Turnover numbers, also known as k cat values, are fundamental properties of enzymes. However, k cat data are scarce and measured in vitro, thus may not faithfully represent the in vivo situation. A basic question that awaits elucidation is: how representative are k cat values for the maximal catalytic rates of enzymes in vivo? Here, we harness omics data to calculate k vivo max , the observed maximal catalytic rate of an enzyme inside cells. Comparison with k cat values from Escherichia coli, yields a correlation of r 2 = 0.62 in log scale (p < 10 −10 ), with a root mean square difference of 0.54 (3.5-fold in linear scale), indicating that in vivo and in vitro maximal rates generally concur. By accounting for the degree of saturation of enzymes and the backward flux dictated by thermodynamics, we further refine the correspondence between k vivo max and k cat values. The approach we present here characterizes the quantitative relationship between enzymatic catalysis in vitro and in vivo and offers a highthroughput method for extracting enzyme kinetic constants from omics data. (1-6). Many models of cellular metabolism include k cat values, the maximal turnover rates of enzymes, as key inputs to predict the behavior of metabolic pathways and networks (7-9). However, most values have never been measured experimentally. Escherichia coli is the most intensely biochemically characterized organism, but k cat values are available for only about 10% of its ≈ 2, 000 enzyme-reaction pairs (Dataset S1). Indeed, k cat values are missing for several central metabolic enzymes. The scarcity of kinetic data limits the scope of models and necessitates generic parameter assignments that significantly reduce the predictive power of cellular models.Even if a larger collection of k cat values was made available, their current use poses a major difficulty: k cat values are measured through in vitro enzyme assays, representing the initial rate of the reaction, i.e., full substrates saturation and negligible levels of products. Such assays may underrepresent factors like cellular metabolite concentrations, thermodynamic constraints, posttranslational modifications, chaperones, cellular crowding, and activating and inhibiting molecules, which can substantially alter enzyme kinetics in vivo. These omissions call into question the relevance of k cat measurements in vivo (10-12). Furthermore, an effort to measure a large number of k cat values under in vivo-like conditions presents a daunting challenge, given how many unknown biochemical factors might be involved.Several studies grapple with missing k cat values by sampling from the distribution of k cat values measured in vitro or by using measurements of the same enzyme from related species (13-16). These approximations systematically ignore any errors resulting from the differences between in vitro and in vivo environments. Approximations of this sort may also introduce significant errors, as k cat values can deviate by orders of magnitude between isozymes in the same organism as well a...

SummaryCan a heterotrophic organism be evolved to synthesize biomass from CO2 directly? So far, non-native carbon fixation in which biomass precursors are synthesized solely from CO2 has remained an elusive grand challenge. Here, we demonstrate how a combination of rational metabolic rewiring, recombinant expression, and laboratory evolution has led to the biosynthesis of sugars and other major biomass constituents by a fully functional Calvin-Benson-Bassham (CBB) cycle in E. coli. In the evolved bacteria, carbon fixation is performed via a non-native CBB cycle, while reducing power and energy are obtained by oxidizing a supplied organic compound (e.g., pyruvate). Genome sequencing reveals that mutations in flux branchpoints, connecting the non-native CBB cycle to biosynthetic pathways, are essential for this phenotype. The successful evolution of a non-native carbon fixation pathway, though not yet resulting in net carbon gain, strikingly demonstrates the capacity for rapid trophic-mode evolution of metabolism applicable to biotechnology.PaperClip

Libraries of S. cerevisiae and E. coli promoter reporters measured under different conditions reveal scaling relationships between expression profiles across conditions and suggest that most changes in activity are due to global effects.

Protein levels are a dominant factor shaping natural and synthetic biological systems. Although proper functioning of metabolic pathways relies on precise control of enzyme levels, the experimental ability to balance the levels of many genes in parallel is a major outstanding challenge. Here, we introduce a rapid and modular method to span the expression space of several proteins in parallel. By combinatorially pairing genes with a compact set of ribosome-binding sites, we modulate protein abundance by several orders of magnitude. We demonstrate our strategy by using a synthetic operon containing fluorescent proteins to span a 3D color space. Using the same approach, we modulate a recombinant carotenoid biosynthesis pathway in Escherichia coli to reveal a diversity of phenotypes, each characterized by a distinct carotenoid accumulation profile. In a single combinatorial assembly, we achieve a yield of the industrially valuable compound astaxanthin 4-fold higher than previously reported. The methodology presented here provides an efficient tool for exploring a high-dimensional expression space to locate desirable phenotypes.