Database Commons

a catalog of worldwide biological databases

Microorganisms are ubiquitous on earth and have diverse metabolic transformative capabilities important for environmental biodegradation of chemicals that helps maintain ecosystem and human health. Microbial biodegradative metabolism is the main focus of the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD). UM-BBD data has also been used to develop a computational metabolic pathway prediction system that can be applied to chemicals for which biodegradation data is currently lacking. The UM-Pathway Prediction System (UM-PPS) relies on metabolic rules that are based on organic functional groups and predicts plausible biodegradative metabolism. The predictions are useful to environmental chemists that look for metabolic intermediates, for regulators looking for potential toxic products, for microbiologists seeking to understand microbial biodegradation, and others with a wide-range of interests.

The University of Minnesota Pathway Prediction System (UM-PPS, http://umbbd.msi.umn.edu/predict/) is a rule-based system that predicts microbial catabolism of organic compounds. Currently, its knowledge base contains 250 biotransformation rules and five types of metabolic logic entities. The original UM-PPS predicted up to two prediction levels at a time. Users had to choose a predicted product to continue the prediction. This approach provided a limited view of prediction results and heavily relied on manual intervention. The new UM-PPS produces a multi-level prediction within an acceptable time frame, and allows users to view prediction alternatives much more easily as a directed acyclic graph.

The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://umbbd.msi.umn.edu/) began in 1995 and now contains information on almost 1200 compounds, over 800 enzymes, almost 1300 reactions and almost 500 microorganism entries. Besides these data, it includes a Biochemical Periodic Table (UM-BPT) and a rule-based Pathway Prediction System (UM-PPS) (http://umbbd.msi.umn.edu/predict/) that predicts plausible pathways for microbial degradation of organic compounds. Currently, the UM-PPS contains 260 biotransformation rules derived from reactions found in the UM-BBD and scientific literature. Public access to UM-BBD data is increasing. UM-BBD compound data are now contributed to PubChem and ChemSpider, the public chemical databases. A new mirror website of the UM-BBD, UM-BPT and UM-PPS is being developed at ETH Zürich to improve speed and reliability of online access from anywhere in the world.

During wastewater treatment, many organic micropollutants undergo microbially mediated reactions resulting in the formation of transformation products (TPs). Little is known on the reaction pathways that govern these transformations or on the occurrence of microbial TPs in surface waters. Large sets of biotransformation data for organic micropollutants would be useful for assessing the exposure potential of these TPs and for enabling the development of structure-based biotransformation prediction tools. The objective of this work was to develop an efficient procedure to allow for high-throughput elucidation of TP structures for a broad and diverse set of xenobiotics undergoing microbially mediated transformation reactions. Six pharmaceuticals and six pesticides were spiked individually into batch reactors seeded with activated sludge. Samples from the reactors were separated with HPLC and analyzed by linear ion trap-orbitrap mass spectrometry. Candidate TPs were preliminarily identified with an innovative post-acquisition data processing method based on target and non-target screenings of the full-scan MS data. Structures were proposed following interpretation of MS spectra and MS/MS fragments. Previously unreported microbial TPs were identified for the pharmaceuticals bezafibrate, diazepam, levetiracetam, oseltamivir, and valsartan. A variety of previously reported and unreported TPs were identified for the pesticides. The results showed that the complementary use of the target and non-target screening methods allowed for a more comprehensive interpretation of the TPs generated than either would have provided individually.

MOTIVATION: Current methods for the prediction of biodegradation products and pathways of organic environmental pollutants either do not take into account domain knowledge or do not provide probability estimates. In this article, we propose a hybrid knowledge- and machine learning-based approach to overcome these limitations in the context of the University of Minnesota Pathway Prediction System (UM-PPS). The proposed solution performs relative reasoning in a machine learning framework, and obtains one probability estimate for each biotransformation rule of the system. As the application of a rule then depends on a threshold for the probability estimate, the trade-off between recall (sensitivity) and precision (selectivity) can be addressed and leveraged in practice.
RESULTS: Results from leave-one-out cross-validation show that a recall and precision of approximately 0.8 can be achieved for a subset of 13 transformation rules. Therefore, it is possible to optimize precision without compromising recall. We are currently integrating the results into an experimental version of the UM-PPS server.
AVAILABILITY: The program is freely available on the web at http://wwwkramer.in.tum.de/research/applications/biodegradation/data.
CONTACT: kramer@in.tum.de.

The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://umbbd.ahc.umn.edu/) provides curated information on microbial catabolism and related biotransformations, primarily for environmental pollutants. Currently, it contains information on over 130 metabolic pathways, 800 reactions, 750 compounds and 500 enzymes. In the past two years, it has increased its breath to include more examples of microbial metabolism of metals and metalloids; and expanded the types of information it includes to contain microbial biotransformations of, and binding interactions with many chemical elements. It has also increased the ways in which this data can be accessed (mined). Structure-based searching was added, for exact matches, similarity, or substructures. Analysis of UM-BBD reactions has lead to a prototype, guided, pathway prediction system. Guided prediction means that the user is shown all possible biotransformations at each step and guides the process to its conclusion. Mining the UM-BBD's data provides a unique view into how the microbial world recycles organic functional groups. UM-BBD users are encouraged to comment on all aspects of the database, including the information it contains and the tools by which it can be mined. The database and prediction system develop under the direction of the scientific community.

The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://umbbd.ahc.umn.edu/) provides curated information on microbial catabolic enzymes and their organization into metabolic pathways. Currently, it contains information on over 400 enzymes. In the last year the enzyme page was enhanced to contain more internal and external links; it also displays the different metabolic pathways in which each enzyme participates. In collaboration with the Nomenclature Commission of the International Union of Biochemistry and Molecular Biology, 35 UM-BBD enzymes were assigned complete EC codes during 2000. Bacterial oxygenases are heavily represented in the UM-BBD; they are known to have broad substrate specificity. A compilation of known reactions of naphthalene and toluene dioxygenases were recently added to the UM-BBD; 73 and 108 were listed respectively. In 2000 the UM-BBD is mirrored by two prestigious groups: the European Bioinformatics Institute and KEGG (the Kyoto Encyclopedia of Genes and Genomes). Collaborations with other groups are being developed. The increased emphasis on UM-BBD enzymes is important for predicting novel metabolic pathways that might exist in nature or could be engineered. It also is important for current efforts in microbial genome annotation.

The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://www.labmed.umn.edu/umbbd/i nde x.html) first became available on the web in 1995 to provide information on microbial biocatalytic reactions of, and biodegradation pathways for, organic chemical compounds, especially those produced by man. Its goal is to become a representative database of biodegradation, spanning the diversity of known microbial metabolic routes, organic functional groups, and environmental conditions under which biodegradation occurs. The database can be used to enhance understanding of basic biochemistry, biocatalysis leading to speciality chemical manufacture, and biodegradation of environmental pollutants. It is also a resource for functional genomics, since it contains information on enzymes and genes involved in specialized metabolism not found in intermediary metabolism databases, and thus can assist in assigning functions to genes homologous to such less common genes. With information on >400 reactions and compounds, it is poised to become a resource for prediction of microbial biodegradation pathways for compounds it does not contain, a process complementary to predicting the functions of new classes of microbial genes.