LIGAND: database of chemical compounds and reactions in biological pathways

Goto, Susumu; Okuno, Yasushi; Hattori, Masahiro; Nishi­oka, Takaaki; Kanehisa, Minoru

doi:10.1093/nar/30.1.402

Cited by 262 publications

(176 citation statements)

References 9 publications

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“…If so, a reaction with superessentiality that is much higher than the superessentiality of another reaction would still be a better candidate drug target. Second, our comparison of essential reactions with enzyme occurrence in organisms with sequenced genomes depends on the quality of available metabolic genome annotation (28,36). This annotation currently has numerous limitations, which we discussed.…”

Section: Discussionmentioning

confidence: 99%

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Superessential reactions in metabolic networks

Barve

Rodrigues²,

Wagner³

2012

Proc. Natl. Acad. Sci. U.S.A.

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The metabolic genotype of an organism can change through loss and acquisition of enzyme-coding genes, while preserving its ability to survive and synthesize biomass in specific environments. This evolutionary plasticity allows pathogens to evolve resistance to antimetabolic drugs by acquiring new metabolic pathways that bypass an enzyme blocked by a drug. We here study quantitatively the extent to which individual metabolic reactions and enzymes can be bypassed. To this end, we use a recently developed computational approach to create large metabolic network ensembles that can synthesize all biomass components in a given environment but contain an otherwise random set of known biochemical reactions. Using this approach, we identify a small connected core of 124 reactions that are absolutely superessential (that is, required in all metabolic networks). Many of these reactions have been experimentally confirmed as essential in different organisms. We also report a superessentiality index for thousands of reactions. This index indicates how easily a reaction can be bypassed. We find that it correlates with the number of sequenced genomes that encode an enzyme for the reaction. Superessentiality can help choose an enzyme as a potential drug target, especially because the index is not highly sensitive to the chemical environment that a pathogen requires. Our work also shows how analyses of large network ensembles can help understand the evolution of complex and robust metabolic networks.drug resistance | drug target identification | essential reactions

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Section: Discussionmentioning

confidence: 99%

“…This known universe currently comprises more than 5,000 stoichiometrically defined reactions (27,28) that are known to take place in some organisms. Based on this information, our approach can generate random samples of metabolic genotypes (metabolic networks) with a given phenotype (22, 23) (SI Appendix, SI Methods).…”

mentioning

confidence: 99%

Superessential reactions in metabolic networks

Barve

Rodrigues²,

Wagner³

2012

Proc. Natl. Acad. Sci. U.S.A.

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“…Synonyms are needed for searching Reaction Database and Explorer because many people use different names for the same compound and few use the often-unwieldy IUPAC-approved names. There are excellent small molecule databases, such as ChEBI [27] and KEGG LIGAND [28].…”

Section: Discussionmentioning

confidence: 99%

Tracing metabolic pathways from enzyme data

McDonald

Tipton

Boyce

2009

Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics

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The IUBMB Enzyme List is widely used by other databases as a source for avoiding ambiguity in the recognition of enzymes as catalytic entities. However, it was never designed for activities such as pathway tracing, which have become increasingly important in systems biology. This is because it often relies on generic or representative reactions to show the reactions catalysed by enzymes of wide specificity. It is necessary to go to databases such as BRENDA to find further, more detailed, information on what is known about the range of substrates for any particular enzyme. In order to provide a framework for tracing pathways involving any specific enzyme or metabolite, we have created a Reactions Database from the material in the Enzyme List. This allows reactions to be searched by substrate/product and pathways to be traced from any selected starting/seed substrate. An extensive synonym glossary allows searches by many of the alternative names, including accepted abbreviations, by which a chemical compound may be known. This database was necessary for the development of the application Reaction Explorer (http://www.reaction-explorer.org), which was written in REALbasic to search the Reactions Database and draw metabolic pathways from reactions selected by the user. Having input the name of the starting compound (the "seed"), the user is presented with a list of all reactions containing that compound and then selects the product of interest as the next point on the ensuing graph. The pathway diagram is then generated as the process iterates. A contextual menu is provided, which allows the user to (i) remove a compound from the graph, along with all associated links; (ii) search the reactions database again for additional reactions involving the compound and (iii) search for the compound within the Enzyme List.

