Literature DB >> 19053521

Eliciting possible reaction equations and metabolic pathways involving orphan metabolites.

Masaaki Kotera1, Andrew G McDonald, Sinéad Boyce, Keith F Tipton.   

Abstract

The development of metabolomics has resulted in the discovery of an increasing number of orphan metabolites, which are defined as compounds that are known to be present in living organisms but whose synthetic/degradation pathways are unknown. In this paper, we describe a procedure for identifying possible products and/or precursors of such orphan metabolites and for suggesting complete reaction equations and the corresponding EC (Enzyme Commission) number simultaneously. Chemical structure comparison is performed for a pair of compounds consisting of a reported substrate and its corresponding product and also for pairs of randomly selected compounds. Possible combinations of compounds registered in the KEGG database were used for generating putative enzyme reaction equations, which resulted in 77% of the reported equations being generated, as most of the remainder represent classes of compounds, rather than specific compounds, or contain Markush structures. The quality was checked using chemical structure comparison and the random-tree method, which gave 98% accuracy in suggesting EC subsubclasses for reported equations in cross-validation tests. The equations generated in this study can be seen using the Web-based program GREP (Generator of Reaction Equations & Pathways; http://bisscat.org/GREP/ ). The usefulness of our method for constructing possible metabolic pathways was demonstrated by mapping the generated equations for several groups of compounds, such as the betalain alkaloids. The possible development of our method so that alternative substrates for reported enzymes can be found and for annotating enzyme functions in genomic research is also discussed.

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Year:  2008        PMID: 19053521     DOI: 10.1021/ci800213g

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  11 in total

1.  An efficient algorithm for de novo predictions of biochemical pathways between chemical compounds.

Authors:  Masaomi Nakamura; Tsuyoshi Hachiya; Yutaka Saito; Kengo Sato; Yasubumi Sakakibara
Journal:  BMC Bioinformatics       Date:  2012-12-13       Impact factor: 3.169

2.  MetaMapp: mapping and visualizing metabolomic data by integrating information from biochemical pathways and chemical and mass spectral similarity.

Authors:  Dinesh K Barupal; Pradeep K Haldiya; Gert Wohlgemuth; Tobias Kind; Shanker L Kothari; Kent E Pinkerton; Oliver Fiehn
Journal:  BMC Bioinformatics       Date:  2012-05-16       Impact factor: 3.169

3.  MUCHA: multiple chemical alignment algorithm to identify building block substructures of orphan secondary metabolites.

Authors:  Masaaki Kotera; Toshiaki Tokimatsu; Minoru Kanehisa; Susumu Goto
Journal:  BMC Bioinformatics       Date:  2011-12-14       Impact factor: 3.169

4.  KCF-S: KEGG Chemical Function and Substructure for improved interpretability and prediction in chemical bioinformatics.

Authors:  Masaaki Kotera; Yasuo Tabei; Yoshihiro Yamanishi; Yuki Moriya; Toshiaki Tokimatsu; Minoru Kanehisa; Susumu Goto
Journal:  BMC Syst Biol       Date:  2013-12-13

5.  Simultaneous prediction of enzyme orthologs from chemical transformation patterns for de novo metabolic pathway reconstruction.

Authors:  Yasuo Tabei; Yoshihiro Yamanishi; Masaaki Kotera
Journal:  Bioinformatics       Date:  2016-06-15       Impact factor: 6.937

Review 6.  Metabolic pathway reconstruction strategies for central metabolism and natural product biosynthesis.

Authors:  Masaaki Kotera; Susumu Goto
Journal:  Biophys Physicobiol       Date:  2016-07-15

7.  E-zyme: predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs.

Authors:  Yoshihiro Yamanishi; Masahiro Hattori; Masaaki Kotera; Susumu Goto; Minoru Kanehisa
Journal:  Bioinformatics       Date:  2009-06-15       Impact factor: 6.937

Review 8.  Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways.

Authors:  Hayat Ali Shah; Juan Liu; Zhihui Yang; Jing Feng
Journal:  Front Mol Biosci       Date:  2021-06-17

9.  Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets.

Authors:  Masaaki Kotera; Yasuo Tabei; Yoshihiro Yamanishi; Toshiaki Tokimatsu; Susumu Goto
Journal:  Bioinformatics       Date:  2013-07-01       Impact factor: 6.937

10.  Metabolome-scale prediction of intermediate compounds in multistep metabolic pathways with a recursive supervised approach.

Authors:  Masaaki Kotera; Yasuo Tabei; Yoshihiro Yamanishi; Ai Muto; Yuki Moriya; Toshiaki Tokimatsu; Susumu Goto
Journal:  Bioinformatics       Date:  2014-06-15       Impact factor: 6.937

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