Dataset Information

Modeling the Biodegradability of Chemical Compounds Using the Online CHEmical Modeling Environment (OCHEM).

ABSTRACT: Biodegradability describes the capacity of substances to be mineralized by free-living bacteria. It is a crucial property in estimating a compound's long-term impact on the environment. The ability to reliably predict biodegradability would reduce the need for laborious experimental testing. However, this endpoint is difficult to model due to unavailability or inconsistency of experimental data. Our approach makes use of the Online Chemical Modeling Environment (OCHEM) and its rich supply of machine learning methods and descriptor sets to build classification models for ready biodegradability. These models were analyzed to determine the relationship between characteristic structural properties and biodegradation activity. The distinguishing feature of the developed models is their ability to estimate the accuracy of prediction for each individual compound. The models developed using seven individual descriptor sets were combined in a consensus model, which provided the highest accuracy. The identified overrepresented structural fragments can be used by chemists to improve the biodegradability of new chemical compounds. The consensus model, the datasets used, and the calculated structural fragments are publicly available at http://ochem.eu/article/31660.

SUBMITTER: Vorberg S

PROVIDER: S-EPMC5175213 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Modeling the Biodegradability of Chemical Compounds Using the Online CHEmical Modeling Environment (OCHEM).

Vorberg Susann S Tetko Igor V IV

Molecular informatics 20131128 1

Biodegradability describes the capacity of substances to be mineralized by free-living bacteria. It is a crucial property in estimating a compound's long-term impact on the environment. The ability to reliably predict biodegradability would reduce the need for laborious experimental testing. However, this endpoint is difficult to model due to unavailability or inconsistency of experimental data. Our approach makes use of the Online Chemical Modeling Environment (OCHEM) and its rich supply of mac ...[more]

PMID: 27485201

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Project description:Compound biodegradability tests with natural microbial communities form an important keystone in the ecological assessment of chemicals. However, biodegradability tests are frequently limited by a singular focus either on the chemical and potential transformation products or on the individual microbial species degrading the compound. Here, we investigated a methodology to simultaneously analyze community compositional changes and biomass growth on dosed test compound from flow cytometry (FCM) data coupled to machine-learned cell type recognition. We quantified the growth of freshwater microbial communities on a range of carbon dosages of three readily biodegradable reference compounds, phenol, 1-octanol, and benzoate, in comparison to three fragrances, methyl jasmonate, myrcene, and musk xylene (as a nonbiodegradable control). Compound mass balances with between 0.1 to 10 mg C · liter-1 phenol or 1-octanol, inferred from cell numbers, parent compound analysis, and CO2 evolution, as well as use of 14C-labeled compounds, showed between 6 and 25% mg C · mg C-1 substrate incorporation into biomass within 2 to 4 days and 25 to 45% released as CO2 In contrast, similar dosage of methyl jasmonate and myrcene supported slower (4 to 10 days) and less (2.6 to 6.6% mg C · mg C-1 with 4.9 to 22% CO2) community growth. Community compositions inferred from machine-learned cell type recognition and 16S rRNA amplicon sequencing showed substrate- and concentration-dependent changes, with visible enrichment of microbial subgroups already at 0.1 mg C · liter-1 phenol and 1-octanol. In general, community compositions were similar at the start and after the stationary phase of the microbial growth, except at the highest used substrate concentrations of 100 to 1,000 mg C · liter-1 Flow cytometry cell counting coupled to deconvolution of communities into subgroups is thus suitable to infer biodegradability of organic chemicals, permitting biomass balances and near-real-time assessment of relevant subgroup changes.IMPORTANCE The manifold effects of potentially toxic compounds on microbial communities are often difficult to discern. Some compounds may be transformed or completely degraded by few or multiple strains in the community, whereas others may present inhibitory effects. In this study, we benchmark a new method based on machine-learned microbial cell recognition to rapidly follow dynamic changes in aquatic communities. We further determine productive biodegradation upon dosing of a number of well-known readily biodegradable tester compounds at a variety of concentrations. Microbial community growth was quantified using flow cytometry, and the multiple cell parameters measured were used in parallel to deconvolute the community on the basis of similarity to previously standardized cell types. Biodegradation was further confirmed by chemical analysis, showing how distinct changes in specific populations correlate to degradation. The method holds great promise for near-real-time community composition changes and deduction of compound biodegradation in natural microbial communities.

Project description:BackgroundUnderstanding the synergetic and antagonistic effects of combinations of drugs and toxins is vital for many applications, including treatment of multifactorial diseases and ecotoxicological monitoring. Synergy is usually assessed by comparing the response of drug combinations to a predicted non-interactive response from reference (null) models. Possible choices of null models are Loewe additivity, Bliss independence and the recently rediscovered Hand model. A different approach is taken by the MuSyC model, which directly fits a generalization of the Hill model to the data. All of these models, however, fit the dose-response relationship with a parametric model.ResultsWe propose the Hand-GP model, a non-parametric model based on the combination of the Hand model with Gaussian processes. We introduce a new logarithmic squared exponential kernel for the Gaussian process which captures the logarithmic dependence of response on dose. From the monotherapeutic response and the Hand principle, we construct a null reference response and synergy is assessed from the difference between this null reference and the Gaussian process fitted response. Statistical significance of the difference is assessed from the confidence intervals of the Gaussian process fits. We evaluate performance of our model on a simulated data set from Greco, two simulated data sets of our own design and two benchmark data sets from Chou and Talalay. We compare the Hand-GP model to standard synergy models and show that our model performs better on these data sets. We also compare our model to the MuSyC model as an example of a recent method on these five data sets and on two-drug combination screens: Mott et al. anti-malarial screen and O'Neil et al. anti-cancer screen. We identify cases in which the HandGP model is preferred and cases in which the MuSyC model is preferred.ConclusionThe Hand-GP model is a flexible model to capture synergy. Its non-parametric and probabilistic nature allows it to model a wide variety of response patterns.

Dataset Information

Modeling the Biodegradability of Chemical Compounds Using the Online CHEmical Modeling Environment (OCHEM).

Publications

Modeling the Biodegradability of Chemical Compounds Using the Online CHEmical Modeling Environment (OCHEM).

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