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“…This coarse-grained graph also visualizes the vulnerable connections in the network, and thus could have important implication for disease studies and drug target identifications. In addition, analysis on the reciprocal links and main cores in the GSC part of bow-tie also reveals that the bow-tie structure of metabolic networks has its own intrinsic and significant features which are significantly different from those of random networks.Keywords: bioinformatics; metabolic network; bow-tie; random network.As the results of various genome projects, the genome sequences of many organisms are available and the organism specific metabolic networks can be faithfully reconstructed from genome information [1][2][3][4][5][6] . Thus the prediction of function from the metabolic networks has become an essential step in the post-genomic era [4,[7][8][9][10][11] .…”

mentioning

confidence: 99%

“…As the results of various genome projects, the genome sequences of many organisms are available and the organism specific metabolic networks can be faithfully reconstructed from genome information [1][2][3][4][5][6] . Thus the prediction of function from the metabolic networks has become an essential step in the post-genomic era [4,[7][8][9][10][11] .…”

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confidence: 99%

Bow-tie topological features of metabolic networks and the functional significance

Zhao

Lin

et al. 2007

Chin. Sci. Bull.

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Exploring the structural topology of genome-based large-scale metabolic network is essential for investigating possible relations between structure and functionality. Visualization would be helpful for obtaining immediate information about structural organization. In this work, metabolic networks of 75 organisms were investigated from a topological point of view. A spread bow-tie model was proposed to give a clear visualization of the bow-tie structure for metabolic networks. The revealed topological pattern helps to design more efficient algorithm specifically for metabolic networks. This coarse-grained graph also visualizes the vulnerable connections in the network, and thus could have important implication for disease studies and drug target identifications. In addition, analysis on the reciprocal links and main cores in the GSC part of bow-tie also reveals that the bow-tie structure of metabolic networks has its own intrinsic and significant features which are significantly different from those of random networks.Keywords: bioinformatics; metabolic network; bow-tie; random network.As the results of various genome projects, the genome sequences of many organisms are available and the organism specific metabolic networks can be faithfully reconstructed from genome information [1][2][3][4][5][6] . Thus the prediction of function from the metabolic networks has become an essential step in the post-genomic era [4,[7][8][9][10][11] . However, before it is possible to investigate the potential relationship between structure and functionality of a metabolic network, it is necessary to study how the metabolic networks are actually constructed.In recent years there has been a strongly increasing passion in investigating the intricate structures of link topologies in metabolic networks [12][13][14][15][16][17][18][19][20][21] . Many researchers have applied methods from graph theory to treat the metabolic networks as a directed graph consisting of nodes denoting metabolites connected by directed arcs representing reactions. An important finding is that metabolic networks, as well as other real-world complex networks, have topologies that differ markedly from those found in simple randomly connected networks [22] , which suggests that their non-random structures could imply significant organizing principles of metabolic networks.From the computational analysis of genome-based metabolic networks of 65 organisms, Ma and Zeng discovered that the global metabolic network is organized in the form of a bow-tie [15] . On the other hand, from the view of material flows and information flows in the metabolic system, Csete and Doyle described metabolism as several nested bow-ties [23] . It was pointed out that bow-tie architectures facilitate robust biologic function, and based on their design, also have

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LIGAND: database of chemical compounds and reactions in biological pathways

Cited by 262 publications

References 9 publications

Superessential reactions in metabolic networks

Superessential reactions in metabolic networks

Tracing metabolic pathways from enzyme data

Bow-tie topological features of metabolic networks and the functional significance

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