EXPLORING CHEMODIVERSITY IN METABOLISM TOWARDS THE SUPERVISED INTEGRATION OF CHEMISTRY INTO BIOLOGY

The availability of different levels of omics data helps us to observe cells with higher resolution and from different perspectives. However, complete understanding of metabolism lags behind in explaining the chemodiversity observed in living organisms – the known reactome does not account for the appearance of many metabolites. We applied the computational tool BNICE.ch to build the “ATLAS of Biochemistry” using expert curated generalized enzyme reaction rules. The first version of ATLAS contains all possible reactions (known and hypothetical) between known biological compounds. In the extended version of ATLAS, we extrapolate the known metabolism towards the chemical knowledge space and we integrate in a supervised manner chemical compounds and their associated reactions into a super network of metabolism. We demonstrate that the supervised integration of chemicals into metabolic networks is the key to complete the mechanism of poorly characterized reactions and to integrate thousands of orphan metabolites into metabolic networks. ATLAS is available as an online database (http://lcsb-databases.epfl.ch/atlas) and is equipped with additional data analysis tools. ATLAS can further be used to create hypotheses about the origin of experimentally measured compounds and serve as a tool for metabolic engineers, synthetic biologists and other scientists working with metabolomics and secondary metabolism.

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

Noushin Hadadi, Jasmin Maria Hafner, Vassily Hatzimanikatis

The availability of different levels of omics data helps us to observe cells with higher resolution and from different perspectives. Consequently, the computational exploration of metabolism gained more importance in the last decade to make sense of newly available data from genomics, transcriptomics and metabolomics. However, complete understanding of metabolism lags behind in explaining the chemodiversity observed in living organisms – the known reactome does not account for the appearance of many metabolites. Integrating experimentally measured metabolites into existing metabolic knowledge is a challenge we address here. We extrapolate the known metabolism towards the chemical knowledge space and we selectively integrate chemical compounds and their associated reactions into an overall network of known and potential metabolism we call the “ATLAS of Biochemistry.” We apply the computational tool BNICE.ch to generate known and novel reactions and compounds using expert curated, generalized enzyme reaction rules, and we created the first released version of ATLAS which contains all possible reactions (known and hypothetical) between known biological compounds. We further demonstrate that the selective integration of chemicals into metabolic networks is the key to complete the mechanism of poorly characterized reactions and to integrate orphan metabolites into metabolic networks. Starting with 16’000 biological compounds, we found biochemical reactions which include 60’000 unique PubChem compounds one reaction step away from known metabolism, and 140’000 PubChem compounds two reaction steps away. We organized our findings in an online database (http://lcsb-databases.epfl.ch/atlas) which is equipped with additional data analysis tools. As an example, results from a pathway search can propose previously unidentified enzymatic activities, bridge gaps in metabolic models and provide potential targets for protein and metabolic engineering. The data can further be used to create hypotheses about the origin of experimentally measured compounds and, in general, serve as a tool for metabolic engineers, synthetic biologists and other scientists working with metabolomics and secondary metabolism.

2017

“ATLAS of Biochemistry”: A repository of all possible biochemical reactions for synthetic biology, metabolic engineering and metabolomics studies

Noushin Hadadi, Jasmin Maria Hafner, Vassily Hatzimanikatis, Adrian Shajkofci, Aikaterini Zisaki

Recent technical and analytical progress in the field of metabolomics has lead to the identification of vast amounts of new compounds in living organisms. However, the integration of these compounds into the context of known metabolism remains difficult. To address this challenge of incomplete knowledge, we propose a computational approach that identifies novel hypothetical reactions between known metabolites, integrates experimentally measured molecular structures into existing metabolic networks, and finally predicts chemical compounds that are probable to exist in metabolism. The computational framework BNICE.ch is used to exploit the known biochemistry contained in the Kyoto Encyclopedia of Genes and Genomes (KEGG). We summarize the vastly diverse functionalities of enzymatic reactions in a few hundred expert-curated reaction rules, each generalizing multiple biochemical reactions. We then apply these rules to all metabolites known to KEGG in order to create a database of all the biochemically plausible reactions between compounds reported to occur in living organism. This extrapolation of the known metabolism results in a network of more than 130’000 known and novel reactions, each connecting two or more KEGG compounds. The generated information has been organized into an online database, the “Atlas of Biochemistry”, and is available under http://lcsb-databases.epfl.ch/atlas/.

2016

Modeling, predicting and mining metabolism at atom-level resolution

Jasmin Maria Hafner

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.