Encoding microbial metabolic logic: Predicting biodegradation

Kyeng Hou Bo, Lynda B Ellis, Lawrence P Wackett

Research output: Contribution to journalArticlepeer-review

51 Scopus citations

Abstract

Prediction of microbial metabolism is important for annotating genome sequences and for understanding the fate of chemicals in the environment. A metabolic pathway prediction system (PPS) has been developed that is freely available on the world wide web (http://umbbd.ahc.umn.edu/predict/), recognizes the organic functional groups found in a compound, and predicts transformations based on metabolic rules. These rules are designed largely by examining reactions catalogued in the University of Minnesota Biocatalysis/ Biodegradation Database (UM-BBD) and are generalized based on metabolic logic. The predictive accuracy of the PPS was tested: (1) using a 113-member set of compounds found in the database, (2) against a set of compounds whose metabolism was predicted by human experts, and (3) for consistency with experimental microbial growth studies. First, the system correctly predicted known metabolism for 111 of the 113 compounds containing C and H, O, N, S, P and/or halides that initiate existing pathways in the database, and also correctly predicted 410 of the 569 known pathway branches for these compounds. Second, computer predictions were compared to predictions by human experts for biodegradation of six compounds whose metabolism was not described in the literature. Third, the system predicted reactions liberating ammonia from three organonitrogen compounds, consistent with laboratory experiments showing that each compound served as the sole nitrogen source supporting microbial growth. The rule-based nature of the PPS makes it transparent, expandable, and adaptable.

Original languageEnglish (US)
Pages (from-to)261-272
Number of pages12
JournalJournal of Industrial Microbiology and Biotechnology
Volume31
Issue number6
DOIs
StatePublished - Jul 2004

Keywords

  • Bacteria
  • Biodegradation
  • Metabolic logic
  • Metabolism
  • Prediction

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