Can AI Help Improve Water Quality? Towards the Prediction of Degradation of Micropollutants in Wastewater

Micropollutants have become a serious environmental problem by threatening ecosystems and the quality of drinking water. This account investigates if advanced AI can be used to find solutions for this problem. We review background, the challenges involved, and the current state-of-the-art of quantitative structure-biodegradation relationships (QSBR). We report on recent progress combining experiment, quantum chemistry (QC) and chemoinformatics, and provide a perspective on potential future uses of AI technology to help improve water quality

Can AI Help Improve Water Quality? Towards the Prediction of Degradation of Micropollutants in Wastewater

Micropollutants have become a serious environmental problem by threatening ecosystems and the quality of drinking water. This account investigates if advanced AI can be used to find solutions for this problem. We review background, the challenges involved, and the current state-of-the-art of quantitative structure-biodegradation relationships (QSBR). We report on recent progress combining experiment, quantum chemistry (QC) and chemoinformatics, and provide a perspective on potential future uses of AI technology to help improve water quality

Design of novel enzymed-catalyzed reactions linked to protein sequences for finding enzyme engineering targets

A key challenge in metabolic engineering is to find and to improve biosynthetic pathways that lead to the cellular production of a given industrial, pharmaceutical or specialty chemical compound. In many cases, the enzymatic reactions required for bio-production have not been observed in nature and need to be designed from scratch. Computational approaches are essential to predict possible novel biotransformation and to find enzymes that can potentially catalyze the proposed reactions. In this work, we present two computational tools, BNICE.ch and BridgIT, and we demonstrate their concerted action to (i) predict hypothetical biotransformations and (ii) to link these novel reactions with well characterized enzymatic reactions and their associated genes. BNICE.ch reconstructs known reactions and generates novel reactions by applying its integrated, expert curated, generalized enzyme reaction rules on known metabolites. In order to find enzymes that potentially catalyze the biotransformation of these novel reactions, we assume that molecules with a similar reactive site and a similar atomic structure around the reactive site may be recognized and transformed by the same enzyme. Hence, BridgIT compares every predicted novel reaction to all known enzymatic reactions for which a protein sequence is available. Novel and known reactions are compared based on the reactive site of the substrates, the atoms surrounding the reactive site, and the breakage and formation of atomic bonds during the conversion of the substrate to the product. As a result, BridgIT reports a similarity score for each comparison of known reactions to novel reactions, thus giving an estimate of how possible it is that a given enzyme can catalyze a novel reaction. The results are organized in a database of known and hypothetical reactions called the “ATLAS of Biochemistry”1, where every hypothetical reaction is associated with its structurally most similar known enzymatic reactions, thus suggesting a plausible Gene-Protein-Reaction (GPR) association that can be used as a starting point for enzyme engineering. Our database currently spans more than 130’000 biochemically possible reactions between known metabolites from the Kyoto Encyclopedia of Genes and Genomes (KEGG). The ATLAS database and the BridgIT online tool are available on the web (http://lcsb-databases.epfl.ch/) and they can be used to extract potential reactions and pathways and to identify enzyme targets for metabolic and enzymatic engineering purposes. 1Hadadi, N., Hafner, J., Shajkofci, A., Zisaki, A., & Hatzimanikatis, V. (2016). ATLAS of Biochemistry: A repository of all possible biochemical reactions for synthetic biology and metabolic engineering studies. ACS Synthetic Biology, 2016

Making waves: Enhancing pollutant biodegradation via rational engineering of microbial consortia.

