Experimental and computational studies of cellulases as bioethanol enzymes

Self-assembly of choline-based surface-active ionic liquids and concentration-dependent enhancement in the enzymatic activity of cellulase in aqueous medium

The micellization of choline-based anionic surface-active ionic liquids (SAILs) having lauroyl sarcosinate [Sar]-, dodecylsulfate [DS]-, and deoxycholate [Doc]- as counter-ions was investigated in an aqueous medium. Density functional theory (DFT) was employed to investigate the net interactional energy (Enet), extent of non-covalent interactions, and band gap of the choline-based SAILs. The critical micelle concentration (cmc) along with various parameters related to the surface adsorption, counter-ion binding (β), and polarity of the cores of the micelles were deduced employing surface tension measurements, conductometric titrations and fluorescence spectroscopy, respectively. A dynamic light scattering (DLS) system equipped with zeta-potential measurement set-up and small-angle neutron scattering (SANS) were used to predict the size, zeta-potential, and morphology, respectively, of the formed micelles. Thermodynamic parameters such as standard Gibb's free energy and standard enthalpy change of micellization were calculated using isothermal titration calorimetry (ITC). Upon comparing with sodium salt analogues, it was established that the micellization was predominantly governed by the extent of hydration of [Cho]+, the head groups of the respective anions, and the degree of counter-ion binding (β). Considering the concentration dependence of the enzyme-SAIL interactions, aqueous solutions of the synthesized SAILs at two different concentrations (below and above the cmc) were utilized as the medium for testing the enzymatic activity of cellulase. The activity of cellulase was found to be ∼7- to ∼13-fold higher compared to that observed in buffers in monomeric solutions of the SAILs and followed the order: [Cho][Sar] > [Cho][DS] > [Cho][Doc]. In the micellar solution, a ∼4- to 5-fold increase in enzymatic activity was observed.

Artificial neural networks in the modeling of the catalytic activity of a biosensor composed of conjugated polymers and urease

Thin films of conjugated polymer and enzyme can be used to unravel the interaction between components in a biosensor. Using artificial neural networks (ANNs) improves data interpretability and helps construct models with great capacity for classifying and processing information. The present work used kinetic data from the catalytic activity of urease immobilized in different conjugated polymers to create ANN models using time, substrate concentration, and absorbance as input variables since the models had absorbance in a posterior instant as output value to explore the predictivity of the ANNs. The performance of the models was evaluated by Pearson's correlation coefficient (ρ) and mean squared error (MSE) values. After the learning process, a series of new experiments were performed to verify the generality of the models. As the main results, the best ANN model presented 0.9980 and 3.0736 × 10-5 for ρ and MSE, respectively. For the simulation step, intermediary values of substrate concentration were used. The mean absolute percentage error (MAPE) values were 3.34, 3.07, and 3.78 for 12 mM, 22 mM, and 32 mM concentrations, respectively. Overall, with the simulations, it was possible to ascertain the interpolatory capacity of the model, which has a learning mechanism based on absorbance and time as variables. Thus, the potential of ANNs would be in their use in pre-evaluations, helping to determine the substrate concentration at which there is higher catalytic activity or in determining the linear range of the sensor.

Fungal strain improvement for efficient cellulase production and lignocellulosic biorefinery: Current status and future prospects

Lignocellulosic biomass (LCB) has been recognized as a valuable carbon source for the sustainable production of biofuels and value-added biochemicals. Crude enzymes produced by fungal cell factories benefit economic LCB degradation. However, high enzyme production cost remains a great challenge. Filamentous fungi have been widely used to produce cellulolytic enzymes. Metabolic engineering of fungi contributes to efficient cellulase production for LCB biorefinery. Here the latest progress in utilizing fungal cell factories for cellulase production was summarized, including developing genome engineering tools to improve the efficiency of fungal cell factories, manipulating promoters, and modulating transcription factors. Multi-omics analysis of fungi contributes to identifying novel genetic elements for enhancing cellulase production. Furthermore, the importance of translation regulation of cellulase production are emphasized. Efficient development of fungal cell factories based on integrative strain engineering would benefit the overall bioconversion efficacy of LCB for sustainable bioproduction.