Discovery and Evaluation of Biosynthetic Pathways for the Production of Five Methyl Ethyl Ketone Precursors

Abstract

The limited supply of fossil fuels and the establishment of new environmental policies shifted research in industry and academia towards sustainable production of the 2 nd generation of biofuels, with Methyl Ethyl Ketone (MEK) being one promising fuel candidate. MEK is a commercially valuable petrochemical with an extensive application as a solvent. However, as of today, a sustainable and economically viable production of MEK has not yet been achieved despite several attempts of introducing biosynthetic pathways in industrial microorganisms. We used BNICE.ch as a retrobiosynthesis tool to discover all novel pathways around MEK. Out of 1'325 identified compounds connecting to MEK with one reaction step, we selected 3oxopentanoate, but-3-en-2-one, but-1-en-2-olate, butylamine, and 2-hydroxy-2methyl-butanenitrile for further study. We reconstructed 3'679'610 novel biosynthetic pathways towards these 5 compounds. We then embedded these pathways into the genome-scale model of E. coli, and a set of 18'622 were found to be most biologically feasible ones based on thermodynamics and their yields. For each novel reaction in the viable pathways, we proposed the most similar KEGG reactions, with their gene and protein sequences, as candidates for either a direct experimental implementation or as a basis for enzyme engineering. Through pathway similarity analysis we classified the pathways and identified the enzymes and precursors that were indispensable for the production of the target molecules. These retrobiosynthesis studies demonstrate the potential of BNICE.ch for discovery, systematic evaluation, and analysis of novel pathways in synthetic biology and metabolic engineering studies.3 Graphical abstract4 Limited reserves of oil and natural gas and the environmental issues associated with their exploitation in the production of chemicals sparked off current developments of processes that can produce the same chemicals from renewable feedstocks using microorganisms. 1-3 A fair amount of these efforts focuses on a sustainable production of the 2 nd generation biofuels.Compared to the currently used fossil fuels and bioethanol, these 2 nd generation biofuels should provide lower carbon emissions, higher energy density, and should be less corrosive to engines and distribution infrastructures. Recently, a large number of potential candidates for the 2 nd generation biofuels has been proposed such as nbutanol 4 , isobutanol 4 , 2-methyl-1-butanol 4 , 3-methyl-1-butanol 4 , C13 to C17 mixtures of alkanes and alkenes 5 , fatty esters, fatty alcohols 1 , and Methyl Ethyl Ketone (MEK) 6 .While some of these compounds were detected in living cells, none was produced by native organisms in appreciable quantities. 7 For chemicals whose natural microbial producers are not known, the feasibility of their bioproduction has to be assessed and potential novel biosynthetic pathways for production of these chemicals are yet to be discovered. 8,9 Even when production pathways for target chemicals are known, it is important to find alternativ...

Large-scale kinetic metabolic models of Pseudomonas putida KT2440 for consistent design of metabolic engineering strategies

“…Methods that use thermodynamic data such as the thermodynamics-based flux analysis TFA [35][36][37][38][39] allow to: (i) integrate the metabolomics and fluxomics data into models, and compute values of metabolic fluxes and metabolite concentrations whose experimental measurements are not available; (ii) eliminate in silico designed biosynthetic pathways not obeying the second law of thermodynamics [51,52]; (iii) eliminate infeasible thermodynamic cycles [53][54][55]; and (iv) identify how far reactions operate from thermodynamic equilibrium [46,56]. Despite the fact that usefulness of thermodynamics has been demonstrated in many applications, only a few reconstructed GEMs are curated for this important property [46,[57][58][59][60].…”

Large-scale kinetic metabolic models ofPseudomonas putidafor a consistent design of metabolic engineering strategies

“…Integration of thermodynamics data. Methods that use thermodynamics data such as the thermodynamics-based flux analysis TFA [35][36][37][38][39] allow us to integrate the metabolomics data together with the fluxomics data, to eliminate in silico designed biosynthetic pathways not obeying the second law of thermodynamics [40,41], to eliminate infeasible thermodynamic cycles [42][43][44], and to identify how far reactions operate from thermodynamic equilibrium [45,46]. Despite the fact that usefulness of thermodynamics has been demonstrated in many applications, only a few reconstructed GEMs are curated for this important property [45,[47][48][49][50].…”

Reinforcement Learning for Bioretrosynthesis

“…Secondly, a number of parameters are often included within the pathway search, decided by the software designer and with limited capacity for a user to incorporate his own knowledge, solving for both retrosynthesis and parameters optimisation. Some examples include enzyme performance (Delépine et al 2018), predicted yield (Campodonico et al 2014;Carbonell et al 2014;Liu et al 2014;Cho et al 2010;Tokic et al 2018), thermodynamics or cofactor usage (Kumar et al 2018). Moreover, those tools do not include the latest advances in combinatorial search space exploration, pioneered in the field of Artificial Intelligence.…”