Biodegradation holds promise as an effective and sustainable process for the removal of synthetic chemical pollutants. Nevertheless, rational engineering of biodegradation for pollutant remediation remains an unfulfilled goal, while chemical pollution of waters and soils continues to advance. Efforts to (i) identify functional bacteria from aquatic and soil microbiomes, (ii) assemble them into biodegrading consortia, and (iii) identify maintenance and performance determinants, are challenged by large number of pollutants and the complexity in the enzymology and ecology of pollutant biodegradation. To overcome these challenges, approaches that leverage knowledge from environmental bio-chem-informatics and metabolic engineering are crucial. Here, we propose a novel high-throughput bio-chem-informatics pipeline, to link chemicals and their predicted biotransformation pathways with potential enzymes and bacterial strains. Our framework systematically selects the most promising candidates for the degradation of chemicals with unknown biotransformation pathways and associated enzymes from the vast array of aquatic and soil bacteria. We substantiated our perspective by validating the pipeline for two chemicals with known or predicted pathways and show that our predicted strains are consistent with strains known to biotransform those chemicals. Such pipelines can be integrated with metabolic network analysis built upon genome-scale models and ecological principles to rationally design fit-for-purpose bacterial communities for augmenting deficient biotransformation functions and study operational and design parameters that influence their structure and function. We believe that research in this direction can pave the way for achieving our long-term goal of enhancing pollutant biodegradation

Dynamics of initial carbon allocation after drought release in mature Norway spruce—Increased belowground allocation of current photoassimilates covers only half of the carbon used for fine‐root growth

After drought events, tree recovery depends on sufficient carbon (C) allocation to the sink organs. The present study aimed to elucidate dynamics of tree-level C sink activity and allocation of recent photoassimilates (C$_{new}$) and stored C in c. 70-year-old Norway spruce (Picea abies) trees during a 4-week period after drought release. We conducted a continuous, whole-tree $^{13}$C labeling in parallel with controlled watering after 5 years of experimental summer drought. The fate of C$_{new}$ to growth and CO$_{2}$ efflux was tracked along branches, stems, coarse- and fine roots, ectomycorrhizae and root exudates to soil CO$_{2}$ efflux after drought release. Compared with control trees, drought recovering trees showed an overall 6% lower C sink activity and 19% less allocation of C$_{new}$ to aboveground sinks, indicating a low priority for aboveground sinks during recovery. In contrast, fine-root growth in recovering trees was seven times greater than that of controls. However, only half of the C used for new fine-root growth was comprised of C$_{new}$ while the other half was supplied by stored C. For drought recovery of mature spruce trees, in addition to C$_{new}$, stored C appears to be critical for the regeneration of the fine-root system and the associated water uptake capacity

Should Transformation Products Change the Way We Manage Chemicals?

peer reviewedWhen chemical pollutants enter the environment, they can undergo diverse transformation processes, forming a wide range of transformation products (TPs), some of them benign and others more harmful than their precursors. To date, the majority of TPs remain largely unrecognized and unregulated, particularly as TPs are generally not part of routine chemical risk or hazard assessment. Since many TPs formed from oxidative processes are more polar than their precursors, they may be especially relevant in the context of persistent, mobile, and toxic (PMT) and very persistent and very mobile (vPvM) substances, which are two new hazard classes that have recently been established on a European level. We highlight herein that as a result, TPs deserve more attention in research, chemicals regulation, and chemicals management. This perspective summarizes the main challenges preventing a better integration of TPs in these areas: (1) the lack of reliable high-throughput TP identification methods, (2) uncertainties in TP prediction, (3) inadequately considered TP formation during (advanced) water treatment, and (4) insufficient integration and harmonization of TPs in most regulatory frameworks. A way forward to tackle these challenges and integrate TPs into chemical management is proposed

