Use of the University of Minnesota Biocatalysis/Biodegradation Database for study of microbial degradation

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

Evaluating eukaryotic secreted protein prediction

BACKGROUND: Improvements in protein sequence annotation and an increase in the number of annotated protein databases has fueled development of an increasing number of software tools to predict secreted proteins. Six software programs capable of high throughput and employing a wide range of prediction methods, SignalP 3.0, SignalP 2.0, TargetP 1.01, PrediSi, Phobius, and ProtComp 6.0, are evaluated. RESULTS: Prediction accuracies were evaluated using 372 unbiased, eukaryotic, SwissProt protein sequences. TargetP, SignalP 3.0 maximum S-score and SignalP 3.0 D-score were the most accurate single scores (90–91% accurate). The combination of a positive TargetP prediction, SignalP 2.0 maximum Y-score, and SignalP 3.0 maximum S-score increased accuracy by six percent. CONCLUSION: Single predictive scores could be highly accurate, but almost all accuracies were slightly less than those reported by program authors. Predictive accuracy could be substantially improved by combining scores from multiple methods into a single composite prediction

Activation of Akt at T308 and S473 in alcohol, tobacco and HPV-induced HNSCC:is there evidence to support a prognostic or diagnostic role?

BACKGROUND: Tobacco, alcohol and HPV infection are associated with increased risk of HNSCC. However, little is known about the underlying signaling events influencing risk. We aimed to investigate the relationship between these risk factors and Akt phosphorylation, to determine prognostic value. METHOD: VEGF-positive HNSCC biopsies, with known HPV status, were analyzed by immunohistochemistry (IHC) for Akt, phosphorylated at residues S473 and T308. Comparisons between the tissues were carried out using a Mann–Whitney U test. Associations between the variables and continuous immunohistochemical parameters were evaluated with general linear models. Patient characteristics and pAkt IHC score were analyzed for possible association with overall survival by Cox proportional hazard models. RESULTS: Immunohistochemistry revealed that cancer patients had significantly higher levels of pAkt T308 than S473 (P < 0.001). Smoking and alcohol were found to be independent risk factors for Akt phosphorylation at T308 (P = 0.022 and 0.027, respectively). Patients with tumors positive for HPV or pAkt S473 had a poorer prognosis (P = 0.005, and 0.004, respectively). Patients who were heavy drinkers were 49 times more likely to die than non-drinkers (P = 0.003). Patients with low pAkt T308 were more likely to be HPV positive (P = 0.028). Non-drinkers were also found to have lower levels of pAkt T308 and were more likely to have tumors positive for HPV than heavy drinkers (P = 0.044 and 0.007, respectively). CONCLUSION: This study suggests different mechanisms of carcinogenesis are initiated by smoking, alcohol and HPV. Our data propose higher phosphorylation of Akt at T308 as a reliable biomarker for smoking and alcohol induced HNSCC progression and higher phosphorylation of Akt at S473 as a prognostic factor for HNSCC

The University of Minnesota Biocatalysis/Biodegradation Database: improving public access

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

Data-driven extraction of relative reasoning rules to limit combinatorial explosion in biodegradation pathway prediction

Motivation: The University of Minnesota Pathway Prediction System (UM-PPS) is a rule-based expert system to predict plausible biodegradation pathways for organic compounds. However, iterative application of these rules to generate biodegradation pathways leads to combinatorial explosion. We use data from known biotransformation pathways to rationally determine biotransformation priorities (relative reasoning rules) to limit this explosion. Results: A total of 112 relative reasoning rules were identified and implemented. In one prediction step, i.e. as per one generation predicted, the use of relative reasoning decreases the predicted biotransformations by over 25% for 50 compounds used to generate the rules and by about 15% for an external validation set of 47 xenobiotics, including pesticides, biocides and pharmaceuticals. The percentage of correctly predicted, experimentally known products remains at 75% when relative reasoning is used. The set of relative reasoning rules identified, therefore, effectively reduces the number of predicted transformation products without compromising the quality of the predictions. Availability: The UM-PPS server is freely available on the web to all users at the time of submission of this manuscript and will be available following publication at http://umbbd.msi.umn.edu/predict/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

The University of Minnesota pathway prediction system: predicting metabolic logic

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 increas

Predicting biodegradation products and pathways: a hybrid knowledge- and machine learning-based approach

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 ∼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: [email protected]

Using Vision System Technologies for Offset Approaches in Low Visibility Operations

Flight deck-based vision systems, such as Synthetic Vision Systems (SVS) and Enhanced Flight Vision Systems (EFVS), have the potential to provide additional margins of safety for aircrew performance and enable the implementation of operational improvements for low visibility surface, arrival, and departure operations in the terminal environment with equivalent efficiency to visual operations. Twelve air transport-rated crews participated in a motion-base simulation experiment to evaluate the use of SVS/EFVS in Next Generation Air Transportation System low visibility approach and landing operations at Chicago O'Hare airport. Three monochromatic, collimated head-up display (HUD) concepts (conventional HUD, SVS HUD, and EFVS HUD) and three instrument approach types (straight-in, 3-degree offset, 15-degree offset) were experimentally varied to test the efficacy of the SVS/EFVS HUD concepts for offset approach operations. The findings suggest making offset approaches in low visibility conditions with an EFVS HUD or SVS HUD appear feasible. Regardless of offset approach angle or HUD concept being flown, all approaches had comparable ILS tracking during the instrument segment and were within the lateral confines of the runway with acceptable sink rates during the visual segment of the approach. Keywords: Enhanced Flight Vision Systems; Synthetic Vision Systems; Head-up Display; NextGe