnetworks fulfilling different properties in each input network. We study the case that the input consists of a directed graph D and an undirected graph G on the same vertex set, and the sought pattern is a path P in D whose vertex set induces a connected subgraph of G. In this context, three concrete problems arise, depending on whether the existence of P is questioned or whether the length of P is to be optimized: in that case, one can search for a longest path or (maybe less intuitively) a shortest one. These problems have immediate applications in biological networks and predictable applications in social, information and communication networks. We study the classic and parameterized complexity of the problem, thus identifying polynomial and NP-complete cases, as well as fixed-parameter tractable and W[1]-hard cases. We also propose two enumeration algorithms that we evaluate on synthetic and biological data.

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ExplorEnz: a MySQL database of the IUBMB enzyme nomenclature

Cited by 29 publications

References 6 publications

Understanding the Functional Roles of Amino Acid Residues in Enzyme Catalysis

Understanding the Functional Roles of Amino Acid Residues in Enzyme Catalysis

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions

Finding Supported Paths in Heterogeneous Networks

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