In‐depth characterization of genome‐scale network reconstructions for the in vitro synthesis in cell‐free systems

Abstract

Cell‐free systems containing multiple enzymes are becoming an increasingly interesting tool for one‐pot syntheses of biochemical compounds. To extensively explore the enormous wealth of enzymes in the biological space, we present methods for assembling and curing data from databases to apply them for the prediction of pathway candidates for directed enzymatic synthesis. We use Kyoto Encyclopedia of Genes and Genomes to establish single organism models and a pan‐organism model that is combining the available data from all organisms listed there. We introduce a filtering scheme to remove data that are not suitable, for example, generic metabolites and general reactions. In addition, a valid stoichiometry of reactions is required for acceptance. The networks created are analyzed by graph theoretical methods to identify a set of metabolites that are potentially reachable from a defined set of starting metabolites. Thus, metabolites not connected to such starting metabolites cannot be produced unless new starting metabolites or reactions are introduced. The network models also comprise stoichiometric and thermodynamic data that allow the definition of constraints to identify potential pathways. The resulting data can be directly applied using existing or future pathway finding tools.

In vitro prototyping of limonene biosynthesis using cell-free protein synthesis

“…Crude lysates are becoming increasingly popular for prototyping metabolism because lysates contain endogenous metabolism, alternate substrates, and cofactors (Miguez et al, 2019;Schuh et al, 2019). Additionally, when provided with an energy source, amino acids, NTPs, and excess cofactors, crude lysates contain the translational machinery for cell-free protein synthesis (CFPS) which enables rapid production of proteins (Carlson et al, 2012;Casini et al, 2018;Chen et al, 2020;Des Soye et al, 2019;Huang et al, 2018;Jaroentomeechai et al, 2018;Jewett et al, 2008;Kightlinger et al, 2019;Lin et al, 2020;Silverman et al, 2019;Stark et al, 2019;Stark et al, 2018).…”

A meaningful path finding method without specific starting metabolite

“…The equation in constraint 3 prevents the hub metabolites from appearing in the metabolic pathway. The inequality in constraint 4 allows for the starting metabolite in the metabolic pathway to be chosen from SC at random [24,25]. The equation in constraint 5 and the inequality in constraint 6 distinguish the compound and no cycle appears in the metabolic pathway [23,25].…”

“…However, the pathway search of carbon atom tracking in the process from the starting metabolite to the target metabolite is not achieved by this method. BLA ß et al proposed another novel constraint-based metabolic pathway prediction approach to search for the metabolic pathways from arbitrary starting metabolites to the target metabolite and applied it to search for the metabolic pathways in different species to show the effectiveness of this method [24,25]. Unfortunately, this method only combines the simple topology and stoichiometry of the metabolic network and does not take into account the influence of hub metabolites on path search.…”

Cell-free prototyping of limonene biosynthesis using cell-free protein synthesis

“…Crude lysates are becoming increasingly popular for prototyping metabolism because lysates contain endogenous metabolism, alternate substrates, and cofactors (Miguez et al, 2019;Schuh et al, 2019). Additionally, when provided with an energy source, amino acids, NTPs, and excess cofactors, crude lysates contain the translational machinery for cell-free protein synthesis (CFPS) which enables rapid production of proteins (Carlson et al, 2012;Casini et al, 2018;Chen et al, 2020;Des Soye et al, 2019;Huang et al, 2018;Jaroentomeechai et al, 2018;Jewett et al, 2008;Kightlinger et al, 2019;Lin et al, 2020;Silverman et al, 2019;Stark et al, 2019;Stark et al, 2018).…”