Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds

Bioinformatics

Volumn 27, Issue 11, 2011, Pages 1537-1545

  Mu, Fangping ^a   Unkefer, Clifford J ^b   Unkefer, Pat J ^b   Hlavacek, William S ^a  

^a LOS ALAMOS NATIONAL LABORATORY   (United States)

^b LOS ALAMOS NATIONAL LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ENZYME;

ARTICLE; ARTIFICIAL INTELLIGENCE; BIOCATALYSIS; BIOLOGY; CHEMICAL STRUCTURE; CLASSIFICATION; FACTUAL DATABASE; METABOLISM; METABOLOME; METABOLOMICS; METHODOLOGY;

ARTIFICIAL INTELLIGENCE; BIOCATALYSIS; CLASSIFICATION; COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; ENZYMES; METABOLIC NETWORKS AND PATHWAYS; METABOLOME; METABOLOMICS; MOLECULAR STRUCTURE;

EID: 79957842729     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btr177     Document Type: Article

References (55)

1. Anari,M.R. and Baillie,T.A. (2005) Bridging chemoinformatic metabolite predic-tion and tandem mass spectrometry. Drug Discov. Today, 10, 711-717.
2. Baran,R. et al. (2009) Mass spectrometry based metabo-lomics and enzymatic assays for functional genomics. Curr. Opin. Microbiol., 12, 547-552.
3. Bhalla,R. et al. (2005) Metabolomics and its role in understanding cellular repsonse in plants. Plant Cell Rep., 24, 562-571.
4. Boernke,W.E. et al. (1995) Stringency of substrate specificity of Escherichia coli malate dehydrogenase. Arch. Biochem. Biophys., 322, 43-52.
5. Boobis,A. et al. (2002) In silico prediction of ADME and pharmacokinetics report of an expert meeting organised by COST B15. Eur. J. Pharm. Sci., 17, 183-193.
6. Boyer,S. et al. (2007) Reaction site mapping of xenobiotic biotransformations. J. Chem. Inform. Model., 47, 583-590.
7. Breinbauer,R. et al. (2002) From protein domains to drug candidates - natural products as guiding principles in the design and synthesis of compound libraries. Angew. Chem. Int. Ed., 41, 2878-2890.
8. Breitling,R. et al. (2006) Precision mapping of the metabolome. Trends Biotechnol., 24, 543-548.
9. Chang,C.-C. and Lin,C.-J. (2001) LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
10. Chen,L. and Vitkup,D. (2007) Distribution of orphan metabolic activities. Trends Biotechnol., 25, 343-348.
11. Darvas,F. (1998) Predicting metabolic pathways by logic programming. J. Mol. Graph., 6, 80-86.
12. Dunn,W.B. et al. (2005) Measuring the metabolome: current analytical technologies. Analyst, 130, 606-625.
13. Ellis,L.B.M. et al. (2006) The University of Minnesota Bioca-talysis/Biodegradation Database: the first decade. Nucleic Acids Res., 34, D517-D521.
14. Fenner,K. et al. (2008) Data-driven extraction of relative reasoning rules to limit combinatorial explosion in biodegradation pathway prediction. Bioinformaticse, 24, 2079-2085.
15. Fiehn,O. (2002) Metabolomics - the link between genotypes and phenotypes. Plant Mol. Biol., 48, 155-171.
16. Fiehn,O. (2007) Cellular Metabolomics: the qwest for pathway structure. In Lindon,J.C., Nicholson,J.K. and Holmes,E. (eds) The Handbook of Metabonomics and Metabolomics. Elsevier, Amsterdam, Oxford, pp. 35-54.
17. Fiehn,O. and Weckwerth,W. (2003) Deciphering metabolic networks. Eur. J. Biochem., 270, 579-588.
18. Fischbach,M.A. and Clardy,J. (2007) One pathway, many products. Nat. Chem. Biol., 3, 353-355.
19. Fischer,E. and Sauer,U. (2003)Anovel metabolic cycle catalyzes glucose oxidation and anaplerosis in hungry Escherichia coli. J. Biol. Chem., 278, 46446-46451.
20. Forster,J. et al. (2003) Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res., 13, 244-253.
21. Goto,S. et al. (2002) LIGAND: database of chemical compounds and reactions in biological pathways. Nucleic Acids Res., 30, 402-404.
22. Greene,N. et al. (1999) Knowledge-based expert systems for toxicity and metabolism prediction: DEREK, StAR, and METEOR. SAR QSAR Environ. Res., 10, 299-313.
23. Guyon,I. and Elisseeff,A. (2003) An introduction to variable and feature selection. J. Mach. Learn. Res., 3, 1157-1182.
24. Guyon,I. et al. (2002) Gene selection for cancer classification using support vector machine. Mach. Learn., 46, 389-422.
