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. 2008 Jul 1;36(Web Server issue):W427-32.
doi: 10.1093/nar/gkn315. Epub 2008 Jun 4.

The University of Minnesota pathway prediction system: predicting metabolic logic

Affiliations

The University of Minnesota pathway prediction system: predicting metabolic logic

Lynda B M Ellis et al. Nucleic Acids Res. .

Abstract

The University of Minnesota pathway prediction system (UM-PPS, http://umbbd.msi.umn.edu/predict/) recognizes functional groups in organic compounds that are potential targets of microbial catabolic reactions, and predicts transformations of these groups based on biotransformation rules. Rules are based on the University of Minnesota biocatalysis/biodegradation database (http://umbbd.msi.umn.edu/) and the scientific literature. As rules were added to the UM-PPS, more of them were triggered at each prediction step. The resulting combinatorial explosion is being addressed in four ways. Biodegradation experts give each rule an aerobic likelihood value of Very Likely, Likely, Neutral, Unlikely or Very Unlikely. Users now can choose whether they view all, or only the more aerobically likely, predicted transformations. Relative reasoning, allowing triggering of some rules to inhibit triggering of others, was implemented. Rules were initially assigned to individual chemical reactions. In selected cases, these have been replaced by super rules, which include two or more contiguous reactions that form a small pathway of their own. Rules are continually modified to improve the prediction accuracy; increasing rule stringency can improve predictions and reduce extraneous choices. The UM-PPS is freely available to all without registration. Its value to the scientific community, for academic, industrial and government use, is good and will only increase.

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Figures

Figure 1.
Figure 1.
A three-step predicted pathway and the choice for the fourth step (ring cleavage) for HBCD, a commercial flame retardant and an EU priority pollutant not found in the UM-BBD. This is a plausible, but perhaps not the most plausible, prediction (see text).
Figure 2.
Figure 2.
(a) The benzenoid cis,cis-muconate, intradiol (ortho) lower cleavage pathway super rule (bt0348). The complete rule is found at: http://umbbd.msi.umn.edu/servlets/rule.jsp?rule=bt0348. (b) The graphic for the benzenoid cis,cis-muconate, intradiol (ortho) lower cleavage pathway super rule (bt0348) shown in (a).
Figure 2.
Figure 2.
(a) The benzenoid cis,cis-muconate, intradiol (ortho) lower cleavage pathway super rule (bt0348). The complete rule is found at: http://umbbd.msi.umn.edu/servlets/rule.jsp?rule=bt0348. (b) The graphic for the benzenoid cis,cis-muconate, intradiol (ortho) lower cleavage pathway super rule (bt0348) shown in (a).

References

    1. Ellis LBM, Roe D, Wackett LP. The University of Minnesota biocatalysis/biodegradation database: the first decade. Nucleic Acids Res. 2006;34:D517–D521. - PMC - PubMed
    1. Wackett LP, Ellis LBM, Speedie S, Hershberger CD, Knackmuss H.-J, Spormann A, Walsh CT, Forney LJ, Punch WF, Kazic T, et al. The prediction of microbial biodegradation and biocatalysis. Amer. Soc. Microbiol. News. 1999;65:87–93.
    1. Hou BK, Wackett LP, Ellis LBM. Predicting microbial catabolism: a functional group approach. J. Chem. Inf. Comp. Sci. 2003;43:1051–1057. - PubMed
    1. Hou BK, Ellis LBM, Wackett LP. Encoding microbial metabolic logic: predicting biodegradation. J. Ind. Microbiol. Biotechnol. 2004;31:261–272. - PubMed
    1. Csizmadia F. JChem: Java applets and modules supporting chemical database handling from web browsers. J. Chem. Inf. Comp. Sci. 2000;40:323–324. - PubMed

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