Modeling, predicting and mining metabolism at atom-level resolution

Living organisms can catalyze many thousands of biochemical reactions that they use to convert energy and matter, which provides them with the essentials for life. The sum of these chemical reactions happening in an organism is called metabo-lism. Understanding metabolism is crucial to elucidate the fundamental principles of biology, and further to enable us to redesign it for the sustainable, biosynthetic pro-duction of bio-based fuels, commodity chemicals and medicines. Mathematical mod-els are essential to organize and understand the complexity of metabolism. They usu-ally represent metabolism as a network of reactions, but they tend to neglect the exact molecular structure of the metabolites. To redesign metabolic reactions, how-ever, a mechanistic understanding of metabolic reactions and their catalysts, proteins called enzymes, is essential. In this work, a mathematical description of enzymatic reaction mechanism, called generalized reaction rules, is applied to computationally simulate and predict meta-bolic processes at the level of atoms. Each reaction rule describes the catalytic activi-ty of an enzyme, or a group of enzymes, at the mechanistic level by encoding the re-arrangement of atoms in the reaction. The reaction rules are called âgeneralizedâ, because they mimic the ability of a single enzyme to catalyze multiple reactions by acting on a range of substrates. Using these reaction rules, we first developed a computational representation of me-tabolism that allowed tracking single atoms throughout complex metabolic reaction networks. The principle of atom-tracking was then used to develop a graph-theory based method to represent and analyze metabolic networks, and to reliably identify of metabolic pathways for the biosynthesis of chemicals. Next, we applied the gener-alized reaction rules to predict all possible novel, hypothetical reactions from known biological compounds, and we stored the five million generated novel reactions them in a database called ATLAS. Finally, the developed tools and resources were applied to specific engineering and research problems, such as the biosynthetic pathway design for the biofuel bisabolene and the plastic precursor 1,4-butanediol. We further pre-dicted a biosynthesis route for the pharmaceutical tetrahydropalmatine and engi-neered a yeast strain to produce it. Finally, we show that our tools can be used to mine available genome sequences to find organisms that can degrade xenobiotics. Our findings suggest that the atom-level representation of metabolism can greatly contribute to its understanding, exploration and prediction. Given the complexity of atom-level modeling of metabolic processes, we propose metrics that can approxi-mate the atom-level information to conserve the information at the level of big, hy-pothetical metabolic networks like ATLAS. This database plus the developed pathway search techniques form a valuable resource for scientist to help characterizing un-known biosynthesis pathways towards secondary metabolites, and for metabolic engi-neers for designing novel bioproduction pathways for chemicals. Hopefully, these considerations will contribute to a better understanding of metabolism, advance the exploration of the bioproduction of drugs and other valuable molecules, and acceler-ate metabolic engineering efforts to realize the switch from a petroleum-based chem-ical industry towards a more sustainable, bio-based production of societyâs chemical needs

Publication Program

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Exploring chemodiversity in metabolism towards the selective integration of chemistry into biology

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Systematic handling of environmental fate data for model development – illustrated for the case of biodegradation half-life data

Environmental hazard endpoints describing persistence, mobility, toxicity, or bioaccumulation of chemicals are often associated with high variability in experimental outcomes. The assessment of persistence in the environment is particularly affected due to a multitude of influencing environmental factors, including the taxonomic composition and physiological state of the microbial community present. Biotransformation experiments may therefore result in half-lives spanning several orders of magnitude for the same substance tested with different environmental samples. Due to experimental limitations, values may further be beyond the limits of reliable half-life quantification (i.e., censored data points), and the number of data points per substance may vary considerably. However, reliable data describing average half-lives and their natural variability are an important foundation for building predictive models for environmental hazard endpoints, which are urgently needed by regulatory authorities to manage existing chemicals and by industry for the design of benign, non-persistent chemicals. Here, we propose the application of Bayesian inference to characterize the uncertainty of reported half-lives and to include censored data points to maximize the information extracted from experimental data. Our model estimates the true mean and standard deviation from a set of reported half-lives experimentally obtained for a single substance. Including censored data increases the available data volume, and reporting uncertainties helps estimating the reliability of the half-life data. We apply the inference model to 893 substances with experimental soil half-lives of varying data quantity and quality, and we estimate the true half-life distribution for each compound. Our approach can be easily adapted and applied to other environmental hazard endpoints to estimate uncertainty and to improve data quality for model development