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明尼苏达大学通路预测系统:预测代谢逻辑。

The University of Minnesota pathway prediction system: predicting metabolic logic.

作者信息

Ellis Lynda B M, Gao Junfeng, Fenner Kathrin, Wackett Lawrence P

机构信息

Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, 55455, USA.

出版信息

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

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 In selected cases, these have been replaced by super rules, which include two or more contiguous reactions that form a small pathway of their own.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.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6ec/2447765/d898dbd15271/gkn315f1.jpg

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