Exploring chemodiversity in metabolism towards the selective 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. 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.