25. Harrigan,G.G. and Goodacre,R. (eds) (2003) Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis. Kluwer Academic Publishers, Boston.
26. Hatzimanikatis,V. et al. (2005) Exploring the diversity of complex metabolic networks. Bioinformatics, 21, 1603-1609.
27. Hou,B.K. et al. (2003) Microbial pathway prediction: a functional group approach. J. Chem. Inf. Comput. Sci., 43, 1051-1057.
28. JOELib (2004) August 27, 2004 Version. Availabe at http://sourceforge.net/projects/joelib/ (last accessed date August, 2008).
29. Kell,D.B. (2004) Metabolomics and systems biology: making sense of the soup. Curr. Opin. Microbiol., 7, 296-307.
30. Kind,T. and Fiehn,O. (2008) Hardware and software challenges for the near future: structure elucidation concepts via hyphenated chromatographic techniques. LCGC North America, 26, 176-187.
31. Klopman,G. et al. (1994) META. 1. A program for the evaluation of metabolic transformation of chemicals. J. Chem. Inf. Comput. Sci., 34, 1320-1325.
32. Langowski,J. and Long,A. (2002) Computer systems for the prediction of xenobiotic metabolism. Adv. Drug Deliv. Rev., 54, 407-415.
33. Loh,K.D. et al. (2006) A previously undescribed pathway for pyrimidine catabolism. Proc. Natl Acad. Sci., 103, 5114-5119.
34. Moco,S. et al. (2007) Metabolomics technologies and metabolite identification. Trends Anal. Chem., 26, 855-866.
35. MolConverter (2009) Marvin beans 5.3.3. ChemAxon Ltd, Budapest.
36. Mu,F. et al. (2006) Prediction of oxido-reductase-catalyzed reactions based on atomic properties of metabolites. Bioinformatics, 22, 3082-3088.
37. Muller,M. (2004) Chemical diversity through biotransformations. Curr. Opin. Biotechnol., 15, 591-598.
38. Mueller,L.A. et al. (2003) AraCyc: a biochemical pathway database for Arabidopsis plant physiology. Plant Physiol., 132, 453-460.
39. Nakahigashi,K. et al. (2009) Systematic phenome analysis of Escherichia coli multiple- knockout mutants reveals hidden reactions in central carbon metabolism. Mol. Syst. Biol., 5, Article no. 306.
40. Nobeli,I. et al. (2009) Protein promiscuity and its implications for biotechnology. Nat. Biotech., 27, 157-167.
41. Payne,M.P. (2004) Computer-based methods for the prediction of chemical metabolism and biotransformation within biological organisms. In Cronin,M.T.D. and Livingstone,D.J. (eds) Predicting Chemical Toxicity and Fate. CRC Press, Baca Raton, FL, pp. 205-227.
42. Reed,J.L. et al. (2003) An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol., 4, R54.51-12.
43. Rendic,S. (2002) Summary of information on human CYP enzymes: human P450 metabolism data. Drug Metab. Rev., 34, 83-448.
44. Romero,P. et al. (2003) Computational prediction of human metabolic pathways from the complete human genome. Genome Biol., 4, R54.
45. Saito,N. et al. (2010) Unveiling cellular biochemical reactions via metabolomics-driven approaches. Curr. Opin. Microbiol., 13, 358-362.
46. Schreiber,S.I. (2005) Small molecules: the missing link in the central dogma. Nat. Chem. Biol., 1, 64-66.
47. Schwab,W. (2003) Metabolome diversity: too few genes, too many metabolites? Phytochemistry, 62, 837-849.
48. Silverman,R.B. (2000) The Organic Chemistry of Enzyme-Catalyzed Reactions. Academic Press, San Diego, CA.
49. Soh,K.C. and Hatzimanikatis,V. (2010) DREAMS of metabolism. Trends Biotechnol., 28, 501-508.
50. Steinbeck,C. et al. (2003) The Chemistry Development Kit (CDK): an open-source Java library for chemo- and bioinformatics. J. Chem. Inf. Comput. Sci., 43, 493-500.
51. Stewart,J.J.P. (2007) Optimization of parameters for semiempirical methods V: modification of NDDO approximations and application to 70 elements. J. Mol. Model., 13, 1173-1213.
52. van der Werf,M.J. et al. (2005) Microbial metabolomics: replacing trial-and-error by the unbiased selection and ranking of targets. J. Ind. Microbiol. Biotechnol., 32, 234-252.
53. Vapnik,V. (1998) Statistical Learning Theory. Wiley, New York, NY.
54. Vaz,R.J. et al. (2010) The challenges of in silico contributions to drug metabolism in lead optimization. Exp. Opin. Drug Metab. Toxicol., 6, 851-861.
55. Wishart,D.S. (2007) Current Progress in computational metabolomics. Brief. Bioinformatics, 8, 279-293.