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Bioinformatics Advance Access originally published online on August 20, 2009
Bioinformatics 2009 25(22):2975-2982; doi:10.1093/bioinformatics/btp507
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© The Author(s) 2009. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Metabolite and reaction inference based on enzyme specificities

M. J. L. de Groot 1,2,3,4,{dagger}, R. J. P. van Berlo 1,3,{dagger}, W. A. van Winden 2,3, P. J. T. Verheijen 2,3, M. J. T. Reinders 1,3,4 and D. de Ridder 1,3,4,*

1The Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, 2Bioprocess Technology Group, Department of Biotechnology, Delft University of Technology, Julianalaan 67, 2628 BC Delft, 3Kluyver Centre for Genomics of Industrial Fermentation, P.O. Box 5057, 2600 GA Delft and 4Netherlands Bioinformatics Center, 260 NBIC, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands

*To whom correspondence should be addressed.


  Abstract

Motivation: Many enzymes are not absolutely specific, or even promiscuous: they can catalyze transformations of more compounds than the traditional ones as listed in, e.g. KEGG. This information is currently only available in databases, such as the BRENDA enzyme activity database. In this article, we propose to model enzyme aspecificity by predicting whether an input compound is likely to be transformed by a certain enzyme. Such a predictor has many applications, for example, to complete reconstructed metabolic networks, to aid in metabolic engineering or to help identify unknown peaks in mass spectra.

Results: We have developed a system for metabolite and reaction inference based on enzyme specificities (MaRIboES). It employs structural and stereochemistry similarity measures and molecular fingerprints to generalize enzymatic reactions based on data available in BRENDA. Leave-one-out cross-validation shows that 80% of known reactions are predicted well. Application to the yeast glycolytic and pentose phosphate pathways predicts a large number of known and new reactions, often leading to the formation of novel compounds, as well as a number of interesting bypasses and cross-links.

Availability: MATLAB and C++ code is freely available at https://gforge.nbic.nl/projects/mariboes/

Contact: d.deridder{at}tudelft.nl

Supplementary information: Supplementary data are available at Bioinformatics online.

{dagger}The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First authors.

Associate Editor: Jonathan Wren

Received on June 2, 2009; revised on August 7, 2009; accepted on August 9, 2009

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