Reaction Similarities Focusing Substructure Changes of Chemical Compounds and Metabolic Pathway Alignments

Tohsato, Yukako; Nishimura, Yu

doi:10.2197/ipsjtbio.2.15

Cited by 4 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this scheme, each reaction is decomposed into a set of structurally related substrate-product pairs at the atomic scale. The data are also available from the RPAIR database [ 14 ], and the same method has been used in several recent works [ 15 - 17 ]. This representation avoids bias originating from currency metabolites.…”

Section: Methodsmentioning

confidence: 99%

Reconstructing phylogeny from metabolic substrate-product relationships

2011

View full text Add to dashboard Cite

BackgroundMany approaches utilize metabolic pathway information to reconstruct the phyletic tree of fully sequenced organisms, but how metabolic networks can add information to original genomic annotations has remained open.MethodsWe translated enzyme reactions assigned in 1075 organisms into substrate-product relationships to represent the metabolic information at a finer resolution than enzymes and compounds. Each organism was represented as a vector of substrate-product relationships and the phyletic tree was reconstructed by a simple hierarchical method. Obtained results were compared with several other approaches that use genome information and network properties.ResultsPhyletic trees without consideration of network properties can already extract organisms in anomalous environments. This efficient method can add insights to traditional genome-based phylogenetic reconstruction.ConclusionsStructural relationship among metabolites can highlight parasitic or symbiont species such as spirochaete and clamydia. The method assists understanding of species-environment interaction when used in combination with traditional phylogenetic methods.

show abstract

Section: Methodsmentioning

confidence: 99%

Reconstructing phylogeny from metabolic substrate-product relationships

2011

View full text Add to dashboard Cite

show abstract

“…In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on pathway alignment to find similar pathways within or between organisms using the Enzyme Commission (EC) numbers to define reaction similarities [ 8 – 11 ]. Due to limitations of the EC classification, the notion of reaction similarity for pathway alignment was improved using metabolite similarity [ 12 ] or substructure changes [ 13 ]. Another approach, that does not require predefined pathways, was based on the detection of motifs in a reaction network [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

A new network representation of the metabolism to detect chemical transformation modules

2015

View full text Add to dashboard Cite

BackgroundMetabolism is generally modeled by directed networks where nodes represent reactions and/or metabolites. In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on graph alignment to find similar pathways. Few years ago, the concept of chemical transformation modules, also called reaction modules, was introduced and correspond to sequences of chemical transformations which are conserved in metabolism. We propose here a novel graph representation of the metabolic network where reactions sharing a same chemical transformation type are grouped in Reaction Molecular Signatures (RMS).ResultsRMS were automatically computed for all reactions and encode changes in atoms and bonds. A reaction network containing all available metabolic knowledge was then reduced by an aggregation of reaction nodes and edges to obtain a RMS network. Paths in this network were explored and a substantial number of conserved chemical transformation modules was detected. Furthermore, this graph-based formalism allows us to define several path scores reflecting different biological conservation meanings. These scores are significantly higher for paths corresponding to known metabolic pathways and were used conjointly to build association rules that should predict metabolic pathway types like biosynthesis or degradation.ConclusionsThis representation of metabolism in a RMS network offers new insights to capture relevant metabolic contexts. Furthermore, along with genomic context methods, it should improve the detection of gene clusters corresponding to new metabolic pathways.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0809-4) contains supplementary material, which is available to authorized users.

show abstract

“…Wernicke et al 8 followed Pinter’s definition of aligning metabolic pathways and provided a faster algorithm. In yet another example of pathway alignment, Tohsato and Nishimura 9 used the similarity of substructure changes to detect similar sequences of metabolites along known metabolic pathways. Ay et al 10 developed a method that combined EC number similarity and metabolite similarity, as well as the metabolic network topology similarity to conduct the pathway alignment.…”

Section: Introductionmentioning

confidence: 99%

Modular Architecture of Metabolic Pathways Revealed by Conserved Sequences of Reactions

Muto

Kotera

Tokimatsu

et al. 2013

J. Chem. Inf. Model.

View full text Add to dashboard Cite

The metabolic network is both a network of chemical reactions and a network of enzymes that catalyze reactions. Toward better understanding of this duality in the evolution of the metabolic network, we developed a method to extract conserved sequences of reactions called reaction modules from the analysis of chemical compound structure transformation patterns in all known metabolic pathways stored in the KEGG PATHWAY database. The extracted reaction modules are repeatedly used as if they are building blocks of the metabolic network and contain chemical logic of organic reactions. Furthermore, the reaction modules often correspond to traditional pathway modules defined as sets of enzymes in the KEGG MODULE database and sometimes to operon-like gene clusters in prokaryotic genomes. We identified well-conserved, possibly ancient, reaction modules involving 2-oxocarboxylic acids. The chain extension module that appears as the tricarboxylic acid (TCA) reaction sequence in the TCA cycle is now shown to be used in other pathways together with different types of modification modules. We also identified reaction modules and their connection patterns for aromatic ring cleavages in microbial biodegradation pathways, which are most characteristic in terms of both distinct reaction sequences and distinct gene clusters. The modular architecture of biodegradation modules will have a potential for predicting degradation pathways of xenobiotic compounds. The collection of these and many other reaction modules is made available as part of the KEGG database.

show abstract

Reaction Similarities Focusing Substructure Changes of Chemical Compounds and Metabolic Pathway Alignments

Cited by 4 publications

References 17 publications

Reconstructing phylogeny from metabolic substrate-product relationships

Reconstructing phylogeny from metabolic substrate-product relationships

A new network representation of the metabolism to detect chemical transformation modules

Modular Architecture of Metabolic Pathways Revealed by Conserved Sequences of Reactions

Contact Info

Product

Resources

About