Protein constraints in genome-scale metabolic models: Data integration, parameter estimation, and prediction of metabolic phenotypes.

Genome-scale metabolic models provide a valuable resource to study metabolism and cell physiology. These models are employed with approaches from the constraint-based modeling framework to predict metabolic and physiological phenotypes. The prediction performance of genome-scale metabolic models can be improved by including protein constraints. The resulting protein-constrained models consider data on turnover numbers (kcat ) and facilitate the integration of protein abundances. In this systematic review, we present and discuss the current state-of-the-art regarding the estimation of kinetic parameters used in protein-constrained models. We also highlight how data-driven and constraint-based approaches can aid the estimation of turnover numbers and their usage in improving predictions of cellular phenotypes. Finally, we identify standing challenges in protein-constrained metabolic models and provide a perspective regarding future approaches to improve the predictive performance.

Production, concentration and partial characterization of an enzymatic extract produced by an Aspergillus niger mutant in solid state fermentation.

An enzymatic extract from Aspergillus niger 3T5B8 was produced by Solid State Fermentation (SSF) in aerated columns, using wheat bran as substrate. A combination of extracts produced using three different process conditions varying temperature, pH and aeration formed the final extract (Mixture). The Mixture was concentrated by an ultrafiltration process that partially purified and provided an efficient recovery of the enzymatic activities of xylanase (88.89%), polygalacturonase (89.3%), ß-glucosidase (93.15%), protease (98.68%) and carboxymethylcellulase (CMCase) (98.93%). SDS-PAGE analysis showed 15 visible protein bands in the crude and concentrated Mixture with molecular weights ranging from 15.1 to 104.6 kDa. Thin layer chromatography confirmed the effective action of ß-glucosidase and xylanase hydrolysis activities over cellobiose and xylan, respectively. A central composite design (CCD) with two variables and four replicates at the center points was used to determine the optimal temperature and pH for CMCase and ß-glucosidase. The optimal temperature was 78.9 °C and pH 3.8 for CMCase and 52.8 °C and pH 4.8 for ß-glucosidase, respectively.

Experimental and mathematical modeling approaches for biocatalytic post-consumer poly(ethylene terephthalate) hydrolysis.

The environmental impact arising from poly(ethylene terephthalate) (PET) waste is notable worldwide. Enzymatic PET hydrolysis can provide chemicals that serve as intermediates for value-added product synthesis and savings in the resources. In the present work, some reaction parameters were evaluated on the hydrolysis of post-consumer PET (PC-PET) using a cutinase from Humicola insolens (HiC). The increase in PC-PET specific area leads to an 8.5-fold increase of the initial enzymatic hydrolysis rate (from 0.2 to 1.7 mmol L-1 h-1), showing that this parameter plays a crucial role in PET hydrolysis reaction. The effect of HiC concentration was investigated, and the enzymatic PC-PET hydrolysis kinetic parameters were estimated based on three different mathematical models describing heterogeneous biocatalysis. The model that best fits the experimental data (R2 = 0.981) indicated 1.68 mgprotein mL-1 as a maximum value of the enzyme concentration to optimize the reaction rate. The HiC thermal stability was evaluated, considering that it is a key parameter for its efficient use in PET degradation. The enzyme half-life was shown to be 110 h at 70 ºC and pH 7.0, which outperforms most of the known enzymes displaying PET hydrolysis activity. The results evidence that HiC is a very promising biocatalyst for efficient PET depolymerization.

Size product modulation by enzyme concentration reveals two distinct levan elongation mechanisms in Bacillus subtilis levansucrase.

Two levan distributions are produced typically by Bacillus subtilis levansucrase (SacB): a high-molecular weight (HMW) levan with an average molecular weight of 2300 kDa, and a low-molecular weight (LMW) levan with 7.2 kDa. Previous results have demonstrated how reaction conditions modulate levan molecular weight distribution. Here we demonstrate that the SacB enzyme is able to perform two mechanisms: a processive mechanism for the synthesis of HMW levan and a non-processive mechanism for the synthesis of LMW levan. Furthermore, the effect of enzyme and substrate concentration on the elongation mechanism was studied. While a negligible effect of substrate concentration was observed, we found that SacB elongation mechanism is determined by enzyme concentration. A high concentration of enzyme is required to synthesize LMW levan, involving the sequential formation of a wide variety of intermediate size levan oligosaccharides with a degree of polymerization (DP) up to ∼70. In contrast, an HMW levan distribution is synthesized through a processive mechanism producing oligosaccharides with DP <20, in reactions occurring at low enzyme concentration. Additionally, reactions where levansucrase concentration was varied while the total enzyme activity was kept constant (using a combination of active SacB and an inactive SacB E342A/D86A) allowed us to demonstrate that enzyme concentration and not enzyme activity affects the final levan molecular weight distribution. The effect of enzyme concentration on the elongation mechanism is discussed in detail, finding that protein-product interactions are responsible for the mechanism shift.